SINGLE-MOLECULE SPECTROSCOPY STUDIES OF THE CONFORMATIONAL DYNAMICS OF ENZYMES

Maolin Lu

A Dissertation

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

December 2014

Committee:

H. Peter Lu, Advisor

Lewis Fulcher Graduate Faculty Representative

John Cable

Massimo Olivucci ii

ABSTRACT

H. Peter Lu, Advisor

Conformational motions of enzymes are highly dynamic and intrinsically stochastic.

Obtaining molecular level insights into conformational dynamics of enzymes is critical for unraveling the complex intimate structure-to-function relationship. This dissertation describes the investigation of conformational dynamics of HPPK (6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase) and T4 lysozyme by single-molecule FRET (Förster/fluorescence resonance energy transfer) spectroscopy and photon stamping spectroscopy. This dissertation also demonstrates the developments of corresponding single-molecule spectroscopic approaches to serve scientifically experimental demands.

Multiple conformational intermediate states and multi-dimensional conformational motions of T4 lysozyme have been observed. The Markov process has successfully reproduced the experimental observations, suggesting that T4 lysozyme hinge-bending open-close conformational changes follow multiple pathways involving multiple intermediate states. The combination of lifetime and anisotropy results presents a whole picture of multi-dimensional conformational dynamics in the process of T4 lysozyme open-close hinge-bending. The non- exponential features of both lifetime and anisotropy autocorrelation functions reveal dynamic and static inhomogeneity/complexity of multi-dimensional conformational fluctuations.

The investigations of probing and manipulating HPPK conformational dynamics has been described. The consistency between the decay rate of donor lifetime and rising rate of acceptor lifetime gives a direct observation of FRET dynamic process at single-molecule level. The autocorrelation analysis of donor lifetimes have revealed intermittent conformational coherence iii of multiple HPPK Loop3-active site conformational states, regulated by substrate-enzyme interactions. Mechanically manipulating a targeted dye-labeled single HPPK in pinpoint nano- scale precision and simultaneously monitoring the conformational changes during the AFM pulling event has been achieved. The observed results of different lifetime fluctuations, distinct anisotropy fluctuations and various dynamic rates have suggested the existence of function-inert and function-active scenarios of HPPK Loop 3-active site conformational dynamic motions.

The developments of single-molecule spectroscopic approaches have been demonstrated, including 1) single molecule photon stamping FRET spectroscopy, on the basis of only measuring the donor’s lifetime trajectory; 2) single-molecule AFM-FRET nanoscopy, capable of effectively pinpointing and mechanically manipulating a targeted dye-labeled single protein in a large sampling area; and 3) single-molecule multi-parameter photon stamping spectroscopy system, integrating fluorescence anisotropy-FRET-lifetime and capable of observing single- molecule multi-dimensional conformational motions.

iv

TO THE MEMORY OF MY BELOVED GRANDPARENTS FOR THEIR UNCONDITIONAL

LOVE AND SELFLESS SUPPORT v

ACKNOWLEDGMENTS

In the past five and half years, I have received valuable knowledge, experiences and support from many talented people. I would love to express my appreciations to them at this moment.

I would love to express my deep thanks to my advisor, Dr. H. Peter Lu, for all the financial support, academic advice, professional training, and patient understanding. His professional attitude and quantitative thinking have great influence on my future career. His comprehensive interdisciplinary knowledge motivates me to expand my visions to new field and combine different knowledge together. I would like to thank my committee members: Dr. John

R. Cable, Dr. Massimo Olivucci, and Dr. Lewis Fulcher for their valuable time.

I am thankful to the faculty and staff working in the Center for Photochemical Sciences, particularly Dr. Alexander N. Tarnovsky, Dr. Ksenija D. Glusac and Dr. Thomas H. Kinstle for their teaching, Nora Cassidy, Alita Frater, Charles Codding, and Doug Martin for their help and coordinates.

I feel delighted to thank my current and past group members for sharing their valuable experiences, especially Dr. Yuanming Wang, Dr. Yufan He, Dr. Desheng Zheng, Dr. Nipedita

Pal, Dr. Dibyendu Kumar Sasmal and Dr. Vishal Govind Rao. I would love to thank my dear friends: Wendy Jenkins, Ken Jenkins, Marie Derkis, Julie George, William Bradley Burgess,

Yenlin Goh, Ying-Wei Sung, Papatya C. Sevinc, Valentina Prusakova, Qian Wang, and Min Gu for their friendship, support, company and encouragement.

I am very grateful to my big peasant family for their love and support, specifically my parents, my sisters, my brother, my nieces and nephews. I want to express my special thanks to

Yan Han for unconditional love, persistent support, unwritten understanding, long-time waiting and company. vi

TABLE OF CONTENTS

Page

CHAPTER 1. INTRODUCTION: SINGLE-MOLECULE SPECTROSCOPY AND SINGLE-

MOLECULE ENZYMATIC DYNAMICS ...... 1

1.1. Single-Molecucle and Ensemble-Level Studies ...... 1

1.2. Single-Molecule Fluorescence Spectroscopy...... 4

1.2.1. Basic Requirements of Single-Molecule Fluorescence Spectroscopy ...... 4

1.2.2. Single-Molecule Fluorescence Imaging and Detection ...... 5

1.3. Single-Molecule FRET Theory ...... 12

1.3.1. FRET Fundamentals ...... 12

1.3.2. FRET Pairs ...... 20

1.3.3. FRET Detection ...... 22

1.4. Biological Applications of Single-Molecule FRET ...... 23

1.5. Single-Molecule Fluorescence Anisotropy ...... 24

1.6. Single-Molecule Conformational Dynamics of Enzymes ...... 26

1.7. References ...... 28

CHAPTER 2. EXTRACTING MULTIPLE INTERMEDIATE STATES OF SINGLE-

MOLECULE T4 LYSOZYME FROM BUNCHED SUB-STEP CONFORMATIONAL

MOTIONS ...... 37

2.1. Introduction ...... 38 vii

2.1.1. Conformational Flexibility...... 38

2.1.2. Multiple Conformational Intermediate States ...... 39

2.1.3. Introduction of T4 Lysozyme ...... 40

2.2. Materials and Methods ...... 41

2.2.1. Materials ...... 41

2.2.2. Single-Molecule Measurements ...... 42

2.2.3. Markov Model Analysis ...... 43

2.3. Results and Discussion ...... 47

2.4. Conclusions ...... 59

2.5. References ...... 60

CHAPTER 3. MANIPULATING PROTEIN CONFORMATIONS BY SINGLE-MOLECULE

AFM-FRET NANOSCOPY ...... 64

3.1. Introduction ...... 64

3.1.1 Single-Molecule Conformational Changes ...... 64

3.1.2. Optical-AFM Correlated Approaches ...... 65

3.1.3. Biological Functions and Catalytic Reactions of HPPK Kinase ...... 68

3.2. Experimental Sections ...... 71

3.2.1 Sample Preparation ...... 71

3.2.2. AFM-FRET Correlated Measurements...... 73

3.3. Results and Discussion ...... 79 viii

3.3.1. Successful Pulling Events under Optical Tracing ...... 79

3.3.2. Protein Unfolding by AFM Pulling ...... 82

3.3.3. Identification of Substructures of Protein Domain ...... 85

3.3.4. The Limitations of AFM-FRET Nanoscopy ...... 89

3.4. Conclusions ...... 89

3.5. References ...... 90

CHAPTER 4. SINGLE-MOLECULE PHOTON STAMPING FRET SPECTROSCOPY STUDY

OF ENZYMATIC CONFORMATIONAL DYNAMICS ...... 94

4.1. Introduction ...... 94

4.2. Experiments and Theoretical Analysis ...... 97

4.2.1. Experimental Sample Condition ...... 97

4.2.2. Experimental Set-Up ...... 100

4.2.3. Statistical Analysis ...... 101

4.3. Results and Discussion ...... 102

4.4. Conclusions ...... 110

4.5. References ...... 110

CHAPTER 5. PROBING COHERENT CONFORMATIONAL DYNAMICS OF HPPK BY

SINGLE-MOLECULE PHOTON STAMPING FRET SPECTROSCOPY ...... 115

5.1. Background Knowledge ...... 116

5.1.1. The Roles of Enzyme Conformational Changes ...... 116 ix

5.1.2. Single-Molecule Photon Stamping FRET Measurements ...... 117

5.1.3. Conformational Dynamics Studies of Loop3-Active Site Motions ...... 118

5.2. Materials and Methods ...... 118

5.2.1. Dyes-Labeled Single HPPK Molecule...... 118

5.2.2. Single-Molecule Photon Stamping FRET Spectroscopy ...... 119

5.2.3. Autocorrelation Function Analysis ...... 119

5.3. Results and Discussion ...... 120

5.3.1. Real-Time Observation of Single Molecule FRET Process ...... 120

5.3.2. Multiple Conformational States of HPPK under Enzymatic Reactions ...... 124

5.3.3. Intermittent Coherence of Bunched Multiple Conformational States ...... 127

5.3.4. Conformational Coherence Induced by Substrate-Enzyme Interactions ...... 132

5.4. Conclusions ...... 135

5.5. References ...... 136

CHAPTER 6. PROBING PROTEIN MULTI-DIMENSIONAL CONFORMATIONAL

FLUCTUATIONS BY SINGLE-MOLECULE MULTI-PARAMETER PHOTON STAMPING

SPECTROSCOPY ...... 142

6.1. Introduction ...... 143

6.2. Materials and Methods ...... 148

6.2.1. Materials ...... 148

6.2.2. Single-Molecule Measurements ...... 148 x

6.2.3. Single-Molecule FRET ...... 148

6.2.4. Fluorescence Anisotropy ...... 149

6.2.5. Autocorrelation Analysis ...... 150

6.2.6. New Approach of Four-Channel Single-Molecule Microscopy ...... 150

6.3. Results and Discussion ...... 153

6.4. Conclusions ...... 166

6.5. References ...... 167

CHAPTER 7. SINGLE-MOLECULE MULTI-PARAMETER RATE ANALYSIS OF HPPK

ENZYMATIC CONFORMATIONAL DYNAMICS ...... 174

7.1. Introduction ...... 175

7.2. Materials and Methods ...... 178

7.2.1. Sample Preparation ...... 178

7.2.2. Single-Molecule Multi-Parameter Photon Stamping Spectroscopy ...... 179

7.2.3. Fluorescence Anisotropy ...... 180

7.2.4. Hydration of Globular Protein HPPK ...... 182

7.3. Results and Discussion ...... 182

7.4. Conclusions ...... 195

7.5. References ...... 196

xi

LIST OF FIGURES

Figure Page

1.1. The difference between ensemble emission and single-molecule emission. ……...... 2

1.2. (A) Confocal microscopy geometry including the light pathways. (B) Comparison between wide-field scanning and point scanning…………………………………………………………....6

1.3. Conceptual diagram of total internal reflection fluorescence...... 8

1.4. (A) Schematic diagram of confocal microscopy with excitation beam waist around 250-

300nm. (B) Schematic diagram of TIRF microscopy.…………...... 11

1.5. Flow chart of addressing FRET theory from transition dipole-dipole interaction to energy transfer rate and then to energy transfer efficiency…………………………………………….....14

1.6. Jablonski diagram of FRET process between a donor and an acceptor…..…………………17

1.7. Energy transfer efficiency EFRET as a function of donor-to-acceptor distance r and

R0.…………………………………………………………………………………………...... 20

1.8. Structures of Cy3 and Cy5 dyes. ………………………………………………………….21

2.1. (A) Crystal structure of wild-type T4 lysozyme (PDB-code, 3LZM). (B) Normalized fluorescence spectrum of Cy3-Cy5 labeled T4 lysozyme………………………………………...45

2.2. (A) Scheme of responsive donor-acceptor distance and FRET efficiency changes associated with enzyme open-close hinge-bending motion in the process of ES* formation. (B) A Markov process model of multiple intermediate states.……………………...... 46

2.3. (A) A typical portion of single-molecule intensity trajectories recorded from single Cy3-Cy5 labeled T4 lysozyme. (B) The corresponding FRET efficiency trajectory calculated from donor/acceptor intensity trajectory in (A).…………...... 50 xii

2.4. (A panel) Distributions of experimental formation times or open times during T4 lysozyme open-close hinge-bending conformational motions under enzymatic reactions. (B panel)

Distributions of simulated formation times on the basis of Markov process model associated with different transition steps (n=1, 2, 3, 4, 5, 6)……………………………………………………….54

2.5. Linear regression between Ln (occurrence) and Ln (formation time).………...... 56

2.6. 2D joint probability distribution of adjacent open times for multiple transition steps (n=1, 3,

6) with 50 ms×50 ms x-y plane.……………………………………………...... 58

3.1. (A) Crystal Structure of HPPK. (B) HPPK catalyzed pyrophosphorylation reaction transferring two phosphor groups from ATP to HP……………...... 69

3.2. (A) Single-molecule AFM-FRET nanoscopy. (B) Single-molecule fluorescence photon counting images of the donor (Cy3, left) and accepter (Cy5, right).……………………………...70

3.3. Protocol for preparing sample that pulling HPPK (Cy3-Cy5 labeled at 88, 142) between possible lysine residue…………………………………………………………………………....72

3.4. Protocol for preparing biotinlated AFM tip………………………………………………...72

3.5. Experimental setup of single-molecule AFM-FRET Nanoscopy………..………………...74

3.6. Schematic diagram of coaxial laser and AFM tip…………….……………………………75

3.7. (A) Diagram of single-molecule AFM-FRET pulling matrix. (B) FRET time trajectory of donor (green) and acceptor (red) recorded under the AFM tip matrix pulling. (C) A zoom-in portion of the trajectory from (B)...... 78

3.8. (A) A typical FRET time trajectory of donor (green) and acceptor (red) associated with one single-molecule AFM-FRET force pulling event. (B) Zoom-in intensity trajectory of donor and acceptor from (A). (C) FRET efficiency time trajectory of one single-molecule AFM-FRET pulling event. (D) The correlated force curve...... 80 xiii

3.9. (A) Histogram of extension length distribution of AFM-FRET force unfolding single- molecule proteins. (B) Histogram of protein ruptures force distribution.………………………...84

3.10. (A) The amino acid sequence of the Apo-HPPK protein. 45,46 (B) The structure of the HPPK protein. 47…………………………………………………………………………………………84

3.11. (A-C) Three types of single-molecule force pulling curves of HPPK. (D) Histogram of protein rupture distance distribution. (E) The structure of the HPPK mutant (the site of lysines and cysteine are illustrated).…………………………………………………………………………..88

4.1. Photon stamping concept and definition of the parameters……………………...... 99

4.2. Single molecule FRET lifetime microscopy.……………...... 101

4.3. Single-molecule photon-stamping measurement and data analysis...... 105

4.4. Single-molecule fluorescence images (10m × 10m) of Cy3 (A) and Cy5 (B) labeled HPPK molecules. (C) Single-molecule fluorescence intensity trajectory of donor. (D) Lifetime trajectory of Cy3. (E) EFRET trajectory of Cy3-Cy5 labeled HPPK in (D). (F, G, H) Autocorrelation analyses from the intensity trajectories…………………………………………………………………...106

4.5. (A) A Fluorescence intensity-time trajectory of donor (green) and acceptor (red) in a single- molecule AFM-FRET measurement on one HPPK (labeled Cy3-Cy5 on 88c, 142c). (B) intensity- based FRET efficiency-time trajectory calculated from trajectory (A). (C) Lifetime-based FRET efficiency-time trajectory from the lifetime measurement...... 109

5.1. Crystal structure of Apo HPPK (PDB-code, 1HKA).………………………...... 118

5.2. (A) 10 seconds portion of the single photon time-stamping raw data of Cy3. (B) Histogram of all delay times in 100 seconds from donor photons. (C) Histogram of delay times in 100 seconds from acceptor fluorescence photons.……………………………………………………………121 xiv

5.3. (A) Jablonski diagram of excitation, de-excitation, and Förster resonance energy transfer process between a FRET pair. (B) Scattering plot (ka2 vs kd2) of acceptor rising rate as a function of donor faster decay rate from 23 single HPPK molecules.………………………………….....123

5.4. The plot between FRET Efficiency and Distance.……...... 124

5.5. (A, B) Single Cy3-Cy5 labeled HPPK fluorescence images (10 m by 10 m). (C) Four seconds portion of single-molecule intensity trajectories of Cy3 (green) and Cy5 (red) of single

Cy3-Cy5 labeled HPPK. (D) Four seconds portion of lifetime trajectory of Cy3 recorded simultaneously as intensity trajectories in (C). (E) The Cy3 fluorescence intensity distribution picked up from (C). (F) The donor lifetime distribution derived from lifetime trajectory

.………………………………………………………………………...... 126

5.6. (A) An example of typical donor lifetime trajectory of Cy3-Cy5 labeled HPPK. (B) The corresponding autocorrelation of donor lifetime.……...... 129

5.7. (A) Intermittent coherence. (B) The dephasing of coherence in (A).……………………...129

5.8. (A) Autocorrelation of donor lifetime of Cy3-Cy5 labeled HPPK under enzymatic reaction conditions of 100 M ATP and 100 M HP. (B) The coherence frequency distribution under enzymatic reactions of 100 M ATP and 100 M HP. (C) Autocorrelation of donor lifetime of

Cy3-Cy5 labeled HPPK under enzymatic reaction conditions of 100 M ATP, 100 M HP and

4M denaturant GuHCl. (D) The coherence frequency distribution under the same condition as (C).

………………………………………………………………...... 130

5.9. (A) The dephasing of coherence shown in Figure 5.8A. (B) The dephasing of coherence shown in Figure 5.8C.………………………………………...... 131 xv

5.10. (A, B) The coherence frequency distributions of Cy3-Cy5 labeled HPPK under enzymatic reactions with various substrate concentrations. (C) Statistic results of the coherence frequency vs substrate concentrations………………………………………………………………………...134

6.1. Multi-dimensional conformational motions of wild-type T4 lysozyme (PDB-code, 3LZM), including hinge-bending motions along -helix and rotational motions of each domain..………147

6.2. Single-molecule multi-parameter photon stamping spectroscopy...... 152

6.3. Multi-dimensional conformational motions of T4 lysozyme probed by single-molecule multi- parameter spectroscopy: dynamic anisotropy and lifetime-based FRET.……………...... 158

6.4. Dynamic and static disorder of T4 lysozyme multi-dimensional conformational fluctuations along FRET coordinate and orientation coordinates via autocorrelation analysis of lifetime and anisotropy.……………...... 159

6.5. Two-component donor lifetime decays associated with two major open and close conformational states.…………………………………………………………………………..163

6.6. Conceptual presentation of T4 lysozyme multi-dimensional conformational dynamics…165

7.1. (A) The HPPK molecule (PDB code: 1HKA) is displayed as a ribbon diagram. (B) The diagram of HPPK catalytic reaction…………………………………………………………….177

7.2. (A) Ensemble-level anisotropy distribution of HPPK without substrates. (B) single-molecule anisotropy distribution of HPPK.……………………………………………………………….179

7.3. (A) Experimental home-built four-channel set-up for simultaneously measuring single- molecule FRET, lifetime and anisotropy. (B) The demonstration of single molecule photon- stamping. (C) Intensity trajectory of the donor with 10 ms binning time. (D) The distribution of the photons’ delay times in Fig 7.3B.……………………………………………………………181 xvi

7.4. Single HPPK two-level conformational motions.…………………………………………185

7.5. Two different dynamic behaviors of single HPPK conformational motions.……...... 186

7.6. Direct visualization of changing probability and distribution of two-level conformational fluctuations.……………………………………………………………………………………..189

7.7. (A) Hydration trajectory calculated on the basis of Equation (7.1). (B) Three-dimensional plot of fluorescence lifetime, molecular anisotropy, and hydration……………………………..190

7.8. Proposed HPPK two-scenario conformational dynamic behaviors: function-inert scenario and function-active scenario. …………………………………………………………..……….193

xvii

LIST OF TABLES

Table Page

7.1. Two distinct dynamic lifetime decay rates behaviors of HPPK conformational motions...... 187

1

CHAPTER 1. INTRODUCTION: SINGLE-MOLECULE SPECTROSCOPY AND SINGLE-

MOLECULE ENZYMATIC DYNAMICS

1.1. Single-Molecule and Ensemble-Level Studies

The entire history of single-molecule studies about complex biological systems

(excluding the single-ion channel recording) is less than 40 years old. In 1989, W. E. Moerner

(In 2014 he was awarded the Nobel Prize in Chemistry) and Lothar Kador first successfully detected an individual molecule in the condensed phase by absorption measurement using frequency modulation spectroscopy at liquid helium temperature. 1 Since then, the number of reports on single molecule spectroscopy, mainly about fluorescence studies, have been grown rapidly. The first of them was made by Michel Orrit and Jacky Bernard in 1990.2 They were able to show the detection of the single molecules from fluorescence excitation spectra at low temperature. Later, Eric Betzig and Robert J. Chichester extended the single-molecule observation to room temperature by near-field scanning optical microscopy.3 Sunny Xie and H.

Peter Lu further applied single-molecule studies to enzymatic systems in 1998. 4 For sure, many other scientists not mentioned here have also contributed to the emergence of the field of single- molecule biophysics and biochemistry. These pioneers in single-molecule studies have made significant contributions to open up possibilities for studying biological process.

The complexity of many biological process or reactions makes fully comprehending the mechanisms of enzymes, DNA molecules, or RNA molecules beyond the use of conventional ensemble techniques. Figure 1.1 describes the difference between ensemble measurements and single-molecule measurements, for the example of molecular fluorescence emission. In ensemble measurements, the properties of bulk collection of molecules are investigated to yield the average value of a parameter for a large number of (presumably identical) copies of the molecule 2 under study. Signals from ensemble level are usually strong, however, the individual behavior can not be distinguished, and only average characteristics can be measured. On the other hand, single-molecule techniques studying one biological macromolecule at a time can reach the dynamic views of biological molecules in action. In single-molecule measurements, the properties of an individual molecule can be distinguished. One molecule at a time is detected and the value of parameter of interest is recorded. After recording the parameters from a number of individual molecules, the distribution of the parameter is made out. This distribution contains more information than the average value alone, especially in heterogeneous systems and dynamic systems (changing in time).

Figure 1.1. The difference between ensemble emission and single-molecule emission. The ensemble measurements record the overall fluorescence emission of the bulk molecules in the focal volume, while single-molecule emission measures the individual fluorescence emission of one single molecule.

Besides the common spatial and temporal sensitivity or resolution, three remarkable merits of single-molecule studies have been pointed out by W. E. Moerner: “Observing a single 3 molecule removes the usual ensemble average, allowing exploration of hidden heterogeneity in complex condensed phases as well as direct observation of dynamical state changes arising from photophysics and photochemistry, without synchronization.” 5

(1) Removing the ensemble averaging, allows construction of a frequency histogram of the actual distribution of an experimental parameter

For example, in the studies of folded-unfolded states, different conformations, or different stages of an enzymatic cycle, the multiple peaks or a skewed shape of parameter distributions contain more useful information than the average value alone. Especially when the biological system is heterogeneous, single molecule studies enable us to scrutinize each individual molecule and therefore allow the details of subpopulations in structure or dynamics under study to be uncovered.

(2) Removing the need of synchronization when single molecules undergo a dynamic process

For instance, to study catalytic intermediate states in one catalytic cycle, the initial synchronization is required in the ensemble measurement. However, this synchronization is unstable as the individual enzymes may have their own dynamic pathways which are stochastic and generally uncorrelated. On the other hand, single molecule measurement intrinsically requires no such synchronization because any one enzyme of the ensemble is observed separately. Since the single enzyme under observation can be in only one state at a given time, therefore the intermediate states can be probed under analysis with proper temporal resolution.

(3) Raising the possibility of observing new effects in unexplored regimes 4

For example, the memory effect characterized by the observation that a short open time tends to be followed by another short open time in enzyme’s two-state open-close model under enzymatic reactions was demonstrated by H. Peter Lu and Sunny Xie in 1998. 4

1.2. Single-Molecule Fluorescence Spectroscopy

1.2.1. Basic Requirements of Single-Molecule Fluorescence Spectroscopy

In order to accomplish single-molecule fluorescence spectroscopy by using optical radiation to detect a single molecule on a surface, two steps are of great importance: (1) making sure that only one molecule is probed in the volume under study, and (2) distinguishing the light emitted by the molecule from the experimental noise.6-8.

The first step is generally achieved by a combination of ultralow concentration of target molecules and a small probed volume focused by a pumping laser. The concentration of molecules of interest is dependent on the volume probed by the laser. For example, at room temperature a concentration of roughly 10-10 M in a volume of 10 µm3 is required to achieve the limit of one molecule in detection. This can be fulfilled by focusing a laser beam into a dilute solution, in which only one molecule can be probed in a focused laser beam and the photons from this molecule will be detected by single-molecule spectroscopy. The second step of providing a signal-to-noise ratio for single-molecule detection requires the optimal condition of maximizing the signal but minimizing the background noise. In order to get as large single-to- noise ratio as possible, close attention should be put on small focal volume, large absorption cross-section, high fluorescence quantum yield, high photo-stability, reducing Raman scattering and Rayleigh scattering. 5

1.2.2. Single-Molecule Fluorescence Imaging and Detection

Direct visualizing approaches generally involve the detection of single fluorophores (for example, rhodamine dyes), or individual fluorescent macromolecules (for example, green fluorescence protein), using high-resolution microscopy. For single-molecule visualization, single-molecule imaging is usually performed in either wide-field or confocal geometry, detected by charge-coupled device (CCD) cameras or single-photon point detectors, respectively. 9

Specifically, confocal and total internal reflection fluorescence (TIRF) microscopies are two of the most common methods used for directly visualizing fluorescent molecules. Recalling the history, the possibility of individual fluorophore visualization emerged in 1989 with the detection of an individual single molecule at low temperature in a confocal geometry.2 Later on, the developments of CCD (charge-coupled device) cameras opened a door to detect individual single fluorophores in the wide-field microscopy and make it possible to take the video of single molecules in the living cell.

Confocal microscopy is an optical imaging technique offering higher optical resolution and better contrast over conventional optical microscopy by eliminating out-of-focus light in specimens that is not from the microscope’s focal plane. 10,11 In a conventional wide-field optical microscopy, the image of specimens in the area of interest is often influenced by fluorescence emitted from out-of-focus region. In contrast, a confocal microscope, which uses a pinhole in an optically conjugate plane in front of the fluorescence detector to eliminate the out-of-focus signal

(the name "confocal" stems from this configuration), provides significant improvements in both axial (Z-direction, or depth of field) and lateral resolution. As shown in Figure 1.2A, only the emitted fluorescence by specimens very close to the focal plane can reach the detector after pinhole and the out-of-focus fluorescence is excluded. Figure 1.2B illustrates the difference 6 between wide-field scanning and point scanning. The illumination of point scanning used in confocal microscopy is achieved by scanning one or more focused beams of laser light, while that of wide-field scanning is fulfilled by bathing the entire specimen in light from a mercury or xenon source. Higher z-resolution and reduced out-of-focus blur make confocal images better than conventional wide-field images. As only a small volume can be visualized by confocal microscopes at once, bigger volumes need time consuming sampling and image reassembling. In this case, total internal reflection fluorescence microscopy (TIRF) is powerful since it allows for the visualization of single molecules containing high concentrations of fluorophores in a bigger volume.

Figure 1.2. (A) Confocal microscopy geometry including the light pathways. (B) Comparison between wide-field scanning and point scanning.

In the confocal microscopy, relatively small optical sections free of out-of-focus background fluorescence are generated by using two pinhole apertures placed in conjugate planes near the illumination source and detector. In TIRF microscopy, an induced evanescent wave in the 7 interface between two media with different refractive indices is produced to selectively illuminate and excite fluorophores in a limited specimen region.

The basic concept of TIRF microscopy, as shown in Figure 1.3, requires an excitation light beam traveling at an incident angle propagating a first medium of higher refractive index n1 reaches an interface with a second medium of lower refractive index n2. Total internal reflection occurs only in situations where the incident angles  are greater than a critical angle c, according to Snell’s Law. The critical angle is defined as

c  arcsine  n21/ n  (1.1)

When the incident angles are greater than c, the light is totally reflected back to the first medium and an evanescent electromagnetic field is created in the second medium immediately.

This termed evanescent field capable of exciting molecules decays exponentially with the distance z from the interface, according to the Equation (1.2),

E(z) E (0) exp( z / d ) (1.2)

where E(z) is the energy at distance z from the interface, and E(0) is the energy at the interface.

