Structure and Dynamics of Elastin Cross-linking …...Abstract Structure and Dynamics of Elastin Cross-linking Domains Aditi Ramesh Master of Science Graduate Department of Biochemistry

Structure and Dynamics of Elastin Cross-linking Domains

by

Aditi Ramesh

A thesis submitted in conformity with the requirementsfor the degree of Master of Science

Graduate Department of BiochemistryUniversity of Toronto

Copyright c© 2015 by Aditi Ramesh

Abstract

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 pro-

teins 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. Regis Pomes for 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 elas-

tomeric 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 phys-

iological 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 pro-

duction 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


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 avail-

able 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.


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 struc-

tural 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 el-

ements while above this temperature, coacervates of tropoelastin and recombinant pep-

tides 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.


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 approxi-

mately 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 trans-

ported 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 ε-amino group in

the lysine side-chain, forming peptidyl α-aminoadipic-δ-semialdehyde (allysine) [29] us-

ing a ping-pong kinetic mechanism [30]. Subsequent spontaneous aldol condensation and

Schiff base reactions with nearby aldehydes or ε-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.


desmosine

di- and tri-tyrosine

polyalanine beta-sheetlysinonorleucine

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).


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.


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 re-

ported 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 polyala-

nine peptides adopt largely random-coil structures in aqueous solution. Most polyalanine


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 corrobo-

rates 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 environ-

ment (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 environ-

ment 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 struc-

tures 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


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 do-

mains 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 do-

mains [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


[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 dynam-

ics (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


Ace-AAAAAAAAKKAAAAAAAA-NH2

Ace-AAAAAAAAKAKAAAAAAA-NH2

Ace-AAAAAAAKAAKAAAAAAA-NH2

Ace-AAAAAAAKAAAKAAAAAA-NH2

Ace-AAAAAAKAAAAKAAAAAA-NH2

Ace-KAAAAAAAAAAAAAAAAK-NH2

A0

A1

A2

A3

A4

A16

Ace-AAAAAKAAKYGA-NH2

Ace-AAAAAKAAAKAA-NH2

A2Y

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.


A16

A0 A1 A2

A3 A4

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 macro-

molecules in simulation.

2.1.1 Molecular mechanics

Molecular mechanics uses classical mechanics to model atomic interactions and the poten-

tial 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 mo-

tions 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):

V (r) =∑

bonds(i)

kd2

(di − d0)2 +

∑angles(i)

kθ2

(θi − θ0)2 +

∑dihedrals(i)

kφ2

(1 + cos(nφi − φ0))

+∑

impropers(i)

kψ2

(ψi − ψ0)2 +

∑non−bondedpairs(i,j)

4εij[(σijrij

)12 − (σijrij

)6] +∑

non−bondedpairs(i,j)

qiqjεDrij

(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.


rijφ

θ

d

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.


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 quan-

tum 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 experi-

mental 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 re-

spect 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.


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 (back-

bone and side chain torsional parameters were refit to QM data) and the transferability

of parameters was demonstrated using the same alanine dipeptide-fitted backbone tor-

sional 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 pep-

tide 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.


QM and MM calculations on alanine, glycine, and proline dipeptides were combined

with MD simulations of proteins in crystal and aqueous environments. Monte Carlo sim-

ulated 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 refine-

ment 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 simula-

tion of unfolded proteins in urea, and crystal structure simulations. Quantum mechanical

calculations of glycine and proline dipeptides were performed and 2D CMAP potentials

were compared to NMR 3J 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, un-

folded 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 opti-

mized 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-


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 pep-

tide 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).


Water models

Water models are used to simulate hydrogen bonding and aqueous solutions. These mod-

els 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.


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 Nose-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 ther-

mostat 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), pres-

sure (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 Nose-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 pep-

tides. 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-


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 pep-

tides (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 sys-

tem 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 baro-

stat. 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 electro-

static 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.


