Structure and Dynamics of Elastin Cross-Linking Domains by Aditi Ramesh a Thesis Submitted in Conformity with the Requirements F

Structure and Dynamics of Elastin Cross-linking Domains

Aditi Ramesh

A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Biochemistry University of Toronto

Structure and Dynamics of Elastin Cross-linking Domains

Aditi Ramesh Master of Science Graduate Department of Biochemistry University of Toronto 2015

The secondary structure of elastin cross-linking domains has been shown to be sequence and context dependent, but the role of these domains in the function of elastomeric proteins remains unclear. We use molecular dynamics simulations (MD), circular dichroism spectroscopy (CD), and nuclear magnetic resonance to probe the conformational equilib- ria of model elastin-like cross-linking peptides. We tested four recently developed force

ﬁelds using MD to select the one that best reproduces the amount of alpha-helix seen in

CD. Simulation studies of the aggregative properties of the cross-linking domains found that they occasionally interact, but not in any speciﬁc way. Additionally, multifaceted studies of biphasic systems show that these domains do not partition preferentially into or on the interface of a hydrophobic surface. Further experiments on constructs of cross- linking and hydrophobic domains will help elucidate how cross-linking modulates the self-assembly and mechanical properties of elastomeric proteins.

ii Dedication

To my parents.

iii Acknowledgements

I would like to thank my supervisors Dr. Simon Sharpe and Dr. RégisPomèsfor their constant guidance and advice throughout my graduate work. They have shaped the scientist I am today and fostered my deep passion for the biological sciences. I also wish to thank the members of my supervisory committee, Drs. Hue Sun Chan, Fred Keeley, and Julie Forman-Kay, for their critical analysis of my work and suggestions along the way. I wish to thank the members, both past and present, of both labs for their constant advice, help, and, most importantly, moral support. I would especially like to thank Drs. Chris Neale, Loan Huynh and Sarah Rauscher for their invaluable help in getting me started in the simulation work and teaching me the ropes. I would like to thank Zhuyi Xue for our daily discussions about elastin and Chris Ing for his scripting help in times of distress. My deepest, most heartfelt thanks to Dr. Grace Li, Kethika Kulleperuma, and Dr. Nilu Chakrabarti for their advice about everything in life. I thank Dr. Patrick Walsh and Jason Yau for leading the way in the peptide work in the Sharpe lab and teaching me the ins and outs of working with peptides for the first time. Greg Cole and Dave Davidson are thanked for their constant help and support in the lab. I have Karen Simonetti to thank for her patience, support and encouragement as I struggled in the wet lab and tried not to break equipment. I wish to thank my amazing friends Tracy Stone, Noor Alnabelseya, and countless other friends I have made over the years from my labs and the rest of the department in various labs who have kept me sane inside and outside the lab. These friends have heard me vent and cry through the years and I treasure their patience, love and support. They kept me going with their encouragement and optimism during the rough and turbulent times and have become a second family for me in Toronto. I also wish to thank Daniel Schep for his support and patience during the long months of my thesis writing. I thank with all my heart my family, especially my parents, for supporting me in all my endeavours and being the loving, encouraging people they have always been and for always having faith in my abilities, even though my own belief sometimes faltered.

iv Contents

List of Tables vii

List of Figures viii

List of Acronyms and Symbols x

1 Introduction 1 1.1 Elastin ...... 1 1.2 Elastin cross-linking ...... 5 1.3 Elastin structure and mechanism ...... 8 1.4 Peptide studies ...... 8 1.5 Recombinant elastin-like polypeptides ...... 10 1.6 Rationale and aims ...... 11

2 Methods 14 2.1 Molecular Dynamics Simulations ...... 14 2.1.1 Molecular mechanics ...... 14 2.1.2 Force fields ...... 17 OPLS force fields ...... 18 CHARMM force fields ...... 18 CHARMM27 (CHARMM22/CMAP) ...... 18 CHARMM36 ...... 19 CHARMM22* ...... 19 AMBER force fields ...... 20

v AMBER ﬀ03w ...... 20 AMBER ﬀ99sb*-ildn ...... 20 Water models ...... 21 Periodic boundary conditions ...... 21 Temperature and pressure coupling ...... 22 2.1.3 System setup ...... 22 2.2 Biophysical techniques ...... 23 2.2.1 Peptide synthesis ...... 23 2.2.2 Peptide sample preparation ...... 25 2.2.3 Circular dichroism ...... 25 2.2.4 Partitioning and analytical RP-HPLC ...... 25 2.2.5 NMR ...... 26 2.3 Data Analysis ...... 27

3 Results 28 3.1 Choice of force ﬁeld ...... 28 3.2 Spectroscopic characterization of the monomeric cross-linking domains . 32 3.3 Aggregative properties of the cross-linking domains - a simulation perspective 37 3.4 Tying biophysical results back to simulation ...... 58 3.4.1 Solution NMR of the model peptides ...... 58 3.4.2 Circular dichroism spectra calculated helicity of the model peptides 60 3.5 Biphasic systems as a way to model the coacervate ...... 64

4 Discussion 66

5 Future Directions 74

Bibliography 76

vi List of Tables

2.1 Summary of force ﬁelds and water models used...... 24

vii List of Figures

1.1 Domain architecture of the tropoelastin monomer ...... 3 1.2 Cross-linking domain sequences in natural elastin ...... 3 1.3 Pseudo-periodic hydrophobic domain sequences in natural elastin . . . .3 1.4 Molecular view of how cross linking is achieved in diﬀerent types of elastomeric proteins ...... 6 1.5 Mechanism of cross-linking ...... 7 1.6 List of model peptides ...... 12 1.7 Position of lysines in the model peptides in a perfect α-helix ...... 13

2.1 Schematic illustrating the diﬀerent energy terms of the potential energy formula for a force ﬁeld ...... 16

3.1 PMFs of backbone dihedral angles of A2 in the force ﬁelds tested . . . . 30

3.2 Time evolution of α-helix in KA16K and A7KAAKA7 ...... 31 3.3 Time evolution of secondary structure in molecular dynamics ...... 33 3.4 Average α-helix percentages in the A2 peptide for the four force ﬁelds tested 34 3.5 Circular dichroism spectra of model peptides in TFE ...... 35 3.6 Circular dichroism spectra of model peptides in NaF ...... 36 3.7 Circular dichroism spectra of model peptides in MeOH ...... 37 3.8 Average peptide-peptide distance in dimer simulations ...... 38 3.9 Histograms of the end-to-end distance of the model peptides in the monomer, dimer, and tetramer simulations ...... 40 3.10 Histograms of the radius of gyration of the model peptides in the monomer, dimer, and tetramer simulations ...... 41

viii 3.11 Histograms of the probability of having 0 through 18 helical residues . . 42

3.12 Time evolution of the radius of gyration of A8KKA8 in CHARMM22* . . 44 3.13 Time evolution of the radius of gyration for a representative A0 dimer simulation ...... 45 3.14 Sample contact maps for the A0 dimer system ...... 47 3.15 Snapshots at 100ns of six A0 dimer replicas ...... 48 3.16 Sample contact maps for the A0 tetramer system ...... 49 3.17 Snapshots at 100ns of six A0 tetramer replicas ...... 50 3.18 Dimer contact maps for the six model peptides averaged over all replicas 52 3.19 Tetramer contact maps for the six model peptides averaged over all replicas 53 3.20 Comparison of total fraction helix formed by all peptides over the 1, 2, and 4 peptide simulation systems ...... 54 3.21 Histograms of pairwise distances between the centers of mass of all peptides 54 3.22 Average fraction helicity for dimer simulations of all peptides with and without formation intermolecular contacts ...... 55 3.23 Average fraction helicity for tetramer simulations of all peptides with and without formation intermolecular contacts ...... 56 3.24 Solution NMR of the A2 peptide ...... 59 3.25 Secondary chemical shifts of Cα (red) and Cβ (blue) atoms for the A2, A3, and A16 peptides and A3 at diﬀerent temperatures ...... 61 3.26 Comparison of helicity calculated from MD and CD ...... 62 3.27 Average helicity per residue for all the model peptides in CHARMM22* . 63 3.28 Integrated peptide peak areas from RP-HPLC chromatograms for each phase in octane and octanol partitioning experiments ...... 65

4.1 Position of lysines in the helical aggregated multimer simulations . . . . . 71 4.2 Schematic of the proposed cross-linking mechanism in elastin ...... 72

ix List of Acronyms and Symbols

A˚ Angstrom

AMBER assisted model building and energy reﬁnement

CD Circular dichroism

DIEA N,N-Diisopropylethylamine

DMF Dimethylformamide

DSSP Dictionary of protein secondary structure

EBP elastin-binding protein

ELP elastin-like peptide

EM electron microscopy

Fmoc Fluorenylmethyloxycarbonyl fs femtosecond

GROMACS Groningen machine for chemical simulations

HFIP 1,1,1,3,3,3-Hexaﬂuoro-2-propanol

HPLC High-performance liquid chromatography

K Kelvin

MD molecular dynamics

x nm nanometer

NMR Nuclear magnetic resonance ns nanosecond

OPLS optimized potentials for liquid simulations

TIP3P transferable intermolecular potential function, three point model

xi Chapter 1

Introduction

1.1 Elastin

Elastic proteins, which are found in many animal species [1], include abductin (which is found in the ﬂexible hinge ligament of a scallop’s shell) [2], resilin (found in the cuticle of many insects) [3], spider silks [4], and elastin [5]. These proteins are found to fulﬁll a diverse set of functions and showcase a wide range of properties, with some spider silks demonstrating incredible rigidity while others are more elastic and resilient. The mechanical properties of elastomeric proteins motivates the study of these proteins from a biomaterials and bioengineering perspective.

Elastin is an extracellular matrix protein found in a number of tissues including skin, blood vessels, and lungs. Elastic ﬁbers are characterized by their ability to undergo repetitive cycles of stretching and relaxation, properties which are integral to the physiological function of these tissues. This function is achieved with very little turnover (with the exception of the uterine wall [6]) of the elastin protein, which is an insoluble biopolymer with a half-life of about 70 years [7], and is thus able to undergo billions of cycles of stretching and recoil over a lifetime [8]. Elastin confers vital function to numerous tissues, thus many diseases are associated with abnormalities of elastin production and deposition. Fragmentation of elastin or an overall decrease in the amount of elastin produced leads to diseases such as atherosclerosis [9], pulmonary emphysema

1 Chapter 1. Introduction 2

[10, 11, 12], cutis laxa [13, 11], which is characterized by wrinkled and sagging skin, and Menkes syndrome, a disease resulting in the inability to absorb copper in the intestines and distribute it to other cells in the body [14]. Excessive accumulation of elastin leads to scleroderma, elastomas, and endocardial ﬁbroelastosis, which is characterized by a thickened lining in the heart [15].

Modern biological and biochemical techniques have made it possible to generate arti- ficial mimics of elastin, which self-assemble into fibrils and membranes with properties resembling those of human elastin. Gaining structural insight into the molecular basis of tissue elasticity will help in the design of both biomimetic materials with application to medicine (such as grafts for skin and heart tissues) and non-immunogenic materials that absorb and release drugs with defined binding constants [16, 17, 18, 19].

Tropoelastin, the soluble, monomeric precursor of elastin, is composed of alternating hydrophobic and cross-linking domains. Figure 1.1 depicts the domain architecture of the tropoelastin monomer. Self-association of these monomers in vitro occurs by an in- verse temperature transition called coacervation. There are two types of cross-linking domains in elastin, the KA-type and the KP-type. The KA-type domains are composed primarily of stretches of alanine residues containing two or three lysines spaced three or four residues apart, while the KP-type domains resemble hydrophobic domains in amino acid sequence with the addition of precisely spaced lysine pairs [20]. The hydrophobic domains are comprised largely of glycine (G), valine (V), proline (P), and alanine (A) residues arranged in pseudo-repetitive motifs, or tandem repeats, with typical motifs such as PGV, GVA, GV, and GGV [21]. Figure 1.2 shows a couple of sequences of each of the two types of cross-linking domains. Figure 1.3 shows the sequences of some elastin hydrophobic domain exons in human and chicken, highlighting the key repetitive motifs. Previous studies indicate that the aggregation of the hydrophobic domains is modulated by their combined proline and glycine content, whereby elastomeric proteins have a higher content of proline and glycine than amyloid-forming proteins and peptides [21]. Amyloid forming proteins, as compared to elastomers, switch from disordered or unfolded states Chapter 1. Introduction 3

Figure 1.1: Domain architecture of the tropoelastin monomer (adapted from [7]). White boxes represent hydrophobic domains, yellow represents KA-type cross-linking domains, maroon boxes are KP-type cross-linking domains.

Figure 1.2: Cross-linking domain sequences in natural elastin. The lysine residues available for cross-linking are highlighted in red.

Figure 1.3: Pseudo-periodic hydrophobic domain sequences in natural elastin. The coloured parts of the sequences indicate the various periodic motifs. PGV is shown in red, GGV in blue, GVA in green and GV in orange. Chapter 1. Introduction 4 into rigid β-sheet assemblies [22] - a fate that is avoided by elastin through its unique amino acid composition. A higher content of proline and glycine results in greater structural disorder [21, 23]. Tropoelastin molecules are thought to aggregate by interactions between hydrophobic domains, which confer the properties of self-aggregation and exten- sibility to elastin [24] and cause the protein to deposit in ordered, ﬁbrous structures. In vitro, the self-association of hydrophobic domains, driven by the hydrophobic eﬀect, is thought to drive coacervation and, because elastomeric chains do not fold into an ordered, native structure, their aggregates are amorphous and hydrated and able to undergo en- tropically driven extension and recoil. The temperature at which coacervation occurs depends on a number of factors, including ionic strength, pH, protein concentration and the relative proportions of hydrophobic and hydrophilic residues [25]. Upon coacervation, the turbidity of the solution increases (as measured by a drop in light intensity in light scattering experiments). Birefringence is seen at the surface of coacervate droplets, as shown by dye binding studies [23], implying there is some level of ordering at the surface of coacervate droplets.

Coacervation is a reversible process; upon cooling a coacervated solution, tropoelastin monomers go back into solution. However, if a coacervated solution is left to mature, the coacervate droplets can settle and form an organized fibrillar structure. There are a specific set of requirements and controlling factors that govern coacervation. As such, coacervation is thought to occur not by nonspecific aggregation of monomers but by an increase in the secondary structure and specific intermolecular contacts.. Below the transition temperature, tropoelastin monomers look like polyamorphous unstructured elements while above this temperature, coacervates of tropoelastin and recombinant peptides with the ability to coacervate take on a fibrillar structure with a diameter of ∼ 5nm. These fibres are similar in structure to mature elastin fibres. Occasionally, lateral association of the fibres or 100-150nm wide banded filaments, representative of the elastin network, are also formed [26]. Coacervation is an important step in ordering tropoelastin monomers and coacervation produces the assembled elastin state, but how this process occurs is largely unknown. Chapter 1. Introduction 5

Elastin fibres have several other components apart from elastin, including fibrillins, fibu- lins, and glycoproteins, collectively referred to as microfibrils [20]. Tropoelastin mRNA is translated at the surface of the rough endoplasmic reticulum in a number of different types of cells, including the smooth muscle, endothelial, and fibroblast cells [5]. The approximately 70kDa precursor protein is transported as a nascent chain to the ER lumen where its signal peptide is cleaved cotranslationally. Tropoelastin is bound by the chaperone FKBP65 once it is synthesized in the endoplasmic reticulum (ER) and is then transported to the cell surface via the Golgi apparatus (where it is bound by elastin-finding protein (EBP)) and is then released and secreted from the cell [5, 27]. It is thought to deposit on microfibril scaffolds [24] and that alignment of tropoelastin monomers occurs via coacervation, which results in ordering and alignment for subsequent cross-linking [21, 1].