The penetration depth (d) is given by

 2 2 2 1/2 12 d nsin  n  4 (1.3) 8 where λ corresponds to the wavelength of light and  is the incident angle. The penetration depths can be in a range of 70~300 nm.12-14

Figure 1.3. Conceptual diagram of total internal reflection fluorescence. The evanescent field generated at the interface between two different media, decays exponentially with the distance z from the interface, and is capable of selectively exciting molecules.

Focusing on the single-molecule studies, confocal microscopy has been successfully used in single-molecule imaging. 6,15-18 The advantages of applying confocal microscopy for single- molecule studies can be summarized as below: 1) The special resolution is about the diffraction limit, which is around half of the excitation wavelength. It usually ranges from 250 nm to 300 nm in single-molecule studies; 2) By means of confining the individual single molecule in the conjugated diffraction limited focal volumes where the excitation and detection are conducted, within this decreased detection volume, confocal microscopy is capable of effectively extracting the weak fluorescence signals from background noise; 6,19,20 3) It can be combined with time- resolved single photon counting and ultrafast pulse laser excitation. This configuration has been widely used in single-molecule time-resolved fluorescence measurements and fluorescence 9 correlation spectroscopy to uncover the mystery of molecular dynamics in terms of molecular interactions and chemical reactions in complex biophysical systems. 21-24

For TIRF microscopy, the merits are also obvious in single-molecule studies: 1) A wider view field can be achieved, about ten thousand times bigger than the typical confocal focal spot by generating the evanescent field of exponential intensity decay at the interface between two media; 12,25,26 2) The background signal from fluorescent molecules more than 200 nm away from the evanescent field interface can be eliminated, because the evanescent electromagnetic field used for single-molecule excitation exhibits single-exponential intensity decay within about

200 nm vertically from the reflection interface; 3) The TIRF configuration has been widely used to visualize more molecules in biochemical systems and in living cells when combined with a

CCD camera. 20,27-29

As discussed above, both confocal microscopy and TIRF microscopy have been serving as powerful approaches to probe single-molecule dynamics.6,16,20-22 Nevertheless, in the case of requiring both wide view field and high time-resolution, their own limitations show up. For example, equipped with avalanche photodiodes, confocal microscopy is remarkably favorable for high time resolution single-molecule measurement especially for time-resolved single-molecule dynamics studies. On the other hand, the pinpoint detection makes it hard for us to detect spatially and temporally random-distributed single-molecule events, for instance, the product releasing and diffusing in the catalytic reactions. Therefore, confocal approach has desirable temporal resolution but suffers from very low throughput. For TIRF microscopy, although the wide-field imaging dramatically improves the possibility of detecting spatially random fluorogenic events, it cannot achieve high time-resolution, limiting the application in molecular 10 dynamics studies. In other words, wide-field imaging with a camera allows TIRF to look at more molecules on a surface or in live cells, but this is limited by the finite frame rate and camera’s noise.

Recently, the integrated apparatus making use of both TIRF z-axial resolution and confocal lateral resolution have been developed to improve the special resolution in single- molecule imaging.30-33 In addition, an integrated spectroscopy system which fulfills the field from TIRF and high time resolution from confocal configuration has been demonstrated.20 It has shown noticeable advantages of addressing the challenges from probing temporally and spatially stochastic events of enzymatic reactions.

11

Figure 1.4. (A) Schematic diagram of confocal microscopy with excitation beam waist around

250-300nm. Only one molecule under the focus can be pinpointed and the fluorescence from this molecule will be recorded. (B) Schematic diagram of TIRF microscopy. The basic characteristics of TIRF microscopy include a wider excitation area (around ten thousand times larger detection area than that of confocal microcopy), more than one molecule can be probed simultaneously and the vertical resolution is around 200 nm within the evanescent field.

12

1.3. Single-Molecule FRET Theory

Fluorescence resonance energy transfer (FRET) is also known as Förster resonance energy transfer in the honor of Theodor Förster who first developed the main FRET theory.

FRET is distance-dependent radiationless energy transfer from an excited donor fluorophore to a nearby acceptor fluorophore, and FRET sensitivity is in nanoscale distance changes. Since the

FRET between two molecules is an important physical phenomenon of considerable interest for the understanding of complex biological systems, it has been widely applied to probe molecular dynamics and interactions at the levels of both ensemble and single molecules. For single- molecule FRET, Taekjip Ha is considered as the pioneer who first used single-molecule FRET for biophysical systems by observing the energy transfer between a single donor fluorophore and a single acceptor fluorophore at the single molecule level.34 Since then, this technique has significantly and extensively contributed to the deep understanding of complex biological systems through the perspectives of dynamics of protein molecules, nucleic acids, and their interactions with other molecules.

1.3.1. FRET Fundamentals

The electric dipole moment is a measure of the separation of positive and negative charges in a system with SI units of Coulomb-meter (C m), or Debye (D). For a pair of opposite charges (one with charge +q and one with charge –q) of magnitude q, the electric dipole moment p is given by, p = qr, where r is the displacement vector pointing from the negative charge to the positive charge. Thus, the electric dipole moment vector p points from the negative charge to the positive charge. The electric dipole moment has a defined orientation with respect to the positions of the charges and is a measure of a molecule’s overall polarity. 13

Transition dipole moment, Mnm, is the electronic dipole moment p associated with the transition between an initial state (m) and a final state (n). It can be calculated from an integral taken over the product of the wave-functions of the initial (m) and final (n) states and the dipole moment operator ( d ),

* M  d  d   | d |   nm n m n m (1.4)

The dipole moment operator is given as

n n n d e xi ,  y i ,  z i (1.5) i 1 i  1 i  1 where the summations are over all the coordinates of all the electrons in the system. The properties of transition dipole moments are: 1) The transition dipole moment is a complex vector quantity that includes the phase factors associated with the two states; 2) Its direction gives the polarization of the transition. For example, in the excitation process of fluorophores, it determines how the system will interact with an electromagnetic wave of a given polarization.

Fluorophores preferentially absorb photons whose electronic vectors are aligned parallel to the transition dipole moment of the fluorophores; 3) Its square gives the strength of the transition dipole interaction, which is due to the distribution of charges within the system.

The sophisticated FRET equations and formulas are further solved based on following assumptions: 1) Weak coupling between donor and acceptor, Hamiltonian Perturbation Theory

H=H0 + V. Since the donor and acceptor are weakly coupled, we can write our Hamiltonian for this problem in a form that can be solved by perturbation theory; 2) Born-Oppenheimer 14 approximation. The B-O approximation states that nuclear and electronic motions can be separated, allowing to express the matrix elements separately in terms of electronic and nuclear

(vibrational, and rotational) parts while the rotational part is neglected; 3) Frank Condon approximation. It states that the electronic transition occurs at a shorter time scale compared to nuclear motion so that the transition probability can be calculated at a fixed nuclear position, allowing one to assume that dipole operators are considered only to act on the electronic states and to be independent of nuclear configuration; 4) Thermal equilibrium system. It introduces

Boltzmann factors for donor excited states and acceptor ground states.

The complete details about how to address the FRET theory will be skipped here, the general FRET theory will be introduced in the next couple of paragraphs. Figure 1.5 illustrate the flow chart of addressing the FRET theory, from transition dipole-dipole interaction to energy transfer rate and then to energy transfer efficiency (one of few key parameters in single-molecule

FRET).

Figure 1.5. Flow chart of addressing FRET theory from transition dipole-dipole interaction to energy transfer rate and then to energy transfer efficiency.

The mechanism of fluorescence resonance energy transfer involves the non-radiative energy transfer of an electronic excitation from a donor to an acceptor, without involving the emission and re-absorption of photons. To be specific, FRET involves a donor in an excited 15 electronic state, which may (distance-dependent) transfer its excitation energy to a nearby acceptor in a non-radiative fashion through dipole-dipole interactions. The theory of energy transfer is based on treating an excited fluorophore as an oscillating dipole that can undergo an energy exchange with a second dipole having a similar resonance frequency. In this manner, resonance energy transfer is analogous to the behavior of coupled oscillators vibrating at the same frequency. Energy transfer occurs when the oscillations of an optically induced electronic donor are resonant with the electronic energy gap of the acceptor. The strength of the donor- acceptor interaction is relevant to the magnitude of the transition dipole interaction.

To describe FRET, there are four electronic states that must be considered: the electronic ground and excited states of the donor (D, D*) and acceptor (A, A*), as shown in Equation (1.6).

Firstly, a donor fluorophore absorbs photons due to the excitation of the incident light. Secondly, the excited donor transfers the excitation energy to a nearby acceptor fluorophore. Finally, the excited acceptor which accepts the energy from donor emit photons with different frequency as the incident light. D hv D* DADA**   (1.6) A*' A hv

The FRET process is often represented by a Jablonski diagram (in the honor of

Aleksander Jabłoński) which illustrates the electronic states and the coupled transitions involved between the donor and acceptor in FRET (Figure 1.6). 35 Absorption and emission transitions are shown in solid lines with arrows (green for donor absorption, yellow for donor emission, and red for acceptor emission). Vibrational relaxation of excited donor is indicated by wavy arrows. The coupled transitions between excited donor and ground acceptor are presented by dashed lines. 16

When the oscillations of an excited donor are resonant with the electronic energy gap of the acceptor, the donor fluorophore can transfer its excited state energy directly to the acceptor instead of emitting a photon or losing energy as heat.

17

Figure 1.6. Jablonski diagram of FRET process between a donor and an acceptor. When the oscillations of an excited donor are resonant with the electronic energy gap of the acceptor, the donor fluorophore can transfer its excited state energy directly to the acceptor instead of emitting a photon.

18

FRET is due to non-radiative dipole-dipole interaction and is characterized by a rate

35 KFRET or EFRET, which is given by Equation (1.7) and Equation (1.8), respectively. EFRET vs donor-to-acceptor distance is plotted in Figure 1.7. The EFRET measurement is sensitive when the separation distance is in the range from 3 nm to 8 nm. 36,37

6 1 R0  KFRET     (1.7)  0 r 

1 EFRET  6 (1.8) 1+rR / 0 where 0 is the donor fluorescence lifetime in the absence of acceptor, r is the donor-to-acceptor separate distance and R0 is the Förster radius. R0, describing the donor-to-acceptor distance where FRET efficiency equals 50 %, is given in Equation (1.9). It is a function of the orientation

2 factor κ , the donor-acceptor spectral overlap J , the donor quantum yield ΦD and the refractive index of the medium n. κ2 can attain the values from 0 to 4. For donor and acceptor which exhibit fast free rotation, κ2 is taken as its average value < κ2 >, which equals 2/3 for isotropic

2 rotation. In general, κ is given by Equation (1.10), where T is the angle between the donor emission dipole and the acceptor absorption dipole, D is the angle between the donor-acceptor connection line and the donor emission dipole, and A is the angle between the donor-acceptor connection line and the acceptor absorption dipole. The spectral overlap integral J involves

-1 normalized donor fluorescence fD, inverse wavenumber λ (cm ) and the acceptor molar

-1 -1 extinction coefficient εA (M cm ), which is given by Equation (1.11).

 2 J R68.79 10 23 D 0 n4 (1.9) 19

22 (cos TDA 3cos  cos  ) (1.10)

 4 Jf DA( )   d   (1.11) 0

In summary, several criteria must be satisfied in order for resonance energy transfer to occur, including the spectral overlap between the donor and acceptor, the relative orientation of the donor-acceptor transition dipole moments, the quantum yield of the donor, and the distance separating the donor-acceptor. Any process that influences the distance of donor-to-acceptor will affect the FRET rate or efficiency, which enables us to refer FRET as a spectroscopic/molecular ruler. 36-38 For example, FRET can be used to sense the distance changes between donor and acceptor that have been labeled on a host molecule or two different molecules, and then the conformational changes of one host molecule or the relative motions of two molecules can be monitored by tracing the FRET efficiency.

20

Figure 1.7. Energy transfer efficiency EFRET as a function of donor-to-acceptor distance r and R0.

EFRET can be sensitively detected when the donor-to-acceptor distance is within the effective range of about 3-8 nm.

1.3.2. FRET Pairs

In most cases, the donor and acceptor employed as a FRET pair are different. It is very important to choose a favorable FRET pair. Referring to the FRET theory discussed in the previous section, the common criteria of choosing dyes for FRET pairs may be summarized: 1)

Donor and acceptor molecules must be in close proximity (typically 30-80 Å); 2) The fluorescence emission spectrum of the donor must have a certain overlap with the absorption spectrum of the acceptor, as described in Equation (1.11); 3) Donor and acceptor transition dipole orientations must be approximately parallel, referred to Equation (1.10). For example, blue fluorescent protein (BFP) coupled with GFP (blue fluorescent protein), or cyan fluorescent protein (CFP) paired with yellow fluorescent protein (YFP), are used as FRET pairs because each pair has spectral overlap between the donor emission and acceptor absorbance, acceptable levels of excitation cross-talk and strong sensitized emission. 39 Quantum dots and organic dyes 21 are also widely used as FRET pairs. 40 In our projects, we choose Cy3-Cy5 FRET pair as fluorescent probes. The reasons why we chose Cy3-Cy5 are as follows: 1) Cy3-Cy5 are water- soluble dyes (with typical quantum yield 0.1 for Cy3 and 0.3 for Cy5 in PBS buffer), which is convenient for protein labeling; 2) R0 of Cy3-Cy5 FRET pair in our calculation is 5.4 nm, which is in the FRET sensitive region (3-8 nm); 3) For the spectra Cy3 (550/570 nm), Cy5 (595/615 nm), emission spectrum of Cy3 and the absorption of Cy5 have good spectral-overlap, no spectral-overlap between absorption of Cy3 and emission of Cy5; 4) 532 nm laser used in our lab locates in the excitation wavelengths region of Cy3, but not in the excitation wavelengths region of Cy5, which can avoid the direct excitation of Cy5 to theoretically ensure that the emission signal from Cy5 is resulted from the energy transfer from Cy3 to Cy5.

Figure 1.8. Structures of Cy3 and Cy5 dyes. The structure difference between Cy3 and Cy5 is that there is one more double bond in Cy5. For Cy5, the electrons are more delocalized and more distributed, the energy gap between the excited state and the ground state is smaller than that of

Cy3. Thus, the absorption spectra of Cy5 will red-shift to longer wavelength. 22

1.3.3. FRET Detection

The efficiency of FRET can be evaluated by ratio-metric methods on the basis of detecting its effects on the photophysics of the fluorophores. Because energy transfer from donor to acceptor will result in a decrease of donor fluorescence intensity and an increase of acceptor fluorescence intensity, a ratio-metric determination of the two signals can be made. Another method is based on the fact that FRET can decrease fluorescence lifetime of the donor fluorophore. This can be made by measuring the fluorescence lifetime of the donor fluorophore in the presence and absence of the acceptor. Therefore, the energy transfer efficiency can be determined on basis of fluorescence intensity of donor and acceptor pair or the donor lifetime in the presence and absence of the acceptor, which is described by 41,42

IA  DA EFRET  1  (1.12) IIDA  D

 AA   (1.13) DD

where IA is the acceptor fluorescence intensity and ID is the donor fluorescence intensity in the

FRET process. DA and D are donor lifetime in the presence (DA) and in absence (D) of acceptor, respectively. γ incorporates the donor and acceptor quantum yields Φ and the detection efficiencies η of both channels, defined in Equation (1.13).

Recently, the FRET measured by fluorescence intensity has been widely employed in single molecule studies of protein conformational dynamics, 43,44 protein folding and unfolding,

37,45,46 RNA folding and unfolding, 36,47 enzyme kinetics, 48,49 and bimolecular associations. 50,51

Nevertheless, the energy transfer efficiency from this method largely depends on the 23 fluorescence intensity of the donor or the acceptor, which makes it highly sensitive to environmental noise. On the other hand, FRET determined by only measuring donor lifetime can be a more accurate method to probe conformational dynamics of protein under complex measurement conditions, such as the AFM tip-enhanced single-molecule spectroscopic and imaging measurements.21,52-54

1.4. Biological Applications of Single-Molecule FRET

Single-molecule FRET is a powerful approach for detecting protein-protein interactions, enzyme activities and small molecules in bimolecular systems in terms of mapping out three- dimensional molecular distances, monitoring conformational motions in real time, and catching molecular motions in action.55 This approach has made significant and extensive contributions to the understanding of complex biological dynamics through the perspectives of heterogeneous dynamics of protein molecules, nucleic acids, and their interactions with other molecules.21,22,36,56-59 For example, single-molecule FRET has been used to study RNA folding pathways,36 hairpin ribozymes intimidate states, 60 DNA bubbles kinetics,61 epidermal growth factor receptor dimerization,62 conformational dynamics of enzymes, 63-65 and four-way DNA

Holliday junction. 52,66

Single-molecule FRET is effective in probing protein conformational dynamics. 54 64,65

Most of enzymatic reactions involve multiple-step process, multiple conformational changes, and complex substrate-enzyme interactions. Single-molecule FRET has been demonstrated as a powerful approach in probing dynamic conformations of proteins and understanding complex structure-function relation.67,68 For example, H. Peter Lu’s group has been focusing on conformational dynamics of enzymes via single-molecule FRET.69 Multiple intermediate states formed during the open time in the open-close hinging-bending motions of T4 lysozyme has 24 been reported and further proved by single molecule electronic circuit studies.70 The results from both the optical and electric perspectives enrich the knowledge about understanding lysozyme’s catalytic activities and conformational motions.

Protein folding and dynamics in three-dimensional conformations are essential for biological functions of most proteins/enzymes/RNA. The complexity nature of protein folding and unfolding, like multiple intermediates states and multiple pathways possibly involved along the process of protein folding into native states, has hindered the application of conventional bulk methods. While ensemble-averaged measurements cannot resolve such complexity, single molecule allow us to observe multiple steps nature of protein folding and distinctive folding pathways.45,71,72 With the support from MD simulations and statistical modelling of folding, single-molecule FRET is playing a significant role in probing protein folding mechanisms. In general, single-molecule FRET has been applied to distinguish folded and unfolded intermediates state of donor-acceptor labeled proteins on the basis of different FRET efficiency subpopulations, as the molecule transits between the unfolded and folded states, and further provide information to predict folding mechanisms.73 74

1.5. Single-Molecule Fluorescence Anisotropy

Fluorescence anisotropy is capable of determining the rotational correlation time of the fluorescence probe, thus providing insights into the motions of probe, and orientation/rotation or mobility of subdomains or the entire molecule.75-77 Changes in probe’s orientation reflects the rotation or mobility of the target macromolecule where the probe is attached. The methods of measuring rotational or tilting motions by fluorescence anisotropy in single molecules have been reviewed and discussed 76,78,79 Lu and co-workers have successfully probed nanosecond protein 25 motions of Calmodulin and T4 lysozyme by single-molecule fluorescence anisotropy.80,81 The fluorescence anisotropy r(t) is defined as the difference between the vertically and horizontally polarized fluorescence emissions divided by the total fluorescence emission, given by

I|| ()() t G* I t rt() (1.14) I|| ( t ) 2 G* I ( t )

where Iǁ (t) and I(t) are the fluorescence intensities of the parallelly (ǁ) and horizontally () polarized emission components under vertically polarized excitation. G is the correct coefficient compensation for the different instrumental detection efficiencies of the various polarized components of the emission, accounting for the ratio of the sensitivities of the detection system for vertically and horizontally polarized light. Typical anisotropy values range from -0.2 for probes with unrestricted motion to 0.4 for those that are immobile.

Several factors can depolarize the measured anisotropy to values lower than 0.4 (the maximum theoretical values), such as the numerical aperture of the objective, the angle difference between the absorption and emission dipole, molecular rotation related rotational diffusion, and et al. 82 In terms of rotational diffusion process, the expected anisotropy is given by the Perrin Equation.75,83

r 0 DA r  (1.15) 1/DA where r0 (assumed to be 0.40 here) is the fundamental anisotropy in the absence of rotational diffusion, and rDA is donor’s anisotropy.  is the rotational correlation time for the diffusion process, an indicator of how rapidly a molecule rotates. 26

1.6. Single-Molecule Conformational Dynamics of Enzymes

The living cell is the spot where tremendous biochemical activity (metabolism) takes place. Metabolism is the process of life-sustaining chemical and/or physical changes within the cells of living organisms. The dynamic biochemical activity, chemical transformations, and physical changes happened inside the living cell are regulated by enzymes in terms of allowing living organisms to grow new tissue, replace the old tissue, dispose waste materials and reproduce life-essential materials. Enzymes, responsible for the thousands of activities that sustain life, are catalysts defined as the acceleration of a chemical reaction with high efficiency and high fidelity but without themselves undergoing any permanent chemical change. They are neither consumed by the reactions they catalyze nor do they alter the equilibrium of these reactions. By lowering the activation energy for a reaction, the reaction rates are increased by millions of times faster than those of reactions without catalysts to pace with the metabolism sufficient for life.

In a typical enzymatic reaction, the catalysis process involves active substrate-enzyme complex formation, chemical transformation, and product releasing, as we know of the

Michaelis-Menten mechanism. 84,85 The basic enzymatic reaction can be expressed as follows, involving an enzyme binding to a substrate to form a complex, and then the complex is further converted into a product and the enzyme

k 1  kcat E S ES  E  P (1.16) k 1 where E represents the enzyme catalyzing the reaction, S is the substrate, ES is the enzyme- substrate complex, and P is the product. K1, K-1, and Kcat are rate constants for this reaction. 27

In the past decades, there are intensive efforts to investigate and understand how the enzymes work for sustaining living cells and are capable of changing the biological activity pathways as well as accelerating the biological reaction rates by millions of times. It has been recognized that the conformational motions are essential for the catalytic functions of enzymes.86-92 For example, molecular dynamics (MD) simulation and statistical modeling have made significant contributions to characterize conformational motions and reaction fluctuations of enzymes under enzyme-catalyzed reactions.93-100 More than often, subtle conformational changes even play a crucial role in enzyme functions, and these protein conformations are highly dynamic rather than being static, involving in multiple intermediate states and multiple conformational coordinates.65,87,90,101-105

The dynamics of enzymatic reaction have been widely investigated by ensemble- averaged experimental approaches. 106-110 Nevertheless, the heterogeneous nature of space-related and time-related reaction dynamics, the complex conformational motions, and the non- synchronization of those complexity among multiple molecules often limit the applications of ensemble-level measurements to acquire comprehensive mechanism of enzymatic reactions. 111

Single-molecule approaches, on the other hand, conquer the non-synchronization issue by studying one molecule under a certain reaction condition at a time. Single-molecule spectroscopy have been serving as an informative tool to unravel the complexity and inhomogeneity of enzymatic reaction. 4,17,63,65,103,112-117 For instance, static disorder (stationary heterogeneity of a physical parameter, like reaction rates, within a large ensemble of molecules) and dynamic disorder (time-dependent fluctuations for each individual molecules) under enzymatic reactions, are intrinsically indistinguishable in ensemble-averaged measurements, have been revealed by probing co-enzyme redox state turnovers rates 4 and enzymatic reaction product formation in real 28 time. 112 The wide distributions of molecular properties, such as reaction rates, dwelling times, transition times and so on, somehow are the results of static and dynamic disorder. It has been suggested that the dynamic rate fluctuations are tightly related to protein conformational changes of enzymes during the catalysis process. 4,112-114 For example, H. Peter Lu et al had observed the enzymatic turnover rate fluctuation of single cholesterol oxidase molecule within the enzymatic reaction time-scale, and attributed this rate fluctuation to the conformational motions of this enzyme during the catalysis process.4 It has been widely accepted that conformational motions are essential for catalytic functions of many enzymes in terms of defining enzymatic dynamics, energy landscape, reaction nuclear coordinates, and reaction pathways.65,70,103,104,108,110,114,116,118-

122 The highly dynamic multiple conformations formed in the catalytic turnover make single- molecule fluorescence spectroscopy as an effective approach to obtain molecular level insights into conformational transition dynamics.17,63,65,103,115-117

My research focus on the studies of enzymatic conformational dynamics of two enzymatic systems, T4 lysozyme (Chapters 2 and 6) and HPPK (Chapters 3,4,5,7), by means of single-molecule one-dimensional FRET spectroscopy (Chapters 2-5) and multi-dimensional spectroscopy (Chapters 6-7). The detailed biological introduction of these two enzymes will be given in corresponding chapters. Motivated by capturing conformational dynamics of enzymes under enzymatic reactions, we have also correspondingly developed single-molecule approaches including single-molecule AFM-FRET nanoscopy (Chapter 3), single-molecule photon stamping

FRET spectroscopy (Chapters 4 and 5), and single-molecule multi-parameter photon stamping spectroscopy (Chapters 6 and 7).

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37

CHAPTER 2. EXTRACTING MULTIPLE INTERMEDIATE STATES OF SINGLE-

MOLECULE T4 LYSOZYME FROM BUNCHED SUB-STEP CONFORMATIONAL

MOTIONS

Protein conformational dynamics play critical roles in enzymatic reactions involving multiple steps of enzyme-substrate interactions. Obtaining molecular level insights into the conformational transition dynamics of enzyme-substrate complex from the inactive state to the active state is fundamental for understanding enzymatic function and dynamics. In this chapter, we have used single-molecule fluorescence resonance energy transfer to characterize the real- time conformational transition dynamics of T4 lysozyme under enzymatic reactions, probing T4 lysozyme domain fluctuations involving open-close hinge-bending conformational motions. Our experimental results suggest complex dynamic behaviors of both Poisson and Non-Poisson statistics including convoluted Poisson distributions and Gaussian-like distributions of the time duration in forming the active enzyme-substrate complex state, and the time bunching features of conformational dynamics. Further analyzing our experimental results by a simulation of state-to- state transitions based on a Markov dynamic model, we are able to obtain a mechanistic understanding: 1) T4 lysozyme conformational changes follow multiple pathways, involving multiple intermediate states; 2) Sub-step conformational motions, associating with multiple nuclear coordinates and being projected in a common FRET-sensitive nuclear coordinate, give rise to multiple conformational intermediate states in the formation process of active enzyme- substrate complex state; 3) The consequence of the multiple pathways, intermediate states, and nuclear coordinates is the time bunching effect revealed and identified by our single-molecule spectroscopic measurements, implying that time durations of conformational changes tend to 38 bunch in a narrowly distributed time window. The physical picture of multiple intermediate states along with bunching effect suggests that the conformational dynamics of T4 lysozyme shows a complementary characteristic behavior of both convoluted enzyme conformation selection and induced-fit dynamics driven by substrate-enzyme interactions.

