Coiled-Coil Stability Analysis

and

Hydrophobic Core Characterization

by

David Brinkmann

B.S., University of Colorado at Colorado Springs, 1994

A thesis submitted to the Faculty of Graduate School of the

University of Colorado at Colorado Springs

In partial fulfillment of the

Requirements for the degree of

Master of Science

Department of Computer Science

2003

ii

©Copyright By David C. Brinkmann 2003All Rights Reserved

iii

This thesis for Master of Science degree by

David Brinkmann

has been approved for the

Department of Computer Science

by

_______________________________________________________Jugal K. Kalita, Chair

_______________________________________________________C. Edward Chow

_______________________________________________________Robert Hodges

_______________________________________________________Karen Newell

Date___________

iv

CONTENTS

CHAPTER

1. INTRODUCTION ............................................................................................

Biology .................................................................................................................. 2

DNA .................................................................................................................. 2

The Central Dogma of Molecular Biology .......................................................... 3

Protein Structure .................................................................................................... 3

Coiled-Coil .......................................................................................................... 13

2. LITERATURE REVIEW........................................................................................

Protein Structure Analysis .................................................................................... 16

Early Proteins Structure Prediction ................................................................... 16

Coiled-coil Characterizations............................................................................ 21

Stability................................................................................................................ 22

Coiled Coil Stability Using Experimental Data..................................................... 25

3. STABLE INPUT ..............................................................................................

UCHSC................................................................................................................ 27

Stable Input Parameters ........................................................................................ 28

Helical Propensity ............................................................................................ 33

Hydrophobicity ................................................................................................ 33

E/G Interactions ............................................................................................... 34

Intra-Chain Electrostatic Interactions................................................................ 35

v

Clusters ........................................................................................................... 36

Entropy ............................................................................................................ 36

Program Flow ...................................................................................................... 37

Output Table ........................................................................................................ 45

Output Graphs...................................................................................................... 47

4. COILED-COIL CLUSTER ANALYSIS ................................................................. 58

Why Coiled-Coils?............................................................................................... 58

Protein Database Analysis .................................................................................... 59

SPTR dataset .................................................................................................... 60

Swiss-Prot Coiled-Coils ................................................................................... 61

Stable Coil Pre-Processing ................................................................................... 65

Coil Analysis........................................................................................................ 68

Summary of Findings ........................................................................................... 95

5. CONCLUSION ...............................................................................................

GLOSSARY ..................................................................................................

BIBLIOGRAPHY ..............................................................................................

APPENDIX A STABLE INPUT GUI....................................................................104

APPENDIX B TABULATED OUTPUT ................................................................106

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TABLES

Table 1-1 Non-polar Amino Acids (hydrophobic) ............................................................................... 6

Table 1-2 Polar Amino Acids (hydrophilic) ...................................................................................... 6

Table 1-3 Electrically Charged (negative and hydrophilic) ..................................................................... 6

Table 1-4 Electrically Charged (positive and hydrophilic) ...................................................................... 7

Table 2-1 Chou-Fasman Table .................................................................................................. 18

Table 3-1 Windowing Algorithm for Window = 7............................................................................. 31

Table 3-2 Helical Propensity Values ............................................................................................ 41

Table 3-3 Hydrophobic Core Values............................................................................................ 42

Table 3-4 Intra-Chain Effect..................................................................................................... 44

Table 3-5 Inter-Chain Electrostatics ............................................................................................ 45

Table 3-6 File Extensions ........................................................................................................ 48

Table 4-1 Helical Propensity and Stability Values............................................................................. 67

Table 4-2 Phe, Ile, Leu, Met, Val, and Tyr Frequency Swiss-Prot ........................................................... 75

Table 4-3 Phe, Ile, Leu, Met, Val, and Tyr Frequency SPTR................................................................. 76

Table 4-4 Clusters 6 Heptad S-P ................................................................................................ 82

Table 4-5 Clusters 6 Heptad+1 S-P ............................................................................................. 82

Table 4-6 Clusters 7 Heptad S-P ................................................................................................ 82

Table 4-7 Cluster 7+1 Heptad S-P .............................................................................................. 82

Table 4-8 Clusters 8 Heptad S-P ................................................................................................ 82

vii

Table 4-9 Clusters 8+1 Heptad S-P ............................................................................................. 82

Table 4-10 Clusters 6 Heptad SPTR ............................................................................................ 83

Table 4-11 Clusters 6 Heptad+1 SPTR ......................................................................................... 83

Table 4-12 Clusters 7 Heptad SPTR ............................................................................................ 83

Table 4-13 Clusters 7+1 Heptad SPTR ......................................................................................... 83

Table 4-14 Clusters 8 Heptad SPTR ............................................................................................ 83

Table 4-15 Clusters 8+1 Heptad SPTR ......................................................................................... 83

Table 4-16 Hydrophobic Cluster Count, Swiss-Prot........................................................................... 87

Table 4-17 Non-Hydrophobic Cluster Count, Swiss-Prot ..................................................................... 88

Table 4-18 Hydrophobic Cluster Count, SPTR ................................................................................ 89

Table 4-19 Non-Hydrophobic Cluster Count, SPTR........................................................................... 89

Table 4-20 Stabilizing Cluster, Swiss Prot ..................................................................................... 90

Table 4-21 Stabilizing Cluster, SPTR........................................................................................... 91

Table 4-22 Cluster Amino Acids Swiss-Prot ................................................................................... 93

Table 4-23 Cluster Amino Acids SPTR......................................................................................... 94

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FIGURES

Figure 1-1 Amino Acid ................................................................................................... 4

Figure 1-2 Phi and Psi Angles ......................................................................................... 5

Figure 1-3 _-Helices...................................................................................................... 10

Figure 1-4 Beta Sheets .................................................................................................. 11

Figure 1-5 Heptad Repeat.............................................................................................. 13

Figure 1-6 Heptad Positions in a Coiled Coil................................................................. 14

Figure 3-1 Coiled Coil A/D and E/G Interactions .......................................................... 34

Figure 3-2 Lateral View Coiled Coil E/G Interaction..................................................... 35

Figure 3-3 Clustered Hydrophobic Core........................................................................ 36

Figure 3-4 Stable Input Program Flow .......................................................................... 38

Figure 3-5 Clusters........................................................................................................ 40

Figure 3-6 Mapping Example........................................................................................ 40

Figure 3-7 Tropomyosin Sequence................................................................................ 49

Figure 3-8 Summary Output.......................................................................................... 51

Figure 3-9 Total Stability .............................................................................................. 52

Figure 3-10 A/D Hydrophobic Stability ........................................................................ 53

Figure 3-11 Helical Propensity...................................................................................... 54

Figure 3-12 E/G Electrostatic Interaction ...................................................................... 55

Figure 3-13 Chain Length ............................................................................................. 56

ix

Figure 3-14 Density Stability ........................................................................................ 57

Figure 4-1 SPTR Protein Entry ..................................................................................... 61

Figure 4-2 Coiled-Coil Retrieval ................................................................................... 63

Figure 4-3 Coiled Coil Entry ......................................................................................... 64

Figure 4-4 Normalized Length Frequency ..................................................................... 69

Figure 4-5 Amino Acid in A and D positions 6&7 Heptads-SPTR................................. 70

Figure 4-6 Amino Acid in A and D positions 6&7 Heptads - Swiss-Prot ....................... 70

Figure 4-7 Normalized Amino Acid Distribution Swiss-Prot......................................... 71

Figure 4-8 Normalized Amino Acid Distribution SPTR ................................................ 72

Figure 4-9 Normalized Cluster by Heptad Length ......................................................... 74

Figure 4-10 Total Clusters and Ratio by Heptad Length Swiss-Prot .............................. 78

Figure 4-11 Total Clusters and Ratio by Heptad Length SPTR...................................... 78

Figure 4-12 Total Clusters by Cluster size Swiss-Prot ................................................... 80

Figure 4-13 Total Clusters by Cluster size SPTR........................................................... 80

Figure 4-14 Hydrophobic Amino Acids in Clusters ....................................................... 85

Figure 4-15 Non-Hydrophobic Amino Acids in Clusters ............................................... 86

Chapter 1

CHAPTER 1

INTRODUCTION

The scientific community now has access to many completely sequenced

genomes of several different species, including the genome of humans. When it comes to

the human genome, however, a complete understanding of the 500000 proteins encoded

by the 30000 genes will take many more years of further study. Not only is there a great

volume of data to be interpreted, but the complexities of the biological systems need to be

understood as well. As a complement to the physical genomic research, proteomics, a

discipline of molecular biology has been initiated for the comparative study of proteomes

under different conditions. Among the research facilities dedicated to the field of

proteomics is the Peptide Chemistry lab of Robert Hodges at the University of Colorado

Health Sciences Center (UCHSC). Dr. Hodges’ group is interested in being able to

determine the absolute stability of the coiled-coil oligomerization domain because the

ability to determine coiled-coil stability would greatly facilitate the prediction of coiled-

coil protein structures and advance protein design.

This thesis explores two areas that are currently being researched at UCHSC.

First, in order to expedite analysis of experimental data a comprehensive tool is needed to

calculate the relative stability of an experimental sequences. Currently, all sequence

stability calculations are done by hand and because of the length of the sequences and the

2

calculations involved this process can take a great deal of time. In addition to performing

the calculations, there is a need for a graphical display of various aspects of the

calculations.

The second part of this thesis examines how hydrophobic amino acids, are

grouped in successive heptads of coiled-coil sequences found in the Swiss-Prot protein

database. The hydrophobic residues appear in the ‘a’ and ‘d‘ heptad position in coiled-

coil conformations. It has been proposed that clusters of hydrophobic amino acids in the

‘a’ and ‘d’ positions play an important role in protein folding and other activities. For

example, when all other stability factors are constant and only the hydrophobic cluster

arrangement is altered, two proteins exhibit different levels of stability.

A comprehensive analysis of all coiled-coils regions is needed found is to be done

to determine if clusters exist in nature. In this analysis, the answers to the following

questions is sought: first, what is the frequency of the hydrophobic amino acids in the ‘a’

and ‘d’ position; second, what is the length of the clusters; third, what amino acids are

present in the different cluster lengths; fourth, how many hydrophobic amino acids and

how many other amino acids are present in clusters of various length; and fifth, Coiled-

coils always start with stabilizing clusters; can these be characterized, and if so how?

Biology

DNA

Physically, DNA is described as a double helix. The double helix is a

conformation that is made up of two anti-parallel sequences connected periodically along

their lengths. The parallel sequences in the DNA molecule are made of series of repeated

sugar and phosphate molecules. This repeated pattern is found along the entire length of

3

the molecule. One of the most important roles DNA plays is that it provides a code that

ultimately leads to the synthesis of proteins in the cell.