1.2 Elastin cross-linking

Elastomeric proteins achieve cross-linking either covalently, as in elastin, or non-covalently, as seen in spider silks. Covalent cross-links are largely formed by lysine residues, though some proteins, such as resilin, have covalent cross-links between tyrosine residues. Non- covalent cross-links are found in many types of spider silks, which achieve cross-linking by the formation of β-sheets between domains consisting largely of alanine residues. Figure 1.4 depicts diﬀerent types of cross-linking schemes in elastomeric proteins. Cross-linking of collagen and elastin occurs through the action of the copper-dependent amine oxidase lysyl oxidase. It catalyzes oxidative deamination of the -amino group in the lysine side-chain, forming peptidyl α-aminoadipic-δ-semialdehyde (allysine) [29] using a ping-pong kinetic mechanism [30]. Subsequent spontaneous aldol condensation and Schiﬀ base reactions with nearby aldehydes or -amino groups result in the formation of di-, tri-, and tetra-functional cross-links such as desmosine and isodesmosine. Figure 1.5 depicts where the lysine residues are catalysed by lysyl oxidase and the subsequent reactions to form the desmosine linkages. Chapter 1. Introduction 6

desmosine lysinonorleucine polyalanine beta-sheet

di- and tri-tyrosine

Figure 1.4: Molecular view of how cross linking is achieved in different types of elastomeric proteins (cross-linked filament network diagram obtained from [28]). Desmosine linkages are found in elastin and are tetrafunctional, pyridinium ring-containing cross-links derived from four lysine residues. The lysinorleucine bivalent elastin cross-link is formed between two lysine residues. Many spider silks form a polyalanine β-sheet, with β-sheet crystalline domains surrounded by semi-amorphous domains. Di- and tri-tyrosine linkages are found in resilin and formed between tyrosine residues interspersed through the elastic repeat motifs (not in specific cross-linking or hydrophobic domains, which are not found in resilin). Chapter 1. Introduction 7

Figure 1.5: Molecular mechanism of cross-linking and the role of lysyl oxidase (figure obtained and modified from [31]). Lysyl oxidase initiates oxidative deamination of the lysine side chains, releasing oxygen and ammonia. The aldehyde formed by this reaction condenses with other aldehydes to form a bivalent aldol condensation product (ACP). The aldehyde can also react with an unmodified lysine side chain and form a dehydrolysi- nonorleucine (dLNL). Spontaneous condensation reactions between ACP and dLNL form tetra-functional cross-links desmosine or isodesmosine. Chapter 1. Introduction 8

1.3 Elastin structure and mechanism

NMR studies of elastin show that most of the backbone carbonyl carbon atoms are highly mobile in polar solvents and that hydrated elastin lacks a defined tertiary structure due to a highly disordered backbone [20, 32, 33, 34, 35, 36]. Previous studies of the hydrophobic domains have concluded that elastin and elastomeric proteins remain disordered upon aggregation [21] and that point mutations in these domains suppress phase separation and promote amyloid-fibril formation [23]. The cross-linking domains provide stability and mechanical integrity, as without these domains, pulling on elastin would cause the polymer to fall apart when extended. They have long been predicted to form α-helices since this would position the lysines on the same side of the helix, allowing the formation of cross-links between two pairs of lysine residues from adjacent tropoelastin monomers [37]. While the cross-linking domains of elastin are α-helical in the polymerized, or cross-linked, state [38], the cross-linking domains of other elastomeric proteins, such as the alanine-rich domains of spider silk, are made of β-sheets [39]. Recent studies have shown that single-point mutations (K to Y or K to A) can switch the conformation of crosslinking domains from α-helical to β-sheet [40]. Furthermore, SSNMR studies have shown that the cross-linking domains of recombinant elastomeric peptides are unstable α-helices in the monomeric state, form β-sheet upon coacervation, but are found to be very stable α-helices when they are cross-linked after coacervation [40]. Thus it is clear that the cross-linking domains of elastin have the potential to affect the aggregation propensity of elastin-like recombinant peptides, as well as the mechanical properties of the assembled protein.

1.4 Peptide studies

The cross-linking domains of elastin and spider silks are rich in alanine, which is reported to be a strong helix former by secondary structure propensity scales [41, 42, 43]. However, experimental [44, 45, 46] and computational work [47, 48, 49] show that polyalanine peptides adopt largely random-coil structures in aqueous solution. Most polyalanine Chapter 1. Introduction 9 peptides, except the very shortest, are insoluble. Solvation of these peptides has been achieved by inserting polar or charged resides at the ends [50]. A a result, a number of studies have been conducted with polyalanine peptides interspersed with amino acids such as lysine, glutamine, and arginine. Some studies have shown that the helicity of alanine-rich peptides can be increased by introducing charged or polar residues such as lysine, arginine, or glutamine into the sequence [51], while other studies show that these residues decrease the helicity of alanine-rich peptides [52]. Yet another study corroborates the helix stabilizing effect of charged residues, but shows that increasing the number of these solubilizing residues is a factor in decreasing helix stability [53]. Thus environment (sequence and context) plays an important role in modulating secondary structure. Polypeptide sequences have been the subject of much study over the last few decades [54, 46, 55]. The secondary structure of small peptides is highly dependent on environment and sequence. In particular, polyalanine peptides have been extensively studied in different solvents and shown to be highly sensitive to environment and sequence length, as well as the guest residues [43, 44, 53]. Chakrabartty and coworkers [43] studied guest amino acids in a series of alanine-based peptides without helix-stabilizing interactions. Using circular dichroism, they found that the helix propensities of all residues, except alanine, leucine, and arginine, oppose folding. Marqusee and coworkers [53] found that 16-residue alanine-based peptides containing between 3 and 6 lysines and glutamates formed stable helices, also determined by circular dichroism. Sung [48] corroborated these findings by simulation methods (Monte Carlo simulations) on the same peptides. The 3K peptides formed 60-80% helix while the 6K peptides formed significantly less helix (only 8-14%). Polyalanine peptides have been studied in aqueous environments, SDS, TFE, and in the presence of hydrophobic interfaces by both experiment [56, 57] and molecular dynamics simulations [58, 59, 60] yielding different percentages of the three major secondary structure types - α-helix, β-sheet and random coil. Best and coworkers [58] tested various MD force fields and gauged the extent of different secondary structures formed in the peptides Ala5 and Ac-(AAQAA)3-NH2. They found anywhere from

15 to 30% helix in the Ala5 peptide and as high as 94% helix in the ff03 force field for Ac-(AAQAA)3-NH2. These studies are of note because the cross-linking domains in Chapter 1. Introduction 10 tropoelastin are alanine-rich and have been long proposed to act as alpha-helical linkers between tropoelastin monomers, thus positioning the lysines for cross-linking and con- ferring strength and stability to the elastin fiber. Notably, the method used to gauge helicity also affects the percent helicity measured in the studies cited above.

1.5 Recombinant elastin-like polypeptides

Diﬃculty in obtaining detailed structural information for full length tropoelastin has prompted the study of synthetic elastin-like peptides (ELPs). Elastin is characterized by its ability to undergo repetitive stretching and relaxation and return to its original shape after large deformations. Additionally, elastomeric proteins are characterized by their ability to self-assemble into a polymeric matrix. Previous studies have shown that recombinant elastin polypeptides based on repetitive motifs found in human elastin have physical and mechanical properties that are similar to full length elastin. These peptides encapsulate key features of the entire protein, such as the ability to self-assemble and organize into ﬁbrillar structures and form lysine derived cross-links [8, 23, 26]. They also recapitulate the local structural propensities found in full-length elastin. The means to recombinantly express proteins has allowed the study of homogenous protein prepara- tions to study structural characteristics.

Studies of recombinant elastin-like polypeptides have shown that the hydrophobic domains are required for coacervation (the cross-linking domains do not coacervate on their own) [8]. Electron microscopy (EM) of coacervates of EP 20-24 and EP 20-24-24, where exons 20 and 24 are hydrophobic domains and the hyphen represents the two cross-linking exons 21 and 23, show fibrillar structures similar to tropoelastin upon self-aggregation [8]. Proline-rich hydrophobic domains influence coacervation by lowering the temperature at which it occurs [61]. Helix-breaking proline residues in the hydrophobic domains imply that the small abount of helix observed is confined to the alanine-rich cross-linking domains [62, 63, 64, 65]. The EP 20-24-24[21Y/A] mutant shows no changes in the amount of α-helix compared to wild type but has a coacervation temperature that is 7◦C higher Chapter 1. Introduction 11

[37]. Thus the biophysical properties of the cross-linking sequences are highly susceptible to point mutations and these domains also play an important role in the mechanical properties of elastin. Essentially, mutations and domain rearrangements [66] aﬀect the properties of the materials formed by recombinant elastin-like polypeptides.

1.6 Rationale and aims

The elastic properties of self-assembled elastomeric proteins depend on cross-linking: how do the cross-linking domains modulate the structure, self-assembly, and mechanical properties of elastomeric proteins? To answer this broad question, I aim to examine and characterize how the cross-linking of hydrophobic domains modulates the structure, self-assembly, and mechanical properties of elastomeric proteins using molecular dynamics (MD) simulations and experiments on model peptides. We adopted a reductionist approach to study the cross-linking domains. I first studied the cross-linking domains separately to characterize their inherent structural and self-assembly properties. I then investigated the effects of lysine spacing on the conformational equilibrium and secondary structure characteristics of the peptides, as well as their aggregation properties in silico. Figure 1.6 lists the peptides I have studied. The A0, A1, A2, A4, and A16 peptides have an alanine background and are 18 resides in length, akin to real cross-linking domains found in elastin. The lysines are centrally placed so as to study lysine spacing (from zero through four alanines apart and at the ends) but not register. The A2Y and A3A peptides are designed to be more like actually cross-linking domains, where the lysines are C-terminally located and the second lysine sometimes followed by a tyrosine residue instead of an alanine residue. Substrate recognition by lysyl oxidase is said to be partly dependent on local conformation, leading to the hypothesis that there must be prior alignment of the cross-linking domains before cross-linking can occur [26]. Coacervation has been found to promote the formations of ordered filaments and thus play a role in the alignment of monomers [23]. As a result, we wanted to probe whether the cross-linking domains order at the surface of coacervate droplets. The model peptides were studied in the presence of a hydrophobic phase to determine whether they partition preferentially Chapter 1. Introduction 12

Ace-AAAAAAAAKKAAAAAAAA-NH2 A0

Ace-AAAAAAAAKAKAAAAAAA-NH2 A1

Ace-AAAAAAAKAAKAAAAAAA-NH2 A2

Ace-AAAAAAAKAAAKAAAAAA-NH2 A3

Ace-AAAAAAKAAAAKAAAAAA-NH2 A4

Ace-KAAAAAAAAAAAAAAAAK-NH2 A16

Ace-AAAAAKAAKYGA-NH2 A2Y

Ace-AAAAAKAAAKAA-NH2 A3A

Figure 1.6: List of model peptides. The A0, A1, A2, A4, and A16 peptides have an alanine background and are 18 resides in length, akin to real cross-linking domains found in elastin. The lysines are centrally placed so as to study lysine spacing (from zero through four alanines apart and at the ends) but not register. The A2Y and A3A peptides are designed to be more like actually cross-linking domains, where the lysines are C-terminally located and the second lysine sometimes followed by a tyrosine residue instead of an alanine residue. into this phase (mimicking a coacervate droplet surface). Figure 1.7 shows the position of lysines in an idealized helix. It is of note that the A2 and A3 peptides, which have spacing akin to those found in natural elastin, position the lysine residues on the same side of the helix. The other peptides do not position the lysines as distinctly to one side of the helix. Chapter 1. Introduction 13

A0 A1 A2

A3 A4 A16

Figure 1.7: Position of lysines in the model peptides in a perfect α-helix. Helical wheels generated from http://kael.net/helical.htm. Chapter 2

Methods

2.1 Molecular Dynamics Simulations

The molecular structure and interactions of biological macromolecules can be predicted in computer simulations from ﬁrst principles using quantum mechanics [67]. However, quantum mechanical calculations are computationally expensive. This necessitates a simpliﬁcation of the method to calculate the structure and dynamics of biological macromolecules in simulation.

2.1.1 Molecular mechanics

Molecular mechanics uses classical mechanics to model atomic interactions and the potential energy of the system is calculated with force fields. Essentially, the electronic degrees of freedom are ignored and separated from the nuclear motions, which are the only motions considered in all calculations (atoms move in the Born-Oppenheimer ground-state energy surface [68]). A force field is the form and set of parameters of the function used to describe the potential energy of the particles in the system. Classical force fields have terms associated with the potential energy of five physically interpretable entities:

1. stretching and compression of bonds 2. bending of angles 3. rotation about torsion angles 4. electrostatic interactions

14 Chapter 2. Methods 15

5. van der Waals forces

These terms can be expressed in the following formula for the potential energy of a molecular system, V(r):

X kd X kθ X kφ V (r) = (d − d )2 + (θ − θ )2 + (1 + cos(nφ − φ )) 2 i 0 2 i 0 2 i 0 bonds(i) angles(i) dihedrals(i)

X kψ 2 X σij 12 σij 6 X qiqj + (ψi − ψ0) + 4ij[( ) − ( ) ] + 2 rij rij Drij impropers(i) non−bonded non−bonded pairs(i,j) pairs(i,j) (2.1) The ﬁrst term in equation 2.1 is the bond stretching term, where each bond is approx- imated as a spring, so the potential energy becomes the harmonic potential as determined by Hooke’s law. This potential is suitable for small deviations from the initial bond length. The second term is the potential energy upon deformation of angles. The third term is the torsional term, and represents the potential energy of the system in terms of rotations about the dihedrals. The fourth term considers the planarity of geometrically ﬂat groups and chirality [67]. The last two terms consider the non-bonded components of the potential energy. They are the van der Waals interactions Lennard-Jones potential and the electrostatic potential respectively, where rij is the distance between nuclei i and j. These interactions are shown in Figure 2.1. Chapter 2. Methods 16

φ d rij

Figure 2.1: Schematic illustrating the diﬀerent energy terms of the potential energy formula for a force ﬁeld. d is the bond length, θ is the angle between topological triples of atoms, φ is the dihedral angle and rij is the distance between nuclei i and j. Chapter 2. Methods 17

2.1.2 Force ﬁelds

The formula for the potential energies is one component of a force ﬁeld. The other is a set of parameters for each atom type, including the partial charges for individual atoms (q), van der Waals radius (σ), atomic mass, spring constant values for each potential energy term (kd, kθ, kφ, kψ), and equilibrium values of various bond lengths, bond angles, and dihedral angles (d0, θ0, φ0, ψ0).

The parametrization or re-parametrization of force ﬁelds is a complex process as there are an endless array of parameter combinations where one subset of parameters can compensate for another subset in order to reproduce experimentally observed structural and energetic data. Each potential energy term needs to be calibrated relative to quantum mechanical data, vibrational spectra, crystal information and other experimental data. Recent advances in computer hardware and software have allowed long all-atom molecular dynamic simulations on the tens of µs to ms timescale. These studies have en- abled detailed understanding of protein folding and conformational dynamics on a longer timescale than ever before. However, these long-scale simulations show inaccuracies in the physical models on which the force ﬁelds are based and inconsistencies with experimental data [69].