2.1. Introduction

2.1.1. Conformational Flexibility

According to the Michaelis-Menten mechanism, a typical enzymatic reaction process consists of substrate binding (E + SES), substrate-enzyme complex formation (ESES*), chemical reaction (ES*EP), and product releasing (EPE + P), where E, S, and P represent enzyme, substrate, and product, respectively. In this process, the interplay between conformational stability and flexibility is critical for enzymatic activity: stability is required for retaining native three-dimensional structures, and flexibility is necessary for allowing efficient substrate binding, complex formation, and product releasing. In terms of flexibility, it is generally recognized that conformational motions are essential for catalytic functions of many enzymes in defining enzymatic dynamics, energy landscape, reaction nuclear coordinates, and reaction pathways.1-14 To function, an enzyme adjusts its conformational flexibility from inactive state to a catalytically competent active state ES*, the specific binding enzyme-substrate complex in which the reactive groups are brought into close proximity in a position-favoring catalysis. Enzymes may adopt multiple intermediate states or transient states in the microseconds to milliseconds timescale before reaching the catalytically competent active state. 5,9,10,15-17 In the ensemble-averaged studies, a set of experimental methods, such as X-ray crystallography,18

NMR relaxation dispersion,12,19 and mass spectroscopy,14,20 have been developed to identify intermediate states. In the single-molecule studies, single-molecule fluorescence spectroscopy 39 has served as an effective approach to obtain molecular level insights into conformational transition dynamics.5,9,13,21-24

2.1.2. Multiple Conformational Intermediate States

The results of steady-state kinetic measurements, such as single-exponential decay of waiting time distribution or two-state Gaussian-like distribution of FRET efficiency, have suggested that many enzymes exhibit two major states (open/close or on/off). In a dynamic equilibrium, the two states associate with two distinct grooving structures, from which the enzyme can be envisioned as an open-close hinge with the active site locating between the two halves of the hinge. Furthermore, more sequential or parallel states in the dynamic non- equilibrium buried in a two-state model, have been reported on the basis of multiple-exponential or non-exponential decay of the waiting time distributions.24,25 Those multiple-exponential or non-exponential behaviors have been attributed to fluctuating reaction rates with dynamic disorder in terms of catalytic or conformational dynamics.24-26 Correspondingly, different statistical modeling analyses have been applied to reveal the hidden events under those behaviors.16,27,28 For example, the hidden Markov model has been reported to extract the sequence of hidden states from observables through the construction of probabilistic model parameters such as transition probability matrix, emission probability matrix and initiation probability matrix.29-32 By all possible approaches, although the complex hidden dynamics is still difficult to resolve, the complexity of multiple-state conformational dynamics can be better identified and characterized by single-molecule spectroscopic experiments and related model analyses. 40

2.1.3. Introduction of T4 Lysozyme

T4 lysozyme, a member of the lysozyme family produced by bacteriophage, has been extensively studied in both ensemble-average 33-35 and single-molecule measurements 7,8,11,16,22,36-

38 to uncover the catalytic mechanism and conformational dynamics. T4 lysozyme has two domains connected by an -helix. The two domains undergo hinge-bending open-close conformational motions under enzymatic reaction conditions.34,35 T4 lysozyme catalyzes the hydrolysis of poly-saccharide chains in bacterial cell walls by attaching and binding to the cell walls and further degrading the cell.33 The enzyme specifically cleaves the glycosidic bonds connecting the repeating subunits of cell walls between N-acetylglucosamine (NAG) and N- acetylmuramic acid (NAM) that are substituted with peptide side chains.39 Previously, we have probed T4 lysozyme hinge-bending conformational motions by single-molecule FRET spectroscopy and imaging.16,22 Inhomogeneity of overall enzymatic reaction rate constants from molecule to molecule and a time bunching effect of conformational motion dynamics have been revealed by single-molecule spectroscopic results, molecular dynamics simulation, and a random walk model analysis.16,22 In addition, our above observations have been further confirmed by recently reported works using single-molecule electronic circuits to sense T4 lysozyme conformational motions through circuit conductance.11,38 In the chapter, we report a new progress on our single-molecule spectroscopic experiments and model analysis. In this work, we are able to continuously observe single T4 lysozyme for extended periods of time, for example, a couple hundreds of seconds. More hidden information about conformational transition dynamic and bunching structure are directly observed from single-molecule experimental results. A

Markov process model is applied to reproduce our experimental results and to decipher 41 intermediate states from bunched sub-step conformational motions during the multi-step open- close conformational process under the enzymatic reactions.

2.2. Materials and Methods

2.2.1. Materials

Wild-type T4 lysozyme plasmid is provided by Prof. Brian Matthews from the University of Oregon through Addgene Company. The wild-type T4 lysozyme has two cysteines groups

(residue 54 on N-domain and residue 97 on C-domain), which are accessible to thiolation reactions. Two dyes of a Cy3-Cy5 FRET pair (GE Healthcare Company) are non-selectively tethered on these two cysteines to sense the relative motion between two domains in T4 lysozyme. The individual donor-acceptor labeled T4 lysozyme can be distinguished and selected by two-channel optical images because only donor-acceptor labeled molecule can simultaneously exhibit emission spots in two-channel optical images. The detailed description of site-specific donor-acceptor dye labeling protocol and the discernible optical images of single donor-acceptor labeled T4 lysozyme can be found in our previous publication.40 Figure 2.1 shows the crystal structure of wild-type T4 lysozyme labeled with a FRET pair (Cy3-Cy5) and the corresponding ensemble-level emission spectrum. The emission peaks of Cy3 and Cy5 are well separated, which is a favorable condition for FRET measurements. Peptidoglycan from Micrococcus luteus, a major component of the bacterial cell wall, is purchased from Sigma-Aldrich and used as a substrate without further purification. The substrate is suspended to a final concentration of

25µg/mL in PBS at pH 7.3 during experimental measurements. 42

2.2.2. Single-Molecule Measurements

In our single-molecule FRET experiments, T4 lysozyme is tethered through a bi- functional cross-linker molecule to a hydrocarbon modified glass cover-slip surface. The glass cover-slip is first sonicated with acetone for half an hour, followed by rinsing with alcohol solution and distilled water three times. The clean cover-slip is treated overnight with a 10%

(v/v) mixture of 3-mercaptopropyl-trimethoxysilane and isobutyltrimethoxysilane (1/1000 ratio) in 15.0 mL DMSO. After rinsing with ethanol and water, the cover-slip is put in the PBS buffer solution (pH 7.3) for one hour to remove un-reacted solvent. The cover-slip is then incubated with 40.0 μL 250 mM bi-functional crosslinker stock solution (NHS-PEG6-Malemiade, Thermo

Scientific) in 12.0 mL PBS buffer solution for two hours at 4°C. The amine-to-sulfhydryl cross- linkers with hydrophilic polyethylene glycol (PEG) spacer arms are attached to the glass cover- slip surface. After additional washing, the cover-slip is incubated with 0.66 nM T4 lysozyme in the PBS buffer for two hours at 4°C followed by rinsing with water and PBS buffer. After the linkage between amine-reactive group of NHS-PEG6-Malemiade and T4 lysozyme’s lysine group, the tethered enzyme sample is assembled on the glass cover-slip surface. During our single-molecule measurements, the assembled T4 lysozyme on the cover-slip is further incubated with 25.0 μg/mL substrate for half an hour at room temperature in PBS buffer solution (pH 7.3).

The Trolox-oxygen scavenger solution, containing 0.8% D-glucose, 1.0 mg/mL glucose oxidase,

0.04 mg/mL catalase, and 1.0 mM Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), 15,41-43 is added to the above sample chamber to prevent the possible photo-bleaching and photo-blinking of the Cy3-Cy5 labeled T4 lysozyme molecules for lengthening the measurements time. 43

A detailed description of our experimental setup is presented in our previous reports.15,40,44 Briefly, an inverted confocal microscope with 532 nm CW (continuous wave) excitation generated from diode-pumped solid-state laser is used to image single T4 lysozyme molecule and record the single-molecule donor/acceptor intensity trajectories. Emission signals from donor Cy3 and acceptor Cy5 are detected separately by a pair of Si avalanche photodiode detectors (APD, SPCM-AQR-14, Perkin Elmer Optoelectronics) after passing through a 640 nm dichroic beam splitter (640dcxr, Chroma). An approximate 15% leakage from donor fluorescence to acceptor channel is taken into account and was corrected by Matlab programming in our data analysis process.

2.2.3. Markov Model Analysis

T4 lysozyme exhibits hinge-bending open-close conformational motions under enzymatic reactions. In our study, the open time or the formation time of active enzyme-substrate complex

(ES*), tES*, is determined by the time duration between when an enzyme opens up to intake substrate and closes down to form ES* as shown in Figure 2.2A. To simulate the probability density of tES* and to determine the number of conformational intermediate states present in this open process, a Markov process model (Figure 2.2B) is proposed here on the basis of experimental observations. We first assume that multiple conformational intermediate states are involved during the open process and the adjacent state-to-state transition is a Poisson process; therefore, the conformational state-to-state transitions are homogenously governed by single exponential decay kinetics, which is defined by

1 f() t et/  (2.1) 44 where f (t) is the probability density function of time duration t of an intermediate state in a single state-to-state transition step, and is the mean value of time duration. We assume =

3.25 ms in our simulation, based on our experimentally determined sub-step time using a

Random Walk Model analysis reported in our previous publications.7,8,16,22 For a standard

Markov process, n (=1, 2, 3, 4, 5, 6) identical Poisson processes in succession are involved in the overall dynamics (Figure 2.2B). Therefore, the formation time of intermediate states ESn or ES* is given by

1 f() t tnt1/ e   n (n  1)! (2.2) where f (t) is the probability density function of the overall time duration, which is the formation time in forming ESn or ES*, and n is the length of transition steps. A specific number of n (n=1,

2, 3, 4, 5, 6) is assigned to generate simulated data.

16,22 The two-dimensional (2D) joint probability distribution described as f(ti, ti+j) is used to identify bunching structures by analyzing pairs of formation times ti and ti+j separated by index number j, on the basis of experimental and simulated data at different transition steps. For any pair of formation times (ti, ti+j), ti vs ti+j is plotted in x-y plane. The occurrence of these pairs is counted as probability in the z dimension and shown by a color bar in the 2D joint probability distribution. 16,22 45

Figure 2.1. (A) Crystal structure of wild-type T4 lysozyme (PDB-code, 3LZM). A Cy3-Cy5

FRET pair are covalently labeled to two cysteines on a single T4 lysozyme: Cys 54 on N-domain and Cys 97 on C-domain, where two Cys are highlighted with dots. The fluorescence intensity or

FRET efficiency fluctuations of this FRET pair reflects relative distance changes between the two domains involved in open-close hinge-bending conformational motions. (B) Normalized fluorescence spectrum of Cy3-Cy5 labeled T4 lysozyme. The emission peaks of Cy3 and Cy5 are spectrally well-separated. In single-molecule FRET measurements, donor and acceptor fluorescence signals are further split by a dichroic beam splitter with appropriate optical filters.

46

Figure 2.2. (A) Scheme of responsive donor-acceptor distance and FRET efficiency changes associated with enzyme open-close hinge-bending motion in the process of ES* formation.

Multiple intermediate states of non-specific enzyme-substrate complex (ES) are involved. (B) A

Markov process model of multiple intermediate states. The conformational intermediate states sequence of T4 lysozyme being modeled is a Markov process. From E+S to ES*, the enzyme can adopt multiple intermediate states in a series of identical state-to-state transitions (n=1, 2, 3,

4, 5, 6). Each state-to-state transition is a Poisson process with transition time governed by single-exponential statistics. The formation time of ES*, also considered as open time, is the time duration from E+S to ES*.

47

2.3. Results and Discussion

To obtain insights into the conformational motions of T4 lysozyme, we have used single- molecule FRET spectroscopy and imaging to probe the relative domain motions by monitoring the fluorescence resonance energy transfer between a Cy3-Cy5 FRET pair covalently tethered to

T4 lysozyme domains involving open-close hinge-bending motions. Figure 2.1A shows the crystal structure of wild-type T4 lysozyme labeled with a FRET pair (Cy3-Cy5) capable of probing the open-close hinge-bending motions. The labeling sites, Cys 54-N domain /Cys 97-C domain, are highlighted using dots shown in Figure 2.1A, where dye substitutions do not perturb

T4 lysozyme activity on the basis of enzymatic reaction control assay.45 Figure 2.1B is the ensemble-level emission spectrum of Cy3-Cy5 labeled T4 lysozyme with well-separated emission peaks. Peaks at approximately 565/610 nm come from Cy3 emission, and the peak at approximately 665 nm comes from Cy5 emission. In single-molecule measurements, T4 lysozyme is tethered through a bi-functional cross-linker molecule to a hydrocarbon modified glass cover-slip surface. In this way, the tethered enzyme is fully mobile and no other perturbations on its activity are left except for spatial confinement from tethering.46 Donor and acceptor intensity trajectories are simultaneously recorded under enzymatic reactions with 25.0

μg/mL peptidoglycan, shown separately in Figure 2.3A. Green dots connected by lines indicate

Cy3 (donor, D) emission and red ones indicate Cy5 (acceptor, A) emission. The corresponding

FRET efficiency trajectory is deduced from EFRET = IA/[IA+ID], where ID, IA are fluorescence intensities of donor and acceptor, respectively (Figure 2.3B). The anti-correlated features of donor intensity decreasing along with the increasing of acceptor intensity (vice versa), are evident in D-A intensity trajectories (Figure 2.3A and inset). Furthermore, the anti-correlated

D-A intensity fluctuation behavior is not observed in the absence of substrates, as we have 48 reported in our previous work.22 From the vertical inset of Figure 2.3B, the FRET efficiency wiggling between an averaged higher FRET state and a lower FRET state is observed beyond the measurement shot noise. Since both the anti-correlated D-A intensity fluctuation and FRET wiggling are the reflections of D-A distance charges associated with the enzyme conformational motions, we attribute each wiggling to individual switching event between open and close conformational states during T4 lysozyme open-close hinge-bending conformational motions under the enzymatic reaction conditions. The bimodal Gaussian-like distribution of FRET efficiency (lateral inset in Figure 2.3B) further proves this attribution.

During the whole process of T4 lysozyme open-close hinge-bending conformational motions, the enzyme first opens up to intake the substrate initiated by electrostatic attraction between enzyme and substrates, and then forms the nonspecific enzyme-substrate complex (ES), corresponding to the process E+SES. After several steps of conformation adaptability, specific enzyme-substrate complex (ESES*, from inactive state/states to active state ready to react) is formed, followed by the chemical reaction and product releasing (ES* EPE+P).

Our previous MD simulation results have implied no significant conformational motions in the process of the chemical reaction or product releasing in T4 lysozyme enzymatic reaction.22

Therefore, reflected in the FRET efficiency or donor-acceptor FRET-dimensional nuclear coordinate, FRET remains high or donor-acceptor distance remains unchanged during the chemical reaction and product releasing process. We have modeled that the open process consists of E+SESES*, in which the time span is the open time or the formation time of

ES*. The following close time is further considered as the time duration of the chemically hydrolysis reaction, product releasing, and the enzyme searching for the next substrate. The left panel in Figure 2.2A shows the response process of donor-acceptor distance and FRET 49 efficiency changes associated with enzyme open-close hinge-bending motions, in which the formation of ES or ES* involve remarkable domain motions reflected in the FRET-dimensional or donor-acceptor distance nuclear coordinate. To quantitatively analyze the hinge-bending conformational dynamics, we have used a thresholding algorithm approach: 22,47 the cutoff between open and closed states is set as 50% of the bimodal FRET efficiency distribution

(Figure 2.3B) to read out the formation time of ES* or open time. We have considered the time duration of each FRET wiggling below the cutoff value as the formation time of ES*.

50

Figure 2.3. (A) A typical portion of single-molecule intensity trajectories recorded from single

Cy3-Cy5 labeled T4 lysozyme under enzymatic reactions with 25.0 μg/mL peptidoglycan.

Green dots connected by lines indicate Cy3 (donor) emission and red ones indicate Cy5

(acceptor) emission. The inset with a magnified time axis shows anti-correlation between donor and acceptor intensity fluctuations. (B) The corresponding FRET efficiency trajectory calculated from donor/acceptor intensity trajectory in (A). The detection of FRET is based on the intensity ratio-metric method. The vertical inset shows energy transfer efficiency wiggling of individual switching event between open and close conformational states. The FRET efficiency distribution fitted with bimodal Gaussian-like functions is shown in the lateral inset.

51

On the foundation of the above thresholding algorithm, we have obtained a series of formation time distributions characterized with distinct mean values from T4 lysozyme FRET trajectories in Figure 2.4 (A1-A6). Figure 2.4A1 shows a single exponential distribution of formation times. We suggest this single exponential distribution as a result of single state-to- state transition regulated by a Poisson process. While, Figures 2.4A2-A6 present neither single- exponential nor multi-exponential distribution, most likely due to consecutive sub-step motions associated with multiple convoluted intermediate states. Furthermore, after scrutinizing into mean values of each formation time distribution, we have found a solid and repeatable pattern: mean formation time derived from each distribution under enzymatic reactions takes on only a discrete set of values. This pattern does not show in control experiments without substrates. The formation times present approximately single or multiple folds of a certain value (3.25 ± 0.3 ms), that is, the formation time increases geometrically if listed with step-by-step rising trend shown in Figure 2.4 (A1-A6). From Figure 2.4A1 to 2.4A6, the distributions gradually exhibit more and more close to Gaussian-like distributions, confirming our previous results that the formation times populate a Gaussian-shaped distribution with mean 19.5±2.0 ms. 16,22 We have used convoluted intermediate states on the basis of modified Mechaelis-Menton mechanism computational modeling to support our expectation that T4 lysozyme transits through several intermediate states as the enzyme forms the ES*.16,22

Considering above formation time pattern, suggested multiple conformational intermediate states formed in T4 lysozyme open-close hinge-bending conformational motions, and Mechanelis-Menton mechanism, we propose a Markov process model for T4 lysozyme conformational dynamics under enzymatic reactions. The Markov model of intermediate states is based on the following assumptions: 1) the state-to-state transitions are governed by single 52 exponential kinetics; 2) The likelihood of next intermediate state exclusively depends on the current state not on the sequence of states that preceded it; 3) The conformational intermediate states sequence being modeled is a Markov chain. The model details are shown in Figure 2.2. In this model, we assume 3.25 ms as the mean time duration of single state-to-state transition and n

(=1, 2, 3, 4, 5, 6) as consecutive state-to-state transition steps. This model of multiple steps and multiple intermediates agrees on the recently result that T4 lysozyme exhibits statistically distinguished slow processive motions and rapid nonproductive motions in the time-scale of 20-

50 s−1 and 200-400 s−1, respectively.39

Figure 2.4 (B1-B6) shows the simulated data of time duration in forming ES* or ES in terms of multiple state-to-state transitions (n=1, 2, 3, 4, 5, 6), on the basis of Markov model process. Unambiguously, the simulated formation time distribution profile of ES* corresponding to different sub-step conformational motions are in good agreement with the real experimental formation time distribution in Figure 2.4 (A1-A6). The Gaussian-like formation time profiles are observed in both experimental and simulated results, implying that the open-close conformational motions of T4 lysozyme in the formation of active state ES* involve multiple intermediate states along with multiple sub-step conformational motion pathway. It has been suggested that the dominant driving force for ES formation is the electrostatic attraction of surface positively charged amino acid residues (Arginine and Lysine) in T4 lysozyme interacting with the negatively charged polysaccharide substrate. The driving force for ESES* process includes the formation of six hydrogen bonds in the active site of ES*.22 The complex heterogeneity and dynamics of driving force may play significant roles in the multiple transition pathway and multiple intermediates of T4 lysozyme conformational dynamics. In the current model, the conformational motions of forming ES or ES* are suggested to involve multiple 53 nuclear coordinates that can be projected to a common FRET-dimensional nuclear coordinate.

Reflected in the experimental results, each sub-step motion cannot necessarily induce a significant increase or decrease in FRET efficiency or in donor-acceptor distance if molecular orientation/domains orientations are taken into account. Although the interpretation of formation time or open time distribution is somewhat model-dependent, underlying conformational transition dynamics of T4 lysozyme should be representable by a Markov chain of state transitions based on the reproducible results between experimental and simulated data in views of open time duration of T4 lysozyme hinge-bending conformational motions.

54

Figure 2.4. (A panel) Distributions of experimental formation times or open times during T4 lysozyme open-close hinge-bending conformational motions under enzymatic reactions. Each experimental histogram is deduced from a single T4 lysozyme FRET trajectory. Formation time is the time-span of each wiggling of the FRET trajectory below the thresholding cutoff, determined by 50% of the bimodal Gaussian-like FRET distribution. (B panel) Distributions of simulated formation times on the basis of Markov process model associated with different transition steps (n=1, 2, 3, 4, 5, 6). 55

A complex chemical reaction is composed of multiple elementary reaction steps, giving rise to non-exponential behavior of probability density function of waiting time. In a feature article of J. Phys. Chem. B, Dr. Cao and Dr. Silbey have provided a useful model to predict the number of elementary rate steps between two states along the shortest pathway.48 In their model, the number of elementary steps is determined by the initial rise of the waiting time distribution

L lim (t )  kL t t (2.3) where ()t is the probability distribution of waiting time t between two states, the exponent L is the minimal number of elementary reaction steps, and kL is the product of the rate coefficients along the shortest pathway.

We apply this initial-rise method to infer the shortest pathway between a pair of states and the minimal elementary steps between E and ES*. In our case, we use occurrence (the product of probability and total number of events N) instead of probability, and formation time instead of waiting time to infer the number of elementary steps. We make the natural logarithm transformation of the whole Equation (2.3), generating a new Equation (2.4).

Ln[ N lim ( t )] Ln [lim ( t ) N ] tt  L (2.4) Ln()()*() NkLL t  Ln Nk  L Ln t

In Equation (2.4), Ln[lim ( t ) N ] vs Ln() t , the slope indicates the minimal number of t elementary reaction steps (L). Figure 2.5 shows the result of the relationship between occurrence and formation time derived from the original data in Figure 2.4A6. From E to ES*, the shortest 56 pathway only needs one elementary step. The same results of one elementary step between E and ES* from Figures 2.4A4 and 2.4A5 are also obtained.

Figure 2.5. Linear regression between Ln (occurrence) and Ln (formation time). The original experimental data is from Figure 2.4A6. One elementary step from E to ES* through the shortest pathway is obtained based on Dr. Cao’s model.

The one elementary step from enzyme to active enzyme-substrate complex obtained by this model agrees with the results from our model in this paper. Our single-molecule experimental results indicate multiple pathways involving multiple transition steps in the formation of active enzyme-substrate complex. Theoretically, the transition from E to ES* can be done through the shortest pathway with one elementary reaction step. From our experimental data, most-likely, this transition needs multiple elementary steps. Regardless of the shortest pathway or other pathways, both our model and Dr. Cao’s model suggest the existence of multiple pathways from E to ES*. In fact, besides one step or six steps, probably two, three, four, five, seven or more steps are involved in different pathways along complex conformational 57 dynamic kinetics. This inhomogeneity can be only observed through single-molecule measurements, not in bulk due to its averaging effect.

The bunching effect characterized by the clustering of conformational motion times during catalysis has been previously reported.16 Nevertheless, the bunching nature of conformational motions has been only probed by the bunching structure of simulated open time distribution because of deficient experimental data. Here, we fill this gap to show bunching structure not only in simulated results but also in experimental results. Figure 2.6 shows 2D joint probability distributions f (ti, ti+j) of adjacent open times for multiple state-to-state transition steps n (=1, 3, 6). Figure 2.6(A1, A2, A3) demonstrates 2D joint probability distributions derived from the data in Figure 2.4(A1, A3, A6). Figure 2.6(B1, B2, B3) illustrates the distributions calculated from simulated data in Figure 2.4(B1, B3, B6). Clearly, the theoretical results from Markov model are in good agreement with the experimental data from single-molecule spectroscopic results. Both Figure 2.6A1 and 2.6B1 show similar wing structures deducted from exponential open time distribution (Figure 2.4A1 and 2.4B1, typical Poisson distributions), implying that there is no bunching effect but stochastic nature of Poisson rate process in single state-to-state transition. For successive multiple state-to-state transitions governed by multiple consecutive

Poisson rate processes, such as 3 or 6 transitions, non-exponential or Gaussian-like distributions

(Figure 2.4A3 or 2.4B3, 2.4A6 or 2.4B6) give rise to bunching effect (in Figure 2.6A2 or 2.6B2,

2.6A3 or 2.6B3), implying the bunching nature in multiple sub-step conformational motions stemming from enzyme-substrate interactions. 58

Figure 2.6. 2D joint probability distribution of adjacent open times for multiple transition steps

(n=1, 3, 6) with 50 ms×50 ms x-y plane. (A1- A2-A3) Experimental results derived from the data in Figure 2.4(A1, A3, A6). (B1-B2-B3) Simulated results calculated from data in Figure 2.4(B1,

B3, B6). Both (A1) and (B1) show similar wing structures deducted from exponential open time distributions (Figure 2.4A1 and 2.4B1), implying that there is no bunching effect in single state- to-state transition. For successive multiple state-to-state transitions, such as 3 or 6 transitions, non-exponential distributions (Figure 2.4A3 or 2.4B3, 2.4A6 or 2.4B6) give rise to the bunching effect in (A3) or (B3), (A6) or (B6).

59

The observed bunching effect in both experimental and theoretical results suggests that the conformational motion time tends to populate in a narrowly distributed time window with the defined first moment and finite second moment as a Gaussian-like distribution. The bunching effect, resulting from consecutive Poisson rate processes, is probably a perspective view of T4 lysozyme conformational flexibility to adjust from inactive state toward a catalytically competent active state in which the reactive groups are brought into close proximity in a similar conformation favoring hydrolyzing catalysis. Our results of T4 lysozyme conformational dynamics show a complementary characteristic behavior of both convoluted enzyme conformation selection and induced-fit dynamics driven by substrate-enzyme interactions.

Conformational selection mechanism predominately regulates the protein conformational fluctuation/flexibility in the process of nonspecific binding between enzyme and substrate, shown in the modeled multiple pathways and multiple intermediate states of ES. The induced-fit mechanism most likely dominates the specific binding process between enzyme and substrate

(from ES to ES*), in which the active state (ES*) drives the system across the intersection between conformational coordinate and catalytic coordinate, leading to the chemically catalytic reaction.

2.4. Conclusions

In conclusion, herein we have investigated underlying conformational transition dynamics of T4 lysozyme from inactive state to active state by single-molecule fluorescence resonance energy transfer along with a statistical modeling. We have observed neither single nor multi-exponential, but Gaussian-like distributions of ES* formation time. The Markov process model has been employed to unravel the hidden intermediate states from sub-step conformational motions behind those distributions. The simulated results on the basis of multi-step state-to-state 60 transitions agree with the real experimental Gaussian-like distributions of ES* formation time.

In addition, the bunching effect, interpreted as a result of a Markov chain conformational state transitions process during catalysis, has been directly observed from both experimental results and simulated results through visualizing the bunching feature in a narrowly distributed time window of conformational changes. Our results suggest multiple transition pathways involved and multiple conformational intermediate states formed in the process of T4 lysozyme open- close hinge-bending conformational motions under enzymatic reactions. Multiple intermediate states are suggested to be configured in convoluted sub-step conformational motions involving non-identical nuclear coordinates (like different domains orientations) besides a common FRET- dimensional nuclear coordinate. The derived results of multiple intermediate states, multiple sub-step pathways, and the bunching effect support a complementary mechanism between convoluted enzyme conformation selection and induced-fit dynamics. The deeper insights into conformational transition and catalytic activity mechanism of T4 lysozyme still require further experimental and modeling efforts, for example, the correlated anisotropy-FRET measurements.

2.5. References

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64

CHAPTER 3. MANIPULATING PROTEIN CONFORMATIONS BY SINGLE-MOLECULE

AFM-FRET NANOSCOPY

Combining atomic force microscopy and fluorescence resonance energy transfer spectroscopy (AFM-FRET), we have developed a single-molecule AFM-FRET nanoscopy approach capable of effectively pinpointing and mechanically manipulating a targeted dye- labeled single protein in a large sampling area, and simultaneously monitoring the conformational changes of the targeted protein by recording single-molecule FRET time trajectories. We have further demonstrated an application of using this nanoscopy on manipulation of single-molecule protein conformation and simultaneous single-molecule FRET measurement of a Cy3-Cy5 labeled kinase enzyme, HPPK (6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase). By analyzing time-resolved FRET trajectories and correlated AFM force pulling curves of the targeted single-molecule enzyme, we are able to observe the protein conformational changes of a specific coordination by AFM mechanic force pulling.

3.1. Introduction

3.1.1 Single-Molecule Conformational Changes

Protein conformations play crucial roles in protein functions.1-5 The new paradigm of the protein structure-function relationship is that the dynamics of protein structural fluctuations play critical roles in protein functions.6-9 For example, protein functions in enzymatic catalysis and protein-protein interactions involve protein conformational fluctuations and folding-binding cooperative interactions.10-13 An enzyme can have different activities with different conformations,14-16 and conformational changes can significantly change the affinity and selectivity of protein interactions, which in turn often contribute to dramatic changes in protein 65 functions.17-19 Thus, manipulating protein conformations can be effective for changing, enhancing, or even creating protein functions. It has been theoretically suggested that an oscillating force applied to an enzyme at a comparable frequency of enzymatic reaction turnover rate changes the enzymatic reaction activities due to force modification of the reaction pathway, potential surface, and enzymatic reaction intermediate state energy.20,21 In recent years, experimental works have demonstrated that external mechanical force can change protein activities;22,23 accordingly, real-time measurements of protein conformational dynamics with a combined external force to manipulate and even control protein structures are a promising approach for protein structure-function studies.