The Central Dogma of Molecular Biology

The transfer of information in cells generally goes from DNA to RNA to the

synthesis of a protein. In brief, a single segment of one DNA strands serves as a template

for the synthesis of a RNA molecule. This process is called transcription because during

this phase of gene expression a transfer of information from one nucleic acid type to

another occurs. Next, the RNA molecule is translated into a protein sequence. The RNA

that is translated into a protein is called messenger RNA (mRNA) and the molecular

machinery which carries out his step is called the ribosome [Becker 200b]. Using

complementary base pairing (3 nucleotides or 1 codon) between a tRNA molecule (which

carries one amino acid) and the mRNA molecule, the ribosome catalyzes the chemical

reaction linking a new incoming amino acid with the previously linked amino acid in the

translated polypeptide chain. Following synthesis, the amino acid sequence can go

through further processing in the endoplasmic reticulum and golgi complex to acquire

post-translational modifications (e.g. glycosylation) [Becker 2000c] to form the final

synthesized protein.

Protein Structure

The Central Dogma of Molecular Biology describes the protein synthesis process.

Although the steps used to synthesis a protein are well known, the processes that causes a

protein to assume a particular physical structure after it is synthesized is not as well

4

understood. The specific structures and substructures a protein ultimately forms, plays an

important role in how the protein will function in the cell. Basic protein structure is

determined by the elemental components of the amino acids and can be described using a

four level hierarchy.

Proteins are generally composed of a linear main amino acid chain or back bone.

Each of the amino acids has a four part molecular substructure. The amino acid begins

with an amide group (--NH2) and end with a carboxylate group (--COOH). In between

these two groups is an _-carbon, C_. Bonded to the C_ are an R group and a hydrogen

atom. The backbone of the amino acid sequence is formed by a linear combination of

amino acids bonding together so that a repeated link of individual amino acids anime

groups, C_, and carboxylate groups form a chain. Figure 1-1, Amino Acid, shows the

details.

Figure 1-1 Amino Acid

Amino acids are connected to each other through a peptide bond that forms

between the carboxylate group of one amino acid and the amine group of its neighbor.

5

Once the bond is formed, the two joined amino acids have only one amine group or N-

terminus and one carboxylate group or C-terminus. The relationship between the joined

amino acids is described using two angles psi and phi. The phi angle is the angle formed

by the amine group to the C_, and the psi angle is the angle formed by the C_, and the

former carboxylate carbon. These angles show the level of twist in the amino acid

backbone and have been used to predict overall structure stability [Gromiha 2002].

Secondary structures are found in globular proteins when the phi and psi angles of

contiguous amino acids in a sequence are repetitive. Figure 1-2, Phi and Psi Angles,

illustrates these relationships.

Figure 1-2 Phi and Psi Angles

The R-group attached to the C_ of each amino acid is called the amino acid side-

chain. Side-chains are what give the amino acids their particular characteristic. It is the

side-chain that makes the amino acid hydrophobic, polar or non-polar. Side-chains range

in size from a simple hydrogen atom as in glycine to relatively large complex aromatic

groups. Nine of the amino acids have non-polar side-chain groups and form the

C_

C

O

C

O

N

H

N

H

C_

RH

_ _

6

hydrophobic amino acids. The remaining 11 amino acids can be further categorized as

hydrophilic charged and hydrophilic uncharged. The different categories of amino acids

are listed in Tables 1-1 though 1-4.

Amino Acid Three Letter Code Single Letter CodeGlycine Gly GAlanine Ala AValine Val V

Leucine Leu LIsoleucine Ile I

Methionine Met MPhenylalanine Phe F

Tryptophan Trp WProline Pro P

Table 1-1 Non-polar Amino Acids (hydrophobic)

Amino Acid Three Letter Code Single Letter CodeSerine Ser S

Threonine Thr TCysteine Cys CTyrosine Tyr Y

Asparagines Asn NGlutamine Gln Q

Table 1-2 Polar Amino Acids (hydrophilic)

Amino Acid Three Letter Code Single Letter CodeAspartic Acid Asp DGlutamic Acid Glu E

Table 1-3 Electrically Charged (negative and hydrophilic)

7

Amino Acid Three Letter Code Single Letter CodeLysine Lys K

Arginine Arg RHistidine His H

Table 1-4 Electrically Charged (positive and hydrophilic)

Protein structure is influenced by the type and number of side-chains present in its

sequence. Hydrophobic amino acids have side chains that will not form hydrogen bonds

or ionic bonds with other groups. These hydrophobic amino acids tend to be buried in the

center of proteins away from the surrounding aqueous environment. The amino acids in

this category are listed in Table 1-1, Non-polar Amino Acids (hydrophobic). Some

references to glycine include it in the hydrophobic category and some consider its side

chain neutral. This amino acid has no strong hydrophobic or hydrophilic properties.

Amino acids with uncharged but polar side chains are uncharged at physiological pH.

These are listed in Table 1-2, Polar Amino Acids (hydrophilic). Amino acids with acidic

side chains have a carboxylic acid group in their side chain and are very hydrophilic.

These amino acids are listed in Table 1-3, Electrically Charged (negative and

hydrophilic). Amino acids with basic side chains have a positive charge on these side

chains that makes them hydrophilic and they are likely to be found at the protein surface.

These are listed in Table 1-4, Electrically Charged (positive and hydrophilic). In addition

to these amino acid characteristics, the Van der Waals forces, hydrogen bonds,

electrostatic interactions and hydrophobic effect also affect protein structure.

The Van der Waals forces are the attractions and repulsions atoms have for one

another that gives matter its general cohesion [Lesk 2002]. These come from the

positively charged nucleus of one atom and the negative charge from the electron cloud

8

of another. Hydrogen bonds are the weaker attractions between uncharged, yet polarized

atoms. Hydrogen bonds commonly form between the O and H atoms. Electrostatic

interactions form the basis for the Van der Waals interactions and the Hydrogen bond.

These interactions are common at the N and C termini of the peptide chains. Electrostatic

side chain interactions occur between Lys, Arg, His, Asp, and Glu. These are listed in

Table 1-4, Electrically Charged (negative and hydrophilic) and Table 1-5, Electrically

Charged (positive and hydrophilic).

The hydrophobic effect is the force that is imposed on the overall structure by the

non-polar side chain groups. The association of the non-polar groups reduces the

collective surface area, and therefore the amount of water that can influence the proteins’

structure. This association forces the side-chains closer together.

Protein structures are classified according to a four level hierarchy. These levels

begin with a simple linear arrangement to complex multiple substructure aggregates.

These levels are commonly referred to as the protein’s primary, secondary, tertiary, and

quaternary structures. A protein’s primary structure is the linear amino acid sequence list

of the amino acid chain or chains. These are those with which are commonly used to

describe the protein in the various databases. Secondary protein structures are local

structures of linear segments of amino acid backbone atoms that do not take into account

the effects of the side chains. The major arrangements found in the secondary structure

category are turns, sheets, and helices. These account for about 70% of the substructures

present in a protein. Tertiary structures are an organization of secondary structures linked

by weak interactions. These are best thought of as a three-dimensional arrangement of all

9

atoms in a single polypeptide chain. Quaternary structures are the aggregation of separate

polypeptide chains into the functional protein.

The primary protein structure is the linear arrangement of amino acids in the order

in which they appear in the protein. When describing a protein, the sequence begins at the

N-terminus and ends at the C-terminus. Once assembled into a primary structure, the

individual amino acid side chains are referred to as amino acid residues. Fredrick Sanger

reported the first amino acid sequence of the insulin hormone [Becker 2000].

The secondary structure of a protein is the result of the local interaction of the

amino acid residues. These interactions form three different structures or conformations.

The _-helix, also know as a repetitive secondary structure get its name because the

relationship of one amino acid to the next is the same. The parameters “n” and “r” are

two parameters that are used to characterize a general helix. The convention nr is used to

describe the helix. The “n” is the number of residues per turn and the subscript “r” is the

rise per helical residue. An _- helix is designated 3.64. It has 3.6 residues per turn and

raises 4 residues in height. In the helix, there is a possible hydrogen bond between every

fourth amino acid. This relationship allows an amino acid to form a bond with the amino

acids “above” it and “below” it. Figure 1-3, Coiled _-Helices [UWK 2003], shows this

relationship and the 3.6 residues per turn.

10

Figure 1-3 _-Helices

A beta strand is an amino acid string that does not form a coil. It zigzags in a

more extended way than a helix. One of three types of beta-sheets is formed when two or

more beta strands link side by side. The links are hydrogen bonds between the main

carboxyl ate and amide groups in the amino acid chains. The three types of beta-sheets

are anti-parallel, parallel, and mixed. In anti-parallel sheets the strands run in opposite

directions, in parallel sheets the strands run in the same directions and in the mixed

conformation there is a mix of anti-parallel and parallel strands. The beta sheet is

characterized by a maximum of hydrogen bonding. Unlike the intra-molecular hydrogen

bonds in the _- helix, the hydrogen bonds in the beta sheet are perpendicular to the plane

of the sheet that link amino acids of different amino acid chains or distant members of the

same amino acid chain.

11

The _ turn is the third type of general secondary structure and involves about one-

third of residues in a globular protein. Turns are important substructures in proteins.

Antibody recognition, phosporylation, glycosylation, hydroxzylation and intron/exon

splicing are found frequently at or adjacent to turns. It has also been proposed that turns

are a mechanism used for tertiary folding of globular proteins. Turns usually occur

between two anti-parallel beta strands and are generally less than seven residues in

length. The turn enables the amino acid chain to reverse itself by 180˚. Turns come in

four types, gamma turns, Type I, Type II and Type III turns. Turns are distinguished by

the hydrogen bonds between the ith, ith+1, ith+2, and ith +3 residues [Brook 2003]. Figure

1-4, Beta Sheets [UOFG 2003], illustrates the how beta sheets are organized.

Figure 1-4 Beta Sheets

12

While the secondary protein structures form because of the repetitive nature of the

amino acid chains and the hydrogen bonds between the amino acids, the tertiary protein

structures develop mainly because of the variety in the amino acid side chains. The

tertiary structure is not a repetitive structure and is highly dependant on the interaction of

the side chains. For example, the hydrophobic residue will be drawn to the center of the

protein while the hydrophilic residues will seek other polar molecules including water.

These interactions will force the tertiary structure to fold, bend and twist in unpredictable

way.