There are a number of systematic projects underway in many academic labs to refine the parametrization of various force fields. Each force field is parametrized with respect to experimental values and optimized for different systems. There are three pop- ular force fields that are commonly used in current molecular dynamics work. Below, I briefly discuss the major force field developments pertinent to my project and the key re-parametrizations and empirical comparisons for recent force fields. It is important to consider and validate different force fields, as they are parametrized in different ways and in reference to different empirical data. The same simulation system may adopt different conformational ensembles in different force fields. It is therefore useful to consider how each force field in my validation studies was parametrized. Chapter 2. Methods 18

OPLS force ﬁelds

The OPLS (Optimized Potentials for Liquid Simulations) force fields were first developed in the the early 1980s [70]. The potentials in this set of force fields were developed for simulating liquid state properties (initially water, but later more than 40 other organic liquids) [68]. The emphasis was on non bonded interactions and these were compared to liquid-state thermodynamics and optimizing charges and van der Waals parameters from simulations of pure liquids. The weights for each fitting point were based on the magnitudes of the potential-energy gradient. Quantum chemical data was used to evaluate the current OPLS-AA force field (backbone and side chain torsional parameters were refit to QM data) and the transferability of parameters was demonstrated using the same alanine dipeptide-fitted backbone torsional parameters for all other dipeptides (with appropriate side-chain refitting) and the alanine tetra-peptide. This re-parametrization of Coulombic charges and van der Waals interactions was validated by reproducing gas-phase energies of complex formation of heats of vaporization and densities of pure model liquids. [71, 72].

CHARMM force ﬁelds

The CHARMM (Chemistry at HARvard using Molecular Mechanics) force ﬁelds were also initially developed in the early 1980s [73]. Parametrization was initially achieved using model compounds such as form amide and N-methylacetamide and aimed to get balanced interactions between solute-water and water-water interactions. The Lennard- Jones parameters were reﬁned to reproduce densities and heats of vaporization of liquids.

CHARMM27 (CHARMM22/CMAP) This particular force field was developed by the MacKerell lab in 2004 [74]. In addition to the preceding versions of the CHARMM force field, MacKerell’s group performed additional parameter optimization via Monte Carlo simulated annealing. The potential energy function was extended to contain peptide backbone φ, ψ dihedral cross terms or φ, ψ grid-based energy correction terms. Empirical adjustments to grid-based corrections for alanine and glycine were applied to account for their systematic differences in the helical and sheet regions. Chapter 2. Methods 19

QM and MM calculations on alanine, glycine, and proline dipeptides were combined with MD simulations of proteins in crystal and aqueous environments. Monte Carlo simulated annealing was used to optimize parameters and MD simulations of seven proteins in crystalline environments were used to validate these parameters.

CHARMM36 The parametrization of the CHARMM36 force field involved a refinement of the backbone CMAP potential for non-Gly, non-Pro residues and compared to solution NMR data for weakly structured peptides [75]. This resulted in a force field that was intended to rebalance the α-helix and extended regions of the Ramachandran map, correcting the overwhelming helical bias seen in CHARMM22/CMAP.

Re-parametrization was performed using simulation of Ala3 and other short peptides, as well as replica exchange simulations of Ac-(AAQAA)3-NH2, solute tempering simulation of unfolded proteins in urea, and crystal structure simulations. Quantum mechanical calculations of glycine and proline dipeptides were performed and 2D CMAP potentials

3 were compared to NMR J scalar couplings (Ala5) and carbonyl chemical shifts (Ac-

(AAQAA)3-NH2) data to optimize backbone parameters - comparisons were made to calculated NMR chemical shifts and J couplings from SPARTA+ [76]. Additionally, unfolded ubiquitin and GB1, a 19-residue disordered fragment of hen lysozyme and dimeric coiled-coil 1U0I were used as test systems.

CHARMM22* This force field is based on the CHARMM22 force field [77]. The details of the re-parametrization can be found in [78], though generally CHARMM22* is CHARMM22 with newly modified backbone torsions potentials. The CMAP corrections were replaced with new backbone torsions terms for all residues, except proline and glycine. Partial charges for asparagine, glutamate, and arginine side chains were modified

to get a better description of salt-bridge interactions as well as χ1 and χ2 torsion terms for asparagine side chains as done for AMBER ﬀ99SB [79]. The backbone torsions parameters for non-proline, non-glycine residues were optimized to match the φ-ψ energy map of di-alanine and NMR data on polyalanine peptides in water. Additionally, simulations were conducted by the Shaw lab on the villin head- Chapter 2. Methods 20

piece and Cα RMSDs from PDB structures, the order of helix formation, and various kinetic and thermodynamics properties were evaluated. Of note is that simulations were conducted with CHARMM-modified TIP3P (a flexible water model)[80]. Each force field discussed has been parametrized with a different water model. A brief discussion of the different water models used is found in the following section.

AMBER force ﬁelds

The AMBER (Assisted Model Building and Energy Reﬁnement) force ﬁeld was initially developed in the early 1980s in Peter Kollman’s group [81, 82].

AMBER ff03w The AMBER ff03w force field [83] was re-parametrized from previous versions of the force field with small backbone modifications to match the population of helical states obtained with a new water model, a highly optimized TIP4P/2005, to experiment. Experimental data was used to re-parametrize the backbone dihedral potential correction for AMBER ff03* so that the fraction helix for the 15 residue peptide Ac-(AAQAA)3-NH2 was correctly reproduced in optimized TIP4P/2005 water. To compare to experiment, SPARTA was used to compute temperature-dependent carbonyl chemical shifts for the same peptide.

AMBER ff99sb*-ildn The Shaw lab optimized side-chain torsion potentials of the I, L, D, and N residues to parametrize AMBER ff99sb*-ildn [79]. The re-parametrization was done to match new quantum mechanical calculations. Millisecond scale molecular dynamics simulations were performed in explicit solvent to validate the resulting force field against experimental NMR measurements. Problematic residue types were identified by comparing the distribution of χ1 dihedrals in simulations of short helical peptides with statistics for residues in helices in the PDB. The water model used in this work was TIP3P or TIP4P-Ew (depending on the system used for validation). Chapter 2. Methods 21

Water models

Water models are used to simulate hydrogen bonding and aqueous solutions. These models are derived from quantum mechanical calculations and comparisons to experiments (as with the parametrization of any force field or computational model). The number of interaction points, the rigid or flexible nature of the model, and whether polarization effects characterize different types of water models. In the work that follows, I use a three-site water model (TIP3P) and a four-site water model (TIP4P), depending on the force field used. A three-site model is characterized by three interactions points, which correspond to the three atoms of a water molecule. The TIP3P model [84] is a three-site, rigid model, with a 104.5◦ HOH angle. This water model is rigid, implying that only non-bonded interactions are considered. That is, holonomic constraints, which are constraints on coordinates, are applied on all bonding interactions. The TIPS3P is a flexible version of this water model. TIP4P is a four-site water model, whereupon a dummy atom with a negative charge is used to improve the electrostatic distribution around the entire water molecule. The TIP4P/2005 model extended the TIP4P model to simulate the entire phase diagram of condensed water.

Periodic boundary conditions

Periodic boundary conditions (PBC) are used to approximate a much larger/inﬁnite system and minimize the artifacts from phase boundaries by replicating a unit cell, or simulation box, along its axes. Net neutrality of the system is important in order to avoid summing to an inﬁnite charge. Interactions between nearest neighbours are the only ones counted, so as to avoid duplication of interactions. Care must also be taken to ensure a large enough simulation box so that artifacts from unphysical interactions do not arise. For example, if the box is too small, a molecule can interact with its own image in a neighboring box. That is, the ’head’ of a molecule could ostensibly interact with its own ’tail’, leading to an unphysical interaction. Chapter 2. Methods 22

Temperature and pressure coupling

The canonical, or NVT, ensemble ensures conservation of the number of particles (N), volume (V), and temperature (T) in the system. The energy of endothermic and exother- mic processes are exchanged with a thermostat. Velocity rescaling considers the velocities at each step and rescales them so that the kinetic energy yields the target temperature. The Nosé-Hoover thermostat allows temperature fluctuation about an average value and this oscillation is minimized by the use of a damping factor that controls the oscillation. The canonical ensemble is produced with this thermostat [85, 86]. The Berendsen thermostat ensures fast equilibration by allowing exponential decay of temperatures to the target value [87]. The isothermal-isobaric, or NPT, ensemble ensures conservation of particles (N), pressure (P), and temperature (T). In addition to a thermostat, a barostat is required to con- serve pressure. This equilibration setup can be likened to an open flask equilibrated to ambient temperature and pressure. As with thermostats, there are a couple of schemes to pressure-couple the system to the environment. Depending on the type of integra- tion used (leap-frog and velocity Verlet are two common methods), the pressure coupling method will vary. For leap-frog, the Berendsen or Parinello-Rahman [88] barostats can be used, whereas the Martyna-Tuckerman-Tobias-Klein barostat [89] can be used in com- bination with the Nosé-Hoover thermostat for velocity Verlet integrated (a numerical method used to integrate Newton’s equations of motion) systems [90].

2.1.3 System setup

A significant part of my initial molecular dynamics simulations involved testing various force fields on my systems and comparing to experiments I performed on the same peptides. I needed a force field that would recapitulate the same average secondary structure I saw in my biophysical experiments. To this end, of the the force fields discussed above, I tested OPLS-AA with TIP4P (the initial force field I intended to use before I noticed significant deviations in secondary structure and dynamics in this force field as compared to experiment), CHARMM22* with TIPS3P, AMBER ff03w with TIP4P/2005, and AM- Chapter 2. Methods 23

BER ff99SB*-ILDN with TIP3P. Table 2.1 summarizes the four force fields tested in this work. All peptides were built in PyMOL and solvated in water as terminally capped peptides (N-terminal acetylation, C-terminal amidation). A cubic box was used with size and number of waters varying depending on the size of the system. Protonation states of lysines in the systems simulated were proposed to be that found at neutral pH. The system was equilibrated at 300K and 1atm for 100ps in the NVT ensemble with the velocity rescaled, modified Berendsen thermostat. Another 100ps of equilibration was performed in the NPT ensemble with the Berendsen thermostat and the Parinello-Rahman barostat. All bonds involving hydrogen atoms were constrained using a fourth-order LINCS algorithm. A 10A˚ cutoff was used for Lennard-Jones interactions and short-range electrostatic interactions. Electrostatic interactions were calculated using Particle Mesh Ewald (PME) summation fourth-order interpolation with a grid size of 0.16nm and pair lists were updated every 10fs with a 10nm cutoff. Covalent bonds on hydrogen atoms were constrained using the LINCS algorithm.

2.2 Biophysical techniques

2.2.1 Peptide synthesis

Peptides were synthesized by solid-phase Fmoc synthesis [91] using either PAL-PEG- PS resin or Fmoc-alanine-Rink amide-MBHA resin. Both of these resins are amidated, meaning the first, or C-terminal, residue coupled to the resin will be amidated at the C- terminus. Peptides were acetylated on the resin with 96:1:3 mixture of DMF:DIEA:acetic anhydride and cleaved with an 88:2:5:5 mixture of TFA:TIPS:phenol:water and ether precipitated. The peptides were lyophilized for storage and subsequent purification. The peptides were dissolved in 21% acetonitrile in water and purified by C18 reverse-phase HPLC in a 10% to 90% acetonitrile gradient. Peptide identity was confirmed by MALDI- TOF mass spectrometry. Chapter 2. Methods 24 ' ' lical'peptides based'he % & ' ' phase'energies'of'complex'heats'of' % chain'torsion'potentials'of'residues'that'differed' % coil'transition'in'alanine % Refinement/Validation Reproducing'gas vaporization'and'densities'of'pure'model'liquids Helix Optimized'side from'PDB'statistics Alpha/beta'balance ' ' ' & ' Origin Jorgensen' lab Best'lab Shaw'lab Shaw'lab ' Table 2.1: Summary of force fields and water models used. & ' ' ' Water& Model TIP4P TIP4P/2005 TIP3P TIP3P % ' ' & ' AA % ' Force&Field OPLS Amber'ff03w Amber'ff99SB* ILDN CHARMM22* ! Chapter 2. Methods 25

2.2.2 Peptide sample preparation

5-10mg of lyophilized peptide were solubilized in 200 µl of HFIP and sonicated for 10

minutes. HFIP was subsequently dried under N2(g) and peptide HFIP ﬁlms were solubilized in 500 µl water and sonicated for 10 minutes. All samples were centrifuged at 13000rpm and the top 400 µl of sample was extracted for peptide stocks. Peptide concentrations were determined using the Waddell method [92]. Equation 2.2 shows how the concentration of peptides was calculated based on a cuvette with a 1cm path length.

144µg/ml(A215 − A225) (2.2)

Peptide concentrations were measured on a nanophotometer (MBI Lab Equipment).

2.2.3 Circular dichroism

Peptide stocks, prepared as in section 2.2.2, were diluted to 50 µM for CD samples. Circular dichroism measurements were made using a JASCO J-810 spectropolarimeter in a 1.0-mm quartz cell. Single spectrum measurements were performed at 10C and temperature melts were performed starting at 10◦C and melting at 1◦C/min until 80◦C with a Jasco PFD-425S Peltier temperature controller. Measurements were performed at a scan rate of 1nm/sec in 0.1nm steps. Each reported spectrum is the mean of three stepwise scans between 250nm and 190nm averaged for 1s at each wavelength.

2.2.4 Partitioning and analytical RP-HPLC

Partitioning experiments in vitro [93, 94] were performed in 1.5ml glass vials. Peptide stocks (made as described in section 2.2.2) were diluted to 50 µM in 750 µl. This aqueous peptide sample was added to glass vials and 750 µl of the hydrophobic solvent (either octane or octanol) was added on top. Vials were capped and inverted a few times and then placed on a nutator overnight for equilibration. Phases were separated by extracting 650 µl of the top layer (the hydrophobic phase), 650 µl of the bottom layer (the aqueous phase) and the remaining 200 µl was considered the interface. Quantitative HPLC was performed with a Waters HPLC system whereby 200 µl of each phase were injected into Chapter 2. Methods 26 a 200 µl loop and run on an Xbridge BEH130 C18 analytical column equilibrated with 10% acetonitrile in water. All phases were run on a 10% to 90% acetonitrile gradient. All octane samples were dried down after separating the three phases and re-solubilized in 400 µl water. Peptide was quantitated by integrating the area under the peptide peak and areas were normalized by volume injected relative to volume in the initial phase. The interface was assumed to be of negligible volume. If A, I, and H are the normalized aqueous, interface, and hydrophobic peak areas and a, i, and h are the raw integrated peak areas, the following equations show how normalized areas were calculated for octanol samples:

750 A = a 200 750 H = h 200 100 100 I = i-(A 750 +H 750 ) The equations below show how normalized areas were calculated for octane samples:

400 A = a 200 400 H = h 200 400 100 100 I = i 200 -(A 750 +H 750 )

2.2.5 NMR

I performed a series of Correlation Spectroscopy (COSY), Total Correlated Spectroscopy (TOCSY), Heteronuclear Single Quantum Coherence (HSQC), and HSQC-TOCSY experiments on the A2, A3, and A16 peptides at temperates at, below, and above their melting temperatures. A COSY experiment allows identiﬁcation of spins that are coupled to each other. TOCSY spectra show through bond correlations through spin-spin coupling. Both the COSY and TOCSY experiments are homonuclear. A 1H-13C HSQC experiment yields correlations between aliphatic carbons and their attached protons. All the unique protons attached to the heteronucleus considered, in this case 13C, are seen. This allows us to track the chemical shifts of various atoms (or types of atoms) in the peptide as a function of position and temperature. Chapter 2. Methods 27

Solution NMR samples were prepared by dissolving lyophilized peptides in HFIP and drying as described previously. Samples were run in a Bruker Avance III spectrometer with a 1H frequency of 600MHz. Samples were approximately 500µM peptide, 20mM sodium phosphate buﬀer, and 10% D2O. Temperature was controlled using a variable temperature unit and a high ﬂow rate of dry air for below room temperatures. All samples were run in a 5mm PATXI 1H/D-13C/15N Z-GRD probe. Binomial water suppression was applied to all pulse sequences and isotropic mixing was achieved using a DIPSI sequence in the TOCSY and HSQC-TOCSY experiments. Mixing times were 0.08 seconds for all TOCSY experiments and 0.06 seconds for all HSQC-TOCSY experiments and the relaxation delay of 2 seconds was used for all experiments.