3.1.2. Optical-AFM Correlated Approaches

Single-molecule approaches are proved to be powerful and informative in characterizing protein functions, conformations, and activities, which are beyond the conventional ensemble- averaged measurements.24-26 In another perspective, AFM and correlated single-molecule force spectroscopy has been proved to be specified for studying protein conformations and activities under physiological conditions.27,28 Thus, a combination of correlated single molecule spectroscopy with atomic force microscopy is ideal for obtaining the identified structural information or direct observation of the effect of external mechanical perturbation on the protein and related enzymatic activity in real time. Progresses have been achieved in combining the single-molecule spectroscopy measurements and simultaneous AFM manipulations.29-33 For example, Fernandez and co-workers introduced a combined AFM-TIRF microscopy, and used fluorescence labeled cantilever to pull and unfold tethered polyubiquitin between sample surface and cantilever tip.30 This combination provides a method to monitor fluorescently labeled molecule moving vertically along z-axis and potentially can be used to tracking the activity of 66 single molecules simultaneously. Gaub and co-workers also used an integrated AFM-TIRF to read out the influence on the enzymatic activity by AFM induced periodic stretching and relaxation of enzymatic conformation through simultaneous fluorescence imaging, and they reported that relaxation from the force-induced enzyme conformation lead to higher catalytic activity after the external stretching force on enzymes were released.29 The advantage of the integrated AFM-TIRF microscopy is that collecting optical signals is relatively easy due to the large imaging area of TIRF. However, there also exists an apparent disadvantage in the correlated single-molecule force manipulation and fluorescence total reflection imaging microscopy measurements, which is that the signals from optical measurement may not come from the target molecule that is manipulated by AFM. For example, in this AFM-TIRF study of the influence on the enzymatic activity by AFM induced periodic stretching and relaxation of enzymatic conformation through simultaneous fluorescence imaging experiment, the enzyme molecules themselves cannot be directly labeled for probing the conformational changes or monitoring enzymatic reaction motions under high enzyme concentration within the imaging sample area, although only the fluorogenic product molecules can be measured. Therefore, many of the time-resolved and polarization resolved single-molecule spectroscopy measurements cannot be applied. Furthermore, the measured enzymatic reaction product is probably not the specific one from the exact target enzyme molecule perturbed by the AFM tip.

Kodama and co-workers have made an advancement of using a confocal laser scanning microscope correlated with AFM to probe the relationship between protein structure and function by observing the fluorescence change of green fluorescent protein when a compression or extension force is applied to the protein.31 However, this measurement is not in single-molecule level, about 30 protein molecules under the microbead are attached to ATM tip. In fact, there is 67 an apparent conflict of preference and intrinsic technical dilemma between high concentration requirement of sample molecules for AFM manipulation and low concentration requirement for single molecule optical spectroscopy. Single-molecule spectroscopy requires that the fluorescent probe molecules are distributed at low concentration 10-9 to 10-10 M or averagely one target molecule per µm2 area. In contrast, conventional AFM force pulling experiments require a high concentration, near monolayer, of proteins on the sampling surface as an AFM tip cannot specifically pinpoint a targeted protein molecule in a sample area of larger than one µm2.

There are rich technical approaches of single-molecule spectroscopy, including confocal imaging, TIRF, single molecule FRET, and tip-enhanced near-field spectroscopy and imaging, etc29-33 that have been combined with the AFM correlated microscopy, but to our knowledge, there are no such approaches that have reached to single-molecule level. There is a simple and critical reason that the technical bottle neck that has been prohibiting various above mentioned single-molecule microscopy and spectroscopy approaches to be combined with the AFM force manipulation analysis. The reason is that for these types of single-molecule spectroscopic approaches to be applied, the single-molecule protein has to be fluorescent by intrinsic fluorescence or by dye-probe labeling, and the fluorescence molecule can only be a single one distributed in a 10-10 M concentration within a large sample area, that is, about one molecule in each µm2. The reality of such a diluted sample requires that the AFM tip is capable of pinpointing the individual molecule in a sample area that is much larger than typical AFM single-molecule imaging sampling area. Nevertheless, a direct correlation between measuring single-molecule activity and specifically-manipulating conformational changes has not been achieved yet. In this chapter, we report our new technical approach of an AFM-FRET nanoscopy capable of simultaneous measurements of single-molecule force spectroscopy and FRET 68 spectroscopy for a targeted single protein molecule. By recording and analyzing single-molecule

FRET time trajectories of a Cy3-Cy5 labeled kinase protein and correlated force spectroscopy on the same molecule, we have demonstrated the experimental approach of simultaneous single- molecule spectroscopic measurements of protein conformational changes under AFM tip manipulations. Our AFM-FRET nanoscopy approach enables us to study the relation between protein structure and function at a pinpointing single-molecule level.

3.1.3. Biological Functions and Catalytic Reactions of HPPK Kinase

In our experiments, HPPK (6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase), a

35KDa 158-residue monomer kinase enzyme protein,34,35 was studied. HPPK catalyzes the pyrophosphorylation reaction that leads the conversion of 6-hydroxymethyl-7,8-dihydropterin

(HP) to 6-hydroxymethyl-7,8-dihydropterin pyrophosphate (HPPP) in the presence of ATP during the folate biosynthesis pathway in bacterial cells. There are three flexible loops of HPPK involved in the enzymatic catalysis reaction (Figure 3.1).5,34 Among them, loop 3 undergoes dramatic open-close conformational changes in each catalytic cycle, correlating with substrates

(ATP and HP) binding. To probe the single-molecule conformational change of protein under the

AFM force pulling perturbation, the enzyme, HPPK was labeled with Cy3-Cy5 to the amino acid residue 88 on loop 3 and residue 142 on protein core close to the enzymatic active site of the enzyme,35 respectively (Figure 3.1A). To apply mechanical force to perturb the conformation of single enzyme molecule using AFM tip, we coupled HPPK molecules between a glass cover-slip and a “handle” function group (biotin and streptavidin) for AFM tip through amines groups on the protein (Figure 3.2A). 69

Figure 3.1. (A) Crystal Structure of HPPK. The green spirals represent α helices and the blue arrows represent β strands. The loops are shown by the red pipes. Amino acid residue 88 and 142 has been labeled with FRET dye pair Cy3 and Cy5, respectively. (B) HPPK catalyzed pyrophosphorylation reaction transferring two phosphor groups from ATP to HP.

70

Figure 3.2. (A) Single-molecule AFM-FRET nanoscopy. The zoomed panel in the left presents schematic diagram of one FRET dye-pair (donor-acceptor: Cy3-Cy5) labeled HPPK molecule tethered between a glass cover-slip surface and a handle (biotin group plus streptavidin), and another biotin group is modified on AFM tip. (B) Single-molecule fluorescence photon counting images of the donor (Cy3, left) and accepter (Cy5, right). Each feature is from a single HPPK enzyme labeled with Cy3-Cy5 FRET dyes.

71

3.2. Experimental Sections

3.2.1 Sample Preparation

Sample Preparation processes are shown in Figure 3.3. We first coated the glass cover- slip by covalently linking the amine group in the protein matrix with the isobutyl group of (3- aminopropyl) trimethoxysilane and isobutyltrimethoxysilane in DMSO solution in a ratio of

1:10000. Then we treated the cover-slip surface by amine-to-amine crosslinkers (dimethyl suberimidate•2HCl, Thermo Scientific) and the HPPK kinase solutions respectively, to cross- link an amine group in HPPK with an amine group on the cover-slip surface.36 After rinsing to remove free enzymes and residual bi-function linkers, the enzyme molecules were distributed with a low surface density of about one enzyme per μm2 area on the cover-slip surface, which is suitable and typical for single-molecule FRET fluorescence imaging measurements at the optical diffraction limited spatial resolution. A biotin group was further attached to the tethered enzyme molecule with an amine group by immersing the sample in 10 nM EZ-link NHS-SS-Biotin, 0.1M

PBS buffer (pH=7.4), 0.15M NaCl solution for 4 hours.36 After rinsing to remove un-linked biotin linkers, streptavidin was further attached to biotin group by immersing the sample in

10nM streptavidin solution.

The process of tip modification is shown in Figure 3.4. An Au coated AFM tip

(MikroMasch CSC38/Cr-Au, typical force constant K=0.01, 0.03, 0.05 N/m) used in the experiments was first modified with a monolayer of biotin by immersing the AFM tip in 1mM 2- aminoethanethiol solution for 4 hours. The AFM tip was then further immersed in 10 nM EZ- link NHS-SS-Biotin, 0.1M PBS buffer (pH=7.4), 0.15M NaCl solution for another 4 hours.36 The interaction pair of biotin and streptavidin serves as the primary force “handle” in an AFM tip force pulling experiment. 72

Figure 3.3. Protocol for preparing sample that pulling HPPK (Cy3-Cy5 labeled at 88, 142) between possible lysine residue.

Figure 3.4. Protocol for preparing biotinlated AFM tip.

73

3.2.2. AFM-FRET Correlated Measurements

AFM pulling experiment were performed on Agilent AFM with 9μm scanner. The experiment were processed in 100mM Tris-HCl buffer (pH=8.3) added with 10mM MgCl2 and

200μM AMPCPP solution, the pulling rate was about 10nm/s. To calibrate the data, we used the standard I27O AFM calibrator protein (From AthenaES, a 94kDa synthetic polyprotein composed of eight repeated units of the I27domain of human fibronectin) to calibrate the force spectroscopy. 37

The experimental setup of AFM-FRET nanoscopy is shown in details in Figure 3.5. The first and critical step is to line up the optical focal point and AFM tip for a typical operation of our AFM-FRET nanoscopy. First, we move the x-y two-axis mechanical positioning stage to roughly co-axis the AFM tip with the laser beam focal point by observing the light reflection pattern from the AFM tip; a symmetric light reflection pattern is to be observed from the microscope objective. It indicates that the coaxial position is achieved within a few micrometers.

To co-axis the AFM tip with the laser beam center of Gaussian distribution of the laser focus, we scan the AFM tip cross the area of laser beam that has been aligned, and send one of the photon- counting signal to AFM controller through a gated photon counter SR400 (Stanford instruments).

The image of the optical intensity was taken during an AFM tip scanning as shown in Figure 3.6.

A bright spot of the optical intensity is due to the photons from tip reflection as the tip scans over the laser beam, because the tip can be considered as a micro mirror which can reflect more photons back through the objective. Through this alignment, we are able to align AFM tip with the center of laser beam to a hundred nanometers. 74

Figure 3.5. Experimental setup of single-molecule AFM-FRET Nanoscopy. M: Mirror,

Dichroic beam splitter 1: z532rdc, Chroma Technology, reflecting 532nm excitation laser beam and transmitting fluorescence. Dichroic beam splitter 2:640dcxr, Chroma Technology, splitting the emission signal into two color beams centered at 570 nm and 670 nm representing Cy3 and

Cy5 emissions. APD 1: Si avalanche photodiode single photon counting modules (SPCM-AQR-

16, Perkin Elmer Optoelectronics) for detecting the single-molecule fluorescence Cy5. APD 2:

Si avalanche photodiode single photon counting modules (SPCM-AQR-16, Perkin Elmer

Optoelectronics) for detecting the single-molecule fluorescence Cy3. Filter 1: HQ545lp, blocking 532 excitation laser beam. Filter 2: E835sp, blocking AFM infrared superluminescent diode (SLD) at 950 nm beam.

75

Figure 3.6. Schematic diagram of coaxial laser and AFM tip. (A) The AFM tip scans right on the protein and the laser beam focus spot. (B) An optical image of laser focus spot under AFM tip scanning. The bright spot indicates the laser beam position.

After aligning AFM tip with the laser beam focus in an over-under co-axial configuration, we first obtain an optical image (10m  10m) by raster scanning the close-loop 2D electropiezo-scanning stage with sample over the laser focus at a scanning speed of 4ms/pixel.

Each image has a matrix density of 100 pixels 100 pixels. We collect single-molecule fluorescence intensities of the Cy3 and Cy5 to locate single enzyme molecule positions as shown in Figure 3.2B. We then move the close-loop sample stage to make the target molecule right in the center of the focal point by position control of the close-loop x-y electropiezo-scanner stage.

As shown in Figure 3.2A, the AFM tip is right on top of the microscopic focal point and it is also right on top of the single molecule with a few hundred nanometers precision which is within the optical diffraction limit. Till now, three components of AFM tip-protein-laser beam components are right in the same axis.

We conducted the AFM-FRET combined experiment in 100 mM Tris-HCl buffer and 10 mM MgCl2 (pH=8.3). To protect the FRET dyes from photo-bleaching, we have also added a 76

0.8% D-glucose plus 1 mg/ml glucose oxidase, 0.04 mg/ml catalase, and about 1mM Trolox in the mixture.38 We utilized an approach of combined AFM 2D matrix force pulling scanning and single-molecule FRET imaging measurements. With the aligning of the AFM tip, the laser beam focus, and the target molecule in a co-axial configuration, the AFM tip and single target molecule are both in the laser focus; however, the distance between AFM tip and the target molecule can be still in tens of nanometers away. To ensure a single-molecule AFM-FRET measurement for the same target protein molecule, we use a new approach of AFM matrix pulling (or mapping) and simultaneous single-molecule FRET measurement. The typical size of the coated AFM tip apex is around 20-40nm in diameter, and HPPK enzyme molecule with streptavidin is about 5-10 nm in diameter; therefore, a 20×20 nm2 area (about one pulling event area) is sufficient for each

AFM-tip pulling to ensure a direct contact with the single molecule under the laser focal point.

In a typical experimental protocol shown in Figure 3.7, there is a 16×16 times pulling matrix within an area of 300×300 nm2, in which an AFM-tip force pulling event occurs in every 20 nm interval. Meanwhile, the single protein molecule can be reached under such a sampling matrix of every 20×20 nm2 within the laser focal point where one individual target molecule is located.

Simultaneously, we record the single-molecule fluorescence intensities of the FRET pair of Cy3 and Cy5 by a two-channel photon-stamping module during the AFM matrix scanning.

Figure 3.7 shows AFM matrix pulling experimental scheme illustrating the detailed protocol. There are 16×16 times pulling events in a 128 pixels ×128 pixels scanning area for the

AFM matrix pulling. It constitutes one pulling event in every 8 pixels along a scanning line of

128 pixels and one line of pulling events in every 8 scanning lines. Each pulling is set to take two seconds, and each line scanning without pulling is set to take one second, thus the total experiment time is about 624 seconds. Figure 3.7B shows a FRET time trajectory of the donor- 77 acceptor pair recorded under the AFM tip matrix pulling. The trajectory is about 624s long including 16 domains. Each domain includes 16 discontinuous peaks and 7 continuous oscillatory peaks, corresponding to one pulling line with 16 pulling events and followed 7 continuous scanning lines without pulling. A zoom-in portion of the trajectory (Figure 3.7C) shows one domain. It includes 16 strong discontinuous peaks corresponding to 16 times pulling events in one scanning line and 7 continuous peaks corresponding to 7 times non-pulling events in 7 scanning lines. In one domain (Figure 3.7C), it shows that the fluorescence intensity is enhanced when the AFM tip gets close to the sample, and that the enhancement effect fades away as the tip is moving away from the sample in an AFM pulling event. There are 256 (16×16) AFM tip pulling events in one AFM matrix pulling experiment. Typically, there is only one protein molecule in an area of 300 × 300 nm2 at the laser focus; therefore, there is only one effective pulling event reaching the protein molecule. The effective pulling event can be identified by the recorded force curve and correlated FRET trajectories.

78

Figure 3.7. (A) Diagram of single-molecule AFM-FRET pulling matrix, there are 16×16 pulling activities in a 128×128 pixels scanning area. (B) FRET time trajectory of donor (green) and acceptor (red) recorded under the AFM tip matrix pulling. The trajectory is about 624s long including 16 domains corresponding to 16×16 AFM pulling events. (C) A zoom-in portion of the trajectory from (B), it shows one domain, containing 16 discontinuous peaks and 7 continuous oscillatory peaks, corresponding to one scanning line with 16 pulling activities and followed 7 continuous scanning lines without pulling, respectively. Each discontinuous peak indicates one single pulling activity.

79

3.3. Results and Discussion

3.3.1. Successful Pulling Events under Optical Tracing

In our single-molecule AFM-FRET nanoscopy, time-resolved FRET trajectories and correlated AFM force pulling curves of the targeted single HPPK enzyme are simultaneously recorded during the whole pulling approach-retract cycle. Figure 3.8 presents the typical data recorded from an effective AFM pulling event, showing the FRET donor-acceptor intensity trajectories (Figure 3.8A, 3.8B), the correlated FRET efficiency trajectory (Figure 3.8C), and the correlated AFM force curve (Figure 3.8D). In a single AFM tip approach-retract cycle, AFM tip travels about 600 nm down and 600 nm up, total route is about 1200 nm within 2 seconds. There is a period about 0.5 second when the fluorescence intensity is three times higher than the flat area in the trajectory. Thus, the total traveling distance of back and forth in the field that enhanced fluorescence intensity is 300 nm, so the distance between AFM tip and sample is estimated as zero to 150 nm. The enhancement of the fluorescence intensity is due to a micro- mirror effect; that is, the AFM tip serves as a mirror reflecting the optical signal down to the microscopic objective resulting in a higher single collection efficiency. The micro-mirror effect increases or decreases following the AFM tip gets close or moves away from the laser focus spot on the sample surface in a correlated confocal single-molecule spectroscopic imaging measurement. The micro-mirror effect disappears when the tip is retracted back from the surface beyond 150 nm range. 80

Figure 3.8. (A) A typical FRET time trajectory of donor (green) and acceptor (red) associated with one single-molecule AFM-FRET force pulling event. (B) Zoom-in intensity trajectory of donor and acceptor from (A), the highlighted intensity change is correlated to one pulling event occurred in 0.04s. (C) FRET efficiency time trajectory of one single-molecule AFM-FRET pulling event, in the whole process of AFM tip travelling route from approaching the protein from far away to moving away out of the micro-mirror effect distance range, three efficiency levels are recorded and identified. The error bar shows the ±2SD (standard deviation) indicating

≥ 95% precision of identification of the data points within the range. (D) The correlated force curve, the curve shows the extension length of 24 nm within a period of 0.04 s.

81

Figure 3.8A-3.8C present a detection of FRET intensity, efficiency changes as well as simultaneous AFM force spectrum when a pinpoint specific force pulling event occurs (Figure

3.8D). The changes of FRET efficiency (EFRET) reflect the changes of the donor-acceptor distance associated with the protein unfolding by AFM force pulling.39,40 Figure 3.8C shows a typical EFRET time trajectory of single-molecule HPPK under an AFM tip perturbation. In the whole process of AFM tip travelling route of approaching the protein from far away and then moving away out of the micro-mirror effect distance range. To distinguish a productive force pulling event from a non-productive pulling event involving essentially similar AFM tip approaching-withdrawing movements, we analyzed the error bar of standard deviation on the correlated EFRET signal measured under the tip approaching-withdrawing movements that occurred around a productive pulling event. The mean of the EFRET when the tip is far from the

st surface (no mirror effect) is marked with 1 level. Similarly, the mean of the EFRET when the tip is close to the surface (with a mirror effect) is marked 2nd level (Figure 3.8C). It is statistically

rd identifiable of that the EFRET signal, marked as 3 level, correlated with a productive force pulling event. Therefore, as the tip approaches the single protein but not close enough to reach the micro-mirror effective distance (~150 nm) and as it moves far away beyond the 150 nm- range, the EFRET presents the first level that is the normal level without perturbation of the micro- mirror effect. As tip reaches the range of micro-mirror effect, EFRET shifts to the second level. In this measurement, there are two factors that may affect the EFRET: first is mirror effect (both donor and acceptor fluorescence intensities increase when tip reflects optical signal down to the microscopic objective). In this case, the increased efficiency may be not identical along the whole wavelength range, leading to different extends of efficiency changes in both channels; second is plasma (plasma is generated from Au coated AFM tip excited by laser). In this case, 82 the intensity of plasma is also probably not uniform in different wavelengths, that is, it may have different intensity in different wavelength range. The third level EFRET only lasts 0.04 s, which corresponds to the rapid extension process in which the protein is stretched by AFM tip until the connection between AFM tip and the protein ruptures. In the third level-0.04 s period, EFRET suddenly switches from the second level to the third one and then switches back; those changes reflect the protein conformational changes pulled by AFM tip. Figure 3.8D shows the force curve from the simultaneous AFM measurement that is correlated to the FRET trajectory (Figure 3.8A-

C). The AFM force pulling curve (Figure 3.8D) shows two peaks and the total extension length of 24 nm. In this typical force curve, the extension length of 24 nm takes about 0.04 s to run across, which is corresponding to the 0.04 s extension period in the corresponding FRET trajectory (highlighted in Figure 3.8A, 3.8B and 3.8C).

3.3.2. Protein Unfolding by AFM Pulling

The combination of force spectrum and correlated FRET recording enables us to identify the exact pulling site on single HPPK molecule. Figure 3.9A, the statistical result of rupture distance, shows the primary distribution within a range of 20-40 nm, and the mean extension length about 24-28 nm. In this experiment, the amine groups on lysine residues were used to link the protein molecule to the cover-slip surface and to the biotin handler for AFM tip manipulation. For Cy3-Cy5 labeled (88c,142c) HPPK molecule, there are still five lysine residues (23, 85, 119, 154, 157) available. Therefore, there are a number of configurations associated with different linking residues for the single-molecule force pulling measurements, although there are only four possible unfold configurations (between residue 23 and 85, or between residue 85 and 154, or between 85 and 157, or between 23 and 119 (Figure 3.10) that gives the possible extension length ranging from 20 nm to 40 nm (Figure 3.9A). Among these 83 possible unfold configurations, the most possible linker is at the residue 85. According to the literature, residue 85 is on loop 3, one of the important catalytic flexible loops of HPPK, correlating with substrate ATP binding, undergoes the most dramatic open-close conformational changes in each catalytic cycle. Therefore, the force perturbation at residue 85 on loop 3 provides a most likely possibility of perturbing the enzyme-substrate binding and accordingly perturbing the enzymatic catalysis activity. Figure 3.9B, the histogram of the protein rupture force distribution shows two peaks, 16-18 pN and 50-52 pN, the most possible rupture forces are in the range of 16-18 pN. Our result is essentially consistent with the reported 10-60 pN rupture force values in single-molecule protein pulling, which is typically much smaller than the result of about a few hundreds pN in the rupture force measured in polymer force pulling experiments.

We attribute that, in our single-molecule force pulling experiment, the rupture force is due to unfolding single loop, segment or single domain that contain one or several hydrogen bonding and other non-covalent chemical interactions. 41 However, we note that there is a significant difference between the rupture force we reported here and the typical protein unfolding rupture forces reported in the previous literature: a rupture force in protein polymer protein pulling 42,43 is the force of unfolding a whole protein within a protein polymer, i.e., the rupture force includes not only the force of rupturing the multiple hydrogen bonds in a protein but also the force of rupturing all the associated hydrophobic forces and related chemical bonds that hold the whole protein in its fold state. Furthermore, the single enzyme domain rupture forces observed in our experiments are in a comparable scale of other reported single-molecule protein domain rupture forces.28,44 84

Figure 3.9. (A) Histogram of extension length distribution of AFM-FRET force unfolding single-molecule proteins. The primary extension length within a range of 20-40 nm, and the mean extension length is about 24-28 nm. (B) Histogram of protein ruptures force distribution.

The distribution shows two peaks with most probable rupture forces at16-18 pN and 50-52 pN.

Figure 3.10. (A) The amino acid sequence of the Apo-HPPK protein. 45,46 (B) The structure of the HPPK protein. 47 Amino acid residue 88 and residue 142 were mutated and labeled with Cy3-

Cy5. (C) The distance between the possible residues that linked the protein molecule to the cover-slip surface and to the biotin handle. 85

3.3.3. Identification of Substructures of Protein Domain

To further prove our attribution of the possible unfold configurations between lysine residues in our single-molecule pulling experiment, we did a control experiment of HPPK mutant

(142C, Figure 3.11E). In one end of linking, we chemically tether the HPPK molecule to glass cover-slip surface at a specific amino acid residue position 142 by mutation. For the other end, in order to be linked by chemical reaction EZ-linker (NHS-SS-Biotin), the -NH2 group on HPPK amino acid side chains must be from one of the lysine residues except site 142, which are site

23, 85, 119, 154 and 157. However, the configuration between 142 and 154 or 157 are too close to be the origins of the measured force pulling curves, so there are actually only three possible unfolding configurations, and they are between amine acid residues 142 and amine acid residue

23, 85, and 119, respectively. Assuming the average distance is 3.8Ǻ in each amine acid residue, the overall distance of the force pulling curves are about 45.2 nm, 21.7 nm, and 8.7 nm, respectively.

The experimental results (Figure 3.11), obtained in tris-buffer plus MgCl2 with the present of enzyme prohibitor (AMPCPP, -Methyleneadenosine 5’-triphosphate), show three typical force curves. These force curves consist of saw teeth shaped peaks. These peaks are the results of unfolding single HPPK molecules. The distances to rupture the protein from the force curves are 9±2 nm, 22±3 nm, and 46±7 nm, respectively (Figure 3.11D). These results correspond to the possible unfolding configurations correlated to the protein domains of between amine acid residue 142 and amine acid residue 119, 85, and 23, respectively. In addition, these experimental results (9±2 nm, 22±3 nm, and 46±7 nm) are consistent with the theoretical results

(8.7 nm, 21.7 nm, and 45.2 nm above). Based on above results, we propose that three domains exist between residue 23 and residue 142, namely, DomA (green in Figure 3.11), DomB (purple 86 in Figure 3.11), and DomC (red in Figure 3.11). Since there is only three unfolding configurations as discussed above, we investigate them separately (Figure 3.11A-C) and integrate the results into one probability distribution of rupture distances (Figure 3.11D). In the first configuration (119,142), as shown in Figure 3.11A, one end of HPPK is tethered to the glass cover-slip through residue 142 when the other end is linked to the AFM tip via residue 119. In our model, DomC is unfolded by AFM tip pulling, and the experimental rupture distance is about 9 nm which is not only consistent with the theoretical value 8.7 nm, but also completely agree with the first peak (9 nm) of rupture distance distribution (Figure 3.11D). In the second configuration (85,142), the pulling and tethering sites on HPPK are residue 85 and residue 142, respectively. As shown in Figure 3.11B, DomB and DomC are unfolded with the rupture distance around 22 nm; this experimental result is also consistent with the theoretical value 21.7 nm and the second peak (22 nm) of rupture distance distribution (Figure 3.11D). In the third configuration (23, 142), as shown in Figure 3.11C, three proposed domains (DomA, DomB and

DomC) between site 23 and site 142 are all unfolded and thus giving a larger rupture distance compared to only unfolding DomC (Figure 3.11A) or unfolding DomB and DomC (Figure

3.11B). Furthermore, the experimental value (45 nm), the expected theoretical value (45.2 nm) and the third peak (45 nm) in the distribution of rupture distance are all almost identical to each other. Therefore, in Figure 3.11, all the three force curves fit well in our proposed three domains model. Moreover, the rupture distance consistence of different domains among experimental values, theoretical values and the distribution peaks reinforces our proposed three domains explanation.

The results (Figure 3.11) show a number of significant characteristics: (1) There are multiple peaks appearing in single domain from unfolding a single protein molecule. The 87 multiple peaks in the single-molecule pulling force spectroscopy are primarily attributed to the traces associated with unfolding of the single segments, loops, or domains. The data are also associated with fluctuations due to the rugged landscape of protein folding with multiple local minima. For example, in Figure 3.11A, the force pulling curve shows two small peaks that come from the unfolding single protein domain DomC between residue 119 and residue 142, which suggests that our AFM-FRET nanoscopy approach is capable of probing the substructures of the protein domain from force curves. Although, at this stage, we are not able to identify each peak with the exact fragment in the protein domains, this observation of substructures in single molecule force spectroscopy is highly promising, which allows AFM force pulling to be a potentially powerful tool for offering insight into the details of the protein domains; (2) In the single molecule force curve, the order of these peaks did not follow the protein structure sequence order (Figure 3.11E), indicating the order of the rupture did not follow the exact pattern of protein substructure sequence order in the molecule (Figure 3.11). For example, the observed order of the peaks in Figure 3.11A (DomC) or 3.11B (DomB and DomC) did not show the same order as they appeared in Figure 3.11C (DomA, DomB and DomC). We attributed this order variance to the different overall affinity among amino acid residues in substructures to resist pulling force, resulting in that some of the substructures are easier to be unfolded or ruptured, while others are not. We also attributed this result to cooperative unfolding nature of three domains; individual domains were not unfolded independently when not just one domain was stretched, as described in Figure 3.11B and 3.11C. (3) The force for rupturing a single protein molecule is small; the range of the force distribution is between 5 to 20 pN, as shown in the three force curves.