Stabilizing the tertiary structure is achieved through both covalent and non-

covalent bonds. The non-covalent stabilizers are the hydrogen bond, electrostatic and

hydrophobic interactions. The most common stabilizing covalent bond is the disulfate

bond. This type of bond is formed between two linearly distant cystines that are situated

near each other. A protein will maintain its stable shape for a given set of environmental

conditions.

The quaternary protein structure is formed by an aggregation of tertiary

component of the same or different proteins. This form of structure applies to multi-meric

proteins. Many proteins belong to this class, particularly those of molecular weight

greater than 50000 [Becker 2000a]. The same forces that stabilize the tertiary structure in

a particular environment stabilize these structures. Anfinsen proposed in his

"Thermodynamic Hypothesis", that the native conformation of a protein is adopted

spontaneously. In other words, there is sufficient information contained in the protein

sequence to guarantee correct folding from any of a large number of unfolded states

[Anfinsen 1973].

13

Coiled-Coil

The coiled-coil is a tertiary oligomerization domain that is formed when two or

more _- helices wrap around each other in a left-handed super coil. Coiled-coils are found

throughout nature and occur in a wide variety of proteins and play an important role in

basic biology. Two examples of this are the kinesin [Thormahlen 1998] and myosin

[Tripet 1997] proteins. Kinesin is a molecule that transports cellular components from

place to place in the cell. The ability to perform this is due in part to the coiled-coil.

Myosin, a fundamental protein used in muscle contractions, is another protein that

employs the coiled-coil conformation. Studies have shown that Myosin depends, in part,

on the coiled-coil to function properly [Chakrabarty 2002]. In both these proteins, the

ability of the coiled-coil to uncoil allowing the attached heads to move gives the protein

the mobility needed to perform its function.

Coiled-coils are found to have hydrophobic amino acids spaced at every third and

then every fourth residue within its sequence. A grouping of seven residues forms a

heptad repeat designated (abcdefg), where the ‘a’ and ‘d’ positions are occupied by

hydrophobic amino acids. An example of the heptad repeat pattern aligned with an amino

acid sequences is in Figure 1-5, Heptad Repeat. This figure shows the amino acid

sequence and directly below it the heptad repeat position each residue occupies.

Sequence: CGG-EVGALKA-EVGALKA-QIGALQK-QIGALQK-EVGALKK-heptadposition: gabcdef-gabcdef-gabcdef-gabcdef-gabcdef

Figure 1-5 Heptad Repeat

14

This pattern repeats and on average places a hydrophobic side-chain every 3.5

residues in the sequence. A typical _-helix has 3.6 residues per turn and takes less than

two full heptads to turn twice.

Figure 1-6 Heptad Positions in a Coiled Coil.

In the coiled-coil, the two _-helices bury their hydrophobic residues in the center

of the coil that causes the coiled-coil itself to form a super coil. These are depicted in as

positions a, a`, d, and d` in Figure 1-5. The super coil character of the coiled-coil also

gives rise to other interactions within the individual _-helices and between the _-helices

in the super coil. A portion of a coiled-coil is illustrated in Figure 1-3, _-Helices. This

figure shows the relative position of the different amino acids in their heptad positions.

The on-going research [Kwok 2003, Tripet 2000, Wagschal 1999] of coiled-coils

at the UCHSC and the University of Alberta has demonstrated that there are a number of

possible factors that determines if a stable coiled-coil exists. Using these stability factors,

15

proteins can be evaluated to find possible coiled-coil domains. Once these domains are

found, they can be further studied. Information about the domain’s composition and other

statistic can be gathered and used to predict their presence in newly sequenced proteins.

16

Chapter 2

CHAPTER 2

LITERATURE REVIEW

Protein Structure Analysis

Protein structure analysis is borne out of the desire to determine protein

characteristics without doing it experimentally or through crystallography. These two

methods can be expensive and time consuming. Processes based on protein statistics and

past experimental data have been generalized to create methods and algorithms to provide

quick answers to protein structure questions. This chapter describes some of the

approaches that have been used to characterize proteins in general and coiled-coils in

particular.

Early Proteins Structure Prediction

Early protein structure prediction algorithms [Chou 1974, Garnier 1978] were

derived by gathering statistics from a relatively small group of proteins. The statistics

related four different protein secondary structures to the amino acids that comprised

them. This information was then generalized in an attempt to predict the secondary

17

structures of other proteins. These approaches proved to be about 60%-65% accurate and

only considered the local amino acid neighborhood.

Outlined in a 1974 paper, “Conformational Parameters for Amino Acids in

Helical, _-Sheets, and Random Coil Regions Calculated from Proteins”, the Chou-

Fasman algorithm is one of the oldest algorithms that attempted to predict the secondary

protein structures using a larger number of proteins [Chou 1974]. Previous attempts used

far fewer than the 15 proteins and 2400 residues used by Chou-Fasman. Up to this point,

the two Zimm-Bragg parameters, _ and s, where investigated for the individual amino

acids. _ is the cooperativity factor for helix initiation and s is the equilibrium constant for

converting a coil residue to a helix. These investigations lead to some generalizations

about how some of the amino acids participate in certain conformations in some proteins.

Chou and Fasman studied all 20 amino acids in 15 proteins and compared the frequency

of the amino acids’ occurrence in various conformational states to the _ and s values. The

result of Chou and Fasman’s research was a better understanding of protein structure

prediction, which led them to develop a table of values called the “Frequency of Helical,

Inner Helical, _, and Coil Residues in the 15 Proteins with Their Conformation

Parameters P_, P_i, P_, and Pc.”

Derived from observed protein structures and their propensity to form different

structures, the Chou-Fasman parameter table consists of seven columns and has twenty

rows. The values assigned to each amino acid in the first three columns, P(_), P(_), and

P(turn), are roughly equivalent to the propensity of an amino acids to form an _-helix, _-

strand and hairpin turn respectively. To provide a sense of the information in the Chou-

18

Fasman parameter table, the first two rows of the table are listed below in Table 6, Chou-

Fasman table.

Name P(_) P(_) P(turn) f(i) f(i+1) f(i+2) f(i+3)Alanine 1.42 .83 .66 .06 .076 .035 .058Arginine .98 .93 .95 .07 .106 .099 .085

Table 2-1 Chou-Fasman Table

The Chou-Fasman algorithm can be explained in three parts. The first part detects

the presence of alpha helices, the second detects the presence of beta sheets and the third

part detects hairpin turns.

The helix detection algorithm starts by dividing the sequence into two regions.

The first region comprises areas where the amino acids have a P_ value greater than 1,

everything else are in the second region. Next, groups of four out of six peptides having

P_ values greater than 1 are identified. These form the base regions of the helix. From

these bases, the amino acids immediately before and after are included in the proposed

helix until the region is found to contains four peptides that have an average P_ value of

less than 1. These are the regions predicted as alpha helices. Beta sheets are predicted in

a similar fashion. This time regions of four or six amino acids with P_ values less than 1

are examined. These regions are expanded until four amino acids average a P_ of less

than 1 are found. A region is declared a beta sheet if over the entire region the P_ average

is greater than 1 and the sum of all P_ is greater than the sum of the P_’s.

19

Beta turns are calculated by calculating a turn propensity value, Pt, for all the

amino acids in the sequence based on that amino acid and the next three that follow. If

the product of all four is greater than .000075 and the Pturn average is greater than 1 and

the sum of Pturn value is greater than both P_ and P_ value, then the amino acid is

predicted to turn at that point.

To improve on the Chou-Fasman algorithm, “Analysis of the Accuracy and

Implications of Simple methods For Predicting the Secondary Structure of Globular

Proteins”, was written by Garnier, Osguthorpe and Robson (GOR) in 1978 [Garnier

1978]. This paper was an attempt to describe and test the simple statistical procedures for

determining secondary protein structures that have been developed. The GOR paper took

particular interest in the performance of the Chou-Fasman algorithm. At the time, the

Chou-Fasman approach was considered one of the best ways to determine a protein’s

structure using amino acid statistics.

Ultimately, the GOR paper sets forth the GOR algorithm. Over the past 25 years

the GOR algorithm has been improved a number of times. The latest was set forth in

1996 in the form of GOR IV. Today the GOR algorithm is an alternative to the Chou-

Fasman algorithm in the area of statistical models.

The GOR algorithm, like the Chou-Fasman algorithm, seeks to predict four

secondary structures of a protein by evaluating the weighted position of the amino-acid

sequence. GOR divides the predicted structures into four types; helix, extended sheets,

turns and coils. The first three structures have been introduced earlier. The coil or

aperiodic state is defined as not being of the first three conformations. In developing this

20

method, the GOR algorithm used 30 proteins; the paper did not provide the number of

residues.

The GOR algorithm implementation is straightforward. The paper provides four

tables, one for each secondary structure to be predicted. Each of the tables lists all 20

amino acids and each acid has 17 spatial parameters derived from experimental

observations.

This implementation of the algorithm starts by progressively calculating an

information value, “I”, for each amino acid in the sequence. The “I” value is defined as

I(Sj;R1, R2, R3, R4,… Rlast)=∑I(Sj; Rj +m or -m)

where last = 17 and –m < j < +m m=8.

The “I” value calculated for the jth amino acid is based on the preceding and succeeding

eight residues. In each of the tables, the 17 parameters are based on the acid’s relative

distance from the jth “I” value being calculated. The “I” values for each amino acid in the

sequence are calculated. This is done four times using values from each of the four

different tables. Once the four values are determined the one with greatest value

determines in which of the four structures the amino acid is likely to participate.

Following the “I” calculation, another statistically determined value can be

applied. The decision constant, DC, can be used to further optimize the evaluation of

each of the four calculated “I” values. The DC values are determined on a protein-by-

protein basis.

There is a second approach outlined in the GOR paper. It is called the “single

residue information method.” As the name suggests, the only information considered is

21

the information that a residue carries about its own conformation. This approach was not

introduced to provide a simpler approach, but to see how much influence adjacent amino

acids have on the predicted structure.

Coiled-coil Characterizations

Predictions

Proteins can be statistically analyzed for important features like charge-clusters,

repeats, hydrophobic regions, and compositional domains. As one of the many important

structural domains, much attention has been directed at developing algorithms to

determine the characteristics of coiled-coils. The basic heptad repeats is what makes the

coiled-coil particularly conducive to computer-based characterization.

