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Scholarworks | University of Nevada, Reno University of Nevada, Reno Developing Novel Non-Natural Amino Acids as Spectroscopic Reporters of Structure for Peptide Systems A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Chemistry by Amy R. Cunningham Dr. Matthew J. Tucker/Thesis Advisor August 2018 University of Nevada, Reno The Graduate School We recommend that the thesis prepared under our supervision by AMY R. CUNNINGHAM entitled Developing Novel Non-Natural Amino Acids as Spectroscopic Reporters of Structure for Peptide Systems be accepted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Matthew J. Tucker, Ph.D., Advisor Laina Geary, Ph.D., Committee Member Ian Wallace, Ph.D., Graduate School Representative David W. Zeh, Ph.D., Dean, Graduate School August 2018 i Abstract Properly folded proteins are essential to living organisms. Mis-folded proteins can lead to some serious diseases such as Alzheimer’s disease, Lou Gehrig’s disease (ALS), and muscular dystrophy which affect many people. The key to potentially preventing such diseases lies in understanding how and why protein mis-folding occurs and determining ways to prevent it from happening in the first place. This thesis describes a possible method of determining how proteins fold in solution using vibrational probes, two types of infrared spectroscopy (Fourier Transform Infrared and two-dimensional infrared), and molecular dynamics simulations. With these three techniques, the angle, distance, and coupling constant between vibrational probes can be determined. These three quantities allow for the determination of orientation of the backbone of each peptide or protein as well as the orientation of the probes within the peptide or protein systems in solution. These studies lay the groundwork for technical advances in monitoring protein folding in solution on the femtosecond timescale. ii Table of Contents Abstract………………………………………………………………………………….....i Introduction………………………………………………………………………………..1 Chapter 1: Experimental and Computational Methods……………………………………7 1.1 Peptide Synthesis……………………………………………………………...7 1.2 Two-Dimensional Infrared Spectroscopy……………………………………..8 1.2.1 Theory…………………………...………………………………...8 1.2.2 Utilizing a 2D IR Spectrum to Determine Coupling Constants…..8 1.2.3 Experimental Layout and Design………………………………...11 1.2.4 Pulse Shaper Layout…………………………………………..…11 1.3 Performing 2D IR Experiments Using a Mid-IR Pulse Shaper……………...11 Chapter 2: Cyanophenylalanine and Azide Derivitized Amino Acids…………………..15 2.1 Introduction…………………………………………………………………..15 2.2 Experimental Methods……………………………………………………….16 2.2.1 FTIR Spectra……………………………………………………..16 2.2.2 Computational Methods……………………………………….....17 2.3 Experimental Results………………………………………………………...17 2.4 Transition Dipole Coupling Model and Simulations………………………...19 2.5 Discussion……………………………………………………………………19 Chapter 3: Comparing Glycine and Proline as a Spacer in Short Peptides……………...30 3.1 Introduction…………………………………………………………………..30 3.2 Computational Methods……………………………………………………...31 iii 3.2.1 Calculating Peptide Length and Dihedral Angles……………….32 3.3 Results………………………………………………………………………..33 3.4 Conclusions…………………………………………………………………..35 Chapter 4: Fourier Transform Infrared Spectroscopy of Plants…………………………44 4.1 Introduction…………………………………………………………………..44 4.2 Experimental…………………………………………………………………44 4.3 Results and Discussion………………………………………………………46 4.4 Conclusion………………………………………………………………...…47 Summary…………………………………………………………………………………54 References………………………………………………………………………………..55 Appendix A: VMD and MatLab Code…………………………………………………...63 Appendix B: Supplemental Figures……………………………………………………...76 iv List of Tables Table 2.1 Frequencies and bandwidths of cyanophenylalanine derivatives in water and tetrahydrofuran (THF)…………………………………………………………………...22 Table 2.2 Extinction coefficients and transition dipole strengths of cyanophenylalanine derivatives………………………………………………………………………………..23 Table 2.3 Extinction coefficients and transition dipole moment strengths of selected infrared probes…………………………………………………………………………...24 v List of Figures Figure I.1 Definition of the dihedral angles phi/psi that are used to construct Ramachandran plots ………………………………………………………………………4 Figure I.2 Allowed and favored regions of a Ramachandran plot………………………...5 Figure I.3 Peak movement in the weak coupling regime……………………………….....6 Figure 1.1 General example of a 2D IR spectrum……………………………………….13 Figure 1.2 Determining coupling constants using frequency of IR bands……………….14 Figure 2.1 Linear infrared spectra of cyanophenylalanine derivatives………………..…25 Figure 2.2 FTIR spectra of short peptides in water……………………………………...26 Figure 2.3 FTIR spectrum of a short peptide that has been fitted with two different ratios of Gaussian curves……………………………………………………………………….27 Figure 2.4 FTIR spectrum of a long peptide that has been fitted with four different ratios of Gaussian curves…………………………………………………………………….