88

Figure 3.11. (A-C) Three types of single-molecule force pulling curves of HPPK, as HPPK was chemically linked to a glass coverslip at residue 142, and AFM tip pulling occurs at the possible lysine residue site 119, 85, and 23. In the insets above the force curves, three proposed domains are colored (green for DomA, purple for DomB and red for DomC) and depicted. (A) The unfolding force curve of DomC (red), which corresponds to the rupture distance 9 nm. (B) The unfolding force curves of DomB (purple) and DomC (red), corresponding to the rupture distance

22 nm. (C) The unfolding force curve of DomA, DomB and DomC, and the rupture distance is

45 nm. (D) Histogram of protein rupture distance distribution. The distribution of the rupture distances shows three peaks, at about 9 nm (DomC), 22 nm (DomB and DomC), and 45 nm

(DomA, DomB and DomC). (E) The structure of the HPPK mutant (the site of lysines and cysteine are illustrated). Amino acid residue 142 was mutated to cysteine for specific site tethering of HPPK on the glass cover-slip. 89

3.3.4. The Limitations of AFM-FRET Nanoscopy

Our AFM-FRET nanoscopy presents a significant advancement comparing current reported techniques 29-33 in terms of conducting simultaneous single-molecule force manipulation and FRET measurement probing the corresponding conformational changes of a single targeted enzyme molecule, which is particularly powerful for studying enzyme function-conformation mechanisms and relationships between function and conformations. Nevertheless, as a typical new approach under a new development, there are still technical limitations that need to be addressed in a further development of this combined AFM-FRET nanoscopy. For example, a major technical limitation is that the AFM tip light reflection changes the microscope photon collection efficiency depending on the tip-to-laser focus spot distance in a simultaneous AFM force manipulation and optical FRET recording experiment, the so called AFM tip micro-mirror effect of changing the microscopic photon collection solid angle. In an AFM-FRET single- molecule protein pulling experiment, as AFM tip approaches close to the sample surface, the micro-mirror effect of AFM tip enhances the fluorescence signal collection. The enhanced signal in both channels of donor and acceptor makes the conformational analysis from FRET trajectory often complicated and susceptible to analysis errors. An alternative approach to remedy this complication is to use fluorescence lifetime dependent FRET measurement 48,49 that records and analyze the single-molecule FRET signal independently from overall intensity changes but the temporal changes in fluorescence decay time. Therefore, a micro-mirror effect will not interfere with the single-molecule FRET measurements in recording protein conformational changes.

3.4. Conclusions

We have demonstrated a novel approach of single-molecule AFM-FRET nanoscopy that is capable of conducting simultaneous single-molecule force manipulation and FRET 90 measurement for a targeted single protein molecule. Using this approach, we are able (1) to locate an individual Cy3-Cy5 labeled enzyme molecule in a pinpoint nanoscale precision; (2) to force pulling and unfolding the target single enzyme molecule; and (3) to simultaneously probe the protein conformational changes by single-molecule FRET spectroscopy measurement during the AFM pulling event. Our demonstrated single-molecule AFM-FRET nanoscopy presents a novel approach of studying protein structure-function dynamics and mechanism. Using the nanoscope, we have specifically demonstrated the force pulling manipulation of a kinase enzyme and simultaneously probed the manipulated conformational changes by correlated single- molecule FRET recording, which showed multiple rupture coordinates in single-molecule enzyme force unfolding processes. The AFM-FRET nanoscopy provides a new approach of analyzing the landscape of protein folding and manipulating protein conformations to explore new properties.

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CHAPTER 4. SINGLE-MOLECULE PHOTON STAMPING FRET SPECTROSCOPY STUDY

OF ENZYMATIC CONFORMATIONAL DYNAMICS

The fluorescence resonant energy transfer (FRET) from donor to acceptor via transition dipole-dipole interactions decreases the donor’s fluorescent lifetime. The donor's fluorescent life- time decreases with the FRET efficiency increases, following the equation: EFRET = 1 - DA /D, where D and DA are the donor fluorescence lifetime without FRET and with FRET.

Accordingly, the FRET time trajectories associated with single-molecule conformational dynamics can be recorded by measuring the donor’s lifetime fluctuations. In this chapter, we report our work about using a Cy3-Cy5 labeled HPPK to demonstrate probing single-molecule conformational dynamics under enzymatic reactions by measuring single-molecule FRET donor lifetime time trajectories. Compared with single-molecule fluorescence intensity-based FRET measurement, single-molecule lifetime-based FRET measurement is independent on fluorescence intensity, which has advantages in terms of eliminating the analysis background noise from acceptor fluorescence detection leak through noises, excitation light intensity noises, or light scattering noises due to local environmental factors, for example, in a AFM-tip correlated single-molecule FRET measurement. Furthermore, the lifetime-based FRET also supports simultaneously for the single-molecule fluorescence anisotropy.

4.1. Introduction

FRET is the energy transfer from the donor to the acceptor via non-radiative transition dipole–dipole interactions. The energy transfer efficiency (EFRET) depends on the distance

1,2 6 between the donor and acceptor , EFRET = 1 / [1+(r/R0) ], as expressed in Equation (1.8). r is the separate distance between two fluorescent dyes, the FRET donor and acceptor, typically the 95 distance is in the range of 30-80 Å. Since EFRET is sensitive to distance changes between donor and acceptor, and the energy transfer efficiency can also be determined by probing fluorescence intensity of donor (ID) and acceptor (IA), EFRET = IA / [IA+ID], as described in Equation (1.12) and

Equation (1.13). Therefore, by labeling donor and acceptor dye probes in the specific positions of a protein and measuring single-molecule EFRET ~ t trajectories, the time-resolved single- molecule conformational dynamics can be probed. 3-10 However, the FRET signals on the basis of photon intensity measurements coming from the ratio of photon intensity typically depends on a number of experimental configuration and detection parameters: such as direct excitation of the acceptor at the donor excitation wavelength (crosstalk), excitation intensity, collection efficiency, and position of dye with respect to the excitation beam, and thermo noise, etc.11,12 A typical

FRET intensity-based efficiency measurement is particularly susceptible to measurement error and background noise when the experimental parameters are variable and time dependent during the measurements. The measurement error can dominate the real signals, especially, for protein or enzymatic conformational dynamics study in catalytic reactions: since the change in the donor-acceptor distance is often only 1-2 nm for the enzyme active-site conformational changes.13-16

Fluorescence lifetime refers to the average time that a molecule stays in its excited state before emitting a photon and is an intrinsic property of a fluorophore.1 At the single molecule level, the fluorescence lifetime fluctuates reflecting the heterogeneity and fluctuations of the local environment. 17-19 The excited-state energy transfer from donor to acceptor via dipole– dipole interactions decrease the donor’s fluorescent lifetime, and the amplitude of donor's fluorescent lifetime decreases with the FRET efficiency increases, EFRET = 1 - DA /D, as described in Equation (1.12). The parameters DA and D are donor’s lifetime in the presence 96

(DA) and in the absence (D) of acceptor, i.e., with and without the FRET interactions, respectively. On the basis of the relation between fluorescence lifetime and FRET efficiency indicated by Equation (1.12), the lifetime based single-molecule FRET measurements have been developed and applied in studying bio-molecule conformational dynamics. 20-23

Compared with the lifetime of donor in the absence of acceptor (D), the lifetime of donor in the presence of acceptor (DA) fluctuates in a relative larger scale, reflecting the different energy transfer efficiency between donor and acceptor due to the D-A distance fluctuation associated with the protein conformational dynamics. Given a subtle fluctuation of donor’s lifetime in the absence of acceptor, the relation between EFRET and donor’s lifetime in the presence of acceptor (DA) is anti-correlated, referring to the Equation (1.12); i.e., the FRET efficiency increases when the donor lifetime (DA) decreases, and vice versa. Therefore, we are able to study the single-molecule conformational dynamics by measuring the donor’s lifetime

24,25 trajectory DA ~ t in the presence of acceptor, where t is the chronic time of data recording.

Compared with the fluorescence intensity-based FRET trajectory EFRET ~ t measurement where both Idonor and Iacceptor need to be measured simultaneously, the fluorescence lifetime-based FRET measurement of lifetime trajectory DA ~ t can be achieved by monitoring only the donor’s lifetime signal (DA) in the presence of acceptor. In this way, the FRET detection is more effective under various single-molecule spectroscopy measurement conditions where fluorescence intensity may be influenced and perturbed by local environment fluctuations irrelevant to the intrinsic protein conformational dynamics. For example, the fluorescence lifetime is not influenced by the measurement factors of direct excitation of the acceptor at the donor excitation wavelength (crosstalk), excitation laser intensity, photon counting collection 97 efficiency, and position of dye with respect to the excitation laser focus position , and thermo noise, etc. 26-29 Overall, the lifetime based FRET can be a more accurate method to probe conformational dynamics of protein under complex measurement conditions, such as the AFM tip-enhanced single-molecule spectroscopic and imaging measurements. 30-33 In this chapter, we report a single molecule lifetime-based FRET method to study conformational dynamics of protein via time-correlated single photon counting (TCSPC) technique.

We chose HPPK as a model system to demonstrate this single molecule lifetime-based

FRET method. As described before, HPPK, a single-subunit kinase of 18 KDa, 158 residues, catalyzes the transfer of pyrophosphate from HP to HPPP with ATP participation. It has been reported that among three catalytic loops involved in the enzymatic conformational changes, loop 3 undergoes the most measurable conformational motions. 34-36

4.2. Experiments and Theoretical Analysis

4.2.1. Experimental Sample Condition

We received the Cy3-Cy5 donor-acceptor labeled HPPK from Prof. Honggao Yan of

Michigan State University, and the Cy3-Cy5 FRET pair are labeled at the amino acid residues 88 on loop 3 and residue 142 on protein core close to the active site of the enzyme (Figure 3.1 in

Chapter 3). In our experiments, 0.1 nM HPPK, 100 M ATP and 100 M HP were sandwiched between two clean glass cover-slips in 1% agarose gel (in 99% water) and in the 1% agarose gel, single HPPK enzyme molecule can rotate freely to perform its enzymatic activity, and the substrates along with the corresponding products can diffuse uninterruptedly. 16 In all measurements, the solution environment was 50 mM tris-buffer (pH=8.3) and 10 mM MgCl2, 98 oxygen scavenger was added into solution to reduce dye’s photo-bleaching and Trolox was added into solution to reduce dye’s unfavorable triplet state. 2

99

Figure 4.1. Photon stamping concept and definition of the parameters: (A) Scheme of a train of laser excitation pulses and detected emission photons; (B) Scheme of the time-stamped photon sequence. The delay time τp is the time delay between the photoexciation event and the photon emission; The real time tp is the chronic time of detecting emission photons; For each detected photon, both τp and tp are simultaneously recorded.

100

4.2.2. Experimental Set-Up

We used femtosecond pulse laser excitation and a home-built two-channel single- molecule FRET lifetime microscope, and the detailed description of our measurement and instrumentation has been reported previously.14 As shown in Figure 4.2, an inverted confocal microscope (Axiovert 200, Zeiss) integrated with an electropiezo-driven nanopositioner (Physik

Instrument) was used for the measurements. An 76 MHz femtosecond Ti:Sapphire laser (Mira

900 oscillator, and Mira OPO, Coherent) operating at 1064 nm was frequency doubled to 532 nm using a BBO crystal. The confocal beam was reflected by a dichroic beam splitter (z532rdc,

Chroma Technology) and was focused by a high-numerical-aperture objective (1.3 NA, 100×,

Zeiss) on the sample at a diffraction limited spot of ~300 nm diameter. To obtain the fluorescence images and intensity trajectories, the emission signal was split by using a dichroic beam splitter (640dcxr) into two color beams centered at 570 nm and 670 nm representing Cy3 and Cy5 emissions, respectively. The two channels of signal were detected by a pair of Si avalanche photodiode single photon counting modules (SPCM-AQR-16, Perkin Elmer

Optoelectronics). Typical images (10m  10m) were acquired by computer-controlled raster- scanning the sample over the laser focus with a scanning speed of 4 ms/pixel, with each image of

100 pixels100 pixels. The fluorescence intensities trajectories of the donor (Cy3) and accepter

(Cy5) were recorded through a PHR800 four channel detector router (PicoQuant) to Picoharb

300 picosecond histogram accumulating real-time processor (PicoQuant) time correlated single photon counting (TCSPC) module.24,25,37,38

The arrival time tp and the delay time (p, between the laser excitation pulse and the fluorescence photon emission) of each fluorescence photon are recorded by TCSPC (Figure 4.1). 101

Not only the intensity time trajectory but also lifetime dynamics of single molecule enzyme can be obtained by analyzing these two parameters of each fluorescence photon (Figure 4.3). And the fluorescence images can be acquired by raster-scanning the sample over the laser focus with an electropiezo scanning stage (Physik Instruments Inc., Germany) at the scanning speed of 4 ms/pixel.

Figure 4.2. Single molecule FRET lifetime microscopy. Two APDs combined with femtosecond pulse laser results in single molecule lifetime-based/intensity-based EFRET measurements. APD= avalanche photodiode, EFRET = fluorescence resonance energy transfer efficiency.

4.2.3. Statistical Analysis

The autocorrelation C(t) function is very useful statistical analytical methods in single- molecule studies. The function is expressed in Equation (4.1) 39-43

Ct() 2  I(0)  I ( t )  I (0) (4.1) (I (0)  I )( I ( t )  I ) ( I (0)  I )2 102 where I(t) represent the signal variables measured in time trajectories I(t). < I > is the means of the fluctuation trajectories of I(t).

4.3. Results and Discussion

Figure 4.3 shows a typical single-molecule lifetime trajectory collected from a Cy3-Cy5 labeled HPPK molecule under enzymatic reaction condition with substrates of 100 M ATP and

100 M HP, which illustrates the basis of single molecule donor’s lifetime trajectory DA ~ t measurements. In this measurement, we record each fluorescence photon’s real arrival time tp and each fluorescence photon’s delay time p related to laser pulse excitation (Figure 4.1).

Figure 4.3A shows a portion of single-molecule photon stamping raw data from the donor channel in a period of 0.8 second. For each detected photon, we recorded two parameters: a chronic arrival time (t) and a delay time related to femtosecond laser pulse excitation (p) (Figure

4.1). The chronic arrival times of the fluorescence photons contain the information about the photon flux so that we can count and bin the photons in a given time scale, for example, of 10- ms binning time to obtain a typical fluorescence intensity trajectory shown in the bottom panel in

Figure 4.3D. Furthermore, we plot the histogram of the photon delay times in a binning period

(10 ms) to obtain the fluorescence lifetime in each time bin (Figure 4.3B). The lifetime (DA) in each 10-ms bin (Figure 4.3B) can be obtained by fitting the histogram of delay time of all the photon in 10-ms bin with exponential function or by calculating the mean of the delay time of all of the photons in 10-ms bins. We typically treat the photon counting distribution in each bin as a

44,45 Poisson distribution that give the means of each distribution as fluorescence lifetime, DA. In

Figure 4.3B, we show typical histograms of delay time of the fluorescence photons in 10-ms bins under different FRET states. The decrease in fluorescence lifetime, comparing left and right 103 panels, indicates an increase of the FRET efficiency, most likely resulting from the decrease of donor-acceptor separate distance (insets in Figure 4.3B). After calculating all the delay times in each 10-ms bin from the original photon stamping data (Figure 4.3A), and connecting the lifetime data resolved in all of the time bins, we obtain the lifetime trajectory of donor (DA ~ t, shown in Figure 4.3C). The lifetime trajectory of donor involving in FRET reports the EFRET fluctuation associated with the enzyme conformational fluctuation. Therefore, by analyzing the donor lifetime trajectory, we are able to detect conformational dynamics, probing real-time donor-acceptor distance changes.

To demonstrate that the dynamics of enzymatic conformation from lifetime correlated with that from the efficiency of FRET energy transfer, we have used single molecule lifetime- based FRET to study the conformational dynamics of Cy3-Cy5 labeled HPPK enzyme in catalytic reaction. Figures 4.4A and 4.4B show donor-acceptor two channel single molecule fluorescence images (10m by 10m) of HPPK (0.1 nM) molecules labeled with Cy3 and Cy5 dye probes in the presence of 100 M ATP and 100 M HP, confined in 1% agarose gel

(containing 99% water) sandwiched between two cover slips. Each individual feature in the images is attributed to a single HPPK molecule, and the intensity variations among the molecules are due to FRET. Figure 4.4C shows the obtained single-molecule fluorescence intensity trajectory of the donor. Figure 4.4D shows lifetime trajectory of donor τDA (purple) from Cy3

(green)/Cy5 (red)-labeled HPPK under enzymatic reaction condition with 100 M ATP and 100

M HP, and lifetime trajectory of τD (green) with Cy3 labeled-only under the same condition. It is clear that the lifetime trajectory of τD (green) with only Cy3 labeled is narrow compared with the lifetime trajectory of τDA with Cy3(green)/Cy5(red)-labeled HPPK in enzymatic reaction 104 condition. This result that narrow distributed τD compared with τDA indicates the change in the lifetime trajectory of τDA is due to the FRET between donor and acceptor reflecting the conformational change in the enzymatic reaction. Figure 4.4E shows the FRET trajectories that calculated by Equation (1.12) from the data in Figure 4.4D, where we use the fitting value 2.65 ns as τD to avoid introducing more measurement error. Fluorescence intensity autocorrelation functions have been demonstrated to be able to extract kinetics constants of chemical reactions or enzymatic reactions with single molecule fluorescence correlation spectroscopy. 46,47 In comparing the validity of lifetime FRET recording, we calculated the autocorrelation of the single-molecule fluorescence intensity (Figure 4.4C), lifetime τDA (Figure 4.4D), and FRET

(Figure 4.4E) trajectories of the donor. The results, as shown in Figure 4.4F, 4.4G, and 4.4H respectively, indicate that the autocorrelation calculated from different methods (intensity, FRET efficiency, lifetime) have the same decay time about 0.14±0.01s, which suggests that both the intensity trajectory and lifetime trajectory or FRET efficiency trajectory based on lifetime measurement give consistent conformational dynamics information.

105

Figure 4.3. Single-molecule photon-stamping measurement and data analysis, all the data are collected from Cy3-Cy5 labeled single HPPK molecule under the enzymatic reaction condition with 100 M ATP and 100 M HP. (A) An example of the Single-molecule photon-stamping raw data from the donor channel in 0.8 second period (5.8-6.6 s). Each data dot corresponds to a detected photon plotted by the delay time (τ) vs its chronic arrival time (t). (B) Histograms of the delay times of the photons in a 10 ms period from the trajectory shown in (A). The left panel is histogram of delay times in 10 ms (6.08-6.09s), corresponding to the low energy transfer efficiency from donor to acceptor. The right panel is histogram of delay times in 10 ms

(6.30-6.31s), corresponding to the high energy transfer efficiency. (C) Lifetime trajectory of donor DA calculated from the trajectory in (A) with 10-ms binning. The arrows show the positions (6.08-6.09s) and (6.30-6.31s) of the lifetime trajectory. (D) Intensity trajectory of donor calculated from trajectory in (A) with 10-ms binning. 106

Figure 4.4. Single-molecule fluorescence images (10m × 10m) of Cy3 (A) and Cy5 (B) labeled HPPK molecules in 1% agarose gel Tris-HCl buffer solution (pH = 8.3). (C) Single- molecule fluorescence intensity trajectory of donor from Cy3-Cy5 labeled HPPK under the enzymatic reaction condition with 100 M ATP and 100 M HP. (D) Lifetime trajectory of

Cy3, τDA (purple) from Cy3-Cy5 labeled HPPK and Lifetime trajectory of Cy3, τD (green) with only Cy3 labeled under the enzymatic reaction condition with 100 M ATP and 100 M

HP. (E) EFRET trajectory of Cy3-Cy5 labeled HPPK, calculated from lifetime time trajectory in

(D), where we use the fitting value of 2.65ns as τD. (F, G, H) Autocorrelation analyses from

the intensity trajectory in (C), lifetime trajectory DA in (D), and EFRET trajectory in E show the same decay time τ = 0.14±0.01s.

107

There are significant advantages and crucial applications of using lifetime-based FRET vs intensity-based FRET in studying molecular conformational changes at single-molecule level.

Theory and experimental methods of single-molecule fluorescence lifetime measurement based on time-correlated single photon counting (TCSPC) have been developed and applied in single- molecule dynamics analyses including single-molecule photon-stamping and single-molecule

FRET measurements. 24,25,37,38,45,48,49 The time resolution of single-molecule fluorescence FRET is usually limited by the binning time of single photon recording, it is difficult to observe molecular dynamics that occur on a time scale faster than the binning time. 50,51 Simultaneous detection and analysis of both lifetime and fluorescence intensities have been successfully applied by using a joint two-dimensional distribution plots of intensity-derived FRET efficiency versus donor lifetime to identify interconverting states, 23,52-55 where intensity-derived FRET efficiency is correlated with the fluorescence lifetime of the donor quenched by FRET.

Furthermore, due to the rotational correlation time for proteins are comparable to the lifetime of fluorophores labeled on the proteins, another important application for the lifetime-based FRET is the simultaneous measurement of single-molecule fluorescence anisotropy that can provide both the rotational diffusion and internal flexibility dynamic information of proteins. 24, 25, 54-56

Another important application for Lifetime-based FRET are the AFM tip correlated single-molecule FRET measurements. Due to the micro-mirror effect of the AFM tip under laser illumination, the intensity of donor or acceptor fluorescence and the calculated intensity-based

31 EFRET were shown to be perturbed by the tip approaching-withdrawing. However, the lifetime- based EFRET can avoid the effect of tip perturbation associated intrinsically with the tip approaching-withdrawing movements. Figure 4.5 shows the AFM tip correlated single-molecule

FRET measurement by using fs pulse laser. The collected intensities (Figure 4.5A) of donor 108

(green) and acceptor (red) as well as the correlated FRET efficiency (Figure 4.5B) that calculated based on the measured fluorescence intensity change as the tip approach to/-withdraw from the

Cy3-Cy5 labeled HPPK on cover glass, Both Figure 4.5A and Figure 4.5B show the strong correlation with the AFM tip approaching-withdrawing movement, which is due to the so called

“mirror effect”. The tip as a mirror to reflect more fluorescence photons to the object as the tip is close to the dye labeled single-molecule proteins. In contract, the lifetime-based EFRET trajectory

(Figure 4.5C) shows no correlation between the tip approaching/withdrawing movements and the lifetime-based FRET efficiency EFRET as that of the intensity-based FRET efficiency EFRET. It is clear that the lifetime-based EFRET can effectively remove the artifact measurement background due to the perturbation of the tip approaching-withdrawing movements, and the lifetime-based

FRET should be particularly useful in the applications of AFM tip correlated single-molecule

FRET manipulation and measurements of protein dynamics.31 Since the single-molecule FRET measurements often involve much smaller changes of the FRET efficiency in probing protein conformational dynamics,56 this technical advantage is particularly important for measuring

FRET fluctuations associated with single-molecule protein conformational dynamics. 109

Figure 4.5. (A) A Fluorescence intensity-time trajectory of donor (green) and acceptor (red) in a single-molecule AFM-FRET measurement on one HPPK (labeled Cy3-Cy5 on 88c, 142c), the experiment was performed in 50 mM tris buffer (pH=8.3) and 10 mM MgCl2, but no enzymatic reaction substrate were added. The donor-acceptor intensities change show the strong correlation with the AFM tip approaching-withdrawing movements. (B) intensity-based

FRET efficiency-time trajectory calculated from trajectory (A), and the EFRET changes obviously in correlation with the AFM tip approaching-withdrawing movements, which is an artifact background complicating the real protein dynamics analysis. (C) Lifetime-based FRET efficiency-time trajectory from the lifetime measurement, the lifetime-based EFRET shows no significant correlation with the AFM tip approaching-withdrawing movements, effectively removed the AFM-FRET measurement artifact background. 110

4.4. Conclusions

In this chapter, we present a single molecule lifetime-based FRET method on the basis of single-molecule photon stamping spectroscopy to characterize conformational dynamics of individual protein. The result shows that the lifetime of donor related to donor-acceptor distance in FRET, and we can probe the single-molecule donor-acceptor distance change with time or the single-molecule conformational dynamics by measuring the donor’s lifetime trajectory.

Moreover, the lifetime-based FRET only measure donor’s lifetime time trajectory and is independent on the factors from measurements, such as, crosstalk, excitation intensity, collection efficiency, and position of dye with respect to the excitation beam, which makes this approach much effective and reliable in single molecule enzymatic conformational dynamic studies, especially for future complex AFM tip correlated single-molecule FRET manipulation and measurements of protein dynamics.

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CHAPTER 5. PROBING COHERENT CONFORMATIONAL DYNAMICS OF HPPK BY

SINGLE-MOLECULE PHOTON STAMPING FRET SPECTROSCOPY

Disclosing potential roles of conformational changes at the active site of enzyme is critical in unraveling the complex intimacy between conformational dynamics and enzyme catalytic cycles. In this chapter, we have investigated the conformational dynamics of HPPK by means of single-molecule photon stamping FRET spectroscopy via only tracing donor lifetime changes. By scrutinizing both the donor and acceptor lifetimes, we have directly observed the

FRET dynamic process between Cy3 and Cy5 at the single-molecule level, derived from the equivalent values between the decay rate of donor lifetime and rising rate of acceptor lifetime.

Furthermore, we have identified intermittent coherence of HPPK Loop3-active site conformational states during catalytic cycles by autocorrelation analysis of donor lifetime trajectories. We have also observed the rising tendency of coherence frequency as the substrates concentration increases to the saturation condition and descending tendency when the denaturant is present. The coherence frequency is heterogeneous from molecule to molecule with distinct approximate bell-shape distributions under denaturant or different substrate concentrations, indicating that different conformational states play relevant roles in the proximity of HPPK open-close conformational motions. The intermittent coherence, interpreted here as a specific dynamic behavior of convoluted conformational open-close time bunching effect, is most likely originated from the reoccurrence of correlated conformational states and regulated by substrates- enzyme interactions coupled with local environment fluctuations. We suggest that the conformational coherence here is likely a common phenomenon in the regulations of both cooperative/adaptive conformational changes and the coupling chemical kinetics in biological systems. 116

5.1. Background Knowledge

5.1.1. The Roles of Enzyme Conformational Changes

Enzyme catalysis is a dynamic process in nature. For example, association and disassociation of substrates or release of products is often accomplished by enzyme conformational changes.1-4 In one catalytic cycle, enzyme molecules typically involve dynamic multiple conformations during substrate binding, substrate-enzyme complex formation, or product releasing.1-15 It has been widely proposed that conformational dynamics and flexibility are part of the enzyme catalytic cycles, and conformational adaptability is clearly significant for enzyme catalytic functions. 1-4,16-32 Therefore, the potential roles of conformational changes at the enzyme active site in the catalytic process have been aimed at discerning the complex intimate linkage between conformational dynamics and enzymatic catalytic cycles,16,18,21-25,33,34 with the reported time-scale of conformational motions ranging from microseconds to milliseconds, even seconds.33,35-37 In the ensemble-level study, motivated by structures of transient intermediates that give brief snapshots into conformational changes, X-ray crystallography,38 NMR,21 mass spectroscopy,20 and ultrafast pump-probe absorption difference spectroscopy 35 have been developed to provide details about kinetics of conformational changes in different steps of the catalytic cycle. Nevertheless, heterogeneity and dynamics of protein motions,39 reaction rate fluctuations,17,39 and complex enzyme-substrate interactions 19,40,41 have become major challenges for conventional fluorescence ensemble-averaged measurements to unravel enzymatic reactions mechanisms. The coupling between enzymatic conformational dynamics and chemical kinetics is always another big concern. Fortunately, molecular dynamics

(MD) simulation and statistical modeling have made significant complimentary contributions to characterize conformational dynamics and reaction fluctuations of enzymes under enzyme- 117 catalyzed reactions.42-49 On the other hand, fluorescence spectroscopy has been widely developed to monitor and understand protein dynamics not only at the ensemble level but also at single- molecule studies, for example, recently developed single-molecule Förster resonance energy transfer (FRET) spectroscopy has been serving as a powerful and informative approach in understanding such complex inhomogeneity and dynamics by monitoring conformational motions of individual molecules. 17,19,50-54

5.1.2. Single-Molecule Photon Stamping FRET Measurements

The introduction of single-molecule photon stamping FRET spectroscopy has been elucidated in Chapter 4. Briefly, intensity-based FRET extracts motion information from calculating the ratio between the acceptor’s intensity and total emission intensity,55 which is sensitive to thermo noise, excitation intensity, collection efficiency, and cross-talking, etc.56,57 In this way, the FRET detection is sensitive under various single-molecule spectroscopy measurement conditions, for example, the environmental fluctuations irrelevant to the intrinsic protein conformational dynamics. Alternatively, lifetime-based FRET measures donor fluorescence lifetime changes in the presence of the acceptor to present FRET changes. It eliminates the non-FRET signals (like thermo noise, cross-talking, collection efficiency, and other environmental factors) which largely disturb intensity-based FRET measurements, making lifetime-based FRET more accurate and more applicable to single-molecule conformational dynamics studies,57 such as AFM tip-enhanced single-molecule spectroscopy and imaging measurements where fluorescence signal magnification by metal tip reflection is a common phenomenon. 58,59

118

5.1.3. Conformational Dynamics Studies of Loop3-Active Site Motions

As described in previous chapters, the wild-type HPPK has three catalytic loops (Loop1,

Loop2 and Loop3) shown in Figure 5.1. 60-62 In our previous work of Chapter 4, a single- molecule photon stamping FRET spectroscopy on the basis of lifetime-based FRET have been demonstrated to study enzymatic conformational dynamics.63 Here, we report our research progress on the conformational dynamics of HPPK Loop3-active site on the basis of donor lifetime changes to present FRET-related conformational changes.