2.3 Data Analysis

Molecular dynamics simulations were all performed in GROMACS version 4.5.5 [95, 96] on the SciNet [97] or MP2 computing clusters using precompiled GROMACS on the cluster. Most analyses were performed using GROMACS analysis tools or using Python scripts written for that purpose in conjunction with Matplotlib for graphing analyzed data. NMR data was processed in NMRPipe [98] and subsequently analyzed in CCPNMR [99]. Chapter 3

Results

Molecular dynamics simulations yield information about low-population intermediate states and conformational dynamics that many other biophysical methods involving ensemble averaging cannot provide. Quantitative comparison of MD studies with experiments will give us insight into protein biophysics at the atomic level and give us the ability to devise more accurate force ﬁelds by using empirical evidence [58]. The following chapter delves into validation of a few recent force ﬁelds for the elastin cross-linking peptides studied by comparing molecular dynamics computational results to biophysical observables in vitro.

3.1 Choice of force ﬁeld

As summarized in the above Methods section, the choice of force field is an important consideration when performing biomolecular simulations. The peptide systems that I have simulated have the ability to sample a diverse set of conformations and, as such, a force field that best recapitulates these properties is optimal for our studies. The force fields OPLS-AA, CHARMM22*, AMBER ff99sb*-ildn, and AMBER ff03w were used to conduct simulations of the A2 peptide with an acetylated N-terminus and amidated

C-terminus as well as amidated and acetylated A18. The Methods section describes the techniques and simulation protocol used. Each system was run 100 times for a 100ns each run, with the monomeric peptide starting in the extended state. The ﬁrst 50ns was

28 Chapter 3. Results 29

discarded before analysis. The cross-linking peptides have historically been hypothesized to be α-helical. This is because this would facilitate cross-linking by bringing the lysine residues of one cross- linking domains onto the same helical face [40]. We thus performed molecular dynamics simulations and investigated the secondary structure of the peptides. More specifically, we measured the extent of α-helicity over the course of the simulation. To compare the conformational space explored by the peptides, we plotted the potentials of mean force (PMFs) of all the backbone dihedral angles in the peptide for each of the four force fields tested. Figure 3.1 shows the results of this analysis. The predominant energetic basin in CHARMM22* is in the α-helical region of the Ramachandran plot. The OPLS and AMBER ff99sb*-ildn force fields have a much more extended basin in the same part of the plot, extending past the canonical α-helical bounds (roughly φ and ψ angles of -64 +/- 7, -41 +/- 7). The ff03w force field has a significant basin in the polyproline/β-sheet region (roughly φ and ψ of -120, +120). Additionally, I used the dictionary of protein secondary structure (DSSP) [100] in- cluded in Gromacs as an analysis tool to compute secondary structure. DSSP identifies intra-backbone hydrogen bonds with a purely electrostatic definition, where a +0.20e partial charge is assigned to amide hydrogens, -0.42e to carbonyl oxygens and the op- posites to the amide nitrogens and carbonyl carbons respectively. A hydrogen bond is defined as an energy E less than -0.5 kcal/mol where

1 1 1 1 E = 0.084[ + − − ] · 332kcal/mol (3.1) rON rCH rOH rCN The computed fraction of α-helix by DSSP are plotted in Figure 3.2 for the acetylated and amidated A18 peptide as well as the A2 peptide from my set of model peptides. The graphs show the fractional amount of helix as a function of time. DSSP calculated values yield significant amount of helix in the CHARMM22* force field, which increases over the course of the simulations since the peptides start from an extended state in the monomer simulations. Comparatively, the other force fields do not yield very helical peptides at all, with significantly less than 15% helix in the AMBER ff03w, ff99sb*-ildn, and OPLS-AA force fields. Chapter 3. Results 30

Figure 3.1: Potentials of mean force of the φ and ψ angles of the A2 peptide (over all residues) for the four force ﬁelds tested. The PMF is given by W(φ,ψ) = -RTlnρ(φ,ψ), where R is the gas constant (8.3145 JK−1mol−1), T is the temperature in Kelvin (K), and ρ(φ,ψ) is the probability distribution of φ and ψ. Chapter 3. Results 31

Figure 3.2: Time evolution of the average fraction of α-helix in (a) A18 and (b)

A7KAAKA7. Fraction helix was calculated using DSSP over all residues in the peptide. Chapter 3. Results 32

Since CHARMM22* produces the most α-helix, I investigated this force field further. I wanted to see to what extent the other secondary structures were formed. The amount of β-sheet and β-turns formed were negligible, as seen in Figure 3.3. Essentially, the predominant secondary structure is α-helix. This is of note because the cross-linking domains have long been hypothesized to be α-helical and we know that the secondary structure of the cross linking domains in the coacervate is actually β-sheet [40]. Figure 3.4 shows the average helicity (using DSSP constraints) for the A2 and A18 peptides in the four force fields tested. As shown in the time trajectories above, the overall helicity is notably higher in CHARMM22* than for the other force fields. The goal of the force field validation was to obtain an amount of α-helix in simulation that was comparable to results obtained in biophysical experiments in vitro. The following section details the results from some of these experiments.

3.2 Spectroscopic characterization of the monomeric cross-linking domains

The analysis in the previous section focused on the secondary structure of the monomeric peptides in water. Comparing the above simulation results with in vitro spectroscopic secondary structure information yields one metric of determining force field quality - or at least a force field that will recapitulate the secondary structure properties seen in vitro. To this end, I performed a series of circular dichroism experiments to probe the secondary structure of the model peptides in various conditions. I first studied these peptides in TFE, a known helix stabilizer, to determine the effect of this solvent on the helicity of the model peptides. As expected, TFE increased helicity

in all the peptides tested, as seen in Figure 3.5. This is denoted by a more negative [θ]222. The A2Y and A3A peptides have a stronger random coil component in water (0% TFE) that disappears upon addition of TFE. However, ﬁtting the melting curves shown on the lefthand panels in Figure 3.5 proved diﬃcult since there is no folded baseline in the curves. We see that TFE stabilizes the Chapter 3. Results 33

Figure 3.3: Time evolution of the average fraction of α-helix, β-sheet and random coil in (a) A2 and (b) A16. Fraction of secondary structure was calculated using DSSP over all residues in the peptide. Chapter 3. Results 34

Figure 3.4: Average α-helix percentages in the A2 peptide for the four force ﬁelds tested. Fraction helix was calculated using DSSP over all residues in the peptide.

helix, but these peptides are never strongly helical and ‘fully folded’. The more ‘realistic’ cross-linking domains, A2Y and A3A, retain the same global properties as the other model peptides, except that they are overall less helical as monomers. Coacervation is promoted by an increase in salt concentration in vitro (among other factors). Therefore, I wanted to monitor the helicity of these peptides in increased sodium fluoride concentration. Although sodium chloride is ordinarily used to represent a physiological salt, circular dichroism experiments preclude the use of large concentrations of chloride because it absorbs strongly below 200nm. Anions such as sulphate or fluoride do not absorb significantly in this range [101] and allow us to increase the ionic concentration to levels used in vitro for coacervation. Figure 3.6 shows that an increase in [NaF] does not noticeably change the intensity of the α-helical minimum at 222nm. Previous simulation studies in the Pomès lab have indicated that the hydrophobic domains of elastin have a greater propensity to form β strands in methanol versus water [102]. A higher secondary structure propensity (namely, β-sheet) is seen for amyloido-

genic sequences like (GV)18, which is similar to the cross-linking poly(GA) sequences in Chapter 3. Results 35

Figure 3.5: Circular dichroism spectra of model peptides in (a,d) 0% TFE, (b,e) 20%TFE, and (c,f) 50%TFE. The left-hand panels show temperature melts following the ellipticity at 222nm as a function of temperature while the right-hand panels show far-UV CD spectra at 10 ◦C. Chapter 3. Results 36

Figure 3.6: Mean residue ellipticity at 222nm from CD in varying [NaF] for the A2, A3, A16, A2Y, and A3A peptides (∼ 66.5 µg/ml) in water and sodium ﬂuoride at 10 ◦C for each of the model peptides.

spider silks, than in elastomeric sequences. Methanol is a poorer solvent of the peptide backbone than water and I wanted to see if the cross-linking domains showed any diﬀerences in CD spectra upon altering the concentration of methanol in the sample. If methanol preferentially solvated the side chains relative to water, then marked diﬀerences in sidechain hydrophobicity would impact solvation. Figure 3.7 shows that the A2Y peptide has a strongly enhanced helicity upon an increase in the concentration of methanol. However, the A2Y peptide has a strong random coil component in its CD spectrum, so this drastic increase in the ellipticity at 222nm simply means that the conformational ensemble favoured is more helical at higher concentrations of methanol. Methanol has a smaller impact on the other two peptides tested, A2 and A3A, but helicity is still slightly increased upon an increase in methanol concentration. Chapter 3. Results 37

Figure 3.7: Mean residue ellipticity at 222nm from CD in varying [MeOH] for the A2, A2Y, and A3A peptides (∼ 66.5 µg/ml) in water and methanol at 10 ◦C.

3.3 Aggregative properties of the cross-linking domains - a simulation perspective

A large part of the data in the previous sections details the properties of the monomeric cross-linking domains. In simulations, this means one peptide solvated in a box of water. In biophysical experiments, one can consider the peptide to be monomeric if it is solubilized by water and not aggregating in solution. In order for cross-linking to occur, two cross-linking domains from different tropoelastin monomers must come together such that a desmosine or isodesmosine linkage can be formed. The monomer molecular dynamics simulations show the intrinsic properties of the peptides in an aqueous environment. However, studying the structure and dynamics of the peptides in the presence of each other better recapitulates cross-linking conditions. To this end, I performed simulations of both two peptides and four peptides in a box of water. We hypothesize that the cross-linking domains have a role in the assembly and order- Chapter 3. Results 38 ing of the elastin fiber. Having characterized these domains as monomers, we wanted to then investigate the aggregation of these domains. MD simulations were conducted for all six peptides in the lysine spacing table where two peptides were solvated in a box of water and run for 100ns starting from 100 different starting conformations (which were equilibrated conformations from monomer simulations of the same peptide). Figure 3.8 shows that, for all peptides, the two monomers come closer together as the simulation progresses.

Figure 3.8: Average peptide-peptide distance in dimer simulations. This was calculated by ﬁnding the distance between the centres of mass of each peptide chain in the simulation.

Figure 3.9 and Figure 3.10 encapsulate two macromolecular properties of the peptides over the course of the simulation: the end-to-end distance and the radius of gyration. Chapter 3. Results 39

The end-to-end distance is the straight-line distance between the ends of a polymer while the radius of gyration is the root-mean-square distance of the segments of a polymer from its centre of mass [103]. Both of these metrics provide a measure of peptide size. The end-to-end distance is largely unchanged, both in terms of average value and distribution, over all peptides. The A16 peptide has a slightly narrower distribution over all system sizes. This correlates with a peptide that has a tendency to be more helical than the rest, and perhaps more compact as a result. The radius of gyration, histograms of which are shown in figure 3.10, is roughly the same for all peptides as well. The average peak is around 1.0nm. The only anomaly is the higher peak/narrower distribution of the A16 dimer. The dashed lines in all panels show the normal distributions calculated from the mean and standard deviation of each data set. We can see that the distribution of the data in each case tends to have a similar shape on its right side to the normal distribution. Also, there is a shift of the maximum peak, which lies to the right of the normal distribution maximum in all cases. In general, the radius of gyration and end- to-end distance, indicators of overall peptide size and compactness, do not vary between peptides and over system size. Figure 3.11 shows the number of residues in the helical conformation over the course of the simulation for the monomer, dimer, and tetramer simulations. The peptides have no helical residues roughly 60% of the time. Additionally, there are no significant differences between the monomer, dimer, and tetramer systems for each model peptide. All peptides have a similar spread in the number of helical residues, tapering off at 16 helical residues. Overall, there are no major differences between peptides for the same system size (monomer, dimer, or tetramer). We don’t see significant differences in the amount of helicity between peptides or system size. We also don’t see any substantial differences in the types of contacts made between peptides and between peptide and water when comparing contacts over all sizes of systems over the entire set of replicas. However, there are a few qualitative observations that can be made for a few specific trajectories. Looking back at the helix histograms in figure 3.10, there is a smaller spread (standard deviation) and mean for the A16 dimer radius of gyration. As a result, we looked at specific trajectories from this system. A few Chapter 3. Results 40

Figure 3.9: Histograms of the end-to-end distance of the model peptides in the monomer, dimer, and tetramer simulations, calculated between the carbon of the N-terminal acetyl group and the oxygen of the C-terminal residue. The end-to-end distance during the last 50ns of the simulation (the equilibrated portion) was divided into 50 bins of the same size for each of the monomer, dimer, and tetramer systems for each of the six peptides. The dashed lines are normal distributions based on the mean and standard deviation calculated in each data set. Chapter 3. Results 41

Figure 3.10: Histograms of the radius of gyration of the model peptides in the monomer, dimer, and tetramer simulations. The radius of gyration during the last 50ns of the simulation (the equilibrated portion) was divided into 50 bins of the same size for each of the monomer, dimer, and tetramer systems for each of the six peptides. The dashed lines are normal distributions based on the mean and standard deviation calculated in each data set. Chapter 3. Results 42

Figure 3.11: Histograms of the probability of having 0 through 18 helical residues (DSSP calculations) in each of the peptides for the monomer, dimer, and tetramer systems. Chapter 3. Results 43 of the peptides were almost completely helical by the end of the trajectory but did not interact. A few formed sporadic peptide-peptide interactions (something that was seen in the other multimer simulations as well), but these interactions were transient and, moving through the simulation, we saw that these peptides then drifted away from each other at a later time point. Some simulations started out completely helical and retained most of this helicity while other were predominantly random coil but formed intermittent β-sheets. A small number formed peptide-peptide interactions. In one instance one of the peptides was largely helical throughout while the other chain sampled all secondary structures. Some inter-strand hydrogen bonding occurred. In certain cases, the peptides actually move apart over the course of the trajectory. In fact, all multimer simulations had very few instances of hydrogen-bonded peptides for large portions of the trajectory. Additionally, helicity within each peptide, if helix was indeed formed, was transient. These trends were also observed in the other systems studied. Although each replica had slightly different behaviour, the entire ensemble of replicas for each system showed no statistically significant differences in bonding and interaction when compared to the other systems, both between peptides and between monomer, dimer, and tetramer simulations. The macromolecular polymer properties of radius of gyration and end-to-end distance plateaued after 50ns of simulation and this, along with block-averaging, was used to determine that the simulations had converged. One question that arose over the course of my studies was whether the secondary structure was correlated to these properties. That is, is the overall size of the monomers correlated to helicity and is this modulated by interactions between peptides in the multimer simulations? Figure 3.12 shows the evolution of the radius of gyration over time for a single replica of the A0 peptide. Snapshots of the peptide conformation, with helix highlighted in magenta, are shown for the 10ns, 20ns, 30ns, 50ns, 80ns, 90ns, and 100ns time points.