PAIRCOILS [Berger 1995] classifies coiled-coils using a statistical approach. It

uses a database of all known coiled-coil sequences from myosin, tropomyosin, and

intermediate filament proteins that was created by extracting sequences from the

GENpept database [OCGC 2003]. These sequences are heptad aligned and form the basis

for PAIRCOILS predictions. From these selected proteins, the conditional probability

that two amino acids are found in any two-heptad position is determined. The frequencies

are normalized and used to determine the probability that a pair of amino acids appear in

a heptad repeat. As a result, PAIRCOIL is able to distinguish two-stranded coiled-coils

from non-coiled-coils and does not produce any false positives or false negative when

tested against a Brookhaven Protein data bank [Brook 2003].

A special coiled-coil is the ‘leucine zipper’. Bornberg-Baur, Rivals and Vingron

[Bornberg 1996] used the Swiss-Prot protein database to retrieve annotated leucine

22

zippers, leucine like zippers, and non-leucine zippers. They made the observation that

there can be two general classes of the leucine zipper. The strict zipper is characterized

by sequences that have leucine appearing regularly in the ‘d’ position in four or more

consecutive heptads. The relaxed zipper is a leucine zipper that has had one of the

leucines replaced by Met, Val, or Ile. Using the TRESPASSER program to predict the

presence of leucine zippers, they evaluated the three groups of proteins. Their results

showed that annotated leucine zippers in the Swiss-Prot database are not often predicted

to follow the strict or relaxed definition of leucine zippers. They did observe, however,

that leucine zippers frequently occur together with DNA binding basic region (bZIP) or a

helix-loop-helix (bHLH-ZIP) domain. These two are hybrid zipper domains and both the

bZIP and bHLH-ZIP regions show coiled-coil characteristics. They concluded that the

presence of a coiled-coil is a better indicator of a leucine zipper than simply the presence

leucine repeat.

Stability

Coiled-coils have been shown to play an important role in many large proteins.

The coiled-coil conformation is found in elongated or fiber-forming proteins such as

myosin, alpha keratin, tropomyosin, and kinesin. Lauzon [Lauzon 2001] analyzes the role

played by coiled-coils in myosin and Tripet [Tripet 1997] examined the coiled-coil in the

kinesin “neck” region.

Kinesin is a microtubule-dependent motor protein. This type of protein is used to

transport other proteins and vesicles from location to location within cells. Kinesin has

two heads, a linker region, and a stalk. Movement is produced when the leading head

23

detaches from the microtubule and moves forward and reattaches. The trailing head then

detaches from the microtubule and reattaches in a location closer to the leading head. The

kinesin travels from the negative to the positive end of the microtubule. The kinesin

counter part is the dynein and travels from the negative to positive end of the

microtubule.

The two heads of the kinesin are globular ATP-binding sites. These two regions

are joined through an alpha helical linker region to the stalk. The linker regions of the

two heads come together and form a coiled-coil stalk. The end of the kinesin is a light

chain region that is used to attach the kinesin to the vesicle being transported.

The “neck” of the kinesin is where the _-helix linker region joins the stalk. This

region forms a coiled-coil stabilized with the classic stabilizing factors plus additional

interactions. The “neck” can be seen as two separate segments, I and II. Segment I does

not have the classic characteristics of a coiled-coil and is considered to be less stable than

segment II [Thormahlen 1998]. Segment I has charged or hydrophilic residues in the

interface; this departs from the classical definition of a stable coiled-coil. Segment II

forms a more classical coiled-coil where the “a” and “d” positions are occupied by

hydrophobic residues.

The model advanced by Tripet [Tripet 1997] suggests that the coiled-coil region

of the “neck” coils and uncoils in response to binding site changes. Segment I is able to

uncoil more than segment II. The action in the model starts with one head bound to the

microtubule and the second detached. The coiled-coil region of the “neck” does not allow

the detached head from finding a binding site in the microtubule. In response to the

leading heads binding, a conformational change occurs that could cause a portion of the

24

“neck” coiled-coil to uncoil. This allows the trailing head to rotate and find a new binding

site in the positive charge direction on the microtubule. In this model the coiled-coil in

the neck is found to be a key element in the performance of the protein.

Myosin II is another protein where the coiled-coil conformation plays an

important role. The myosin II protein plays a fundamental role in muscle contractions and

cellular and intercellular mobility. Structurally, the myosin II protein and kinesin are

similar. Both have a separate globular binding sites connected to stalk or tail through a

polypeptide chain called a “neck”. The tail of the myosin and kinesin are formed from

two helices coiled around each forming a coiled-coil.

The myosin protein has been studied to determine how the stability of the coiled-

coil neck region impacts the head to head interactions, force generations and regulation

[Chakrabarty 2002]. They found that the coiled-coil conformation remains largely intact

in the presences and absence of actin and it is estimated that it would require about 5-

6kJ/mol per residue to uncoil. Another study tested how important neck flexibility was

on the mechanical performance of the myosin [Lauzon 2001]. They showed that the

presence of a stable coiled-coil region at the neck of the myosin significantly impairs the

mechanical performance of the myosin. They also found that a stable coiled-coil region

needed to be 15 heptads removed from the neck before normal mechanical function is

restored. Although the last two studies sites appear to contradict each other, these studies

demonstrate the important role the coiled-coil plays in different proteins.

25

Coiled Coil Stability Using Experimental Data

An approach being explored at the UCHSC, the relative stability of a coiled-coil

substructure within a protein is being determined. It has been shown that the stability of a

coiled-coil varies with the residues that occupy ‘a’ and ‘d’ positions within the

hydrophobic core of a coiled-coil [Tripet 2000, Wagschal 1999]. Core stability may be an

indicator that a coiled-coil may be able to form, but this does not necessarily indicate a

coiled-coil is present. It has also been noted that if the structure within the protein is not

stable, the protein’s structure will not fold and function properly. This would naturally

lead one to conclude that the structure is not present.

The hydrophobic core of a protein has a great influence on the overall stability

and folding rate. It has been shown [Baldi 2000] that by modifying a protein’s

hydrophobic core by a single methyl group, the folding rate can be reduced and the

overall stability can be increased from between 0.8 to 2 kcal/mol. It was suggested that

this change is caused by the overall conformational strain within the core because of the

residue changes

Studies have explored the relationship between selected hydrophobic core amino

acids and coiled-coil stability. Two studies examined the effects that replacing a single

amino acid with each of the 18 other amino acids on the stability and oligomerization

state of the protein. Both of these take a similar approach by replacing one of the

hydrophobic amino acids in the core of the coiled-coil. The first study replaced the amino

acids at the ‘a’ position. The second study replaced the amino acid in the ‘d’ position.

26

The results of these studies allowed the generation of a relative thermodynamic stability

scale for the 19 naturally occurring amino acids in the ‘a’ or ‘d’ position of a coiled-coil.

How does the constituent amino acids in the ‘a’ and ‘d’ positions in the

hydrophobic core of adjacent heptad affect overall stability and protein folding? A

hydrophobic cluster is defined as a consecutive string of three hydrophobic non-polar

amino acids in the hydrophobic core of a coiled-coil [Kwok 2003]. Kwok designed two

proteins with identical properties; the only difference between the two was they have a

different number of hydrophobic clusters. Two proteins were designed for this study.

Protein P2 had two clusters and protein P3 had three clusters. The results of this study

showed that the P3 protein folded more often than that of P2 in benign buffer. It also

showed that P3 was more stable than P2. Kwok suggests that the differences between the

two proteins are due mainly to the burial of the non-polar surface. Kwok further suggests

that clusters may stabilize the proteins in structurally significant regions, while the non-

clustered areas are involved in conformational changes that allow for protein-protein

interactions.

27

Chapter 3

CHAPTER 3

STABLE INPUT

UCHSC

Protein research at the UCHSC has used a model 2 stranded, homo-stranded,

parallel coiled-coil protein to determine the effects that replacing different amino acids in

the sequence has on the relative stability of the protein. From this and other work [Kwok

2003], it is hoped that the relative and absolute stability of the protein can be determined.

There are a number of advantages of studying the coiled-coil domain. The advantages are

[TRI2 2003]:

∞ Abundant motif in proteins∞ There is only one type of secondary structure present, i.e. the α-helix∞ Only two interacting α -helices are required to introduce tertiary and

quaternary structure∞ Diversity in length makes it an ideal system to test predictions∞ All the non-covalent interactions that stabilize the three-dimensional structure

of proteins are found in coiled-coils∞ Experimentally easy to analyze structure and stability

Being able to determine protein stability is important because a minimum

threshold of stability is required to initiate final protein folding and stability is intimately

28

involved in conformational changes and function of proteins [Kwok 2003, Lauzon 2001,

Chakrabarty 2002]. To expedite this work, an analysis tool is needed to calculate the

stability of an amino acid sequence. The “Stable Input” tool was developed in

conjunction with UCHSC to help the center determine coiled-coil stability over an entire

sequence prior to conducting a lengthy experiment.

Stable Input Parameters

An HTML graphical user interface program that is available on University of

Colorado at Colorado Springs Computer Science department Linux server provides input

to “Stable Input” [SI 2003]. This program allows the biologists the opportunity to enter a

sequence, set parameters, and perform calculations based on custom or default parameter

values. The results are provided in the form of up to eight different graphs and a tab

delimited text file of sequence values in kilo-calories per mole (kcals/mol).

The input from the HTML program is parsed and a common gateway interface

PERL program called “stable_coiled_sub.pl” calculates the results. The calculations are

based either user inputs or program defaults. The user settable inputs are summarized in

below.

Sequence Information1. Sequence2. Sequence Name3. Heptad Registry offset4. Window width

29

Tabulated Input1. Helical Propensity2. Hydrophobic core stability between a and d’ and d and a’ positions3. Intra-chain (i to i+3 or i to i+4) electrostatic interactions4. Inter-chain (g-e’ or i to i’+5) electrostatic interactions5. Hydrophobic Clusters6. Entropy-Chain Length

The window width allows the user to determine the number of amino acids over which to

calculate the relative stability. There are two options, 7 and 11. A window size of 7 is the

default window size in the program. The idea is that windowing the results for a

particular amino acid sequence will include the influence of at least one heptad on the

one amino acid being scored. The windowed point, when aligned with the amino acid

sequence, represents the stability trend for that position derived from the amino acids

slightly before and slightly after the current amino acid position. The beginning and end

of the sequence need special handling because there are too few amino acids to populate

a full window. The windowing algorithm is outlined below for a window width of 7, it

takes three parameters, the sequence array, Window array and the widow width and

returns an array of the same len:

WindowingINPUT: Raw Sequence Array

Window Array Window width

current position=0FOR EACH $Amino Acid in the ( Raw Sequence Array ) IF ( current position > window width /2 ) and (current position <= (Raw Sequence length) - window width /2) THEN 7 Windowed Array[current position] = Σ Raw Sequence [i]; i=current-3 ELSE IF ( current position == 0 ) THEN Window width/2 Windowed Array [current position] = Σ Raw Sequence [i]; i=0

30

ELSE IF ( current position == 1 ) THEN

Windowed Array [current position]= Windowed Array [0]+Raw Sequence [4]

ELSE IF ( current position == 2 ) THEN

Windowed Array [current position]= Windowed Array[1]+ Raw Sequence [5]

ELSE IF ( current position == Two From Sequence End ) THEN 6 Windowed Array [current position] = Σ Raw Sequence [i]; i=current position -3

ELSE IF ( current position == one from sequence end ) THEN 5 Windowed array [current position] = Σ Raw Sequence [i]; i=current position -3

ELSE IF ( current position == sequence end ) THEN 4 Windowed array [current position] = Σ Raw Sequence [i]; i=current position -3

current position = current position +1

A similar approach is used to implement the 11 amino acid window width. The

major difference is that the beginning and ending partial windows are extended to include

5 positions before and after the current position.