Figure 5. 1. Crystal structure of Apo HPPK (PDB-code, 1HKA). Three loops (Loop1, Loop2, and Loop3) are shown in deep red pipes. A pair of Cy3 and Cy5 dye molecules is covalently labeled to the cysteine 88 on Loop3 and cysteine 142 of a single HPPK molecule as donor and acceptor for single-molecule FRET measurements.

5.2. Materials and Methods

5.2.1. Dyes-Labeled Single HPPK Molecule

The sample preparation is the same to the description in Chapter 4.2.1. Briefly, two dyes of a Cy3-Cy5 FRET pair are used to label the two cysteines: residue 88 on Loop3 and residue 119

142 near the C-terminal helix to monitor the conformational motions of the host HPPK molecule.

The typical sample condition for single molecule measurement in our experiments is: 0.1 nM

HPPK, 100 M ATP and 100 M HP in 1% agarose gel (99% pH 8.3 100 mM Tris-HCl buffer

55,64 with 10 mM MgCl2). The Trolox-oxygen scavenger solution, is added to the above mixture.

The formed mixture is then sandwiched between two clean transparent cover-slips.

5.2.2. Single-Molecule Photon Stamping FRET Spectroscopy

We have used single photon time-stamping to record each fluorescence photon’s real arrival time (t) and delay time (t) related to laser pulse and further to measure single-molecule

FRET fluctuation trajectories for both donor and acceptor simultaneously.63,65 In our work, both intensity and lifetime trajectories are obtained by analyzing arrival and delay times photon-by- photon. A two-channel single molecule FRET lifetime microscopy is used, and the details of experimental setup have been published in our previous work.5 The detailed descriptions of the concepts and experimental set-up are elucidated in Chapter 4.

5.2.3. Autocorrelation Function Analysis

Autocorrelation function analysis has been widely applied to identify fluctuation rates of single-molecule electron transfer,66,67energy transfer fluctuations,5,68,69 and protein conformational changes.5,39,70,71 We have used correlation function to analyze the donor lifetime fluctuations during FRET process. The autocorrelation function is given by Equation (5.1):

2 Cauto()()()() t DA 0   DA t   DA 0

2 (()DA0   DA )(()  DA t   DA )(()  DA 0   DA ) (5.1) 120 where DA(t) is the donor lifetime in the presence of the acceptor and also the signal variable in lifetime trajectory DA(t) ~ t; t here is the time lag; <DA> is the mean lifetime of the lifetime trajectory DA(t) ~ t. By using autocorrelation analysis, donor lifetime fluctuate rate can be identified, and the HPPK conformational fluctuation dynamics can be further discerned.

5.3. Results and Discussion

5.3.1. Real-Time Observation of Single Molecule FRET Process

Fluorescence lifetime is an intrinsic property of a fluorophore,72 and the fluorescence lifetime fluctuation reflects the heterogeneity of the local environment, 73-75 like quenching effects, fluorophore conformational changes, or thermal fluctuation at room temperature. In

FRET process, a donor fluorophore in an excited electronic state may non-radiatively transfer its excitation energy to a nearby acceptor fluorophore through transition dipole-dipole interactions, as shown in Figure 5.3A; thereby, the donor fluorescent lifetime can be dramatically reduced/quenched by energy transfer to the acceptor.76 Single-molecule photon stamping FRET technique enables us to conduct not only intensity-based FRET measurements, but also lifetime- based FRET measurements by recording the arrival and delay times of each fluorescence photon.65 Figure 5.2A shows an example of 10 seconds raw data from the donor channel recorded by single photon time-stamping. Here, each photon is stamped with two parameters: an arrival time and a delay time. Photons collected in 100 seconds can give rise to the general lifetime by fitting the histogram of delay times. 121

Figure 5.2. (A) 10 seconds portion of the single photon time-stamping raw data of Cy3. The data is collected from Cy3-Cy5 labeled HPPK under the enzymatic reaction condition with 100 M

ATP and 100 M HP. Each dot represents one emitted photon by donor, and each photon is stamped with an arrival time and a delay time, plotted in horizontal and vertical axes, respectively. (B) Histogram of all delay times in 100 seconds from donor photons. Double-

-1 exponential function can smoothly fit the histogram with a slower decay rate kd1 0.28 ns (d1 =

-1 3.5 ns) and a faster decay rate kd2 1.20 ns (d2 = 0.83 ns). (C) Histogram of delay times in 100 seconds from acceptor fluorescence photons. Single-exponential function fitting generates a

-1 -1 rising rate ka2 1.20 ns (a2 = 0.83 ns) and a decay rate 0.54 ns (a1 = 1.86 ns). It shows that the faster decay rate kd2 is approximately equal to the rising rate ka2.

122

To obtain the detailed information about the energy transfer between donor and acceptor associated with lifetime change, we have further analyzed the lifetimes of donor and acceptor labeled on HPPK in the presence of 100 M ATP and 100 M HP by exponential fitting. The instrument response function, about 0.35 ns in our experimental set-up, is de-convoluted in the fitting process. By double-exponential fitting the histogram of donor delay times, we have obtained two components of decay rates or lifetimes as shown in Figure 5.2B; kd1 is relatively

-1 -1 slower than the kd2: 0.28 ns vs 1.20 ns . We interpret the slower decay rate kd1 as the normal fluorescence decay of donor when there is no or low energy transfer from donor to acceptor, while the faster decay rate kd2 is the one when there is high energy transfer. In the meantime, the

-1 acceptor only has one decay rate ka1 0.54 ns (a1 1.86 ns) during FRET process in Figure 5.2C.

-1 We have noticed a remarkable rising rate ka2 1.20 ns (a2 0.83 ns) in the propagating process of the acceptor excited states shown in Figure 5.2C, being equal to the value of the faster decay rate kd2 in the de-excited process of donor excited states.

In order to examine whether this coincidence is common phenomenon in FRET process, we have collected and plotted the faster decay rate kd2 and rising rate ka2 from single Cy3-Cy5 labeled HPPK molecules. As shown in Figure 5.3B, most of the data points are surrounding the diagonal, indicating the approximate equivalence between the faster component of donor lifetime and rising component of acceptor lifetime. This coincidence is understandable according to

Jablonski diagram in Figure 5.3A: 1) During FRET process, non-radiative energy transfer pathway dominates the de-excitation process of the donor and the corresponding lifetime of the donor is dramatically reduced; 2) In the meantime, the acceptor, which alone cannot involve absorption of a light field, accepts the energy from the donor when the oscillations of an optically induced electronic coherence on the donor are resonant with the electronic energy gap 123 of the acceptor; 3) Energy transfer rate equivalent to the rising rate of the acceptor is approximately equal to faster decay rate of the donor. Considering the Förster distance R0 of

77 9 -1 Cy3-Cy5 pair is ~ 54 Å, the energy transfer rate KET is 1.2×10 s , the average distance r between donor and acceptor is estimated to be 4.24 nm for this single molecule in Figures 5.2B-

6 -1 C (KET = 1/d1 [R0/r] , where d1 = 3.5 ns, KET = 1/0.83 ns , R0= 5.4 nm). The calculation details are presented in Figure 5.4.

Figure 5.3. (A) Jablonski diagram of excitation, de-excitation, and Förster resonance energy transfer process between a FRET pair. (B) Scattering plot (ka2 vs kd2) of acceptor rising rate as a function of donor faster decay rate from 23 single HPPK molecules.

124

Figure 5.4. The plot between FRET Efficiency and Distance. The average distance r between donor and acceptor is estimated to be 4.24 nm for the specific single molecule in Figures 5.2B-C.

5.3.2. Multiple Conformational States of HPPK under Enzymatic Reactions

Figure 5.5A and 5.5B shows two fluorescence images from single donor (Cy3, Figure

5.5A) and acceptor (Cy5, Figure 5.5B) labeled HPPK in the presence of 100 M ATP and 100

M HP. Individual peak appear in both donor and acceptor fluorescence images indicate one single HPPK enzyme labeled with donor-acceptor FRET pair. We have known that HPPK enzyme undergoes open-close conformational motions during enzyme catalytic cycles; Loop3 involves the most remarkable conformational motions; the distance of donor-acceptor on single

HPPK varies as host HPPK molecule transits among different conformations.60-62 Hence, the resulting fluorescence intensity fluctuations of both donor and acceptor, as shown in Figure 5.5C, reflect donor-acceptor distance changing trend during HPPK conformational motions under enzymatic reactions. Although the thermal fluctuation or inherent measurement noise may somehow contribute to fluorescence intensity fluctuations, the anti-correlation feature in Figure

5.5C between donor and acceptor intensity indicates that FRET-related intensity fluctuation dominates this process. Figure 5.5D shows the donor lifetime trajectory simultaneously recorded 125 as intensity trajectory (Figure 5.5C). It is generated by averaging donor lifetime in each 10 ms time-frame and connecting means in all of those short 10 ms periods.63 In view of donor-acceptor distance variance driven by HPPK conformational changes in the catalytic process, the donor lifetime also presents dynamic fluctuations similar to intensity fluctuations.

Enzymes are considered as conformations ensembles (at least two conformational states like open and close states) rather than the single conformation during enzymatic reactions.1-

3,19,54,78 The capability of enzymes undergoing rapid conformations transition from one to another is also a significant reflection of the enzyme specificity and overall catalytic activity.25 In this work, we have observed the Gaussian-like distribution of both donor intensity and donor lifetime, as shown in Figure 5.5E and Figure 5.5F, respectively. We interpret the Gaussian-like distribution as the conformations ensemble of HPPK enzyme involving multiple conformational states in each HPPK catalytic turnover. It is likely arising from selective substrate binding in the active and non-active substrate-enzyme formation process, which is consistent with the description of conformation selection mechanism. At current stage, the contributions of each conformational states are difficult to resolve and hard to show spectral discrimination, most likely due to the possible fast inter-conversion kinetics or coupling among states and close FRET signals coming from the various states.

126

Figure 5.5. (A, B) Single Cy3-Cy5 labeled HPPK fluorescence images (10 m by 10 m) in the presence of 100 M ATP and 100 M HP. The emissions are coming from a pair of FRET dyes, donor/Cy3 (A) and acceptor/Cy5 (B). (C) Four seconds portion of single-molecule intensity trajectories of Cy3 (green) and Cy5 (red) of single Cy3-Cy5 labeled HPPK. The result shows anti-correlation between donor and acceptor intensity fluctuations. (D) Four seconds portion of lifetime trajectory of Cy3 recorded simultaneously as intensity trajectories in (C). (E) The Cy3 fluorescence intensity distribution picked up from (C). (F) The donor lifetime distribution derived from lifetime trajectory (D). Gaussian-like distribution shown in both (E) and (F) reveals the involvement of multiple conformational states in HPPK catalytic turnovers.

127

5.3.3. Intermittent Coherence of Bunched Multiple Conformational States

In our previous study, we have observed the coherence of active-site conformational changes involved in HPPK enzymatic reactions by probing the Loop2 motions, identified from cross-correlation analysis of fluorescence intensity.79 Here, we have found that HPPK Loop3- active site conformational motions involves intermittent coherent conformational dynamics during the catalytic cycles via single molecule photon stamping FRET spectroscopy. Statistically, around 10% of overall trajectory segments exhibit coherence in Figures 5.7 and 5.8, and the left shows typical exponential feature shown in Figure 5.6. Figure 5.6B is a typical example of major autocorrelation of donor lifetime trajectory and Figure 5.6A is the corresponding lifetime trajectory. The decay time (0.13±0.02 s) agrees with the dynamic time-scale published in our previous work,63 in which different methods (intensity, FRET efficiency, lifetime) have the same decay time of about 0.14±0.01s.

The intermittent coherence of multiple enzymatic conformational states transitions is observed from the coherent features in autocorrelation functions of donor lifetime fluctuation trajectories. In our current experimental results, the coherence can be maintained as long as about 4.0 s and dephasing appears after that (Figure 5.7). The intermittent property of coherence is most likely resulted from local environment fluctuations (like electrostatic field, electrostatic interaction, hydrophobicity of active site, and thermal fluctuations) and multiple nuclear coordinate fluctuations.5,79 Under those uncontrolled fluctuations, the coherence of bunched conformational states associated with recurrence of the local environment cannot be consistently resolved under enzymatic reactions. The coherence frequency is heterogeneous from molecule to molecule with distinct approximate bell-shape distributions under different reaction conditions.

Figure 5.8A shows the autocorrelation function C(t) of donor lifetime of Cy3-Cy5 labeled HPPK 128 under enzymatic condition of 100 M ATP and 100 M HP, revealing the coherence feature with dephasing at ~2 s (Figure 5.9A) and a coherence frequency of 1.4 s-1. We have further obtained the narrow bell-shape coherence frequency distribution with frequency 1.4±0.22 s-1 under this reaction condition, shown in Figure 5.8B. Previously, we have observed a bunching effect in hinge-bending non-equilibrium conformational motions of T4 lysozyme catalytic reactions, that is, the hinge-bending conformational open-close time is narrowly bunched in a Gaussian-like distribution with a defined second moment.80 The bunching effect originates from the consecutive multiple-step conformational motions in forming an active substrate-enzyme complex, and the Gaussian-like distribution is a result of the convolution of consecutive multiple

Poisson rate processes.80 Likewise, most likely, this specific intermittent coherence is a specific dynamic behavior of time bunching effect as a result of convoluted conformational open-close motions and the coupling catalytic kinetics, originated from the recurrent multiple conformational states during the enzymatic reaction cycles.

129

Figure 5.6. (A) An example of typical donor lifetime trajectory of Cy3-Cy5 labeled HPPK under enzymatic reaction conditions with 100 M ATP and 100 M HP. (B) The corresponding autocorrelation of donor lifetime. The typical decay time τ = 0.13±0.02s is obtained.

Figure 5.7. (A) Intermittent coherence. It is evident in autocorrelation of donor lifetime of Cy3-

Cy5 labeled HPPK under enzymatic reaction condition with 200 M ATP and 200 M HP. (B)

The dephasing of coherence in (A). The coherence can stay for as long as about 4.0 s and dephasing appears after that. 130

Figure 5.8. (A) Autocorrelation of donor lifetime of Cy3-Cy5 labeled HPPK under enzymatic reaction conditions of 100 M ATP and 100 M HP. The coherence is observed with fitted frequency of 1.4±0.22 s-1. (B) The coherence frequency distribution under enzymatic reactions of

100 M ATP and 100 M HP. (C) Autocorrelation of donor lifetime of Cy3-Cy5 labeled HPPK under enzymatic reaction conditions of 100 M ATP, 100 M HP and 4M denaturant GuHCl.

The fitted coherence frequency is lower with 0.8±0.2 s-1. (D) The coherence frequency distribution under the same condition as (C). The coherence frequency with added denaturant is slower than that without denaturant.

131

Figure 5.9. (A) The dephasing of coherence shown in Figure 5.8A. (B) The dephasing of coherence shown in Figure 5.8C. Both coherence in Figure 5.8A and Figure 5.8C can last for ~

2s, the typical coherence dephasing time observed in our experiments.

Since donor lifetime is an estimation of FRET efficiency reflecting the donor-accepter distance, the autocorrelation function of donor lifetime is eligible to describe the correlations of enzyme conformational motions under the enzymatic reactions. For a two-state model in which only open and close conformational states involve under the enzymatic reactions, the autocorrelation function will exhibit single-exponential decay and the decay rate is the sum of the forward (open to close) and backward (close to open) transition rates. For our HPPK enzymatic system in which more than two intermediate conformational states involve, the observed intermittent coherence derived from the autocorrelation function describes the process of conformational motions and estimate how long a given property of multiple intermediates states system persists until it vanishes by interactions with surroundings. The intermittence feature is probably caused by the conformational dynamics of the single HPPK molecule, when the detailed balance of the thermodynamics equilibrium is intermittently violated by local 132 environmental fluctuations.81 We interpret the intermittent coherence of multiple conformational states transitions observed in HPPK Loop3-active site conformational motions as a specific rate process of bunched multiple conformational dynamics.

5.3.4. Conformational Coherence Induced by Substrate-Enzyme Interactions

We have further conducted several control experiments to verify the coherence under denaturant guanidine hydrochloride (GuHCl) and different substrate concentrations ranging from unsaturated to saturated conditions. Figure 5.8C shows the coherence feature of the autocorrelation function of donor lifetime of Cy3-Cy5 labeled HPPK under enzymatic reaction conditions with 100 M ATP, 100 M HP and 4M denaturant GuHCl. The coherence can stay for ~2 s (Figure 5.9B). The distribution of coherence frequency under this condition is given in

Figure 5.8D, and coherence frequency (0.8±0.2 s-1) in the presence of denaturant becomes slower than that in the absence of denaturant. We attribute this descending tendency to the slower conformational motions of HPPK when the denaturant is added to perturb the enzymatic reaction. With 4M GuHCl denaturant, the enzyme is most likely not completely denatured and still has certain reactivity to perform conformational motions. Enzyme conformations favor the unfolded conformational states, resulting in the weak interactions between substrates and enzyme along with the decreased conformational states coherence frequency.

Our results suggest that conformational coherence is induced by substrates-enzyme interactions. This conclusion is further implied by substrates concentration-dependent control experiments. Figures 5.8B and 5.10A-B show coherence frequency distributions of 1.4±0.22 s-1,

1.7±0.33 s-1, and 1.8±0.4 s-1 derived from the autocorrelation functions of donor lifetime trajectories with different substrate concentrations of 100 M, 200 M, and 500 M for both

ATP an HP, respectively. The narrowly distributed coherence frequency under each concentration 133 presents a concentration-dependent rising trend illustrated in Figure 5.10C. To elaborate, frequency increases from 1.4±0.22 s-1 to 1.8±0.4 s-1 as the substrate concentrations increase from

100 M to 500 M. Under saturated substrate concentration conditions, like 200 M and 500

M, the coherence frequencies are slightly different and the variance is within the 0.1 s-1 marginal error bar (1.7±0.33 s-1 and 1.8±0.4 s-1). We attribute the saturated coherence frequency as a result of fully occupied enzyme by saturated substrates:1) In the case of unsaturated substrates, as the concentrations of substrates increase, more and more substrates can bind to the free enzymes to form substrate-enzyme complexes, accelerating the conformational motions and resulting in the faster coherence frequency of bunched conformational states; 2) In the case of saturated substrates, increasing the substrates concentrations cannot enable more substrates to interact and bind with free enzymes; 3) As a result, the coherence frequency induced by the substrate-enzyme interactions most likely tends to be stable under over-saturated substrates. The involvement of a few characteristic fluorescence coherence frequencies indicates that different conformational intermediate states play relevant roles in the proximity of HPPK open-close conformational motions.

134

Figure 5.10. (A, B) The coherence frequency distributions of Cy3-Cy5 labeled HPPK under enzymatic reactions with various substrate concentrations: (A) 200 M ATP and 200 M HP, the saturated condition; (B) 500 M ATP and 500 M HP, the over-saturated condition. (C) Statistic results of the coherence frequency vs substrate concentrations, including the denaturant effect under 100 M ATP and 100 M HP.

We have also noticed that the coherence frequency is sometimes higher than turnover rate

0.70 s-1 at the saturating substrates concentration of 200 M at ensemble-level measurements. 82

Considering that the rate-limiting step in the HPPK catalytic turnover is the products releasing process, 82 the result of the higher conformational coherence rate than products releasing rates is not controversial. Conversely, the result indicates that the conformational changes during the enzymatic reactions involve both slow productive and rapid non-productive motions in forming active or inactive substrate-enzyme complex, whereas only the active complex can lead to the chemical reaction occurring and following products releasing.79,83 The involvement of slow productive and rapid non-reductive motions support a complimentary mechanism of both conformation selection and induced fit loop-gated conformational changes during interactions between enzymes and substrates. The potential roles of productive or non-productive motions and the multiple intermediate states coherence in biological systems are likely examples of conformational cooperative adaptability and selectivity for the enzyme catalytic function. 135

Additional theoretical efforts are still in need for a deeper understanding of the conformational coherence in terms of the coupling between chemical kinetics and conformational transitions at single-molecule level. Recent efforts to understand damped oscillations of the correlation functions of the fluorescence signal, in which the intrinsic dynamics are modelled as the fluctuations of stochastic variables to express the time-dependent transition rates through the use of master equations, 81 might be adapted to the specific conformational coherence raised by our experiments in this report.

5.4. Conclusions

We have reported real-time HPPK conformational dynamics under non-equilibrium conditions on the basis of single-molecule photon-stamping FRET by recording lifetime fluctuation trajectories. We have directly observed the FRET process at single-molecule level from the consistence between the decay rate of donor lifetime and the rising rate of acceptor lifetime. Our findings demonstrate that HPPK involves multiple Loop3-active site conformational states in catalytic turnovers by monitoring donor lifetime changes. Those multiple conformational states present a correlated intermittent coherence with defined frequency distributions, revealed in the coherent features of donor lifetime autocorrelation functions. We suggest that the coherence is originated from a specific rate process of the time bunching effects in non-equilibrium conformational states, and is regulated by the nature of substrate-enzyme interactions coupled with local environment fluctuations. The observed rapid coherence rate beyond HPPK catalytic turnover indicates that conformational motions during enzymatic reactions involve both slow productive and rapid non-productive motions in forming active or inactive substrate-enzyme complex, suggesting a complimentary mechanism integrating both conformational selection and induced fit loop-gated conformational changes. The potential roles 136 of the bunched conformational states coherence are likely direct or indirect reflections of cooperative/adaptive conformational changes and coupling chemical kinetics under enzymatic reactions.

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142

CHAPTER 6. PROBING PROTEIN MULTI-DIMENSIONAL CONFORMATIONAL

FLUCTUATIONS BY SINGLE-MOLECULE MULTI-PARAMETER PHOTON STAMPING

SPECTROSCOPY

Conformational motions of proteins are highly dynamic and intrinsically complex. To capture the temporal and spatial complexity of conformational motions and further to understand their roles in protein functions, an attempt is made to probe multi-dimensional conformational dynamics of proteins besides the typical one-dimensional FRET coordinate or the projected conformational motions on the one-dimensional FRET coordinate. T4 Lysozyme hinge-bending motions between two domains along -helix have been probed by single-molecule FRET.

Nevertheless, the domain motions of T4 lysozyme are rather complex involving multiple coupled nuclear coordinates and most likely contains motions besides hinge-bending. It is highly likely that the multiple dimensional protein conformational motions beyond the typical enzymatic hinged-bending motions have profound impact on overall enzymatic functions. In this report, we have developed a single-molecule multi-parameter photon stamping spectroscopy integrating fluorescence anisotropy, FRET, and fluorescence lifetime. This spectroscopic approach enables simultaneous observations of both FRET-related site-to-site conformational dynamics and molecular rotational (or orientational) motions of individual Cy3-Cy5 labeled T4 lysozyme molecules. We have further observed wide-distributed rotational flexibility along orientation coordinates by recording fluorescence anisotropy and simultaneously identified multiple intermediates conformational states along FRET coordinate by monitoring time-dependent donor lifetime, presenting a whole picture of multi-dimensional conformational dynamics in the process of T4 lysozyme open-close hinge-bending enzymatic turnover motions under enzymatic reaction conditions. By analyzing the autocorrelation functions of both lifetime and anisotropy 143 trajectories, we have also observed the dynamic and static inhomogeneity of T4 lysozyme multi- dimensional conformational fluctuation dynamics, providing a fundamental understanding of the enzymatic reaction turnover dynamics associated with overall enzyme as well as the specific active-site conformational fluctuations that are not identifiable and resolvable in the conventional ensemble-averaged experiment.

6.1. Introduction

The introduction of T4 lysozyme has been given in Chapter 2.1.3. The three-dimensional structure is clearly organized with two domains connected by a long -helix (Figure 6.1). 1-4 The active site cleft, where hydrolysis of the glycosidic linkage takes place, is located at the interface between the two domains. It has been widely accepted that T4 lysozyme exhibit hinge-bending conformational motions, referring to rotation of one domain relative to the other domain along an axis running through the interface of the two domains.3,4 Both ensemble-level and single- molecule measurements have revealed hinge-bending motions in which the opening of the active site cleft is within a nanometer.5,6 As we have reported previously,6,7 the T4 lysozyme enzymatic reaction involves complex conformational state changes in the enzymatic turnovers. A simplified Michaelis-Menten type of mechanism can be presented as:

E + S  ES  ES*  EP  E + P (6.1) where E, S, ES, ES*, EP represents enzyme, substrate, non-specific enzyme-substrate complex, specific or active enzyme-substrate complex, and enzyme-product complex, respectively. It is the process of forming the active complex of ES* that involves multiple conformational states. The process of E + S  ES  ES* essentially involves the enzyme active site opening up to take the substrate, forming a non-specific ES complex, and binding down to form the active complex of 144

ES* ready to react followed by turnover to EP. The process of reaction and product releasing

ES*  EP  E + P may not involve significant enzymatic active site conformational changes.

While T4 lysozyme hinge-bending motions have been extensively probed by single- molecule FRET (fluorescence resonance energy transfer) spectroscopy, the domain motion of T4 lysozyme is rather complex and contains motions besides hinge-bending. It is reasonable to assume that the hinge-bending motion in nature involves multiple coupled nuclear coordinates that can be projected to a nuclear coordinate associated with the -helix. In order to capture the complexity of T4 lysozyme conformational motions, herein we probe multi-dimensional conformational dynamics beyond the one-dimensional FRET coordinate.

Single-molecule spectroscopy is a powerful approach for mechanistic understanding of complex and fluctuating biological processes by resolving time-dependent dynamic process and allowing exploration of hidden heterogeneity beyond non-synchronized ensemble-averaged measurements.8-22 Single-molecule FRET spectroscopy sensitive to single-molecule conformational fluctuation dynamics has offered possible direct observations of biological conformational dynamics by rendering spatial and temporal information between donor and acceptor fluorophores placed within a certain proximity on individual molecules of interest.8,23

This approach has made significant and extensive contributions to the understanding of complex biological dynamics through the perspectives of heterogeneous dynamics of protein molecules, nucleic acids, and their interactions with other molecules.6-8,16-18,24-31

For complex biological systems, such as protein-protein interactions, ion channel receptor activations, protein folding and aggregations, and protein conformational fluctuations in enzymatic reactions, it is more than often that multiple conformational nuclear coordinates simultaneously play critical roles in regulating and gating biological functions. Under such 145 complex multiple coordinate conformational dynamic rate processes, one-dimensional FRET may be insufficient to characterize the intrinsic complexity of molecular dynamics. Hence, it is desirable to have advanced FRET techniques capable of probing more than one-dimensional dynamic information. For example, some efforts has been made to develop three-color and four- color FRET in which more than one FRET pair are used to probe multiple site-to-site distance changes at a time.32-35 Nevertheless, multi-color FRET requires fluorophores with high photo- stability and clear spectral separation, which are much more difficult to achieve than one FRET pair, especially at single-molecule level. In addition, the fact that energy transfer between donor and acceptor obey orientation dependence (2) as expected for a dipole-dipole interaction has seldom been took into account, instead, the assumption 2=2/3 (the fluorephores undergo freely rotation in a time much shorter than fluorescence lifetimes) 36 is widely used for approximate distance estimate. It has been experimentally proved that fluorescence energy transfer between

Cy3 and Cy5 depends on the orientation.37 It has also been reported that the degree of position accuracy relies on the rotational mobility of single molecules.38 Therefore, the understanding of orientation effect could result in more accurate FRET distance approximation and molecular angular information for position determination. For this purpose, several groups have shed light on molecular orientations (or rotations) via imaging and fluorescence anisotropy (or polarization) spectroscopy. For example, out-of-focus and in-focus images have been combined to refer three- dimensional single molecule orientations.39 Dual-color and dual-polarization images of single molecules have been captured by CCD (Charge-Coupled Device) camera.40 A multi-parameter single-molecule fluorescence spectroscopy,41,42 photon distribution analysis,43 along with structural modelling 44 have been developed to quantitatively describe single-molecule FRET in which several FRET-related parameters such as distance, molecular orientation, and dye 146 quenching (or bleaching) are taken into account. Lu and coworkers have demonstrated the use of single-molecule nanosecond anisotropy to study spatially confined rotational diffusion dynamics of individual tethered proteins 45 and nanosecond protein motion dynamics 46.