Force&Field&

Water&

Mod

el&

Orig

in&

Refin

emen

t/Va

lidation&

OPLS%AA

'TIP4

P'Jorgen

sen'

lab'

Reprod

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ergies'of'com

plex'heats'of'

vapo

rization'and'de

nsities'of'p

ure'mod

el'liqu

ids'

Ambe

r'ff03w

'TIP4

P/2005

'Be

st'lab'

Helix%coil'transition

'in'alanine

%based

'helical'pep

tides'

Ambe

r'ff99SB*

%ILDN

'TIP3

P'Shaw

'lab'

Optim

ized'sid

e%chain'torsion'po

tentials'of're

sidue

s'that'd

iffered

'from

'PDB

'statistics'

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MM22*'

TIP3

P'Shaw

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ta'balance'

!

Tab

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odel

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d.


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 sol-

ubilized 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 concen-

trations 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


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:

A = a750200

H = h750200

I = i-(A100750

+H100750

)

The equations below show how normalized areas were calculated for octane samples:

A = a400200

H = h400200

I = i400200

-(A100750

+H100750

)

2.2.5 NMR

I performed a series of Correlation Spectroscopy (COSY), Total Correlated Spectroscopy

(TOCSY), Heteronuclear Single Quantum Coherence (HSQC), and HSQC-TOCSY ex-

periments 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.


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 en-

semble averaging cannot provide. Quantitative comparison of MD studies with exper-

iments 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

E = 0.084[1

rON+

1

rCH− 1

rOH− 1

rCN] · 332kcal/mol (3.1)

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.


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.


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 pep-

tide.


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 simula-

tion 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


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.


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 phys-

iological 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 concentra-

tion 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 Pomes 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


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.


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.


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 do-

mains - 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 wa-

ter. 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 tropoe-

lastin 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 dy-

namics 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-


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 simula-

tion.

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.


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 differ-

ences 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


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.


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.


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.


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


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.


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

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.


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 sim-

ulations 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-


Figure 3.14: Sample contact maps for six replicas of the A0 dimer system showing in-

termolecular 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.


Figure 3.15: Snapshots at 100ns of six A0 dimer replicas. These replicas are the same as

those shown in figure 3.14.


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).


Figure 3.17: Snapshots at 100ns of six A0 tetramer replicas. These replicas are the same

as those shown in figure 3.16.


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, denot-

ing helical contacts, are also seen. Otherwise, all intermolecular contacts and all other

intramolecular contacts are of lower propensity. This underscores the structural hetero-

geneity 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 confor-

mations 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


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.


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.


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.


Figure 3.22: Average fraction helicity for dimer simulations of all peptides with and with-

out 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.


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 pep-

tides, the hatched bar denotes fraction helix formed by the peptides when they are not

making any contacts with other peptides.


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 scatter-

ing (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


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


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.


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


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.


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


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.


3.5 Biphasic systems as a way to model the coacer-

vate

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 par-

titioning 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 representa-

tive 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.


!0.6%

!0.4%

!0.2%

0%

0.2%

0.4%

0.6%

0.8%

1%

1.2%

1.4%

1.6%

A2% A3% A16% A2Y% A3A%

Peak%Area%(A.U)%

Octanol%Par22oning%

Aqueous%

Interface%

Hydrophobic%

!0.6%

!0.4%

!0.2%

0%

0.2%

0.4%

0.6%

0.8%

1%

1.2%

1.4%

1.6%

A2% A3% A16%

Peak%Area%(A.U)%

Octane%Par00oning%

Aqueous%

Interface%

Hydrophobic%

a b

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 ly-

sine 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 liter-

ature [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 recom-

binant 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 com-

putationally 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, how-

ever 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 biophys-

ical 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 compar-

ison 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 exper-

iments, 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


proper balance of secondary structure in a particular system, adequately sampling con-

formational 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 he-

lix 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 parame-

ters 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 par-

ticular 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 disor-

dered 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 specif-

ically, 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 measure-

ments [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


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 litera-

ture and other peptide studies. In this regard, not only are my model peptides represen-

tative 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 be-

tween 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-


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 polypep-

tides (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


the hydrophobic helix interaction face (shaded). Favourable interaction between the hy-

drophobic 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 to-

gether 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-


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 hydropho-

bic 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 hydropho-

bic 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.


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 ag-

gregative 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 pref-

erentially 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 prefer-

entially 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 mo-

bility 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 appro-

priate 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 sys-

tems. 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 differ-

ent 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.

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