The “beginning” case is handled by summing the first window/2+1 positions to

produce the 0th windowed result value, summing the first window/2+2 positions produces

the 1st windowed result value. This continues until the values for the window width/2 -1

result value is calculated. A similar calculation produces the windowed value for the

“end” corner case. When the number of positions goes below the window value, the

remaining values are used until the last four values are used for the last windowed

31

positions. An example of this calculation is illustrated for a small sequence in Table 3-1,

Windowing Algorithm for Window = 7.

AminoAcid D F Y H L A D E R G H A L V L L ITableValue 1 1 2 4 1 2 3 1 2 1 3 1 3 1 1 2 1Values A B C D E F G H I J K L M N O P Q

ValuesUsedForWindow A

BCD

ABCDE

ABCDEF

ABCDEFG

BCDEFGH

CDEFGHI

DEFGHIJ

EFRGHJK

FGHIJKL

GHIJKLM

HIJKLMN

IJKLMNO

JKLMNOP

KLMNOPQ

LMNOPQ

MNOPQ

NOPQ

WindowedValue 8 9 11 14 14 15 14 13 13 14 12 12 12 12 9 8 5

Table 3-1 Windowing Algorithm for Window = 7

The heptad registry position parameter sets the heptad registry offset for the input

sequence. This parameter defaults to ‘g’ if not specified by the user. The heptad registry

offset determines the heptad registry position of the first amino acid in the sequence.

Having set the first registry position, the rest of the sequence is set according to the

heptad repeat (abcdefg)n. The registry position of the sequence is stored in a parallel

array and is used in all the calculations performed by the Stable Input tool.

There are five experimentally determined parameter tables provided by UCHSC

that form the basis of all calculations. The user can override these tables by selecting the

custom radio button for any of the input parameters and providing a complete table of

32

values in the prescribed format. One or all of the five tables can be customized without

affecting the other tables.

Each of the five tables is formatted according to the information being described.

The helical propensity table contains the one helical propensity value for each of the 20

amino acids. The Intra-Chain Electrostatics Interactions table contains a value for a select

set of amino acid pairs and their values are based on the spatial separation of the pair

members. The Inter-Chain E/G Electrostatic Interaction table has two values for each

amino acid. These values are based on whether the amino acid is in the ‘e’ heptad

position or the ‘g’ heptad position. The Hydrophobic core stability table also has two

values per amino acid. These values are based on the relative heptad position, either ‘a’

or ‘d’, for each amino acid. The entropy table has a single entry per amino acid and

represents the amount of energy that should be removed from the final stability

calculation based on the amino acids in the sequence.

All table values in all five tables are listed in kcals/mol and are listed in tables and

represent the amount of relative stability each of these amino acid interactions contribute

to the over all stability of the sequence. Some of these tables represent a characteristic of

the amino acid such as helical propensity. Whereas others are based on amino acid

interactions that are derived not only on the amino acids involved but their relative

position to other amino acids within the coiled-coil.

33

Helical Propensity

The helical propensity value measures the effect a particular amino acid has on

the creation of a helix. The first propensity scale was actually a measure of the statistical

frequency that the different amino acids were found to occur in helices. Ala has the

highest helical propensity while Glu, Met, Leu, and Lys are slightly less helically prone.

Those amino acids with the least helical propensity are Gly, Ser, Thr, and Pro. Pro

actually disrupts helical formations.

Hydrophobicity

Hydrophobicity refers to the tendency of non-polar molecules to associate with

each other rather than with a polar substance such as water. The most hydrophobic

amino acids are those with aliphatic and aromatic non-polar side chains. An aliphatic

compound is one that is not aromatic; i.e., it lacks a particular arrangement of atoms in its

molecular structure. These amino acids are Ile, Met, Leu, and Val. An aromatic molecule

or compound is one that has special stability and properties due to a closed loop of its

electrons. Phe is an aromatic amino acid. The other amino acids Arg, Lys, Tyr, and Trp

have a mixture of hydrophobic, polar and charged characteristics. The experimental

tables used in Stable Input have ‘a’ and ‘d’ hydrophobic core stability values with the

helical propensity component removed. The hydrophobic core of a coiled-coil is depicted

looking down the axis of the coils in Figure 3-1, Coiled Coil A/D and E/G Interactions

[UOFG 2003]. The hydrophobic interaction between amino acids in the ‘a’ and ‘d’

34

positions is one of two interactions that occurs between the amino acids of the different

coils.

Figure 3-1 Coiled Coil A/D and E/G Interactions

E/G Interactions

Also depicted in Figure 3-1, Coiled Coil A/D and E/G Interactions, is the relative

position of the amino acids in the ‘e’ and ‘g’ positions on the different coils. Leu, Ile,

Met, and Val are the only amino acids that can occur in the ‘e’ or ‘g’ position that

impacts the stability. The other amino acids do not contribute to stability when found in

these positions. The arrows in Figure 3-2 Lateral View Coiled Coil E/G Interaction,

depicts the relative positions of the ‘e’ and ‘g’ positioned amino acids along the coiled-

coil pair. The E/G interactions add to overall coiled-coil stability by creating bonds

between these amino acids and pulling the two coiled regions together.

35

Figure 3-2 Lateral View Coiled Coil E/G Interaction

Intra-Chain Electrostatic Interactions

The intra-chain Electrostatics Interaction is the interaction that occurs between

amino acids in the same coil. These interactions only apply between the charged amino

acids His, Arg, Lys, Asp, and Glu that are found at a distance of i+3, i+4, or i+5 from its

pair partner i. In the case of the pair partner being at a distance of i+5, this interaction is

applied only if the i+5 position is in the heptad position ‘e’ or ‘g’. This calculation

determines the additional stability gained by having charged amino acids above and

below the current amino acid. The spatial relationship is due to the relative positions of

the amino acid around the helix.

36

Clusters

When hydrophobic amino acids occupy the hydrophobic core of the coiled-coil in

consecutive heptads, stability increases [Kwok 2003]. The clustering of hydrophobic

amino acids is also considered in the Stable Input program. Considering only heptad

positions ‘a’ and ‘d’, Figure 3-3, Clustered Hydrophobic Core, illustrates clustering in

consecutive heptads. In the figure, the hydrophobic amino acids, Phe, Ile, Leu, Met, Val,

and Tyr are the darkened circle, while all others are open circles. A cluster is defined

starting and ending with three or more consecutive hydrophobic amino acids occupying

the hydrophobic core positions with no more than one of these positions being occupied

by anything else.

Amino Acid Sequence

Seq1Seq2

GabcdefEAEALKA-EIEALKA-KAEAAEG-KAEALEG-KIEALEG-KAEAAEG-KAEALEG-EIEALKAEAEALKA-EAEALKA-KIEAAEG-KAEALEG-KIEALEG-KAEAAEG-KAEALEG-EIEALKA

Schematic Representation of Hydrophobic residue at a and d positions

Seq 1

Seq 2

a d a d a d a d a d a d a d a d3 Clusters

2 Clusters

Figure 3-3 Clustered Hydrophobic Core

Entropy

The entropy table has one experimentally determined entropy value per amino

acid. These values represent the change in system entropy due to the presence of each of

37

the 20 amino acids. Entropy is a measure in the energy distribution within a system. As

an example, the amino acid Pro does not appear in helical conformations. The entropy

table shows that Pro changes the entropy by 17 kcal/mol, this suggests that a large change

in entropy indicates a decrease in coiled-coil stability.

Program Flow

Program flow is illustrated in Figure 3-4, Stable Input Program Flow. The

program begins by reading the five stability parameter tables into five hash tables. The

keys for the hash tables are either the amino acids or, in the case if the intra-chain

interactions table, the amino acid pairs. Each amino acid is then assigned a heptad

position. The first amino acid position is determined by the user; rest of the positions

follow the heptad repeat pattern (abcdefg)n, the heptad positions are saved in a parallel

array. Parallel arrays are also used to save the data for derived from the five input tables

corresponding to each amino acid.

38

Figure 3-4 Stable Input Program Flow

A parallel array is also used to save the final cluster map. A cluster map is used in

the program to identify the regions in the sequence that form hydrophobic core clusters

and includes the positions that separate clusters by at least one position. The pseudo-

code, Cluster Algorithm, below outlines the process of creating the cluster map. This

routine takes three input parameters and returns an array that includes a 1 in each amino

acid position that participates in a cluster.

Input

SequenceHeptad OffsetTablesReq. Graphs

InputTables toHashTables

CreateHeptadRegistryArray

CreateParallelarrays fromtables

ApplyClusterAlgorithm

ApplyWindowingAlgorithm

Windowed Stability =∑ Window ArraysNon-Windowed Stability =∑ Non-Window Arrays

CGI OutputTable/Graphs

39

Cluster Algorithm

INPUT : Raw Sequence Array : Parallel Hydrophobe Map Array

: Parallel Heptad Array : Initial Heptad OffsetLOCAL : Cluster Map

: Position=0 : Next=0

FOR EACH Amino Acid In Raw Sequence Array{IF Amino Acid In Parallel Heptad Array [Position] = “A” OR “D” THEN

IF Amino Acid = PHE, ILE, LEU, MET, VAL, TYR THEN Cluster Map [Next] = 1

ELSECluster Map [Next] = 0

Next=Next+1

Position =Position+1}

WHILE Sub Pattern In Cluster Map { ((1{2,}((\s{1}1{1})*(\s{1}))1{2,})|(1{2,}))/g)

Cluster Bridge = Replace Sub Pattern With All 1’s}Position=Next=0FOR EACH Raw Sequence Array

IF Parallel Heptad Array Position “A” OR “D” THENIF Cluster Bridge [Next] = 1 THEN

Parallel Hydrophobe Map Array [Position] = 1ELSE

Parallel Hydrophobe Map Array [Position] = 0Next=Next+1

ELSE Parallel Hydrophobe Map Array [Position] = 0 Position=Position+1

An examination of all the amino acids in the hydrophobic core’s ‘a’ and ‘d’

heptad positions is used to create the cluster map. All hydrophobic amino acids, Phe, Ile,

Leu, Met, Val, or Tyr that are present in the hydrophobic core are marked with a 1; this

produces the hydrophobe map. After the sequence has been processed, the hydrophobe

40

map is condensed to remove all position but those of the hydrophobic core. This is the

cluster map. At this point the cluster map has no relationship to the sequence and is better

suited for cluster pattern searches. Once a cluster pattern is found, the entire cluster

region is marked 1’s; this produces a bridge map. It is called a bridge map because the

clustered areas are bridged by non-hydophobic amino acids that will be included in the

cluster. Figure 3-5, Clusters, illustrates what cluster map patterns are bridged and which

are not.