In this replica, more extended conformations (larger Rg) are largely random coil while more helical conformations have a smaller radius of gyration. In the dimer simulations, we studied whether peptide-peptide interactions played a role in modulating the radius of gyration and/or end-to-end distances as well as secondary structure. As with the monomer simulations, the peptides are largely random coil. As Chapter 3. Results 44

Figure 3.12: Time evolution of the radius of gyration of A8KKA8 in CHARMM22*. The graph shows the radius of gyration of the peptide monomer at a given point in time. Selected snapshots of the peptide showing secondary structure are shown along with the timepoint at which the snapshot was taken. The radius of gyration for the entire production run (after NVT and NPT equilibration) is depicted in the plot. Chapter 3. Results 45 seen in ﬁgure 3.13, the two peptides each have a slightly diﬀerent radius of gyration but increase and decrease at similar times by the same amount on average. The peptides also form similar amounts of helix at the same time, though this could also be attributed to the fact that both peptides begin from the same starting conformation (albeit far apart). The question here is whether interactions between peptides causes both peptides to adopt the same structure and if helix is preferentially formed when there are contacts. To answer this question, a closer look at the number and type of contacts formed between peptides (if they are formed at all) is important.

0ns 20ns 40ns 60ns 80ns 100ns

Figure 3.13: Time evolution of the radius of gyration of A8KKA8 in CHARMM22*. The blue plot is the Rg of one peptide chain in one replica of the dimer simulation, shown above the graph is a snapshot of the peptide every 20ns over the course of the simulation with secondary structure highlighted. The green plot is the Rg of the other peptide chain, with representative snapshots shown below the graph. Chapter 3. Results 46

We first investigated if the peptides in the multimer simulations interact at all. Figure 3.14 shows contact maps for six different replicas in the A0 dimer simulations. Each map shows all interpeptide and intrapeptide interactions between non polar atoms of each residue in the two peptides in each simulation. Figure 3.15 shows the structure, with secondary structure elements highlighted in different colours, for each peptide and shows the distance of approach between peptides in each replica. We see, for example, that replica 12 (top right panel of figure 3.14) has many i,i+4 contacts within peptide 1. These i+4 contacts suggest that the peptide is forming a significant amount of helix. Furthermore, peptide 2 is forming a diffuse array of contacts, suggesting a largely random coil peptide 2 in this replica. Additionally, the top left and bottom right quadrants of this contact map are mostly black. These quadrants depict intermolecular contacts and thus show that the two peptides hardly interact over the course of the simulation in this replica. Looking at figure 3.15, which shows a snapshot of the simulation at the 100ns time point, we see these descriptions of the system hold true. Replica 83 (bottom right panel of figure 3.14) shows a lot of i,i+4 contacts in the off diagonal quadrants and also a lot of intermolecular contacts in the on diagonal quadrants. This shows two peptides that form helix down nearly their entire length that interact. Other maps show helix-turn-helix motifs and a diverse, heterogeneous, array of intermolecular interactions. Figure 3.17 shows snapshots of the six A0 tetramer replicas shown in figure 3.16. We see that replicas 2, 41, and 47 have formed helical bundles. This is seen by the extended i,i+4 contacts as well as a scattered, nonadjacent, pattern of intermolecular contacts, showing that the peptides interact via specific faces of the helix, essentially disallowing adjacent residues from forming contacts with the same residue (as they will be positioned on different faces of the helix). The contact maps have been averaged over all six two-peptide combinations to yield two-peptide by two-peptide contact maps. As previously discussed for the monomer simulations, the dimer and tetramer simulations also show a lot of heterogeneity in the type of contacts they form. Although clear patterns in inter- and intramolecular contacts are seen in individual replicas, fig- Chapter 3. Results 47

Figure 3.14: Sample contact maps for six replicas of the A0 dimer system showing intermolecular and intramolecular contacts between nonpolar atoms of all residues. The top left and bottom right quadrants identically show intermolecular contacts between peptides 1 and 2. The top right and bottom left quadrants show intramolecular contacts for peptide 2 and 1 respectively. Chapter 3. Results 48

Figure 3.15: Snapshots at 100ns of six A0 dimer replicas. These replicas are the same as those shown in ﬁgure 3.14. Chapter 3. Results 49

Figure 3.16: Sample contact maps for six replicas of the A0 tetramer system showing intermolecular and intramolecular contacts between nonpolar atoms of all residues. The top left and bottom right quadrants identically show intermolecular contacts between peptides 1 and 2 (averaged over all six possible pairs of peptides). The top right and bottom left quadrants show intramolecular contacts for peptide 2 and 1 respectively (again averaged over all six pairs). Chapter 3. Results 50

Figure 3.17: Snapshots at 100ns of six A0 tetramer replicas. These replicas are the same as those shown in figure 3.16. Chapter 3. Results 51 ures 3.18 and 3.19 show that averaging the contacts over all replicas washes out specific interactions. In fact, the only average interactions seen with large propensity are the i+1 interactions as well as i+2 and i+3 turns. A small proportion of i,i+4 turns, denoting helical contacts, are also seen. Otherwise, all intermolecular contacts and all other intramolecular contacts are of lower propensity. This underscores the structural heterogeneity of the cross-linking peptides and shows that there is no preferential arrangement or aggregation state. However, we do see a significant amount of helical structure in these simulations and also see interpeptide interactions. We know from previous work in our labs that the cross- linking peptides are helical in the cross-linked state and perhaps their helicity pushes the peptides to interact or vice versa. Namely, is interaction between chains correlated to helicity? Figure 3.20 shows average fraction helix formed using DSSP calculations for the monomer, dimer, and tetramer simulations for all six lysine-spacing model peptides. On average, the systems with two or four peptides formed more helix, with only the A16 peptide forming significantly more helix in the dimer and tetramer simulations relative to the monomer case. Additionally, figure 3.21 shows a distribution of the distance between the centers of mass (COM) of all pairs of peptides. Panel a shows the distribution for the dimer systems, with two clear distances of approach seen by the two peaks in the distribution. The first peak is also seen in panel b for the tetramer systems. The second peak is a more diffuse plateau, which makes sense because there are many more conformations that can be sampled in the comparatively larger tetramer system. Additionally, only the first peak is indicative of any interactions between peptides. We see two distances of approach in the COM plots (figure 3.21) as well as an increase in helix in the multimer simulations. Figures 3.22 and 3.23 show the fraction helix formed on average for each peptide, divided into two cases: when the peptides are forming intermolecular contacts and when they are not. When the peptide is helical, it is more frequently not in contact with another peptide (greater than factor of 2 in most cases for dimer, still significantly more for the tetramer). However, considering the fraction of time they are in contact, they are largely helical Chapter 3. Results 52

Figure 3.18: Dimer contact maps for the six model peptides averaged over all replicas. Intramolecular contacts are shown above the diagonal and intermolecular contacts are shown below the diagonal for the(a) A0, (b) A1, (c) A2, (d)A3, (e) A4, and (f) A16 peptides. Chapter 3. Results 53

Figure 3.19: Tetramer contact maps for the six model peptides averaged over all replicas. Intramolecular contacts are shown above the diagonal and intermolecular contacts are shown below the diagonal for the(a) A0, (b) A1, (c) A2, (d)A3, (e) A4, and (f) A16 peptides. Chapter 3. Results 54

Figure 3.20: Comparison of total fraction helix formed by all peptides over the 1, 2, and 4 peptide simulation systems. Fraction helix was computed using DSSP and normalized by the total number of frames.

Figure 3.21: Histograms of pairwise distances between the centers of mass of all peptides in the (a) dimer and (b) tetramer systems. Chapter 3. Results 55

Figure 3.22: Average fraction helicity for dimer simulations of all peptides with and without formation intermolecular contacts. The dark grey bar denotes fraction helix formed by the peptides when they are making intermolecular contacts with other peptides, the hatched bar denotes fraction helix formed by the peptides when they are not making any contacts with other peptides. Chapter 3. Results 56

Figure 3.23: Average fraction helicity for tetramer simulations of all peptides with and without formation intermolecular contacts. The dark grey bar denotes fraction helix formed by the peptides when they are making intermolecular contacts with other peptides, the hatched bar denotes fraction helix formed by the peptides when they are not making any contacts with other peptides. Chapter 3. Results 57 when they are in contact with another peptide (results not graphed). That is to say, the peptides in contact are helical but helicity does not imply they are in contact. However, we do see from the previous figure 3.20 that the multimeric peptides form more overall helix, suggesting that increasing peptide concentration in the box yields more helix, regardless of the fraction of time they spend in contact (since the difference in helicity between system sizes when the peptides are in contact is negligible). The monomer simulations were conducted in a 6 nm x 6 nm x 6 nm box, yielding a total volume of 216nm3 available to one peptide. The dimer box also had the same dimensions, thus crowding each peptide to an available volume of 108nm3. The tetramer simulations were conducted in a 12 nm x 6 nm x 6 nm box, also yielding 108nm3 in volume per peptide. Thus the increased helicity in the multimer simulations could be due to a crowding of their environment, as they have two-fold less space available per peptide, even if they are not in contact. There is enough steric hindrance and charge repulsion between lysines to prevent long-lasting contact formation but molecular crowding could cause the peptides to form more compact structures and, if these structures are compact enough, they have backbone hydrogen bonds that need to be fulfilled amongst themselves due to exclusion of water. These bonds are most easily fulfilled by a helical structure. We can conclude that in the dimer and tetramer simulations, both secondary structure and peptide-peptide associations (not necessarily hydrogen bonding) are transient. Per- haps if there is any ordering in the domains, it requires the entire tropoelastin monomer. A study of tropoelastin by Baldock and coworkers [104] used small angle X-ray scattering (SAXS) and neutron scattering and presented a head-to-tail model of tropoelastin assembly, where propagation of the fiber occurred through a stacked spring design. This type of ordered alignment and assembly may only occur when the cross-linking domains are placed in the context of the entire tropoelastin molecule. Previous studies from our labs have shown that the hydrophobic domains lack a defined order on their own as well [21]. Additionally, tropoelastin is notoriously difficult to work with in vitro, leading to study of simplified domains that are chemically synthesized or expressed recombinantly in E. coli. We may thus not see the possible ordered alignment of tropoelastin monomers by studying either the cross-linking or hydrophobic domains in isolation or together but Chapter 3. Results 58 as simplified constructs. This motivates the development of methods that would allow the study of tropoelastin in greater resolution.

3.4 Tying biophysical results back to simulation

Molecular dynamics simulations give us atomistic level detail but comparison to circular dichroism spectra, which only yields average secondary structure of the peptide, is diﬃ- cult. The model peptides used in my work have highly repetitive sequences, which yields high signal to noise in NMR experiments. The peptides are small and soluble enough for study in solution NMR experiments, which are able to resolve diﬀerences between populations of lysines and alanines in the peptides.

3.4.1 Solution NMR of the model peptides

NMR samples were prepared as described in section 2.2.5. The A2, A3, and A16 peptides were used to create three different samples. COSY, TOCSY, HSQC, and HSQC-TOCSY spectra were acquired for each peptide starting at 7◦C, followed by 15◦C, and then 37◦C. The samples were stable for 1-2 weeks, allowing data collection for all experiments. Figure 3.24 shows the HSQC and TOCSY spectra for the A2 peptide at 15◦C. The peaks for various Cα and Cβ atoms are shown and labelled on both spectra. As seen in the HSQC, the alanine Cα peaks overlap in the same general area but do not populate one homogeneous state. They appear to populate two major states, one centered further upfield in the proton dimension. Also of note is that the two lysines, although centered in the model peptide sequence, have different chemical shifts, denoting that they are in slightly different environments. The HSQC and TOCSY spectra allow us to distinguish the different populations of alanine and lysine residues in the peptide and thus allow comparison between peptides and across temperatures. The secondary structure and dihedral angles of the residues in a peptide can be determined by their chemical shifts. One way to determine the secondary structure of a particular residue is to calculate the secondary chemical shift. Equation 3.2 shows that the calculation to determine the secondary chemical shift. δobserved is the chemical shift of Chapter 3. Results 59

Figure 3.24: Solution NMR on unlabelled A2 peptide. The top panel shows a 1H-13C HSQC with the carbon chemical shifts labelled according to atoms in the peptide. The bottom panel shows the correlated peaks from a TOCSY spectra collected on the same sample. Chapter 3. Results 60

the peak observed in the spectrum, and δrandom coil is the published random coil chemical shift value. Wishart et al. [105] have published random coil chemical shift values for C=O, Cα,Cβ, NH, Hα, and Hβ for the 20 amino acids.

∆δ = δobserved - δrandom coil (3.2)

In α-helices, Cα atoms will have positive secondary chemical shifts while Cβ atoms will have negative secondary chemical shifts. The opposite holds for β-strands (i.e. Cα atoms will have negative secondary chemical shifts while Cβ atoms will have positive secondary chemical shifts). The Cα atoms are most strongly correlated to secondary structure of all the types of atoms that can be observed in an experiment. Figure 3.25a shows the secondary chemical shifts for the A2, A3, and A16 peptides at 15◦C. As denoted by the positive secondary chemical shifts for the Cα residues and the negative secondary chemical shifts for the Cβ residues, they are all shifted towards helix, with two populations of roughly equally helical lysine Cαs in A2, only one population of lysine Cα in A3, and two populations of lysine Cα, one of which is much more shifted to helix than the other, in A16. Overall, all residues are helical, and to roughly the same degree over all peptides. In ﬁgure 3.25b, we see a decrease in the magnitude of the secondary chemical shift with increasing temperature for the A3 peptide, denoting loss of helix with increasing temperature, which corroborates our CD melt data (which shows gradual loss of helix, i.e. unfolding, with increasing temperature).

3.4.2 Circular dichroism spectra calculated helicity of the model peptides

Going back to the circular dichroism data, we can calculate percent helicity from the mean residue molar ellipticity at 222nm. It can be calculated as follows:

Fraction helix = −40, 000(1 − 2.5/n) (3.3)

where n is the number of amino acid residues in the peptide. Converting calculated fraction helix into percentages, we can compare the helicity Chapter 3. Results 61

Figure 3.25: Secondary chemical shifts of the Cα (red) and Cβ (blue) atoms for (a) the A2, A3, and A16 peptides at 15 ◦C and (b) the A3 peptide at three different temperatures. Positive Cα and negative Cβ secondary chemical shifts denote helical structure. Chapter 3. Results 62 calculated from CD experiments to what was calculated from DSSP analysis of the MD data. Figure 3.26 summarizes the comparison. We obtain significantly more amounts of helix in CD versus MD for all peptides (A2, A3, A16, A2Y, and A3A). However, if we look at the inset graph of figure 3.26, we see a linear relationship between the CD and MD percent helix, though MD underestimates helicity by a factor of 3 relative to CD (the slope of the graph is about 0.3).

Figure 3.26: Comparison of helicity calculated from molecular dynamics simulations (DSSP constraints) and circular dichroism (molar ellipticity at 222nm) for the model peptides.

Although some residue speciﬁc information can be obtained from NMR experiments, the degeneracy of the sequences of the model peptides does not yield more than general populations of the alanine and lysine residues present in the peptides. To this end, we can use molecular dynamics simulations, which give us atomistic level detail and insight Chapter 3. Results 63 into the system. Figure 3.27 shows the DSSP calculated secondary structure on a per residue basis, averaged over the equilibrated portion of the trajectory (the last 50ns) for the monomer simulations in CHARMM22*. We can see the the helicity is lowest at either end of the peptide, where the helix cannot be hydrogen bonded on both sides. helicity increases towards the middle of the peptide and dips slightly about the lysine residues. This dip is seen in most of the model peptides. This is most probably due to unfavourable helix elongation by the lysine residues, which are long, charged, and bulkier than alanine residues. Furthermore, the helicity is markedly less at the C-terminal end than at the N-terminal end (with the exception of the A1 peptide), which might be due to the high N-cap propensity of the acetyl group [43].