The possibility of probing multi-dimensional conformational dynamics of complex biological systems calls for FRET-anisotropy correlated measurements on demand. Fluorescence anisotropy not only allows for estimating orientation effect on FRET or position determination for imaging mentioned above, but also allows for acquiring information about the motion of the fluorophore, the rotational dynamics of subdomains or the entire proteins. The methods of measuring rotational or tilting motions by fluorescence anisotropy in single molecules have been reviewed and discussed 47-49 Lu and co-workers have successfully probed nanosecond protein motions of Calmodulin and T4 lysozyme by single-molecule fluorescence anisotropy.45,46

Unfortunately, most of fluorescence anisotropy research have been either limited to the study of orientation effect for FRET, or limited to the rotational dynamics of molecules; although the potential ability of fluorescence anisotropy in the identification of multiple conformational states of biological molecules has been mentioned.50 Nevertheless, the potential ability of correlated single-molecule FRET and fluorescence anisotropy for direct observing multi-dimensional conformational dynamics has not been demonstrated yet, to our best knowledge. In this article, we report our new technical approach integrating single-molecule FRET, photo stamping spectroscopy and fluorescence anisotropy to study multi-coordinate conformational dynamics of

T4 lysozyme under enzymatic reaction conditions. From conformational dynamics perspectives, this approach enables us to simultaneously probe multi-dimensional or multi-coordinate conformational dynamics of proteins, including FRET coordinate motions probed by FRET pair and rotational motions monitored by fluorescence anisotropy. 147

Figure 6.1. Multi-dimensional conformational motions of wild-type T4 lysozyme (PDB-code,

3LZM), including hinge-bending motions along -helix and rotational motions of each domain.

Cy3 and Cy5 are covalently labeled to two cysteines: Cys 54 on N-domain and Cys 97 on C- domain. Cy3-Cy5 labeled T4 lysozyme is tethered through an amine-to-sulfhydryl bi-functional cross-linker molecule to thiol-functionalized glass cover-slip surface. The approximate ~4-5 nm spacer allows free rotation of single T4 lysozyme without perturbation or confinement from the modified surface. Distance changes between the two labeling sites involved in hinge-bending conformational motions can be monitored by tracing the dynamic fluctuations of donor lifetime during the FRET process. Besides hinge-bending motions along -helix line, the two domains of

T4 lysozyme also exhibit other type of conformational motions, for example, rotational motions.

The rotational motions can be probed by single-molecule fluorescence anisotropy. 148

6.2. Materials and Methods

6.2.1. Materials

The materials we used in this chapter is the same as in Chapter 2 (2.2.1 Materials). Dye- labeled T4 lysozyme is selected as the model protein to demonstrate our new approach and scientific insights of T4 lysozyme conformational dynamics are provided based on this approach.

The crystal structure of wild-type Cy3-Cy5 labeled T4 lysozyme is shown in Figure 6.1. The individual donor-acceptor labeled T4 lysozyme can be easily distinguished by four-channel optical images as shown in Figure 6.2, because only donor-acceptor labeled molecule can simultaneously exhibit four emission spots (dual color and dual polarization for each color).

6.2.2. Single-Molecule Measurements

The sample preparation procedure of single-molecule measurements is illustrated in

Chapter 2 (2.2.2 Single-Molecule Measurements). In our single-molecule FRET experiments, T4 lysozyme was tethered through a bi-functional NHS-PEG6-Maleimide cross-linker to a modified glass cover-slip surface. This cross-linker is functional between primary amines (NH2) and sulfhydryl (SH) groups in which the N-hydroxysuccinimide ester (NHS) group reacts specifically with primary amino groups of lysine form stable amide bonds and the maleimide group reacts with sulfhydryls to form stable thioether bonds.

6.2.3. Single-Molecule FRET

The detailed descriptions of FRET have been provided in Chapter 1 (1.3.1 FRET

Fundamentals), especially the Equations (1.7-1.11). FRET refers to the non-radiative energy transfer from a donor molecule to an acceptor molecule, arising from a dipole-dipole interaction between the electronic states of the donor and the acceptor. 51,52 FRET efficiency is sensitive to 149 the inter-distance between the donor and acceptor in the range of 30-80 Å, resulting in the capability of probing single molecules conformational dynamics in real time by tracking EFRET changes 17,23,53-55

The detection of EFRET, usually by ratio-metric methods, can be generally classified into intensity-based FRET and lifetime-based FRET in Equation (1.12). FRET detection on the basis of donor’s life is more effective and less sensitive to local environment fluctuations,25 especially in AFM tip-enhanced single-molecule spectroscopic and imaging measurements where amplified fluorescence signal by metal tip reflection exists 24,56.

6.2.4. Fluorescence Anisotropy

Fluorescence anisotropy provides information about detailed motions of Cy3 or Cy5 and conformational dynamics of the T4 lysozyme system being studied here. Fluorescence anisotropy/polarization provides insights into the motions of probe, and orientation/rotation or mobility of subdomains or the entire molecule by determining the rotational correlation time of the fluorescence probe. 47,52,55,57 The fluorescence anisotropy r(t) and Perrin Equation are expressed in Equation (1.14) and Equation (1.15). In our experiments, bright fluorescence microspheres (0.1µm, 540/560nm orange spheres, Invitrogen Molecular Probes) were used to measure the G factor by using horizontally polarized excitation. With the horizontally polarized excitation, the excited-state distribution of the molecules is rotated to lie along this observation axis, so that both the horizontally and vertically polarized components are orthogonal to the incident polarization and the intensities of collected signal are equivalent. G factors are averagely estimated to be ~1.36 and ~2.36 for donor and acceptor, respectively. The unbalanced

G factor for donor and acceptor are most likely due to the detection discrepancy of different 150 detectors and the bias of the optics response to different colors. Autocorrelation Fluctuation

Analysis

6.2.5. Autocorrelation Analysis

We use autocorrelation function to analyze lifetime fluctuation and anisotropy fluctuation. The time-dependent correlation strength of lifetime as well as anisotropy trajectory is evaluated by autocorrelation function given by

(XXXXi )( i t ) Ct()= i 2 ()XXi  i (6.2) where Xi is the donor lifetime/anisotropy and also the signal variable in time-dependent lifetime/anisotropy trajectory; i here is the index number of data point; t here is the time lag; X is the mean value of lifetime/anisotropy. By using autocorrelation analysis, donor lifetime/anisotropy fluctuate rate can be identified, providing insights into the T4 lysozyme conformational fluctuation dynamics.

6.2.6. New Approach of Four-Channel Single-Molecule Microscopy

Combining FRET, lifetime, polarization, and anisotropy at single-molecule sensitivity, we show our compact design for probing multi-dimensional conformational motions. Compared to two-color 6,58 or two-polarization concepts,45 our new approach combines color discrimination and polarization using a four-channel single-molecule epi-illuminated microscopy, as shown in

Figure 6.2A. In short, a femtosecond pulse laser (Ti: Sapphire Mira 900F/P, Coherent Inc.) is combined with an optical parametric oscillator (OPO BASIC, Coherent Inc.) as well as frequency doubling β -barium borate crystal (BBO) to generate linear-polarized pulse laser. The 151

532 nm linearly polarized pulse laser is aligned and focused into coverslip-solution interface by a confocal objective (Plan-APOCHROMAT, 1.40 NA, 63×, Carl Zeiss) to excite molecules. A piezoelectric scanning stage (Nano-H100, MCL) with a positioning resolution of 0.2 nm is further used to scan the coverslip surface and locate individual molecules. The fluorescence signals from single molecule are separated to two pathway (two color) in wavelength ranges below and above 640 nm after passing through a filter (HQ545LP, Chroma) and two dichroic mirrors (545dcxru, 640dcxr, Chroma). Signals in each pathway are further divided into four- channel parallel and perpendicular components for each color by using two polarizing beamsplitter cubes (PBS 201 420~680nm, PBS 202 620~1000nm, respectively). The photons from four-channel (parallel and perpendicular for both donor and acceptor) are detected by four

APDs-Si avalanche photodiode (SPCM-AQR-14, Perkin Elmer Optoelectronics). The time- stamped photon information is recorded through multi-channel detector router (HRT-82, Becker

& Hickl GmbH) to a time-correlated single photon counting module (SPC-830, Becker & Hickl

GmbH) and a personal computer. Figure 6.2B shows the single-molecule photon counting images of individual Cy3-Cy5 labeled T4 lysozymes in which dual-color (donor and acceptor) and dual-polarization (ǁ and ⊥) are captured by four APDs. Four images are taken simultaneously with an illumination of 532 nm and scanning area of 20 m by 20 m. 152

Figure 6.2. Single-molecule multi-parameter photon stamping spectroscopy. (A) Experimental home-built four-channel single-molecule set-up for measuring multi-dimensional conformational dynamics. Basically, it consists of an inverted confocal epi-illumination configured microscopy, a femtosecond pulse laser, four Si avalanche photodiode detectors, a time-correlated single photon counting module, and several optics. 532 nm green linearly polarized pulse laser is used to excite Cy3-Cy5 labeled T4 lysozyme. The emissions (yellow and red) from Cy3 and Cy5 are discriminated by dichroic mirrors. The polarization of the light emitted is further distinguished into parallel (ǁ) and vertical (⊥) components (relative to the polarization of the laser excitation) by two polarizing beamsplitter cubes. (B) Single-molecule photon counting images of individual

Cy3-Cy5 labeled T4 lysozymes. Dual-color (Cy3 and Cy5) and dual-polarization (ǁ and ⊥) images are captured. DM: dichroic mirror; APD: avalanche photodiode; PBS: polarizing beamsplitter cubes; LPF: long pass filter; WP: wave plate; L1/L2: lens; PC: personal computer.

153

6.3. Results and Discussion

Figure 6.3 shows the single-molecule studies of multi-dimensional conformational motions of T4 lysozyme by recording real-time anisotropy trajectory and donor lifetime trajectory. In our single-molecule multi-parameter spectroscopy, time-resolved FRET fluctuations detected by donor lifetime and the correlated fluorescence anisotropy are simultaneously recorded. As described before, each detected photon is stamped with two parameters: a chronic arrival time and a delay time related to femtosecond laser pulse excitation.

25,45,46 Our data analyses are primarily based on those two temporal parameters recorded by

25 single-molecule photon stamping spectroscopy. In this work, we have used donor lifetime (DA)

25,59 to probe FRET fluctuations and fluorescence anisotropy (rDA) to monitor domain rotational motions 47,55,57 of Cy3-Cy5 labeled T4 lysozyme. Figure 6.3B shows a typical single-molecule lifetime trajectory, DA~t. The donor lifetime fluctuates from time to time, implying dynamic change of inter-domain distance along FRET coordinate. The wide Gaussian-like distribution of lifetime (the right panel in Figure 6.3B) indicates the existence of multiple conformational intermediate states characterized with different inter-domain distance along -helix, from FRET perspective.

Multiple conformational intermediate states have often been suggested as the general feature of enzymatic mechanisms and protein motions.6,7,60-70 In single-molecule studies, time- binned FRET or lifetime trajectory has been reliably used to identify the presence of multiple intermediate states corresponding to well-defined stable FRET values in the case of well- separated, low-noise, two-state or three-state systems.17,25,59,71,72 Nevertheless, for complex biological systems, due to the influence from local environmental fluctuations, thermal fluctuations, measurement short noise, photophysical effects, and conformational dynamic or 154 static heterogeneity coupled with fluctuating catalytic activity, multiple intermediate states are often buried in a wide-distributed Gaussian-like distribution.7,73 In this work, the single-molecule lifetime trajectories are limited by 10 ms binning time and the other facts discussed above, resulting in that conformational intermediate states do not present clear separation in the lifetime distribution in Figure 6.3B. Furthermore, we have performed advanced quantitative analysis of lifetime, to be discussed later in this paper (Figures 6.4-6.5), to further resolve buried multiple intermediate states.

The rotational flexibility of the donor molecule Cy3 on T4 lysozyme fluctuates significantly in the course of the enzymatic reaction turnovers, revealed in our single-molecule anisotropy analysis. Figure 6.3A shows a typical single-molecule anisotropy trajectory, rDA ~t, showing that the single-molecule anisotropy fluctuates in a wide range from negative to the maximum value of 0.4. This is most likely associated with the dye molecule fast spinning motions, the slow subdomain motions and entire protein motions of the T4 lysozyme tethered with the dye molecule.55,74 Those three types of motions are all involved but contribute differently to the overall anisotropy measured from the experiments. If there are no rotational diffusion restriction from the subdomains or the whole enzyme, the Cy3 probe on T4 lysozyme should exhibit fast wobbling spin rotation and the resulting Cy3’s lifetime should be in the sub- nanosecond range consisted with a free Cy3 dye in solution.75-76 Our experimental results of lifetime trajectory and distribution (Figure 6.3A) excludes this scenario because most of the measured lifetimes of Cy3 are in nanosecond range instead of sub-nanosecond range. Similar behavior of the lack of rotational freedom and nanoseconds lifetime range of tethered dye molecules have also been reported for Cy3 covalently linked to DNA.75 The reported Cy3 lifetime is around 10 times larger than the fluorescence lifetime of the free dye in solution, which 155 is similar to the result of Cy3 on T4 lysozyme in our measurements. The lifetime of Cy3 is dependent on physical and chemical properties of surroundings. The longer lifetime of Cy3 is directly related to local environment changes deviated from an aqueous environment, such as viscosity. For example, when Cy3 is attached to a strand of DNA or a protein, an additional increase in local viscosity may occur, leading to lifetime increase.77,78 The interaction between

Cy3 and DNA molecules have also been reported to play a role in longer Cy3 lifetime than that measured in aqueous solution.75 Therefore, it is most likely that the increase in local viscosity and the interaction between Cy3 and T4 lysozyme result in longer Cy3 lifetime which evidences the lack of Cy3 free spin and wobbling rotational freedom identified in our anisotropy measurements. The joint distribution of lifetime and anisotropy (Figure 6.3C) further implies that the measured overall anisotropy is not dominated by free dye rotation but rather by restricted rotation indicating that the Cy3 dye probe is significantly regulated by interactions with the T4 lysozyme protein matrix. Presumably, there are two possibilities that may contribute to the restricted rotation: the enzyme-surface interaction and domain rotations of enzyme. Previous studies have reported that the tethered single T4 lysozyme to the hydrocarbon modified surface is mostly in solution phase, and the interaction between enzyme and surface is weak or insignificant in impacting enzyme rotations, giving rise to the absence of rotational rate fluctuations for most of the time during the measurements. 45 Therefore, the restricted rotation of

Cy3 characterized by the dynamic and fluctuating anisotropy is most likely regulated by the domain rotations of enzyme. Figure 6.3B and 6.3C give a broad anisotropy distribution, implying different rotational flexibility of Cy3 regulated by T4 lysozyme domain motions. The different dye rotational flexibility has been found in the study of HIV-1 reverse transcriptase, and they have attributed broad rotational correlation time distribution probably to the clamping of the 156 finger and thumb domains during polymerase activity.42 In our work, different Cy3 rotational flexibility, reflected in the broad anisotropy distribution in Figure 6.3B and 6.3C, is likely regulated by T4 lysozyme domain rotations during the inter-domain hinge-bending conformational motions.

T4 lysozyme exhibits a well-known hinge-bending conformational motions in which the distance changes between the opening and closing of the active site cleft are about 4-6 Å, revealed in crystal structure analyses, MD simulation, and our previous single-molecule spectroscopic analyses. 2,4,6-7,45,79-81 Conceivably, T4 lysozyme conformational motions involve more than just hinge-bending along its -helix. Furthermore, even the hinge-bending motions themselves are actually complex fluctuating conformational motions involving multiple conformations with distinct domain orientations or hinge-bending angles along multiple nuclear coordinates. For example, it has been suggested that T4 lysozyme populates multiple intermediates states with distinct hinge-bending angles trapped in the crystal structures of T4 lysozyme mutants.82

Our single-molecule T4 lysozyme anisotropy fluctuation result (Figure 6.3) suggests that there are a wide range of domain-rotational mobility, indicating different dominant orientations of the domains from time to time, consistent with distinct hinge-bending angles. Typically, single-molecule FRET spectroscopy only probes the conformational motions associated with the

FRET donor-acceptor distance changes, and most likely, such FRET probed conformational motions are actually the projections of much more complex conformational motions of the examined enzyme molecules on the FRET sensitive coordinate. Nevertheless, besides hinge- bending motions along coordinate probed by single-molecule FRET spectroscopy,6,7 T4 lysozyme actually exhibits complex and fluctuating rotational motions along multiple orientation 157 coordinates, leading to a comprehension of the multi-dimensional conformational motions associated with the T4 lysozyme enzymatic reaction turnovers.

T4 lysozyme multi-dimensional hinge-bending conformational motion dynamics presents dynamic and static inhomogeneity, revealed and identified by autocorrelation analysis of the single-molecule lifetime trajectories DA~t and anisotropy time trajectories rDA~t.

Autocorrelation function analysis has been extensively applied to identify inhomogeneous fluctuation rates of single-molecule electron transfer,83,84 energy transfer fluctuations,6,85,86 and protein conformational changes.6,9,10,87 We have analyzed the autocorrelation functions, C(t), of lifetime and anisotropy trajectories for 40 Cy3-Cy5 labeled T4 lysozyme molecules under the enzymatic reaction conditions. Figure 6.4 shows the autocorrelation analysis results of lifetime fluctuation decays (Figure 6.4A) and anisotropy fluctuation decays (τ) (Figure 6.4B). Most of the autocorrelation functions of FRET donor lifetime trajectories (Figure 6.4A, inset) show non- exponential fluctuation decays, implying the dynamic disorder of FRET energy transfer, i.e., the conformational fluctuation rate along FRET-probed hinge-bending coordinate changes from time to time under enzymatic condition in one single-molecule measurement. We have analyzed the autocorrelation functions by bi-exponential fitting and observed a wide range of fluctuation decays from milliseconds to seconds (Figure 6.4A), suggesting a static disorder of conformational fluctuation rate change from molecule to molecule. Autocorrelation functions of anisotropy trajectories also give similar results in terms of non-exponential and inhomogeneity of fluctuation correlation function decays (Figure 6.4B). The non-exponential conformational fluctuation dynamics and the wide-range rate constant distributions of the single-molecule anisotropy indicate dynamic disorder and static disorder of conformational rotational fluctuation along multiple orientation coordinates, respectively. 158

Figure 6.3. Multi-dimensional conformational motions of T4 lysozyme probed by single- molecule multi-parameter spectroscopy: dynamic anisotropy and lifetime-based FRET. (A)

Single-molecule real-time anisotropy trajectory rDA ~t and distribution recorded within 100 seconds. Each dot represents the average anisotropy in every 10 ms binning of Cy3, calculated on the basis of Equation (1.14). The distribution of anisotropy is shown in the right panel. (B)

Single-molecule real-time donor lifetime trajectory DA~t. Each dot is the average of photon delay times in each 10 ms binning of Cy3 collected from single-molecule photon stamping. (C)

2D joint distribution between anisotropy in A and lifetime in B. We note that the band widths of both lifetime and anisotropy distributions, 1.0 ns and 0.25, are significantly larger beyond the measurement error bars of ±0.30 ns and ±0.05, respectively. The broadness of the distributions of the lifetime and anisotropy represent intrinsic physical inhomogeneity beyond the measurement error bars.

159

Figure 6.4. Dynamic and static disorder of T4 lysozyme multi-dimensional conformational fluctuations along FRET coordinate and orientation coordinates via autocorrelation analysis of lifetime and anisotropy. (A) Histogram of fluctuation decays derived from the autocorrelation function of donor lifetime trajectories. (Inset) Typical non-exponential autocorrelation function calculated from a single molecule fluorescence lifetime trajectory DA~t. (B) Histogram of fluctuation decays derived from the autocorrelation function of donor anisotropy trajectories.

(Inset) Typical non-exponential autocorrelation function originated from a single molecule fluorescence anisotropy trajectory rDA~t. For autocorrelation functions of lifetime or anisotropy, τ represents the fluctuation decay. Correspondingly, 1/τ represents the fluctuation rate, that is, conformational fluctuation rate along FRET coordinate or multiple orientation coordinates under

T4 lysozyme enzymatic reactions. ACF: autocorrelation function.

160

The flexibility of the hinge-bending conformational coordinates regulated by substrate binding to the enzymatic active site most likely contributes to the inhomogeneous and complex fluctuation dynamics of lifetime and anisotropy. The flexibility of conformations associated with the process of forming the non-specific binding complex (E + S  ES), the process of enzyme closing down to form the active complex of ES  ES*, and the following enzymatic reaction of

ES*  EP, involves complex local environment and molecular structures of substrate as well as the enzymatic active site. The conformational motions involve multiple coordinates in nature and are regulated by a fluctuating multiple coordinate energy landscape defined by the dynamically changing and statically inhomogeneous molecular interactions as well as local electric field in the process of open-close hinge-bending enzymatic turnovers.

Donor lifetime decays exhibit two major distributions, associated with T4 lysozyme open and close states during the hinge-bending motions of the enzymatic active site. Mean lifetime trajectory and distribution (Figure 6.3A) does not necessarily give a clear separation of multiple intermediate states but rather present a Gaussian-like distribution, due to the limitations from the local environment fluctuations and the time-resolution of our single-molecule spectroscopy. Most likely, the convolution of the multiple Poisson processes gives the rise to the overall wide Gaussian-like distribution.7 To further resolve the intermediate conformational states, we have performed analysis of the temporal decays including all the photons in a single- molecule donor lifetime trajectories. Figure 6.5A shows the representative single-molecule lifetime decay curves of the FRET donor. Two-component lifetime decays, a faster one (1) and a slower one (2), are derived from bi-exponential fits. We note that the lifetime decays (1 and 2) in Figure 6.5A reflects different properties of the enzymatic conformational dynamics from the fluctuation decays ( in Figure 6.4. As a lifetime-based FRET measurement expressed in 161

Equation (1.12), FRET donor lifetime decays are derived by fitting donor photon delay times histogram, essentially a time-correlated single photon counting distribution, to identify the FRET efficiency reflecting the major conformational states along FRET coordinates; whereas the fluctuation decays are calculated by the autocorrelation analysis of mean lifetimes DA to characterize the conformational fluctuations and conformational flexibility. We attribute the two- component FRET donor lifetime decays, corresponding to two different FRET efficiency values, to two major conformational states, open and close states along FRET coordinates: in each enzymatic reaction cycle, the enzyme active site opens up to interact with the substrate forming a non-specific enzyme-substrate complex (E + S  ES), and then closes down to form a specific enzyme-substrate complex (ES  ES*) ready to react and turnover the substrate to product.

Our result of the two-component donor lifetime decays (Figure 6.5B and 6.5C) corresponding to two distinct FRET efficiency distribution is consistent with hinge-bending motion that opens and closes the active site cleft along -helix.6,7 From the distributions of two- component lifetime decays of donor fluorescence, we have identified narrowly distributed faster component (1) and widely distributed slower component (2) (Figure 6.5B). Figure 6.5C shows the mean and standard deviation of the distributions. The results of two-component lifetime decays imply that T4 lysozyme exhibits open-close hinge-bending conformational motions associated with two-component FRET donor lifetime: the faster component (1), when the active- site is closed and the FRET efficiency is high, is relatively narrowly distributed; and the slower component (2), when the active-site is open and the FRET efficiency is low, is widely distributed. The distinct distribution of each component gives a further indication that close state is rigid and spatially narrowly distributed, and in contrast, the open states involve flexible and broadly distributed conformational fluctuations. On the basis of Michaelis-Menten mechanism 162 in Equation (6.1), in the process of forming the active complex ES* (E + S  ES  ES*), the enzyme involves active site opening up to intake the substrate to form the non-specific enzyme- substrate complex ES, and binding down to form the active complex ES*. During this whole open-close hinged-bending rate process, the enzyme essentially involves multiple steps associated with multiple conformational intermediate states. In the process of catalytic reaction and product releasing (ES*  EP  E + P), corresponding to the rigid and narrowly distributed close states, the enzyme may not exhibit significant enzymatic active site conformational changes. In terms of T4 lysozyme hinge-bending open-close conformational motions, our result of two-component lifetime decays is consistent with both ensemble-level measurements3,4 and single-molecule fluorescence measurements.6,7,45,82,88,89 The different modes of open and close motions also agree with the recent study of conformational dynamics of T4 lysozyme through an electric circuit by means of attaching single molecules to single-walled carbon nanotube field- effective transistors,90-91 that is, T4 lysozyme closes up in a single step while the open process requires a minimum of two steps.90

163

Figure 6.5. Two-component donor lifetime decays associated with two major open and close conformational states. (A) The representative histograms of photon delay times of donor in a single-molecule measurement. Bi-exponential fitting (red curve) gives the best estimate to the experimental data (grey), and two-component donor lifetime decays (1 and 2) are observed. (B)

The distribution of two-component lifetime decays of donor. Narrowly distributed faster component (1) and widely distributed slower component (2) are revealed. (C) Statistical results of the two-component feature derived from B. Mean and standard deviation of lifetime decays are illustrated. The results of two-component lifetime imply that T4 lysozyme exhibits open- close hinge-bending conformational motions characterized with two-component lifetime decays.

The distinct distributions of each component even give a further implication that close state is rigid and spatially narrowly distributed while the open states involve flexible and broadly distributed conformational fluctuations.

164

Our work provides a new insight into T4 lysozyme conformational dynamics from a multiple dimensional perspective. Along the domain orientation coordinates, significantly different T4 lysozyme conformations can have similar or same donor-to-acceptor (D-A) distance; therefore, the different T4 lysozyme conformations may not necessarily be identifiable from the

FRET signal alone associated with this D-A sensitive coordinate (Figure 6.6). Typically, FRET measurement is sensitive to the projected FRET-coordinate changes from the multiple T4 lysozyme intermediates states associated with different domain orientation coordinates. The multiple intermediate states involved in the active-site open-close hinge-bending motions, and the hinge-bending conformational motions are intrinsically multi-dimensional, allowing for repositioning and reorienting the sub-domains in forming the active enzyme-substrate complex conformational state ready for a hydrolysis turnover reaction. While the hinge-bending motions along -helix require spatial proximity of two domains to interact with the substrate within the active site, the domain rotational orientation motions are predicted to be important for the enzyme to function,92-94 allowing the substrate to enter and the products to leave the active site.

Our results of T4 lysozyme conformational dynamics obtained from the single-molecule multi- parameter photon-counting spectroscopy highlight the potential significance of probing the multi-dimensional conformational motions along multiple coordinates for characterizing the enzyme-substrate interactions and catalytic efficiency. 165

Figure 6.6. Conceptual presentation of T4 lysozyme multi-dimensional conformational dynamics. T4 lysozyme exhibits multiple conformational intermediates states during open-close hinge-bending motions, involving multiple domain orientation coordinates and FRET coordinate.

FRET-coordinate projections of multiple T4 lysozyme conformations associated with different domain orientation coordinates are presented. Along domain orientation coordinates, different enzyme conformations can have same D-A distance, which can be undetectable and hidden in a conventional single-molecule FRET spectroscopic measurement.

166

Extensive efforts have been put to understand how the enzyme work. It has been widely recognized that the conformational motions are essential for the catalytic functions of enzymes,92,93,95-99 and even play a crucial role in enzyme functions. The protein conformations involving in multiple intermediate states and multiple conformational coordinates,7,31,64,93,95,100-103 are highly dynamic rather than being static. The approach, experimental results, and discussion presented in this report are probably just a step starting in expanding the studies and interpretations of new information about dissecting both the spatially and temporally complex enzymatic conformational dynamics in enzymatic reactions. Apparently, there is still a long way towards a detailed and quantitative analysis of function-related conformational motions. For example, FRET and anisotropy results suggest the existence of multi-dimensional conformational motions that are important to enzymatic activities, such as the hinged-bending motions in T4 lysozyme enzymatic reactions. Additional efforts are still on demand to give a direct spatial and temporal characterization of the exact angles and positions of multi- dimensional conformational coordinates. Temporal transitions of multiple intermediate states associated with FRET coordinate and orientation coordinates or the coupling between them are still unclear. The complimentary MD simulations will likely be helpful and supportive to address those issues.