Cluster Map Cluster Bridge11011011 1111111110111011 0011111111010101 0000000001011010 0000000011100111 11100111

Figure 3-5 Clusters

After all clusters have been found in the sequence, the bridge map is expanded

back using the starting heptad offset provided by the user. Figure 3-6, Mapping Example,

is an example of this process.

Heptad Position GABCDEFGABCDEFGABCDEFGABCDEFGAmino Acid AMHTISCWHKRLDEKLPAKKRSIKRMKACHydrophobe Map 01001000000100010000001001000Cluster Map 11011011Cluster Bridge 11111111Final Map 01001001000100010010001001000

Figure 3-6 Mapping Example

The final map serves as a per amino acid multiplier when the total stability is calculated

for that particular amino acid in the sequence.

41

The five stability parameter tables are used to create sequence-aligned arrays for

the attributes being evaluated. The helical propensity attribute is done by a simple hash

look-up. This attribute is not dependant on heptad position or its relation to any other

amino acid. When completed, each amino acid in the sequences has a helical propensity

value. Table 3-2, Helical Propensity Values, show all values used in the default case and

were derived experimentally and provided by UCHSC [TRI 2003]. The helical propensity

values listed are the amount of stability these amino acids add to the relative stability to

the protein. Note, that Pro has the only negative value and is considered a helix killer

when found in a sequence.

Amino Acid Single Letter

Helical PropensityScore kcal/mol

Alanine A 0.53Cysteine C 0.24

Aspartic acid D 0.12Glutamic acid E 0.18Phenylalanine F 0.26

Glycine G 0.00Histidine H 0.18Isoleucine I 0.33

Lysine K 0.39Leucine L 0.45

Methionine M 0.37Asparagine N 0.18

Proline P -2.5Glutamine Q 0.34Arginine R 0.50Serine S 0.18

Threonine T 0.15Valine V 0.23

Tryptophan W 0.27Tyrosine Y 0.24

Table 3-2 Helical Propensity Values

42

The Hydrophobic core stability between a and d’ and d and a’ positions is

dependant on the heptad positions of the amino acids. In these case a sequence aligned

array is generated that contains a value for only amino acids in the ‘a’ and ‘d’ positions.

The hash lookup for this parameter is the amino acid and is premised on its heptad

position. For example, if an amino acid is in the ‘a’ heptad position it will receive a

different score than the same amino acid in the ‘d’ heptad position. Table 3-3,

Hydrophobic Core Values, shows the default values used in the calculations [TRI 2003].

Amino Acid SingleLetter

PositionA

PositionD

Alanine A 0.72 1.27Cysteine C 0.72 1.27

Aspartic acid D -0.63 0.78Glutamic acid E 0.07 0.27Phenylalanine F 2.49 2.14

Glycine G 0.00 0.00Histidine H 0.47 1.22Isoleucine I 2.87 2.97

Lysine K 0.66 0.51Leucine L 2.55 3.25

Methionine M 2.58 3.03Asparagine N 1.52 1.32

Proline P -5.00 -5.00Glutamine Q 0.86 1.71Arginine R 0.35 -0.15Serine S 0.42 0.72

Threonine T 1.20 1.05Valine V 3.07 2.12

Tryptophan W 1.38 1.48Tyrosine Y 2.11 2.26

Table 3-3 Hydrophobic Core Values

43

The fourth sequence aligned array is for the Intra-chain (i to i+3, i to i+4, and i to

i+5(g)) electrostatic interactions. This calculation is not only sensitive to which heptad

position the amino acid is in, but is also dependant on the amino acids at a sequence

distances of i+3, i+4, and i+5. In this calculation, consideration is only given amino acid

pairs consisting of Asp, Glu, Lys, Arg, and His at the i and i+3 and i+4 positions. If the

amino acid at the ith position is in heptad registry position ‘c’ or ‘a’, then the amino acid

in the i+5 position is considered too with the same pairing restriction applies. Table 3-4,

Intra-Chain Effect, lists the default values used [TRI 2003].

44

Residue Pair i to i+3Score

i to i+4Score

i to i+5Score(e/g)

Lys- Glu 0.2 0.2 0.4Lys-Asp 0.2 0.2 0.4Arg-Glu 0.2 0.2 0.4Arg-Asp 0.2 0.2 0.4His-Glu 0.2 0.2 0.4His-Asp 0.2 0.2 0.4Glu-Lys 0.2 0.2 0.4Glu-Arg 0.2 0.2 0.4Glu-His 0.2 0.2 0.4Asp-Lys 0.2 0.2 0.4Asp-Arg 0.2 0.2 0.4Asp-His 0.2 0.2 0.4Glu-Glu -0.2 -0.2 -0.4Glu- Asp -0.2 -0.2 -0.4Asp-Asp -0.2 -0.2 -0.4Asp- Glu -0.2 -0.2 -0.4Lys- Lys -0.2 -0.2 -0.4Lys- Arg -0.2 -0.2 -0.4Lys- His -0.2 -0.2 -0.4Arg-Arg -0.2 -0.2 -0.4Arg-Lys -0.2 -0.2 -0.4Arg-His -0.2 -0.2 -0.4His-His -0.2 -0.2 -0.4His-Lys -0.2 -0.2 -0.4His-Arg -0.2 -0.2 -0.4

Table 3-4 Intra-Chain Effect

When scoring the intra-chain interaction, 1/2 of the table score is given to each residue

position. If more than one interaction can occur in any of the pair positions then the value

assigned to the amino acids is added.

The fourth sequentially aligned array that is created is the Inter-chain (g-e’or i to

i’+5) electrostatic interactions array. This array is created by considering only those

amino acids in the heptad registry positions ‘e’ and ‘g’. Figure 3-1 and Figure 3-2

illustrate the positional interactions between the two amino acids. Hash lookups for this

45

parameter are straightforward and are only dependent on position. These interactions are

just outside the hydrophobic core and a very few amino acids participate. Ile, Leu, Met,

and Val are the amino acids that have been identified as being significant in these

positions. Table 3-5, Inter-Chain Electrostatics, lists the default values used [TRI 2003].

Amino Acid Position eScore

Position gScore

Ile 0.7 0.8Leu 0.7 0.8Met 0.4 0.5Val 0.4 0.5

Table 3-5 Inter-Chain Electrostatics

Output Table

Appendix A, Tabulated Output, is an example of the 19 column tab-delimited

table produced by Stable Input. The first column is the sequence position number, the

second column is the amino acid in that sequence position, and the third column is the

heptad registry position. Columns 4, 5, 6, and 7 are the values assigned to that amino acid

based on the four of the five input parameter tables. The tenth column is the final cluster

map. Clusters can be identified by 1’s marking consecutive ‘a’ and ‘d’ heptad positions.

Columns 11, 12, 13, and 14 are the helical, intra-chain electrostatic interactions,

hydrophobic core, and inter-chain electrostatic interaction that have had the windowing

algorithm applied. The remaining columns are derived based on the values found in the

five-parameter tables and the cluster map.

46

Column 8, Relative Stability, is the position specific relative stability value for

each amino acid. Amino acids found in clusters are given a full hydrophobicity score in

this calculation. If no clusters are present no hydrophobicity score is added to the relative

stability score.

Relative Stability[i] = Heli Propensity[i]+AD Electro[i]+EG Electro+Cluster[i]*Hydro[i]

Column 9, Windowed Relative Stability, applies the windowing function to the position

specific Relative Stability values calculated above.

Total Stability, column 16, takes into account the entropy in the coiled-coil.

Entropy was introduced late in the project to help reconcile the deviation between the

results obtained using only the four-parameter tables and the experimental data when

longer coiled-coils were used [TRI 2003]. In his research, Dr. Tripet noticed that as the

coiled-coil length was increased, the experimentally measured stability values differed

from the calculated values. The chain length effect, as it has become known as, is an

informal theory advanced to help account for these differences. To assist, the program

was modified to account for entropy in the coiled-coil. The total stability calculation is

made by removing the total accumulated entropy from the total accumulated stability.

i Total Stability [i] = ∑ Relative Stability[j] – Entropy[j]

j=0

47

Column 17, Running Stability is the accumulated stability calculated from the

four input parameters. This represent the amount of stability the sequence gains as a

result of the chain length.

i Running Stability [i] = ∑ Relative Stability[j]

j=0

Column 18, Density stability, is an attempt to normalize the Running Stability

value based on the number of amino acids that were used to determine it. This value is

the total accumulated stability divided by the number of residues used to calculate it.

i Density Stability [i] =( ∑ Relative Stability[j] )/ i

j=0

Finally, the Density window column 19, is the windowed values obtained from

applying the window function to the Density Stability column. These columns are used in

the graphical output.

Output Graphs

When requested, graphs are generated based on the tabulated values. The graphs

are generated using the Linux based program GNUPLOT. This program is called directly

from the Stable Input program and stores the .PNG file in the local directory. The

tabulated and graphical output follows a naming convention that prevents the current

operating directory from getting cluttered by old data. This convention is the system time

48

stamp plus an extension indicating which file it describes. Table 3-6, File Extensions,

shows which files are associated with which data set.

File Extension Data set associated*all.png Graphic with all values graphed*hel.png Helical Propensity*ele.png Intra-Chain Electrostatic Interactions*e_g.png Inter-Chain Electrostatic Interactions*hyd.png Hydrophobic Core*chl.png Stability with Entropy*den.png Stability Density*sum.png Total Stability*ent.png Windowed Entropy*.text Tabulated results

Table 3-6 File Extensions

After all the calculations are complete and the graphs generated, the output is

written to a HTML formatted page. The text file is written to a local file and a HTML

link is provided in the HTML output. The HTML output is formatted to include the

protein sequence with position markers, the initial heptad offset, and all the graphs

requested by the user. After each run, all the old graphs and tabulated data files are

deleted and replaced with the new files. To further assist the biologist, the individual

graphs of the last run are saved in the cgi-bin directory on the server.