Figure 3.27: Average helicity per residue for all the model peptides in CHARMM22*. Helicity was calculated based on structure assignment to each residue from DSSP criteria. Chapter 3. Results 64

3.5 Biphasic systems as a way to model the coacervate

Coacervation is a process whereby an increase in temperature causes a phase separation of a solution into protein rich droplets and a surrounding, protein depleted, solution. It has already been shown that the hydrophobic domains on their own are able to coacervate. They form a partially-hydrated aggregate, as shown by molecular dynamics simulations [21, 106]. We wanted to probe the behaviour of the cross-linking domains at the interface of a coacervate droplet. To simulate this, we performed molecular dynamics simulations in the presence of an octane slab [107]. Additionally, RP-HPLC was used to analyze partitioning experiments in vitro. We hypothesized that the cross-linking domains partition at the interface of a coacervate droplet and, due to positively charged lysine residues, do not insert into the droplets. Rather, we predicted that they would have the ability to order at the surface of a droplet and drive subsequent cross-linking, as cross-linking domains need to come together in some way so that lysyl oxidase can convert lysines to allysines and the spontaneous condensation reaction can happen immediately. The reaction would be most specific and efficient if the cross-linking domains were already near each other after coacervation in preparation for cross-linking. The overarching conclusions of this work were that the cross-linking domains do not partition at the interface. They neither move towards the octane slab by the end of simulation nor do they preferentially partition at or insert into the octane slab. Addi- tionally, HPLC results, shown in figure 3.28, show that peak areas for the interface and hydrophobic phases are negligible and that, with the exception of the A16 peptide in octane, the peptides do not partition into the hydrophobic phase. It is worth noting that the A16 peptide has the lysines at either end of the sequence, which is not representative of the lysine spacing and register in natural cross-linking domains (these lysines are found at the C-terminal end and spaced 2 or 3 residues apart). Essentially, the cross- linking domains in isolation (that is, without the hydrophobic domains) do not partition in a biphasic system. Further experiments will need to be performed on more complex systems involving both the hydrophobic and cross-linking domains. Chapter 3. Results 65

a b Octanol%Par22oning% Octane%Par00oning% 1.6% 1.6% 1.4% 1.4% 1.2% 1.2% 1% 1% 0.8% 0.8% Aqueous% Aqueous% 0.6% 0.6% Interface% Interface% 0.4% 0.4% Hydrophobic% Hydrophobic% 0.2% 0.2% Peak%Area%(A.U)% Peak%Area%(A.U)% 0% 0% A2% A3% A16% A2Y% A3A% A2% A3% A16% !0.2% !0.2% !0.4% !0.4% !0.6% !0.6%

Figure 3.28: Integrated peptide peak areas from RP-HPLC chromatograms for each phase in octane and octanol partitioning experiments. The peptide peak was integrated for each of the three phases run separately in triplicate on an analytical C18 column.

The hypothesis based on current results is that the cross-linking domains are ex- cluded by coacervation when the hydrophobic domains form the interior of the droplet. The hydrophobic environment is unfavourable for the positive residues in the cross-linking domains. They may be positioned, by exclusion, at the surface of the droplet and are oriented for subsequent cross-linking. We know from other studies that the sequence of the cross-linking domains has an effect on their aggregation properties and that lysine residues contribute to preventing aggregation [40]. My results have shown that the cross-linking domains are partially helix in solution as monomers. This helicity is melted below physiological temperatures. These results are corroborated in recent elastin literature [40]. Additionally, Reichheld and coworkers found that the cross-linking domains are β-sheet in the coacervate and predominantly helix in the cross-linked state. Thus modulation of secondary structure is a phenomenon of the state of assembly of the recombinant peptides. We can think of the cross-linking domains as floating on the coacervate droplet surface, rather than inserting into the droplet. They are heavily influenced by their environment but do not have an intrinsic ability to drive coacervation or self-order. Chapter 4

Discussion

The development of molecular mechanics force fields began 40 years ago [108] and the first molecular dynamics simulation of a protein was also conducted at this time. With advances in computational power, the sampling of the energy landscape becomes a lesser concern as longer simulations are being conducted and making it clear that accurate energy functions are required for reliable results. Quantum mechanical calculations are the most accurate way to computationally track atomic movement, but are also computationally costly. Molecular mechanics energy functions are much simpler and more computationally efficient but pre-existing force fields have conformational biases. Choos- ing an appropriate force field for a study is a challenge since different force fields have specific biases towards certain secondary structures. These biases are usually only evident with thorough validation against experimental data, which isn’t always possible [109] due to the lack of experimental data or difficulty in calculating experimental observables from simulation data [78] and the accuracy of the experimental observables themselves. Many force fields are parametrized for peptide and protein simulation studies, however these force fields all have different parametrization and have been validated against different sets of experimental data. As previously mentioned, four force fields were tested for their ability to recapitulate biophysical data for the alanine-rich peptides studied. Alanine-based peptides form varying levels of helix in solution. However, this helicity is in balance with the tendency for these peptides to sample a diverse set of random coil conformations. Thus the force field most suitable for my studies necessitates the right

66 Chapter 4. Discussion 67 balance of helix and random coil in my simulations. Recent developments in computer hardware and simulation methods have allowed simulations on the millisecond timescale [110]. Sampling representative conformations for peptides and small proteins is thus a lesser concern than the reliability and accuracy of force field parametrization. As computational power increases, the development of force field parameters that accurately recapitulate and predict biophysical properties becomes a more important topic of study. Re-parametrization of force fields is done most effectively when simulation data is compared to experimental results, such as NMR J-coupling constants and other biophysical data. Ultimately, the future use of data like what I have obtained over the course of my project can be used to further validate or re-parametrize force fields. Direct comparison between experimental and computational results on the same systems and similar metrics between techniques will enable a more thorough validation of simulation results. In my study, I used the same set of peptides in both simulation and in biophysical experiments, along with similar methods of calculating secondary structure. This led to direct comparison and validation of the best force field for the systems in my study. My work has shown that CHARMM22* leads to the largest amount of helix formation in the tested peptides. This compares most favourably to experimentally determined percent helicity from CD and NMR and also compares best to pre-existing literature results. Robert Best and coworkers [58] found anywhere from 15 to 30% helix in the Ala5 peptide and as high as 94% helix in the ff03w force field for Ac-(AAQAA)3-NH2. Many other peptide studies, as mentioned in Section 1.4, use various simulation and biophysical techniques to quantify the amount of helix in poly-alanine peptides. Ultimately, it is important to select a force field for the system of relevance that compares most favourably to experimental data for the same system. Consequently, the force field to use might be different for a stable membrane protein embedded in a lipid bilayer (where the study might involve rare ion channel events) versus a disordered peptide that forms very little secondary structure and needs to sample a great variety of conformations (such as elastin- like peptides). Force field validation is important for a number of reasons, including achieving the Chapter 4. Discussion 68 proper balance of secondary structure in a particular system, adequately sampling conformational space over the course of a simulation, and being able to compare and validate computational simulation results against experimental findings. In this context, Best and coworkers looked at residue specific α-helix propensities in alanine-based peptides [111] in comparison to experimental Lifson-Roig parameters. This yielded a picture of helix nucleation and propagation propensities for the 20 amino acids within alanine-based peptides for various AMBER force fields, including two force fields that I have tested - AMBER ff03w and AMBER ff99sb*-ildn. Temperature dependent Lifson-Roig parameters from experiment [112, 43] nearly overlapped for the AMBER ff99SB force field with backbone and ILDN side-chain corrections - that is, the AMBER ff99sb*-ildn force field. The agreement for ff03w was also very good. Another important consideration is the quality of the solvent model used with a particular force field. I have used different solvent models for each of the force fields I tested. Previous studies [113] looked at the stability and conformational ensemble of disordered tripeptides as a function of the solvent model used. Careful consideration of protein-solvent interactions is especially important for simulations of intrinsically disordered peptides and proteins, since the energy differences between different conformations of these systems is minimal and interactions with water are significant in these systems. It is precisely for this reason that I used the CHARMM-modified TIP3P water model for my simulations in CHARMM22*, as it is essential to match the solvent and the force field, as they are parametrized together and experimental comparisons are made with respect to this specific pairing of water model and force field. Additionally, comparison to experimental measurements yields a benchmark of force field accuracy. My studies looked at the comparative secondary structure (more specifically, helicity) of my model peptides in silico and in vitro. Other studies have pointed out the limitations of current force fields by comparing simulation data to NMR measurements [114] to benchmark current force fields and gain an idea of force field dependent dynamics and the accuracy of potential energy parameters in penta-peptide simulations [115]. The robustness of protein folding with respect to the force field (i.e is the path of folding the same) is also an important consideration [78] and transferability of force fields Chapter 4. Discussion 69

is important if simulations are used to elucidate folding mechanism [116]. In the context of these, and other, molecular dynamics simulation studies, force field validation is an important consideration for any system of study. Now that temporally longer and spa- cially larger simulations are being conducted, force field accuracy becomes the dominant concern as issues with convergence due to insufficient sampling become less important. The force field validation I performed is important in the context of simulation literature and other peptide studies. In this regard, not only are my model peptides representative cross-linking domains in terms of their sequence composition, but also a peptide set that can be studied within the framework of polyalanine peptide studies. Intrinsic α-helix and β-sheet preferences were studied by Cabellero and coworkers [117] and found to be different for different force fields. Polyalanine peptides have long been found to be α-helical [53, 43, 48] and lysines within these peptides decrease the amount of helix formed. This validates the work presented in my thesis regarding peptide helicity, as I find local decreases in helix about lysine residues and at the termini of each peptide, but find that helix is the dominant secondary structure overall. Similar comparisons between CD and simulation data have been made before [52] to determine the equilibrium structure and folding of helix-forming peptides. Investigation of the formation and stabilization of α-helices in short peptides is in itself a worthwhile study since the α-helix is a dominant secondary structure in proteins and small helical peptides are also finding a role in anti-microbial applications. How- ever, the context in which such peptides are found is also of note. My model peptides, specifically, have been designed to resemble elastin cross-linking domains. It is thus also useful to look at these peptides in the framework of elastin and elastin-based peptide studies. Conformational disorder is integral to elastin function, requiring a force field that achieves the right balance of structure and disorder in an elastin-based peptide [20]. Previous studies on elastin have shown that tropoelastin, the soluble monomer that cross-links and deposits into insoluble fibers to form mature elastin, has an α-helical component in CD measurements [65]. This reinforces the relevance of focusing on the cross-linking domains, since the hydrophobic domains are not α-helical in isolation and show significant structural disorder that is essential to their function in elastin [21]. Mu- Chapter 4. Discussion 70

tations in the hinge region of the cross-linking domain encoded by exons 21 and 23 of human tropoelastin affect the stability of the α-helix formed by this region and found to be important in the function of elastin [37, 118]. We know that short elastin-like polypeptides (ELPs) exhibit the same temperature-induced structural transitions as full-length elastin polymers. This motivates continued study of shorter, representative recombinant constructs that resemble full length elastin. The key structural features of the peptides are similar within the model set. The A2 and A3 peptides, both of which have lysine spacings found in natural elastin, adopt similar amounts of helix as the other model peptides. On their own, these peptides do not show significant difference in secondary structure or interactions. The only exception is the A16 peptide, which adopts significantly more helix in vitro. However, this is a more canonical polyalanine peptide with a longer, uninterrupted stretch of alanine residues and serves as a sequence composition control for our model peptide set. My results also suggest that these peptides do not partition or assemble in a meaningful way on their own. They are perhaps passive in the coacervation process and cross-linking after coacervation may occur simply by exclusion of the cross-linking domains from the interior of the coacervate droplet. Analogous to this hypothesis is the association of charged detergent micelles, where the association of hydrophobic tails brings charged headgroups in close proximity to one another despite coulombic repulsion between the like charges on these headgroups. The exclusion of cross-linking domains from hydrophobic solvents suggests that they are pushed to the surface of these droplets, increasing their local concentration and potentially aiding in cross-link formation. The following chapter proposes a few experiments to probe this hypothesis. The cross-linking domains aggregate in water without the hydrophobic domains and are preferentially helical in their aggregated state. Charge repulsion and steric hindrance between lysines lead to the lysines preferentially pointing away from each other on the outside of the helix dimer. Figure 4.1 shows lysines pointing away from packed helices in a snapshot of (a) an A0 dimer simulation, and (b) an A0 tetramer simulation. Fig- ure 4.2 shows this interaction, where (a) shows a sideview of interacting helices and (b) shows a top down view of the same helix dimer interaction. Figure 4.2 (b) also shows Chapter 4. Discussion 71 the hydrophobic helix interaction face (shaded). Favourable interaction between the hydrophobic faces is what drives association of these helices. Simulation results show similar helix packing but differences in the exact face of the helix forming the interaction (see figure 4.1). This rotational freedom in inter-helix interactions is indicated by the arrows in part (b). This same interaction face is hypothesized to be responsible for interaction with the surface of the coacervate droplet. Figure 4.2 (c) shows the proposed interaction of the aggregated cross-linking domains with the hydrophobic droplet interface, where the hydrophobic interface between the helices and the droplet is similar to the interface between helices in part (b).

Figure 4.1: Position of lysines in the helical aggregated multimer simulations. (a) Helical dimer formed in an A0 two peptide simulation, with lysines (green stick representation) pointing away from the hydrophobic helical interface, (b) Helical trimer formed in an A0 four peptide simulation, again with lysines (green stick representation) pointing away from the helix bundle.

We know that four lysines from two diﬀerent tropoelatin monomers must come together for cross-linking to occur. The model proposed in ﬁgure 4.2 is one hypothesis for how these lysines might come together in the early stages of coacervation. As yet unresolved is the alteration of secondary structure in the cross-linking domains in the coacervate, which is rich in β-sheet [40]. Our experimental data only considers the cross- Chapter 4. Discussion 72

Figure 4.2: Schematic of the proposed cross-linking mechanism in elastin. (a) Helix dimer formed between cross-linking peptides, with lysines pointing away from the hydrophobic helical interface, (b) top down view of a, showing the hydrophobic interaction face between helices and the dynamic nature of this face (arrows show the helices can rotate about their axis), and (c) proposed cross-linking domain interaction with the hydrophobic droplet interface - association between cross-linking domains is as in (b), but rotated such that the hydrophobic helical face is at the surface of the droplet. Chapter 4. Discussion 73 linking domains in isolation. Further experiments with the hydrophobic and cross-linking domains together will be required to probe what type of ordering occurs in the cross- linking domains at the surface of a coacervate droplet. Furthermore, simulation studies of the cross-linking peptides considered two or four peptides in the same box. In vivo, cross-linking in the extracellular matrix occurs in a more crowded environment and the local concentration of elastin is much higher prior to cross-linking. The conformational equilibrium of the cross-linking domains may switch to favour β-sheet or other more extended structures when a suﬃciently high concentration of cross-linking domains is achieved locally and minimization of steric and charge repulsions needs to occur. That being said, we have considered the early stages of aggregation of the cross- linking domains in our simulation results and have a working hypothesis for the assembly mechanism of elastin that will lend us insight for larger scale studies. Chapter 5

Future Directions

The model cross-linking peptides have been studied thoroughly in the monomer and aggregative states. RP-HPLC data and initial simulation data suggest that the cross-linking domains on their own, whether monomeric or somehow assembled, do not partition preferentially at the water-octane interface. We can extrapolate these results to say that they do not drive coacervation by partitioning preferentially to the outside of coacervate droplets. Once it has been conclusively determined whether these domains partition preferentially at the interface of a biphasic system, it is possible to devise experiments to determine whether the cross-linking domains are ordering at this interface. One method is to label the peptides with tryptophan and monitor whether their structure is affected by the presence of other cross-linking domains or a biphasic environment. This can be done by tracking tryptophan fluorescence and monitoring whether there is any quenching of these residues. Alternatively, fluorescence anisotropy measurements can be performed to determine mobility of these peptides. NMR PFG diffusion experiments can also be performed to track changes in peptide oligomeric state and possible aggregation and mobility changes. We can also spin label the lysines and gather more long range distance information than can be obtained by NMR by doing electron paramagnetic resonance (EPR) if we have access to a magnet and even probe partitioning with the use of appropriate paramagnetic compounds that partition into either phase. Eventually, we will want to study larger constructs involving the cross-linking domains

74 Chapter 5. Future Directions 75 in the context of the hydrophobic domains. Such studies have already been conducted by our lab in conjunction with the Keeley lab [40]. Essentially, we want to study the assembly of these larger constructs and cross-link them to get samples that mimic elastin- like aggregates. This will involve coarse-grained methods for simulation due to the size of these constructs. Molecular dynamics simulations are a valuable tool when performed at the atomistic scale, but require large computational resources when performed on large systems. To study these systems atomistically, the simplest system of relevance to study coacervation would be two hydrophobic domains linked by a cross-linking domain. This system can be used to probe whether the cross-linking domains can be forced to lie at the interface of a hydrophobic droplet if multimers of this construct are simulated in the same system. Coarse-graining, in the broadest sense, allows groups of atoms to be lumped together and re-parametrized, thus reducing the number of degrees of freedom and thus lowering computational cost. This enables greater sampling of conformational space for larger systems. One particular coarse-grained force field that can be used for the aforementioned studies is MARTINI [119, 120]. Additionally, coacervated and cross-linked constructs form insoluble aggregates that are not amenable to study by solution NMR. They can be studied by SSNMR of selectively and uniformly 13C/15N labelled constructs with magic angle spinning (MAS) to average orientation dependent parameters. A number of different experiments can be performed to assign chemical shifts and determine internuclear distances. Dihedral angle information can be obtained and compared to results from molecular dynamics simulations. Additionally, information about the mobility of specific residues can be obtained from these experiments. Bibliography

[1] S. Rauscher and R. Pom`es,“Structural disorder and protein elasticity,” in Fuzzi- ness, pp. 159–183, Springer, 2012.