6.4. Conclusions

In closing, we have provided a new insight into T4 lysozyme conformational dynamics from a multiple dimensional perspective. The multi-dimensional conformational probing from our correlated single-molecule FRET and anisotropy measurements implies that T4 lysozyme exhibits much complex conformational motions along multiple orientations and nuclear coordinates beyond hinge-bending coordinate (-helix). Significant information about the 167 complex conformational motions are hidden by using only a conventional single-molecule one- dimensional FRET analysis that is primarily sensitive to the motions projected from the complex and real conformational motions to the FRET distance sensitive coordinate. The results of FRET donor lifetime decays and correlated anisotropy suggest that T4 lysozyme open states involve flexible and broadly distributed conformational fluctuations while the close state is more rigid. In addition, the dynamic and static inhomogeneity of multi-dimensional conformational fluctuations have been revealed by non-exponential features of autocorrelation functions of both lifetime and anisotropy. The developed single-molecule multi-parameter photon stamping spectroscopy provides a possible access to probe multi-dimensional conformational motions of complex enzymatic systems, such as T4 lysozyme, by means of simultaneous acquisition of FRET, fluorescence anisotropy, and FRET donor fluorescence lifetime. There is still a high call for experimentally technical approaches which are capable of probing the complex enzymatic conformational fluctuations without ensemble-averaging as well as measurement synchronization, and multiple parameter measurements with the sensitivity of analyzing the enzyme rotational motion, translational diffusion, intramolecular domain motions, and intermolecular interactions. Our approach reported here holds a promise to characterize not only the enzymatic active site conformational fluctuations and enzyme-substrate interactions, but also the overall enzyme matrix motions surrounding the active site. Evidently, such overall enzyme conformation fluctuations and multiple coordinate in nature, likely play important roles in establishing the catalytic reaction pathways and the overall enzymatic reaction energy landscape.

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CHAPTER 7. SINGLE-MOLECULE MULTI-PARAMETER RATE ANALYSIS OF HPPK

ENZYMATIC CONFORMATIONAL DYNAMICS

HPPK is an important kinase in the folate biosynthetic pathway. Unveiling the roles of conformational dynamics in HPPK catalysis has profound impact on understanding the mechanisms of enzymatic pyrophosphoryl transfer and providing fundamental scientific insights into developing potential antimicrobial agents. Here, we have examined the conformational dynamics of HPPK under the enzymatic reactions by using single-molecule multiple parameters detecting approach. We have directly revealed the existence of function-inert and function-active scenarios of HPPK Loop 3-active site conformational dynamic behaviors. The two-scenario conformational dynamics are characterized with distinguished FRET lifetime fluctuations, anisotropy fluctuations and dynamic lifetime decay rates, suggesting function-related

(inert/active) conformational flexibility and orientational adaptability associated with multiple conformational intermediates states. Under the inert scenario, HPPK adopts multiple conformational states of similar donor-acceptor separate distance and various rotational orientations, implying significant aspects for the enzyme to adjust its optimal conformational orientation for engaging and interacting with substrates. Under the active scenario, HPPK experiences open-close conformational motions and at least two sub-states are involved during the open process. We have also observed different molecular local environment changes

(hydrophilicity and hydrophobicity) under the distinguished two-scenario conformational behaviors, enabling us to better understand the roles of the hydration fluctuation in conformational motions and enzymatic catalysis. Our results have implications of conformational selectivity and adaptability on the processing of function-related conformational 175 motions. Understanding the features of scenario conformational transitions will shed light on structure-function relation during the catalytic process.

7.1. Introduction

As described before in Chapter 3 (3.1.3 Biological Functions and Catalytic Reactions of

HPPK Kinase), HPPK is a key enzyme in the biosynthesis cycle of folates which are essential for all organisms, like mammals and microorganisms.1 While mammals can absorb folates from their diets through the active transport system, most microorganisms lacking such system have to synthesize folates de novo. Therefore, HPPK, as an important kinase in the first reaction of the folate biosynthetic pathway, is an attractive research target for developing potential antimicrobial agents.2-5 From HPPK structure in Figure 7.1, it shows monomeric feature composed of one folded valley and two walls in which three active loops (Loop1, Loop2 and Loop3) serve as one wall and a rigid motif β-loop-α as the other wall.6

HPPK undergoes dramatic conformational changes during catalytic cycles and the conformational changes plays critical roles for its catalytic function. X-ray crystallographic and

NMR studies of HPPK have provided valuable insights into the structural features and conformational changes. Crystallographic models have indicated that HPPK undergoes remarkable conformational changes under the enzymatic reactions at atomic resolution and the conformational changes are essential for catalytic activity.7,8 In addition, the most mobile loop,

Loop 3 (begins with R82 and ends with R92) has been reported to play an important role for assembling the catalytic center and catalysis. 4 NMR spectroscopic studies of HPPK has revealed the internal motions of HPPK on a wide range of timescales and suggested that Loop 3 involves multiple conformations undergoing transition on the millisecond (ms) timescale.8 176

In terms of obtaining enzymatic conformational dynamics as well as heterogeneity, and understanding the role of conformational dynamics in enzymatic catalysis, single-molecule approaches offer new insights into molecular activity, without averaging out effect and synchronization which hinder ensemble-level studies.9-14 For example, single-molecule FRET spectroscopy has been widely used in understanding intrinsic conformational dynamic motions of biological molecules, one of many aspects that are insufficiently well understood under ensemble measurements,14-27 We have used single-molecule FRET spectroscopy to investigate

HPPK FRET-dimensional conformational dynamics under the enzymatic reactions, reporting our progressive efforts on loop-related active-site conformational dynamics.28,29

Nevertheless, one-dimensional single-molecule FRET measurements are most likely insufficient to probe highly dynamic and intrinsically complex HPPK conformational changes, probably involving multi-dimensional motions and versatile scenarios. Methods for characterizing these species are sorely lacking. Thus, correlated measurements combining single- molecule FRET to probe one-dimensional FRET-coded conformational changes and fluorescence anisotropy to study molecular rotations/orientations are practical and promising to provide deep understanding in dissecting the complex mechanism of enzymatic structure-related catalysis. For example, dual-color with dual-polarization imaging,30 and multiparameter fluorescence detection

31,32 have been developed to simultaneously record conformational dynamic signals and extensive spectral fingerprints of single molecules. In Chapter 6, we have developed a single- molecule multi-parameter photon stamping spectroscopy. Here, we have employed single- molecule multi-parameter photon stamping spectroscopy to determine dynamics and complexity of HPPK multi-dimensional conformational motions under enzymatic reactions. We have also evaluated local environmental features during HPPK conformational motions. 177

Figure 7.1. (A) The HPPK molecule (PDB code: 1HKA) is displayed as a ribbon diagram.

Three loops (Loop1, Loop2, and Loop3) are shown in pipes, and Loop 3 is highlighted in yellow.

Cy3 attaching site is indicated as green, and Cy5 attaching site is in Red. Distance changes between the two labeling sites involved in Loop3-active site conformational motions can be monitored by tracing the dynamic fluctuations of donor lifetime during the FRET process.

Besides single-molecule FRET-coded conformational motions, molecular rotations are simultaneously recorded by fluorescence anisotropy. Perrin Equation is used to predict rotational correlation time-molecular mobility indicator. (B) The diagram of HPPK catalytic reaction. It catalyzes pyrophosphoryl transfer involving transformation from HP to HPPP with

ATP and Mg2+ ions participation.

178

7.2. Materials and Methods

7.2.1. Sample Preparation

The sample preparation has been introduced in Chapter 4.2.1. The mutant HPPK

(M142C, R88C) carries two accessible cysteines groups at position 88 on Loop3 and at position

142 near the C-terminal helix, as shown in Figure 7.1. Two dyes of a Cy3-Cy5 FRET pair are labeled with those two cysteines on the mutant HPPK by thiolation reaction to monitor the conformational dynamic motions of the host HPPK molecule. Low concentration of HPPK (0.1 nM HPPK) was treated with saturated substrates (200 M ATP and 200 M HP) in 1% agarose gel buffer solution. The aqueous buffer contains pH 8.3 100 mM Tris-HCl buffer with 10 mM

33,34 MgCl2 and a specific treatment of trolox-oxygen scavenger.

To prove that HPPK can freely rotate in the 1% agarose gel without perturbations from substrates and products diffusing, both ensemble-level and single-molecule measurements of anisotropy distribution of HPPK without substrates were recorded and compared in Figure 7.2.

For ensemble-level measurement, one droplet of concentrated HPPK (~1.0 mM) buffer solution was applied on a glass coverslip. For single-molecule measurement, low concentration of HPPK

(~0.1 nM HPPK) was treated with saturated substrates (200 M ATP and 200 M HP) in 1% agarose gel buffer solution. The narrow-distributed ensemble-level anisotropy centered at 0.29 and the relatively wide-distributed single-molecule anisotropy centered at 0.27 clearly show that

HPPK can exhibit free rotation without confinements or perturbations from ~100-150 nm gel.

179

Figure 7.2. (A) Ensemble-level anisotropy distribution of HPPK without substrates. (B) single- molecule anisotropy distribution of HPPK. The narrow-distributed anisotropy of ensemble-level measurement and the relatively wide-distributed anisotropy of single-molecule measurement indicate that HPPK can freely rotate in the agarose gel without spatial confinements or other perturbations for molecular rotation.

7.2.2. Single-Molecule Multi-Parameter Photon Stamping Spectroscopy

The detailed description of single-molecule multi-parameter photon stamping spectroscopy has been provided in Chapter 6. Briefly, single-molecule multi-parameter photon stamping spectroscopy are performed by a home-built four-channel single-molecule microscopy basically consisting of an inverted confocal epi-illumination configured microscopy, a femtosecond pulse laser, four Si avalanche photodiode detectors and a time-correlated single photon counting module, as shown in Figure 7.3A.

In our home-built set-up, the chronic arrival time, the delay time between the pulse excitation and molecular excited state emission, the polarization, and the color of each photon are recorded. Figure 7.3B shows a portion of typical single-molecule photon stamping raw data from the donor parallel channel in a period of 3 s, plotted as delay time vs chronic arrival time. 180

The chronic arrival times of the fluorescence photons contain the information about the photon flux, so that we can count and bin the photons in a unit of time (10 ms binning time in this report) to obtain a typical fluorescence intensity trajectory shown in the bottom right panel in Figure 7.3C. The distribution of delay times of all the photons in Figure 7.3B gives rise to a nanosecond fluorescence decay curve, as shown in Figure 7.3D. Giving a brief blueprint of our multiple parameters recording from photon stamping spectroscopy, intensity trajectories (Figure

7.3C) and lifetime decay curve (Figure 7.3D) are derived from the photon stamping raw data

(Figure 7.2B).

7.2.3. Fluorescence Anisotropy

Fluorescence anisotropy measurements provide insights into molecular orientation/rotation and mobility. Changes in fluorophore’s orientation can reflect the rotation of a target macromolecule to which a fluorophore is attached. The fluorescence anisotropy r(t) is defined by Equation (6.1) and the Perrin Equation is expressed in Equation (6.2). The details of the theory and methodology of fluorescence anisotropy are provide in Chapter 6 (6.2.4

Fluorescence Anisotropy). 181

Figure 7.3. (A) Experimental home-built four-channel set-up for simultaneously measuring single-molecule FRET, lifetime and anisotropy. (B) The demonstration of single molecule photon-stamping. Each detected photon is stamped with the chronic arrival time as well as the delay between the pulse excitation and molecular excited state emission. It shows a typical example of the raw data from the donor channel in a 3.0 second period. Each dot corresponds to a detected photon, plotted as the delay time vs its chronic arrival time. (C) Intensity trajectory of the donor with 10 ms binning time, calculated from the photon information in Fig 7.3B. (D) The distribution of the photons’ delay times in Fig 7.3B. The nanosecond fluorescence decay curve in red line with fitting residues is obtained.

182

7.2.4. Hydration of Globular Protein HPPK

For globular proteins (HPPK is considered as one of them), the rotational correlation time is approximately related to the volume of rotational unit (V), specific volume of proteins (ν), gas constant (R), molecular weight (M), temperature (T), viscosity (η), and hydration (h), as expressed in Equation (7.1). 20

VM    h (7.1) RT RT

Equation (7.1) can be further converted to Equation (7.2), 20 which is an expression of hydration as a function of rotational correlation time, viscosity, and other physical parameters. RT h  M (7.2)

Values of specific volume for proteins ν are typically around 0.73 ml/g, the viscosity is near 0.0094P (poise), gas constant R equals to 8.31*107 erg/mol °K. In our experiment, molecular weight (M) is 18 kD for HPPK and the experimental temperature is 298.15 °K. For simplicity, the result of hydration is calculated on the basis of above parameters’ typical values for globular proteins. Therefore, the exact values of hydration (gram H2O per gram of protein) are uncertain, but the distribution, the fluctuation or the relevant values may provide some useful information for predicting the local environmental effect on anisotropy fluctuations, such as hydrophobic interaction, hydrophilic interaction, and solvation.

7.3. Results and Discussion

We have investigated the conformational dynamic motions of Cy3-Cy5 labeled HPPK from multi-dimensional perspectives by means of single molecule multi-parameter photon stamping spectroscopy. The multiple parameters presented in Figure 7.4 are fluorescence 183 lifetime, anisotropy, and presentative lifetime decay curves in diagrams of donor (Cy3) in the presence of acceptor (Cy5). The photon-stamping delay time contains each photon’s lifetime information, so we can either exponentially fit the histogram of delay times or averagely calculate the mean of the delay times of all the photons in each bin (10 ms in this report). In single-molecule photon stamping spectroscopy, we consider the photons within each bin obey a

Poisson distribution, so that the mean of each lifetime distribution can be treated as the fluorescence lifetime. By averaging all the delay times in each 10 ms from the original photon stamping data, we further obtain the donor lifetime trajectory in Figure 7.4C. The lifetime trajectory enables us to detect protein conformational dynamics by probing real-time donor- acceptor distance changes, as discussed in Equation (1.8) and (1.12). The histogram of the occurrence in regards with donor lifetime is given in Figure 7.4D. Two difference levels of lifetime (high and low levels, centered at 2.45 ns and 2.93 ns, respectively) are observed from the fitted two Gaussian-like distributions of lifetime.

In order to check whether the fluorescence anisotropy differs while donor lifetime presents two distinguished levels, the anisotropy trajectory (Figure 7.4A) and the corresponding histogram (Figure 7.4B) are analyzed. Each data point of fluorescence anisotropy trajectory is calculated based on Equation (1.14) and derived from average anisotropy in each 10 ms bin. We have observed the dynamic fluctuation of fluorescence anisotropy between two levels switches asymmetrically with the high level centered at 0.32 and low level centered at 0.10, as shown in

Figure 7.4A and 7.4B. While previous lifetime measurements showing fluctuations switching between two different levels indicate FRET-dimensional conformational changes, the two-level anisotropy fluctuations further imply that intrinsic conformational dynamics involves both the 184

FRET-related donor-acceptor distance fluctuation and the anisotropy-related molecular orientation fluctuation.

Further evidence of the existence of intrinsic two-level conformational motions is obtained by analyzing the dynamic rates at each level. Figure 7.4E shows the dynamic lifetime decay curve at the high level and Figure 7.4F shows the decay curve at the low level. Referring to the Equation (1.14) and (1.15), the low-level lifetime indicates the short distance between two probes, which is a sign of the close proximity of the two labeling sites on HPPK molecule. The high-level lifetime suggests a relatively wide opening of the two labeling sites between the most mobile Loop 3 and the active site of HPPK molecules. Two distinct dynamic behaviors are observed and categorized from both decay curves and corresponding lifetime distributions, shown in Table 7.1 and the Figure 7.5. From the two different dynamic behaviors, two-level conformational motions in synchrony with the low-level and high-level lifetimes are suggested.

Table 7.1 summarizes the characteristics of the lifetimes in each level for a single HPPK molecule and Figure 7.5 further lists the decay curves and overall percentage of time-spent in each distinguishable states. At the low-level, at least two components (dynamic decay rates) present on the basis of the multi-exponential fitting. In contrast, only one dynamic decay rate is obtained for the high-level. Since donor lifetime in the presence of acceptor can reflect the conformational changes of protein/enzyme referring to Equation (1.8) and (1.12), we can further infer that the dynamic decay rates discrepancy between two levels is most likely originated from the intrinsic conformational dynamic motions of HPPK under the enzymatic reactions.

185

Figure 7.4. Single HPPK two-level conformational motions. (A) Single-molecule two-level anisotropy fluctuations under enzymatic reaction condition of 0.1 nm Cy3-Cy5 labeled HPPK,

200 µM ATP and 200 µM HP. Each data point is the average donor anisotropy calculated on the basis of Equation (1.14) in 10 ms bin. (B) Fitted two Gaussian-like distributions of anisotropy fluctuations derived from (A). (C-D) Simultaneously recorded donor lifetime trajectory and the corresponding lifetime distributions. A high-level and a low-level lifetime fluctuations are observed. (E-F) The representative dynamic decay curves of low-level (light purple) and high- level (light blue) lifetime fluctuations, respectively. At least two components (dynamic lifetime decay rates) are requisite for fitting the low-level lifetime decay curve, while only one component dynamic decay rate is necessary for the high-level lifetime decay curve. 186

Figure 7.5. Two different dynamic behaviors of single HPPK conformational motions. Two distinct behaviors are observed from the lifetime decay curves. The decay curves (A-F) correspond to the time ranges (0-80 s) in Table 1 and the decay rates are summarized in Table 1.

The upper panel decay curves show one-component lifetime decay rates, while the lower panel decay curves show multi-component lifetime decay rates along with their probabilities. Two- level conformational motions of single HPPK molecule under enzymatic reactions in synchrony with the upper-panel and low-panel lifetime decay curves/rates are observed.

187

Table 7.1. Two distinct dynamic lifetime decay rates behaviors of HPPK conformational motions

Time Range (s) 0-6 6-20 21-35 36-51 51.5-63.5 64-84

Lifetime (ns) 3.46 ± 0.10 1.23 ± 0.27 3.42 ± 0.09 1.29 ± 0.24 3.43 ± 0.10 1.28 ± 0.29

3.63 ± 2.33 5.20 ± 3.63 3.77 ± 2.55

We have also examined the color map of lifetime fluctuations and analyzed 2D joint distributions to visualize the changing probability and distributions of two-level conformational fluctuations of HPPK under enzymatic reactions. Figure 7.6A shows the color map of two-level lifetime fluctuations. The color map generated by Labview system (National Instruments) is built upon the consecutive lifetime distributions from each 3-second time window and each adjacent time-window has 0.5-second moving overlap. The color map indicates the time trace of changing probability of donor lifetime distributions, giving a direct real-time observation of two- level conformational dynamic fluctuation switching between one and the other. The correlation between FRET-dimensional and orientation-dimensional conformational changes are probed by plotting out the joint distribution between fluorescence lifetime and anisotropy in Figure 7.6B.

Two overlaid lines calculated on the basis of the Perrin Equation (1.15) represent two different rotational correlation times of donor at the center of each cluster. In Figure 7.6B, the joint distribution of correlated lifetime and anisotropy at the low-level has a broad rotational correlation time distribution ranging from 0.1 ns to 2.0 ns centered at 0.82 ns, implying high molecular mobility and orientation fluctuation. Considering the multi-component donor lifetime at low-level as shown in Figure 7.4E, Table 7.1 and Figure 7.5, we suggest that the enzyme

HPPK adopts at least two conformational intermediates states at the low-level. In contrast, the slow rotational correlation time distribution centered at 10.88 ns along with one-component lifetime decay rate shown in Figure 7.4F, Table 7.1 and Figure 7.5 indicates that the perturbed 188 donor predominately exhibit low mobility, and the distance between Loop 3 labeling site and the active site does not exhibit dramatic change at the high-level. One point is worthy to be noted here that the similar distance is probably due to multiple conformational dynamic states which have similar FRET-coordinate conformational site-to-site distance but pose different molecular orientations/rotations along multiple orientation coordinates.

Besides the conformational changes probed by FRET-coded donor lifetime fluctuations along FRET coordinate, here we have spied into the molecular rotation/orientation along orientation coordinates and the correlation between HPPK orientation-dimensional and FRET- dimensional conformational motions. The hydration data further give us some knowledge of hydration fluctuations of HPPK at molecular level during conformational motions under enzymatic reactions. Figure 7.7 shows the hydration trajectory (Figure 7.7A) derived from

Equation (7.1) and the three-dimensional correlation of anisotropy-lifetime-hydration (Figure

7.7B) after applying experimental results of rotational correlation times as well as the typical values of other parameters. More discussion will be provide after proposed mechanism.

189

Figure 7.6. Direct visualization of changing probability and distribution of two-level conformational fluctuations. (A) The color map of two-level lifetime fluctuation. The map is built upon the basis of the lifetime distributions in the consecutive three-second time windows with 0.5 second overlaps. (B) Joint distribution of lifetime and anisotropy plotted with computed curves referring to Perrin Equation (1.15). The color bar indicates the number of occurrence.

Two distinguished distributions are observed and they show different rotational correlation times

(centered at 0.82 ns and 10.88 ns). 190

Figure 7.7. (A) Hydration trajectory calculated on the basis of Equation (7.1). The values of parameters are typically used values. The shape of distribution indicates the hydration fluctuations at molecular level. (B) Three-dimensional plot of fluorescence lifetime, molecular anisotropy, and hydration.

191

Two different conformational dynamic behaviors between function-inert and function- active scenarios in synchrony with the two-level conformational motions of HPPK are proposed on the foundation of above observations. The rotation of donor involves the Loop 3 motion, the overall HPPK motion, and the free dye motion. In Chapter 6, we have discussed 35-38 and suggested that measured overall anisotropy of Cy3 on T4 lysozyme is not dominated by free dye rotation but rather by restricted rotation. Therefore, the rotational correlation times observed in our experiments, to some extent, indicate that donor dye exhibits constrained rotation. The rotation is predominately dominated by Loop 3-active site motion as well as the whole HPPK motion. Based on above observations including the correlation between lifetime and anisotropy, the two different dynamic rates behaviors, FRET-dimensional and orientation-dimensional conformational motions, we propose a mechanism of two-scenario function-related conformational dynamic behaviors (Figure 7.8). The function-inert scenario and function-active scenario are suggested. Under the inert scenario, the broad distribution of rotational correlation time is most likely due to multiple conformational states, and the one-component lifetime dynamic decay rate indicates the similar spatial distances between two labeling sites among multiple orientation-related conformational states. With similar FRET-dimensional conformational configuration, the multiple orientation-dimensional conformational states of the enzyme is probably significant for adjusting its optimal orientation to actively engage substrates and efficiently interact with them. Under the active scenario, distinct two-component lifetime dynamic decay rates imply two major FRET states, agreeing on the fact that the enzyme undergoes open-close motions along FRET coordinate. The high standard deviation of the slow component suggests at least two sub-states involved during the open process. Multiple conformational intermediate states, observed from NMR, crystal structures, MD simulation and 192 single-molecule measurements, have often been suggested as the general feature of enzymatic mechanisms and protein motions.14,21,39-48 Our observation here is also consistent with the multiple sub-states from the ensemble-level HPPK crystal structures.4 In addition to breaking the limit of the averaging effect and synchronization from ensemble-level measurements, two scenarios (inert and active) conformational dynamic behaviors for the first time are distinguished by our sensitive single-molecule correlated multi-parameter photon stamping approaches. Our results imply that HPPK exhibits Loop 3-active site scenario-related conformational changes: conformational changes under inert scenario are useful for assembling the catalytic center; dramatic Loop 3-active site open-close conformational changes under active scenario are essential for assembling and sealing the catalytic center, thus it is critical for catalysis; and the scenarios switching between one and the other are most-likely related to the interaction between enzyme and substrates, thermal energy fluctuations, and local environment fluctuations.

193

Figure 7.8. Proposed HPPK two-scenario conformational dynamic behaviors: function-inert scenario and function-active scenario. Under inert scenario, the mobility and orientations of enzyme are probably significant for adjusting its optimal orientation to engage substrates. Under active scenario, the enzyme undergoes open-close motions and the open motion involves multiple states.

The hydration results also support our proposed HPPK two-scenario conformational dynamic behaviors. It has long been acknowledged that water plays an essential role in protein conformational dynamics.48-52 For example, it has been reported that the dynamics and functions of myoglobin (the protein which gives muscles red color) are coupled to its conformational motions in the bulk solvent and the hydration shell.53 In our work, under the inert scenario, the enzyme predominately exhibit wide-opened configuration where the dye molecule is surrounded by water molecules. Therefore, large values of hydration are observed under relatively hydrophilic environment. Under the active scenario, the narrowly open or close (or partially) configurations dominate the whole catalytic cycle where dye molecule is predominately exposed 194 to a more hydrophobic environment. Under this environment, the dye is buried or partially buried inside the hydrophobic core of the enzyme. Outside of the enzyme is a cage formed by water molecules (hydration shell). The fluctuations in the protein’s hydration shell are essentially related to the conformational motions which involve mainly amino acid side chains and hydrogen-bond network.53 The different molecular local environment changes between hydrophilic and hydrophobic conditions enable us to better understand the difference of the hydration or hydration fluctuation in these two distinguished scenarios. The forces for switching between hydrophilic and hydrophobic environment are most likely driven by electrostatic interaction between the enzyme and substrates, and the thermal energy.

It has been appreciated and suggested that dynamic conformational changes play critical roles in enzyme functions, involving multiple intermediate states and multiple conformational coordinates.14,23,43,54-73 As an essential part of conformational changes, molecular rotations/ orientations of many proteins or enzymes are crucial motions in their mechanisms.1,74 For dye- labeled protein/enzyme, changes in orientation of fluorophore are considered to reflect the relative motions of the fluorophore as well as the rotational motions of the protein/enzyme domain. Single-molecule fluorescence polarization and the relevant mathematical modeling have been demonstrated to determine orientation and rotational mobility of single fluorophore.75 In addition, the orientation factor 2, relying on the dipole orientations of the FRET pair, is not usually taken into account for single-molecule FRET measurements. In the certain case of the single-molecule distance-related FRET measurement where obtaining absolute donor-acceptor distance requires knowing accurate Förster radius (R0), dye orientation which determines factor

2 becomes crucial. For example, FRET obeying the orientation dependence as expected for a dipole-dipole interaction has been experimentally proved and reported.76,77 Fortunately, for 195 single-molecule conformational dynamic studies, in most cases, it is unnecessary to measure absolute distance instead of relative distance changes. Therefore, distance-related FRET based on well-defined stable FRET has been widely used to assign multiple states from protein/enzyme conformational motions values.24,29,78-80 Nevertheless, this FRET-dimensional states assignment predominately considers one-dimensional conformational motion, that is, only the projection of conformational motions on the FRET coordinate is considered. In fact, conformational dynamic motions can be probably projected on multiple coordinates besides FRET coordinate. For example, with the same site-to-site distance between donor and acceptor, the conformations of protein can be different if taking domain orientation into account. As many proteins/enzymes use rotational motions as significant features of the functional output, the approaches to probe these motions become very important in the study of biological macromolecules. The single- molecule multi-parameter detection approaches could be a valuable tool to dissect the multi- dimensional rotational motions besides FRET coordinate, and facilitate single-molecule measurements to the extensive information exploring of the complex cellular biophysical systems.

7.4. Conclusions

In conclusion, we have observed two-level FRET lifetime fluctuations, two-level anisotropy fluctuations and two different behaviors of lifetime dynamic decay rates of HPPK conformational motions under enzymatic reactions. We have further proposed function-inert and function-active scenarios of HPPK Loop 3-active site conformational dynamics in synchrony with two-level behavior in terms of lifetimes, anisotropy, and dynamic rates. Our observations not only agree with multiple states feature of HPPK conformational motions reported in the ensemble-level studies, but also provide insights to the hidden scenario-related features beyond 196 the ensemble-level measurements, allowing a deeper understanding of the complex HPPK conformational motions in the biosynthesis cycle of folates. The approach and the results here may explore a field for dissecting the multi-dimensional rotational conformational motions which are rarely considered but important for cellular enzymes’ functional output, making it promising for unraveling the accurate conformational motions and complex structure-function relationships under the enzymatic reactions.

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