The graphs are produced using the GNUPLOT program is installed on the Linux

server. GNUPLOT uses the various columns from the output text file as the data points

for the graphs. The graphs use as the X-axis the amino acid sequence position, the Y-axis

is the column information found in the text file. The summary (*all.png) plot is a

composite of four columns in the text file and requires GNUPLOT to re-plot the graph

49

for each of the columns used. It has four different line graphs, one for each of the four

input parameter, and a point plot. The point plot is the sequence positions that represent

that clustered ‘a’ and ‘d’ positions. In all the graphs, a legend in set in the upper right

hand portion of the graph that is color and symbol coded.

Figure 3-7, Tropomyosin Sequence, was used to demonstrate the graphical output

of Stable Input. Figures 3-8 through 3-18 are the produced using the tool with the heptad

registry option set to A. Appendix A has the table output generated by the Stable Input

program.

0 MDAIKKKMQM LKLDKENALD RAEQAEADKK AAEDRSKQLE DELVSLQKKL KGTEDELDKY60 SEALKDAQEK LELAEKKATD AEADVASLNR RIQLVEEELD RAQERLATAL QKLEEAEKAA120 DESERGMKVI ESRAQKDEEK MEIQEIQLKE AKHIAEDADR KYEEVARKLV IIESDLERAE180 ERAELSEGKC AELEEELKTV TNNLKSLEAQ AEKYSQKEDR YEEEIKVLSD KLKEAETRAE240 FAERSVTKLE KSIDDLEDEL YAQKLKYKAI SEELDHALND MTSI

Figure 3-7 Tropomyosin Sequence

Figure 3-8, Summary Output, shows the summary plot produced. This plot and

all other plots produced have as the X-axis the sequence position and as the Y-axis the

Relative stability in kilo-calories per mole. The Summary Output plot shows a composite

of the windowed helical propensity, E/G and A/D interactions, and the clustered

positions. The clustered regions are identified as points at their respective positions in the

sequence.

This graph shows that in nature a coiled-coil protein, such as tropomyosin, has

significant regions of high helical propensity and hydrophobic clusters. This graph also

shows a correlation between the clustered regions and A/D stability. Since this graph is

an analysis of the entire protein, there are regions in the protein that are not stable coiled-

50

coils. In the graph this is shown in the region around position 175. This region shows that

all the indicators of coiled-coil stability fall off significantly. This is a region where no

clusters form, helical propensity is low and the A/D stability is very low.

51

Figure 3-8 Summary Output

52

Figure 3-9 Total Stability

Figure 3-9, Total Stability, is the sum of all stability factors. It shows that regions

in the tropomyosin protein have a great amount of stability in many different regions.

These are regions in the protein that can be examined in greater detail to determine what

amino acids are in these regions.

53

Figure 3-10 A/D Hydrophobic Stability

Figure 3-10, A/D Hydrophobic Stability, is a graph that shows the amount of

stability gained because of the interaction between the amino acid in the ‘a’ and ‘d’

position Positions ~45 - ~75, ~80 - ~115, and ~235 - ~275 are three regions that stand

out as having in the tropomyosin protein that have high hydrophobic contributions to

stability. There are other regions but these stand out because they represent large trend

regions.

54

Figure 3-11 Helical Propensity

Figure 3-11 Helical propensity, is the helical propensity of the windowed values

for the individual amino acids. This graph shows regions where coil formation is

favored. Since a single amino acid cannot for an amino acid, the windowing of the helical

propensity shows the propensity for a region. The strongest region shown here is in the

region between ~60 and ~120, but to a lesser degree the entire protein show a propensity

to form coils.

55

Figure 3-12 E/G Electrostatic Interaction

Figure 3-12, E/G Electrostatic Interaction, windowed or not, is one of the sparest

graphs generated. The E/G interactions are based on finding two charged (Lys, Glu,

Asp,Arg, or His) amino acids in the ‘e’ or ‘g’ heptad positions. This indicates that the

tropomyosin protein does not rely heavily on the E/G interactions for stability.

56

Figure 3-13 Chain Length

Figure 3-13 Chain Length, is a graph that shows the average amount of stability

gained for each additional amino acid. The idea is that as the length of the sequence

increases there would be a corresponding increase in stability. This was a part of the

research that continues to have trouble and the theory is not set [TRI 2003]. Keeping in

mind that all output graphs are optional, this graph was included here because as the

theory becomes more refined the algorithm can be changed and this graph can become

meaningful.

57

Figure 3-14 Density Stability

Figure 3-14, Density Stability is a graph of Figure 3-13 with the windowing

algorithm. This graph shows how the stability changes over the length of the protein by

dividing the accumulated relative stability as each new amino acid is added divided by

the number of amino acids used to calculate it.

58

Chapter 4

CHAPTER 4

COILED-COIL CLUSTER ANALYSIS

Why Coiled-Coils?

This chapter describes the analysis of hydrophobic amino acids clusters in the ‘a’

and ‘d’ heptad positions in coiled-coil proteins of length 42 amino acids or greater. The

‘a’ and ‘d’ positions are the only significant positions because they form the hydrophobic

core of the coiled coil. The minimum sequences length of 42 was chosen as the minimum

length because the stabilizing effect has been observed when there are at least 3 minimum

length (3 amino acids) clusters separated by at least one minimum length destabilizing

cluster. Kwok cluster experiments used two proteins. The first protein had 3, 3 amino

acid, clusters and 2, 3 amino acid, destabilizing clusters, and the second protein had 2, 3

amino acids, cluster and 1, 3, and 1, 2, amino acid destabilizing clusters [Kwok 2003]. A

sequence length of 42 is the smallest full heptad length in which 3 minimum length

clusters this can be observed.

Hydrophobic interactions contribute significantly to protein stability because

the burial of the hydrophobic surfaces is thermodynamically favorable in aqueous

solutions. Hydrophobic core clustering may play an important role in the structure and

the function of long native coiled-coil proteins as well as be an important mechanism for

59

long coiled-coil proteins to maintain chain integrity. Hydrophobic core clusters can also

serve as “knots” to keep that chain together while allowing regions flexible regions to

function. The stabilizing regions can control protein stability in structurally important

regions and destabilizing clusters of a coiled-coil may be involved in conformational

changes that allow protein-to-protein interactions. Finally, the hydrophobic core clusters

are a natural nucleation sites for protein folding intermediates. Investigating the structural

and functional roles of hydrophobic clusters will improve the understanding of the

mechanism of coiled-coils and protein folding in general [Kwok 2003]. Because the

hydrophobic core can have non-hydrophobic amino acids and form destabilizing clusters

that separate stabilizing clusters. Hydrophobic clusters are those clusters of the amino

acids Phe, Ile, Leu, Met, Val, and Tyr while destabilizing clusters are those clusters of the

amino acids Ala, Ser, Thr, Gln, Asp, Glu, and Lys. Both cluster types are characterized in

this analysis.

Protein Database Analysis

This analysis compares annotated coiled-coil domain data to that of a complete

database of all protein sequences after each has been pre-processed through a modified

coiled-coil prediction algorithm. This analysis will attempt to find answers to five

questions concerning coiled-coil clusters. First, how often do the hydrophobic amino

acids (Phe, Ile, Leu, Met, Val, and Tyr) occur in the ‘a’ and ‘d’ position; second, what are

the lengths of these clusters; third, what amino acids are present in clusters of different

cluster lengths; fourth, how are the various amino acids distributed in various length

60

clusters; and fifth, Coiled-coils always start with stabilizing clusters, can these be

characterized, and if so how? Two sources of data were used to find the answers to these

questions. The first source of data comes from the annotated coiled-coil domain found in

the Swiss-Prot database via SWall on the European Bioinformatics Institute (EBI) servers

[EBI 2003]. The second source of data is the entire protein database found in Swiss-Prot

and TrEMBL [SIB 2003], or SPTR, database via the ExPASy server [EXP 2003]. Since

the SPTR data is a collection of all proteins, a method for determining where in the

proteins coiled-coils may appear is necessary. Both datasets are pre-processed using an

algorithm similar to that found in the Stable Coil program to identify stable coil regions.

After both sets of data have been processed, two working files of data are produced. The

dataset derived from the annotated coiled-coils is referred to as the Swiss-Prot dataset and

the dataset derived from the entire Swiss-Prot TrEMBL database is referred to as the

SPTR dataset.

SPTR dataset

The SPTR dataset was derived from data retrieved from ExPASy Molecular

Biology Server. The ExPASy (Expert Protein Analysis System) proteomics server of the

Swiss Institute of Bioinformatics is dedicated to the analysis of protein sequences.

ExPASy provides a number of different tools, databases, and, other documentation

dedicated to the study of proteins. This server provides access to Swiss-Prot and

TrEMBL Protein Knowledgebase. Swiss-Prot is a protein sequence database that strives

to provide a high level of annotations (such as the description of the function of a protein,

61

its domains structure, post-translational modifications, variants, etc.), a minimal level of

redundancy and high level of integration with other databases. TrEMBL is a computer-

annotated supplement of Swiss-Prot database that contains all the translations of EMBL

nucleotide sequence entries not yet integrated in the Swiss-Prot database. As of mid

August 2003 the Swiss-Prot Release 41.20 had 132675 entries.

The ExPASy server provides a file that contains a copy of the latest Swiss-Prot

database. From the Swiss-Prot TrEMBL web page, a link can be followed to the database

file download page. The complete database is available on CD or by FTP. FTP

downloads can be done from seven different mirror sites. The SPTR dataset used in this

analysis was done using a weekly updated-complete non-redundant database-from the US

mirror site. The database contained 132000 formatted protein entries packet into 55

megabytes. The raw data found in this file is in the format shown in Figure 4-1, SPTR

Protein Entry. This entry contains the Entry name, 108_LYCES, the primary accession

number, Q43495, and the protein name, Protein 108 precursor - Lycopersicon esculentum

(Tomato). The rest of the entry is the protein sequence.

sp|Q43495|108_LYCES (Q43495) Protein 108 precursor. - Lycopersicon esculentum (Tomato).MASVKSSSSSSSSSFISLLLLILLVIVLQSQVIECQPQQSCTASLTGLNVCAPFLVPGSPTASTECCNAVQSINHDCMCNTMRIAAQIPAQCNLPPLSCSAN

Figure 4-1 SPTR Protein Entry

Swiss-Prot Coiled-Coils

Swiss-Prot TrEMBL is a nearly non-redundant protein database consisting of the

SwissProt, SwissProtNew, TrEMBL, and TrEMBLNEW data repositories [SPTR 2003].