[2] B. Bochicchio, A. Pepe, and A. M. Tamburro, “Circular dichroism studies on re- peating polypeptide sequences of abductin,” Chirality, vol. 17, no. 7, pp. 364–372, 2005.

[3] J. Gosline, M. Lillie, E. Carrington, P. Guerette, C. Ortlepp, and K. Savage, “Elas- tic proteins: biological roles and mechanical properties,” Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, vol. 357, no. 1418, pp. 121–132, 2002.

[4] J. M. Gosline, M. E. DeMont, and M. W. Denny, “The structure and properties of spider silk,” Endeavour, vol. 10, no. 1, pp. 37–43, 1986.

[5] M. B. van Eldijk, C. L. McGann, K. L. Kiick, and J. C. van Hest, “Elastomeric polypeptides,” in Peptide-Based Materials, pp. 71–116, Springer, 2012.

[6] B. Starcher and S. Percival, “Elastin turnover in the rat uterus.,” Connect. Tissue Res., vol. 13, no. 3, pp. 207–15, 1985.

[7] S. M. Mithieux and A. S. Weiss, “Elastin,” Advances in Protein Chemistry, vol. 70, pp. 437–461, 2005.

[8] F. W. Keeley, C. M. Bellingham, and K. A. Woodhouse, “Elastin as a self– organizing biomaterial: use of recombinantly expressed human elastin polypeptides

76 Bibliography 77

as a model for investigations of structure and self–assembly of elastin,” Philosoph- ical Transactions of the Royal Society of London. Series B: Biological Sciences, vol. 357, no. 1418, pp. 185–189, 2002.

[9] L. B. Sandberg, N. T. Soskel, and J. G. Leslie, “Elastin structure, biosynthesis, and relation to disease states.,” N. Engl. J. Med., vol. 304, pp. 566–79, Mar 1981.

[10] R. M. S. Richard A. Pierce, Thomas J. Mariani, “Elastin in lung development and disease,” Ciba Foundation Symposium 192 - The Molecular Biology and Pathology of Elastic Tissues, 1995.

[11] B. Levinson, J. Gitschier, C. Vulpe, S. Whitney, S. Yang, and S. Packman, “Are X-linked cutis laxa and Menkes disease allelic?,” Nat. Genet., vol. 3, p. 6, Jan 1993.

[12] S.-H. Lee, S. Goswami, A. Grudo, L.-Z. Song, V. Bandi, S. Goodnight-White, L. Green, J. Hacken-Bitar, J. Huh, F. Bakaeen, H. O. Coxson, S. Cogswell, C. Storness-Bliss, D. B. Corry, and F. Kheradmand, “Antielastin autoimmunity in tobacco smoking-induced emphysema.,” Nat. Med., vol. 13, pp. 567–9, May 2007.

[13] M. C. Zhang, M. Giro, D. Quaglino, and J. M. Davidson, “Transforming growth factor-beta reverses a posttranscriptional defect in elastin synthesis in a cutis laxa skin ﬁbroblast strain.,” J. Clin. Invest., vol. 95, pp. 986–94, Mar 1995.

[14] C. Vulpe, B. Levinson, S. Whitney, S. Packman, and J. Gitschier, “Isolation of a candidate gene for Menkes disease and evidence that it encodes a copper- transporting ATPase.,” Nat. Genet., vol. 3, pp. 7–13, Jan 1993.

[15] L. Bader, “Disorders of elastic tissue: a review.,” Pathology, vol. 5, pp. 269–89, Oct 1973.

[16] Y. Wu, J. A. MacKay, J. R. McDaniel, A. Chilkoti, and R. L. Clark, “Fabrica- tion of elastin-like polypeptide nanoparticles for drug delivery by electrospraying.,” Biomacromolecules, vol. 10, pp. 19–24, Jan 2009. Bibliography 78

[17] J. R. McDaniel, D. J. Callahan, and A. Chilkoti, “Drug delivery to solid tumors by elastin-like polypeptides.,” Adv. Drug Deliv. Rev., vol. 62, pp. 1456–67, Dec 2010.

[18] S. S. Amruthwar and A. V. Janorkar, “Preparation and characterization of elastin- like polypeptide scaﬀolds for local delivery of antibiotics and proteins.,” J Mater Sci Mater Med, vol. 23, pp. 2903–12, Dec 2012.

[19] M. S. Kim, D.-W. Lee, K. Park, S.-J. Park, E.-J. Choi, E. S. Park, and H. R. Kim, “Temperature-triggered tumor-speciﬁc delivery of anticancer agents by crgd- conjugated thermosensitive liposomes.,” Colloids Surf B Biointerfaces, vol. 116, pp. 17–25, Apr 2014.

[20] L. D. Muiznieks, A. S. Weiss, and F. W. Keeley, “Structural disorder and dynamics of elastin,” Biochemistry and Cell Biology, vol. 88, no. 2, pp. 239–250, 2010.

[21] S. Rauscher, S. Baud, M. Miao, F. W. Keeley, and R. Pom`es,“Proline and glycine control protein self-organization into elastomeric or amyloid ﬁbrils,” Structure, vol. 14, no. 11, pp. 1667–1676, 2006.

[22] T. P. J. Knowles, M. Vendruscolo, and C. M. Dobson, “The amyloid state and its association with protein misfolding diseases.,” Nat. Rev. Mol. Cell Biol., vol. 15, pp. 384–96, Jun 2014.

[23] M. Miao, C. M. Bellingham, R. J. Stahl, E. E. Sitarz, C. J. Lane, and F. W. Keeley, “Sequence and structure determinants for the self-aggregation of recombinant polypeptides modeled after human elastin,” Journal of Biological Chemistry, vol. 278, no. 49, pp. 48553–48562, 2003.

[24] B. Vrhovski and A. S. Weiss, “Biochemistry of tropoelastin,” European Journal of Biochemistry, vol. 258, no. 1, pp. 1–18, 1998.

[25] C. Bellingham, K. Woodhouse, P. Robson, S. Rothstein, and F. Keeley, “Self- aggregation characteristics of recombinantly expressed human elastin polypeptides,” Biochimica et Biophysica Acta (BBA)-Protein Structure and Molecular En- zymology, vol. 1550, no. 1, pp. 6–19, 2001. Bibliography 79

[26] G. C. Yeo, F. W. Keeley, and A. S. Weiss, “Coacervation of tropoelastin,” Advances in colloid and interface science, vol. 167, no. 1, pp. 94–103, 2011.

[27] M. Miao, S. E. Reichheld, L. D. Muiznieks, Y. Huang, and F. W. Keeley, “Elastin binding protein and FKBP65 modulate in vitro self-assembly of human tropoelastin.,” Biochemistry, vol. 52, pp. 7731–41, Nov 2013.

[28] G. Bratzel and M. J. Buehler, “Sequence-structure correlations in silk: Poly-ala repeat of N. clavipes MaSp1 is naturally optimized at a critical length scale,” Journal of the Mechanical Behavior of Biomedical Materials, vol. 7, pp. 30–40, 2012.

[29] A. Sethi, R. J. Wordinger, and A. F. Clark, “Focus on molecules: lysyl oxidase.,” Exp. Eye Res., Nov 2012.

[30] H. M. Kagan and W. Li, “Lysyl oxidase: properties, speciﬁcity, and biological roles inside and outside of the cell.,” J. Cell. Biochem., vol. 88, pp. 660–72, Mar 2003.

[31] J. E. Wagenseil and R. P. Mecham, “Vascular extracellular matrix and arterial mechanics.,” Physiol. Rev., vol. 89, pp. 957–89, Jul 2009.

[32] D. Torchia and K. Piez, “Mobility of elastin chains as determined by 13C nuclear magnetic resonance,” Journal of Molecular Biology, vol. 76, no. 3, pp. 419–424, 1973.

[33] J. Lyerla Jr and D. Torchia, “Molecular mobility and structure of elastin deduced from the solvent and temperature dependence of 13C magnetic resonance relaxation data,” Biochemistry, vol. 14, no. 23, pp. 5175–5183, 1975.

[34] A. Perry, M. P. Stypa, B. K. Tenn, and K. K. Kumashiro, “Solid-state 13C NMR re- veals eﬀects of temperature and hydration on elastin,” Biophysical Journal, vol. 82, no. 2, pp. 1086–1095, 2002.

[35] M. S. Pometun, E. Y. Chekmenev, and R. J. Wittebort, “Quantitative observation of backbone disorder in native elastin,” Journal of Biological Chemistry, vol. 279, no. 9, pp. 7982–7987, 2004. Bibliography 80

[36] X. Yao and M. Hong, “Structure distribution in an elastin-mimetic peptide (VPGVG)3 investigated by solid-state NMR,” Journal of the American Chemical Society, vol. 126, no. 13, pp. 4199–4210, 2004.

[37] M. Miao, J. T. Cirulis, S. Lee, and F. W. Keeley, “Structural determinants of cross- linking and hydrophobic domains for self-assembly of elastin-like polypeptides,” Biochemistry, vol. 44, no. 43, pp. 14367–14375, 2005.

[38] L. D. Muiznieks, S. A. Jensen, and A. S. Weiss, “Structural changes and facilitated association of tropoelastin,” Archives of Biochemistry and Biophysics, vol. 410, no. 2, pp. 317–323, 2003.

[39] A. H. Simmons, C. A. Michal, and L. W. Jelinski, “Molecular orientation and two-component nature of the crystalline fraction of spider dragline silk,” Science, vol. 271, no. 5245, pp. 84–87, 1996.

[40] S. Reichheld, L. Muiznieks, R. Stahl, K. Simonetti, S. Sharpe, and F. Keeley, “Conformational transitions of the cross-linking domains of elastin during self- assembly,” Journal of Biological Chemistry, vol. 289, no. 14, pp. 10057–10068, 2014.

[41] P. Y. Chou and G. D. Fasman, “Empirical predictions of protein conformation,” Annual Review of Biochemistry, vol. 47, no. 1, pp. 251–276, 1978.

[42] C. Nick Pace and J. Martin Scholtz, “A helix propensity scale based on experimental studies of peptides and proteins,” Biophysical Journal, vol. 75, no. 1, pp. 422– 427, 1998.

[43] A. Chakrabartty, T. Kortemme, and R. L. Baldwin, “Helix propensities of the amino acids measured in alanine-based peptides without helix-stabilizing side-chain interactions,” Protein Science, vol. 3, no. 5, pp. 843–852, 1994.

[44] R. Ingwall, H. Scheraga, N. Lotan, A. Berger, and E. Katchalski, “Conformational studies of poly-L-alanine in water,” Biopolymers, vol. 6, no. 3, pp. 331–368, 1968. Bibliography 81

[45] K. Platzer, V. Ananthanarayanan, R. Andreatta, and H. Scheraga, “Helix-coil stability constants for the naturally occurring amino acids in water. IV. Alanine parameters from random poly (hydroxypropylglutamine-co-L-alanine),” Macro- molecules, vol. 5, no. 2, pp. 177–187, 1972.

[46] S. E. Blondelle, B. Forood, R. A. Houghten, and E. P´erez-Pay´a,“Polyalanine-based peptides as models for self-associated β-pleated-sheet complexes,” Biochemistry, vol. 36, no. 27, pp. 8393–8400, 1997.

[47] V. Daggett, P. A. Kollman, and I. D. Kuntz, “A molecular dynamics simulation of polyalanine: an analysis of equilibrium motions and helix–coil transitions,” Biopoly- mers, vol. 31, no. 9, pp. 1115–1134, 1991.

[48] S.-S. Sung, “Folding simulations of alanine-based peptides with lysine residues,” Biophysical Journal, vol. 68, no. 3, pp. 826–834, 1995.

[49] A.-S. Yang, B. Honig, et al., “Free energy determinants of secondary structure formation: I. Alpha-helices.,” Journal of Molecular Biology, vol. 252, no. 3, p. 351, 1995.

[50] J. S. Miller, R. J. Kennedy, and D. Kemp, “Short, solubilized polyalanines are conformational chameleons: Exceptionally helical if N-and C-capped with helix stabilizers, weakly to moderately helical if capped with rigid spacers,” Biochemistry, vol. 40, no. 2, pp. 305–309, 2001.

[51] J. A. Vila, D. R. Ripoll, and H. Scheraga, “Physical reasons for the unusual α-helix stabilization aﬀorded by charged or neutral polar residues in alanine-rich peptides,” Proceedings of the National Academy of Sciences, vol. 97, no. 24, pp. 13075–13079, 2000.

[52] J. M. Stewart, J. C. Lin, and N. H. Andersen, “Lysine and arginine residues do not increase the helicity of alanine-rich peptide helices,” Chemical Communications, vol. 39, pp. 4765–4767, 2008. Bibliography 82

[53] S. Marqusee, V. H. Robbins, and R. L. Baldwin, “Unusually stable helix formation in short alanine-based peptides,” Proceedings of the National Academy of Sciences, vol. 86, no. 14, pp. 5286–5290, 1989.

[54] L. Zhong and W. Johnson, “Environment aﬀects amino acid preference for secondary structure,” Proceedings of the National Academy of Sciences of the United States of America, vol. 89, no. 10, pp. 4462–4465, 1992.

[55] D. Roccatano, M. Fioroni, M. Zacharias, and G. Colombo, “Eﬀect of hexaﬂuoroiso- propanol alcohol on the structure of melittin : A molecular dynamics simulation study,” Protein Science, vol. 14, no. 10, pp. 2582–2589, 2005.

[56] C. Huang, Z. Getahun, Y. Zhu, J. Klemke, W. DeGrado, and F. Gai, “Helix formation via conformation diﬀusion search,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 5, pp. 2788–2793, 2002.

[57] P. Thompson, V. Mu˜noz,G. Jas, E. Henry, W. Eaton, and J. Hofrichter, “The helix-coil kinetics of a heteropeptide,” Journal of Physical Chemistry B, vol. 104, no. 2, pp. 378–389, 2000.

[58] R. B. Best and G. Hummer, “Optimized Molecular Dynamics Force Fields Applied to the Helix-Coil Transition of Polypeptides,” The Journal of Physical Chemistry B, vol. 113, no. 26, pp. 9004–9015, 2009.