…28 Figure 2.5 Coupling constant with respect to distance………………………………….29 Figure 3.1 Depiction of the ‘bond’ that was used to determine the length of the peptides in the MD simulations……………………………………………………………………37 Figure 3.2 Dihedral angles between the nitrile probes….……………………………….38 Figure 3.3 Distance distributions for short and long peptides…………………………..39 vi Figure 3.4 Distance distributions from multiple starting conformations………………..40 Figure 3.5 Distance distributions after 10 ns and 100 ns………………………………..41 Figure 3.6 Ramachandran plots………………………………………………………….42 Figure 3.7 Distance, angle, and coupling constant maps………………………………...43 Figure 4.1 Depiction of the multiple bounce behavior that occurs in the crystal of the ATR instrument………………………………………………………………………….48 Figure 4.2 ATR-FTIR spectra of Astragalus leaves (unprepared) ………………………49 Figure 4.3 ATR-FTIR spectra of Astragalus leaves (without wax coating)………….…50 Figure 4.4 ATR-FTIR spectra of methanol extraction of Astragalus leaves with wax….51 Figure 4.5 ATR-FTIR spectra of methanol extraction of Astragalus leaves w/o wax…..52 Figure 4.6 ATR-FTIR spectra of Astragalus leaves after methanol extraction with wax.53 1 Introduction Many diseases, such as Alzheimer’s disease, Lou Gehrig’s disease (ALS), and muscular dystrophy, are caused by misfolded peptides and proteins. If the correctly folded structure and/or the aggregation mechanism of β-amyloid peptide, one of the main peptides that cause Alzheimer’s disease, can be elucidated, then potential treatments for this disease could be pursued. The studies that follow illustrate a combination of methods that can be used to determine structural information for larger peptide systems. Protein structure can be divided into four categories: primary (a chain of amino acids), secondary (helices and sheets), tertiary (coordination of helices and sheets into a single structure), and quaternary (coordination of fully folded proteins with other fully folded proteins). Peptides are shorter than proteins and they exhibit secondary structures such as α-, 310-, π-, poly- proline, and poly-glycine helices as well as β-sheets in solution. The α-, 310-, and π-helices are all right-handed helices with hydrogen bond connections between i···i+4, i···i+3, and i···i+5 in the backbone, respectively.1,2 Poly-glycine and poly-proline chains form left-handed helices. Since the handedness of the helix changes when these residues are added to a sequence, proline and glycine can be called ‘helix breakers’ of the common right-handed helices.3,4 Dihedral angles, defined as the angle between C1–N–Cαʹ–C1ʹ (φ) and N–Cαʹ–C1ʹ–Nʹ (ψ), formed by the peptide backbone create φ/ψ pairs that can be plotted into a Ramachandran plot (Figure I.1).5,6 Since each secondary structure has a unique set of φ/ψ angles, this type of plot explains the different secondary structures that the peptide could be exhibiting based on their φ/ψ pair. The favored conformational regions shown in the regular Ramachandran plot do not work for poly-proline or poly-glycine peptides because the glycine residues create a very flexible backbone that can have 2 nearly any φ/ψ combination whereas the proline residue creates such a rigid structure that the peptide angles are constrained to a small region of the plot (Figure I.2).7 Spectroscopic techniques, such as infrared spectroscopy, can be used to determine the various secondary structures within a peptide through the application of molecular probes in the system. Common probes are the carbonyl group (C=O) which is found in the peptide backbone and exists as a part of every amino acid residue, isotopic labels such as deuterium (C–D stretch)8,9,10 that can be substituted for hydrogen atoms on the amino acid side chains, large metal 11,12 carbonyls such as tungsten hexacarbonyl (W(CO)6), or smaller probes such as azide (R– 11,13 11 N3) and nitrile (C≡N). These probes all have transition dipole moments that can interact with each other either through space or through molecular bonds. This interaction is referred to as vibrational coupling and can be used to determine structural information about each peptide. This vibrational coupling is evident in linear infrared spectroscopy when the splitting between coupled transitions and intensity changes are observed relative to the uncoupled transitions (Figure I.3). Unfortunately, if the coupling is very small there will not be any clear evidence of coupling in the linear spectrum, such as changes in peak intensities or peak frequency shifts. A technique that has much more sensitivity than linear infrared spectroscopy
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