[59] G. Hummer, A. Garc´ıa,and S. Garde, “Conformational diﬀusion and helix formation kinetics,” Physical Review Letters, vol. 85, no. 12, pp. 2637–2640, 2000.

[60] A. E. Garc´ıaand K. Y. Sanbonmatsu, “α-helical stabilization by side chain shield- ing of backbone hydrogen bonds.,” Proc. Natl. Acad. Sci. U.S.A., vol. 99, pp. 2782– 7, Mar 2002.

[61] A. Pepe, D. Guerra, B. Bochicchio, D. Quaglino, D. Gheduzzi, I. Pasquali Ronchetti, and A. M. Tamburro, “Dissection of human tropoelastin: supramolecular organization of polypeptide sequences coded by particular exons.,” Matrix Biol., vol. 24, pp. 96–109, Apr 2005. Bibliography 83

[62] L. B. Sandberg, N. Weissman, and W. R. Gray, “Structural features of tropoelastin related to the sites of cross-links in aortic elastin,” Biochemistry, vol. 10, no. 1, pp. 52–56, 1971.

[63] D. P. Varadi, “Studies on the chemistry and ﬁne structure of elastic ﬁbers from normal adult skin,” Journal of Investigative Dermatology, vol. 59, no. 3, pp. 238– 246, 1972.

[64] W. R. Gray, “Molecular model for elastin structure and function,” Nature, vol. 246, pp. 461–466, 1973.

[65] A. M. Tamburro, A. Pepe, and B. Bochicchio, “Localizing α-helices in human tropoelastin: assembly of the elastin puzzle,” Biochemistry, vol. 45, no. 31, pp. 9518–9530, 2006.

[66] P. Toonkool, S. A. Jensen, A. L. Maxwell, and A. S. Weiss, “Hydrophobic domains of human tropoelastin interact in a context-dependent manner.,” J. Biol. Chem., vol. 276, pp. 44575–80, Nov 2001.

[67] L. Monticelli and D. P. Tieleman, “Force ﬁelds for classical molecular dynamics.,” Methods Mol. Biol., vol. 924, pp. 197–213, 2013.

[68] J. W. Ponder and D. A. Case, “Force ﬁelds for protein simulations.,” Adv. Protein Chem., vol. 66, pp. 27–85, 2003.

[69] S. Piana, J. L. Klepeis, and D. E. Shaw, “Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations,” Current opinion in structural biology, vol. 24, pp. 98–105, 2014.

[70] W. L. Jorgensen and J. Tirado-Rives, “The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin,” Journal of the American Chemical Society, vol. 110, no. 6, pp. 1657–1666, 1988. Bibliography 84

[71] G. A. Kaminski, R. A. Friesner, J. Tirado-Rives, and W. L. Jorgensen, “Evaluation and reparametrization of the OPLS-AA force ﬁeld for proteins via comparison with accurate quantum chemical calculations on peptides,” The Journal of Physical Chemistry B, vol. 105, no. 28, pp. 6474–6487, 2001.

[72] W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, “Development and testing of the OPLS all-atom force ﬁeld on conformational energetics and properties of organic liquids,” Journal of the American Chemical Society, vol. 118, no. 45, pp. 11225– 11236, 1996.

[73] B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus, “Charmm: A program for macromolecular energy, minimization, and dynamics calculations,” Journal of Computational Chemistry, vol. 4, no. 2, pp. 187–217, 1983.

[74] A. D. MacKerell, M. Feig, and C. L. Brooks, “Extending the treatment of backbone energetics in protein force ﬁelds: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations,” Journal of Computational Chemistry, vol. 25, no. 11, pp. 1400–1415, 2004.

[75] R. B. Best, X. Zhu, J. Shim, P. E. Lopes, J. Mittal, M. Feig, and A. D. MacK- erell Jr, “Optimization of the additive charmm all-atom protein force ﬁeld targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles,” Journal of Chemical Theory and Computation, vol. 8, no. 9, pp. 3257–3273, 2012.

[76] Y. Shen and A. Bax, “SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artiﬁcial neural network.,” J. Biomol. NMR, vol. 48, pp. 13–22, Sep 2010.

[77] A. D. MacKerell, D. Bashford, M. Bellott, R. L. Dunbrack, J. D. Evanseck, M. J. Field, S. Fischer, J. Gao, H. Guo, S. Ha, D. Joseph-McCarthy, L. Kuchnir, K. Kucz- era, F. T. Lau, C. Mattos, S. Michnick, T. Ngo, D. T. Nguyen, B. Prodhom, W. E. Reiher, B. Roux, M. Schlenkrich, J. C. Smith, R. Stote, J. Straub, M. Watanabe, Bibliography 85

J. Wi´orkiewicz-Kuczera, D. Yin, and M. Karplus, “All-atom empirical potential for molecular modeling and dynamics studies of proteins.,” J Phys Chem B, vol. 102, pp. 3586–616, Apr 1998.

[78] S. Piana, K. Lindorﬀ-Larsen, and D. E. Shaw, “How robust are protein folding simulations with respect to force ﬁeld parameterization?,” Biophysical Journal, vol. 100, no. 9, p. L47, 2011.

[79] K. Lindorff-Larsen, S. Piana, K. Palmo, P. Maragakis, J. L. Klepeis, R. O. Dror, and D. E. Shaw, “Improved side-chain torsion potentials for the Amber ff99SB protein force field,” Proteins: Structure, Function, and Bioinformatics, vol. 78, no. 8, pp. 1950–1958, 2010.

[80] A. D. MacKerell, J. Wiorkiewicz-Kuczera, and M. Karplus, “An all-atom empirical energy function for the simulation of nucleic acids,” Journal of the American Chemical Society, vol. 117, no. 48, pp. 11946–11975, 1995.

[81] S. J. Weiner, P. A. Kollman, D. A. Case, U. C. Singh, C. Ghio, G. Alagona, S. Profeta, and P. Weiner, “A new force ﬁeld for molecular mechanical simulation of nucleic acids and proteins,” Journal of the American Chemical Society, vol. 106, no. 3, pp. 765–784, 1984.

[82] W. Cornell, P. Cieplak, C. Bayly, I. Gould, K. Merz Jr, D. Ferguson, D. Spellmeyer, T. Fox, J. Caldwell, and P. Kollman, “A new force ﬁeld for molecular mechanical simulation of nucleic acids and proteins,” J. Am. Chem. Soc, vol. 117, pp. 5179– 5197, 1995.

[83] R. B. Best and J. Mittal, “Protein simulations with an optimized water model: cooperative helix formation and temperature-induced unfolded state collapse,” The Journal of Physical Chemistry B, vol. 114, no. 46, pp. 14916–14923, 2010.

[84] W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. W. Impey, and M. L. Klein, “Comparison of simple potential functions for simulating liquid water,” The Journal of Chemical Physics, vol. 79, no. 2, pp. 926–935, 1983. Bibliography 86

[85] S. Nos´e,“A molecular dynamics method for simulations in the canonical ensemble,” Molecular Physics, vol. 52, no. 2, pp. 255–268, 1984.

[86] W. G. Hoover, “Canonical dynamics: equilibrium phase-space distributions,” Phys- ical Review A, vol. 31, no. 3, pp. 1695–1697, 1985.

[87] H. J. Berendsen, J. P. M. Postma, W. F. van Gunsteren, A. DiNola, and J. Haak, “Molecular dynamics with coupling to an external bath,” The Journal of Chemical Physics, vol. 81, no. 8, pp. 3684–3690, 1984.

[88] M. Parrinello and A. Rahman, “Polymorphic transitions in single crystals: A new molecular dynamics method,” Journal of Applied Physics, vol. 52, no. 12, pp. 7182– 7190, 1981.

[89] G. J. Martyna, M. E. Tuckerman, D. J. Tobias, and M. L. Klein, “Explicit reversible integrators for extended systems dynamics,” Molecular Physics, vol. 87, no. 5, pp. 1117–1157, 1996.

[90] W. C. Swope, H. C. Andersen, P. H. Berens, and K. R. Wilson, “A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters,” The Journal of Chemical Physics, vol. 76, no. 1, pp. 637–649, 1982.

[91] E. Atherton and R. Sheppard, “Solid phase peptide synthesis,” 1989.

[92] W. J. Waddell, “A simple ultraviolet spectrophotometric method for the determi- nation of protein.,” J. Lab. Clin. Med., vol. 48, pp. 311–4, Aug 1956.

[93] W. C. Wimley and S. H. White, “Quantitation of electrostatic and hydrophobic membrane interactions by equilibrium dialysis and reverse-phase HPLC.,” Anal. Biochem., vol. 213, pp. 213–7, Sep 1993.

[94] W. C. Wimley, T. P. Creamer, and S. H. White, “Solvation energies of amino acid side chains and backbone in a family of host-guest pentapeptides.,” Biochemistry, vol. 35, pp. 5109–24, Apr 1996. Bibliography 87

[95] S. Pronk, S. P´all,R. Schulz, P. Larsson, P. Bjelkmar, R. Apostolov, M. R. Shirts, J. C. Smith, P. M. Kasson, D. van der Spoel, B. Hess, and E. Lindahl, “GRO- MACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit.,” Bioinformatics, vol. 29, pp. 845–54, Apr 2013.

[96] D. Van Der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark, and H. J. C. Berendsen, “GROMACS: fast, ﬂexible, and free.,” J Comput Chem, vol. 26, pp. 1701–18, Dec 2005.

[97] C. Loken, D. Gruner, L. Groer, R. Peltier, N. Bunn, M. Craig, T. Henriques, J. Dempsey, C.-H. Yu, J. Chen, L. J. Dursi, J. Chong, S. Northrup, J. Pinto, N. Knecht, and R. V. Zon, “SciNet: Lessons Learned from Building a Power- eﬃcient Top-20 System and Data Centre,” Journal of Physics: Conference Series, vol. 256, p. 012026, 2010.

[98] F. Delaglio, S. Grzesiek, G. W. Vuister, G. Zhu, J. Pfeifer, and A. Bax, “NMRPipe: a multidimensional spectral processing system based on UNIX pipes.,” J. Biomol. NMR, vol. 6, pp. 277–93, Nov 1995.

[99] W. F. Vranken, W. Boucher, T. J. Stevens, R. H. Fogh, A. Pajon, M. Llinas, E. L. Ulrich, J. L. Markley, J. Ionides, and E. D. Laue, “The CCPN data model for NMR spectroscopy: development of a software pipeline.,” Proteins, vol. 59, pp. 687–96, Jun 2005.

[100] W. Kabsch and C. Sander, “Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.,” Biopolymers, vol. 22, pp. 2577–637, Dec 1983.

[101] S. M. Kelly, T. J. Jess, and N. C. Price, “How to study proteins by circular dichroism.,” Biochim. Biophys. Acta, vol. 1751, pp. 119–39, Aug 2005.

[102] Z. Xue, “Understanding the molecular mechanism of elasticity in elastin from a solvation perspective,” Master’s thesis, University of Toronto, 2013.

[103] A. Rudin and P. Choi, “The elements of polymer science and engineering,” 2012. Bibliography 88

[104] C. Baldock, A. F. Oberhauser, L. Ma, D. Lammie, V. Siegler, S. M. Mithieux, Y. Tu, J. Y. H. Chow, F. Suleman, M. Malfois, S. Rogers, L. Guo, T. C. Irving, T. J. Wess, and A. S. Weiss, “Shape of tropoelastin, the highly extensible protein that controls human tissue elasticity.,” Proc. Natl. Acad. Sci. U.S.A., vol. 108, pp. 4322–7, Mar 2011.

[105] D. S. Wishart, C. G. Bigam, A. Holm, R. S. Hodges, and B. D. Sykes, “(1)H, (13)C and (15)N random coil NMR chemical shifts of the common amino acids. i. investigations of nearest-neighbor eﬀects.,” J. Biomol. NMR, vol. 5, pp. 67–81, Jan 1995.

[106] S. Rauscher, Protein Non-Folding: A Molecular Simulation Study of the Structure and Self-Aggregation of Elastin. PhD thesis, University of Toronto, 2012.

[107] A. Nikolic, S. Baud, S. Rauscher, and R. Pom`es,“Molecular mechanism of β-sheet self-organization at water-hydrophobic interfaces,” Proteins: Structure, Function, and Bioinformatics, vol. 79, no. 1, pp. 1–22, 2011.

[108] M. Bixon and S. Lifson, “Potential functions and conformations in cycloalkanes,” Tetrahedron, vol. 23, pp. 769–784, Jan 1967.

[109] E. A. Cino, W.-Y. Choy, and M. Karttunen, “Comparison of Secondary Structure Formation Using 10 Diﬀerent Force Fields in Microsecond Molecular Dynamics Simulations.,” J Chem Theory Comput, vol. 8, pp. 2725–2740, Aug 2012.

[110] K. Lindorﬀ-Larsen, P. Maragakis, S. Piana, M. P. Eastwood, R. O. Dror, and D. E. Shaw, “Systematic validation of protein force ﬁelds against experimental data,” PLoS One, vol. 7, no. 2, pp. 3787–3791, 2012.

[111] R. B. Best, D. de Sancho, and J. Mittal, “Residue-speciﬁc -helix propensities from molecular simulation,” Biophysical Journal, vol. 102, no. 6, pp. 1462–1467, 2012.

[112] R. J. Moreau, C. R. Schubert, K. A. Nasr, M. T¨or¨ok,J. S. Miller, R. J. Kennedy, and D. S. Kemp, “Context-independent, temperature-dependent helical propensi- Bibliography 89

ties for amino acid residues.,” J. Am. Chem. Soc., vol. 131, no. 36, pp. 13107–16, 2009.

[113] P. S. Nerenberg and T. Head-Gordon, “Optimizing Protein-Solvent Force Fields to Reproduce Intrinsic Conformational Preferences of Model Peptides,” Journal of Chemical Theory and Computation, vol. 7, no. 4, pp. 1220–1230, 2011.

[114] K. A. Beauchamp, Y.-S. Lin, R. Das, and V. S. Pande, “Are protein force ﬁelds getting better? A systematic benchmark on 524 diverse NMR measurements,” Journal of Chemical Theory and Computation, vol. 8, no. 4, pp. 1409–1414, 2012.

[115] W. A. Hegefeld, S.-E. Chen, K. Y. DeLeon, K. Kuczera, and G. S. Jas, “Helix formation in a pentapeptide: experiment and force-ﬁeld dependent dynamics.,” J Phys Chem A, vol. 114, pp. 12391–402, Dec 2010.

[116] J. Mittal and R. B. Best, “Tackling force-ﬁeld bias in protein folding simulations: folding of Villin HP35 and Pin WW domains in explicit water.,” Biophys. J., vol. 99, pp. L26–8, Aug 2010.

[117] D. Caballero, J. Määttä,A. Q. Zhou, M. Sammalkorpi, C. S. O’Hern, and L. Regan, “Intrinsic α-helical and β-sheet conformational preferences: a computational case study of alanine.,” Protein Science, vol. 23, pp. 970–80, Jul 2014.

[118] J. Djajamuliadi, T. F. Kagawa, K. Ohgo, and K. K. Kumashiro, “Insights into a putative hinge region in elastin using molecular dynamics simulations.,” Matrix Biol., vol. 28, pp. 92–100, Mar 2009.

[119] M. Seo, S. Rauscher, R. Pom`es,and D. P. Tieleman, “Improving Internal Peptide Dynamics in the Coarse-Grained MARTINI Model: Toward Large-Scale Simula- tions of Amyloid- and Elastin-like Peptides.,” J Chem Theory Comput, vol. 8, pp. 1774–1785, May 2012.

[120] J. Barnoud and L. Monticelli, “Coarse-grained force ﬁelds for molecular simulations.,” Methods Mol. Biol., vol. 1215, pp. 125–49, 2015.