62

This database will provide the sub-sequences for queried coiled-coil domains. In this

case, the coiled-coil domain is sought for all annotated proteins. What follows is the

method used to retrieve the coiled-coil domain sequences. Unfortunately the coiled-coil

information is not available in a concise format.

The Swiss-Prot database [SWPR 2003] provides two classes of data: the core

protein data and the annotations. The core data has the sequence data, the citation

information (bibliographical references), and the taxonomic data (description of the

biological source of the protein). The annotation data consists of the description of the

functions of the protein, domains and sites, secondary structure, quaternary structure,

similarities to other proteins, diseases associated with any number of deficiencies in the

protein and sequence conflicts, variants.

A query of the Swiss-Prot TrEMBL database for the coiled-coil domain with the

protein sequence option set will return a list of 70 proteins from SWall (SPTR on the EBI

SRS server) with additional 2600 proteins available on additionally linked pages. Figure

4-2, Coiled-Coil Retrieval, is the method using PERL to retrieve the coiled-coil

sequences.

63

Figure 4-2 Coiled-Coil Retrieval

To get the additional pages, the database is re-queried with the display set to display as

many as 3000 entries on a single page. The retrieved page contains a link to every SWall

entry and another link through the accession number to all 2600 proteins that contain a

coiled-coil reference.

Following one of the SWall entry links opens a detailed page describing the

protein. Near the bottom of the page, the feature section shows the different domain types

identified in the protein. Each of these domains is a link to another page with detailed

information about the particular domain. For the purpose of this analysis, only the Coiled

Coil (POTENTIAL) domain is of interest. Each of the protein links will have at least one

Coiled Coil domain link, but could also have multiple domain links. The coiled-coil

Open SWall2643 Proteins

Parse and Saveall ProteinAccessionNumbers

Assemble “GET”Command fromProtein AccessionData

Parse and Save allCoiled Coil DomainRef’s

Assemble “GET”Command fromCoiled Coil Refs

Parse Coiled CoilDomain Information

Save Coiled CoilSequences

64

domain link provides a page that greatly simplifies coiled-coil sub-sequence retrieval.

The sub-sequence pages contain only the sequence, sequence ID, length, and start/end

position.

Retrieving the coiled-coil data from each entry is done in a three-step process.

First, a list of HTML links is extracted from the 2600 entry protein query page. Second,

the HTML links are used to form a PERL GET call to retrieve the Swiss-Prot entry page

for each protein. The contents of all the Swiss-Prot pages are parsed to find all the links

to the COILED COIL (POTENTIAL) link. The final step uses the coiled-coil linked

pages to form another PERL GET call to retrieve the page that has only the details of the

coiled region.

The information retrieved from this page is shown in Figure 4-3, Coiled Coil

Entry. This entry has the identifier ID A2S3_Human and was the parent protein in which

it was found. This is followed by the domain identification and the sequence positions in

which it was found. Finally, the domain sequence is displayed along with the length of

the region.

ID A2S3_HUMAN_1; parent: A2S3_HUMANFT DOMAIN 134 354 COILED COIL (POTENTIAL).SQ Sequence 221 AA; QALLKRNHVL SEQNESLEEQ LGQAFDQVNQ LQHELCKKDE LLRIVSIASE ESETDSSCST PLRFNESFSL SQGLLQLEML QEKLKELEEE NMALRSKACH IKTETVTYEE KEQQLVSDCV KELRETNAQM SRMTEELSGK SDELIRYQEE LSSLLSQIVD LQHKLKEHVI EKEELKLHLQ ASKDAQRQLT MELHELQDRN MECLGMLHES QEEIKELRSR S//

Figure 4-3 Coiled Coil Entry

From this page, the coiled-coil sub-sequence, name and start and end position

information are save to a file.

65

The saved file is not perfect. There are over 2600 protein links that were followed.

This process took over one hour and forty minutes using a high-speed network

connection. During this process there were a number of “server time-out” errors that

were also written to output file. There were multiple attempts to get an error free run.

This was not possible. These entries had to be removed by hand. Of the 2600 links

followed, about nine “time-out” errors were found. Since this was a small proportion of

all the links removing them should not affect the overall results.

Stable Coil Pre-Processing

Before any analysis can begin, the specific coiled regions of each sequence need

to be determined. Coiled-coils are composed of multiple coiled coils that wrap around

each other. The individual coils are not necessarily aligned on the same heptad registry.

To identify the coils on different heptad alignments, the modified Stable Coil algorithm is

used. Even though the Swiss Prot dataset has already identified purported coiled-coil

regions, the individual coils have not been. Using the modified Stable Coil algorithm,

both datasets can be processed to determine specific coiled regions and the heptad

registry offset in which they exist.

Stable Coil is offered by Pence, The Canadian Protein Engineering Network [SCP

2003], and is a program designed to predict the location and stability of alpha-helical

coiled-coil conformations within protein sequences. The program uses experimentally

derived alpha-helical propensity and stability coefficients as reported by [Zhou 1994,

Wagschal 1999 and Tripet 2000]. By summing the residue scores over variable window

66

widths and comparing the total score assigned to each amino acid to a known globular

and cytoskeletal coiled-coil containing sequences, the program displays the region and

probability (in kcal/mol) that a particular sequence will adopt a coiled-coil conformation.

The modified version of the algorithm uses a 42 amino acid window with a probability

that the sequence is a coiled region set to 38kcal/mol.

The modified Stable Coil analysis algorithm uses coil stability and helical

propensity to identify coiled regions. Each sequence is processed seven times; once for

each heptad position. Each amino acid has the combined helical propensity and stability

coefficient applied to it based on its heptad registry position. The value the amino acid

position assigned is determined by which heptad position it occupies in the heptad

alignment. The amino acid position is set to one of three different values whether the

amino acid is in the ‘a’, ‘d’, or one of the other five positions. Table 4-1, Helical

Propensity and Stability Values, lists the values that are used.

67

AminoAcid

PositionA

PositionB Other

A 1.245 1.8 0.528C 1.245 1.8 0.237D -0.75 0.9 0.116E 0.255 0.45 0.176F 2.75 2.4 0.264G 0 0 0H 0.67 1.4 0.182I 3.185 3.3 0.325K 1.045 0.9 0.385L 2.985 3.7 0.446M 2.96 3.4 0.369N 1.67 1.5 0.182P -10 -10 -5Q 1.18 2.05 0.336R 0.86 0.35 0.495S 0.605 0.9 0.182T 1.345 1.2 0.154V 3.295 2.35 0.231W 1.635 1.75 0.27Y 2.285 2.5 0.237

Table 4-1 Helical Propensity and Stability Values

After applying these values to the sequence, windowing is applied to locate coils.

Starting with the sequence values and a zeroed parallel array, a window of the first 42

values is summed. This sum is applied to the parallel array if the present value in a

position is less that the new sum. This process is repeated until the entire parallel array is

set. After the windowing process is complete, the regions that have at least 3 heptads with

a value of greater than 38 are deemed to be coiled regions. These regions are then

extracted from the sequences and saved along with the heptad registry positions with

which it was found. When preprocessing is complete, all coiled regions in all the protein

sequences are identified and each coiled sequence has a starting heptad offset assigned to

it. These new sequences are place in one of two new datasets that are used in this

analysis. The first dataset, containing 2817 coil sequences, is the Swiss-Prot data having

68

originally come from the Swiss-Prot coiled-coil annotated database, and the set second

dataset, containing 67358 coil sequences, is the SPTR data having been derived from the

entire SPTR database.

Coil Analysis

The Swiss-Prot and SPTR dataset have a great variety sequence lengths. A graph

depicting this variety in total sequence length is shown in Figure 4-4, Normalized Length

Frequency. This graph shows that both datasets have a similar distribution of sequence

length when normalized to the greatest sequence length in the set. Both datasets had

recorded the most frequent length at 44 amino acids. The Swiss-Prot dataset was

normalized to 216 sequences and the SPTR dataset was normalized to 8660 sequences.

69

Normalized Length Frequency

0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

0.1200

0.1400

42 49 56 63 70 77 84 91 98 105

Sequence Length

No

rma

lize

d C

ou

nt

Swiss-Prot SPTR

Figure 4-4 Normalized Length Frequency

Having collected about 70000 coil sequences between the two dataset the first question to

be answered is at what frequency do the hydrophobic amino acids Phe, Ile, Leu, Met,

Val, and Tyr occupy the hydrophobic core positions ‘a’ and ‘d?’

The frequency at which the different amino acids appear in the hydrophobic core

are listed in Figure 4-5, Amino Acid in A and D positions 6&7 Heptads -SPTR and

Figure 4-6, Amino Acid in A and D 6&7 Heptads -Swiss-Prot. These two tables show the

data for sequences that are 6 and 7 heptads in length in both the ‘a’ and ‘d’ heptad

positions. Going from left to right the bars in each graph represent the frequency of the

amino acid in the A then D position with the 6 heptad data set first the 7 heptad data.

70

6&7 Heptad Amino Acid SPTR Data

0.00

0.05

0.10

0.15

0.20

0.25

0.30

A C D E F G H I K L M N P Q R S T V W Y

Amino Acids

No

rm

alize

d C

ou

nt

A 6 Heptad D 6 Heptad A 7 Heptad D 7 Heptad

Figure 4-5 Amino Acid in A and D positions 6&7 Heptads-SPTR

6&7 Heptad Amino Acid Swiss Prot Data

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

A C D E F G H I K L M N P Q R S T V W Y

Amino Acids

No

rma

lize

d C

ou

nt

A 6 Heptad D 7 Heptad A 7 Heptad D 7 Heptad

Figure 4-6 Amino Acid in A and D positions 6&7 Heptads - Swiss-Prot

71

In both sets of data the in raw numbers for the first two full heptads show that in

either case Leu is the dominate amino acid in either the ‘a’ or the ‘d’ position, but Leu is

preferred in the ‘d’ position. Ile and Val are the next two dominant amino acids. These

two are preferred in the ‘a’ position in the Swiss-Prot data, but in the SPTR data the

preference is strong in Val but almost even in Ile. Phe appears to favor the ‘a’ position in

the SPTR data but is very sparse in the Swiss-Prot data. Surprisingly the non-

hydrophobic amino acid Ala appears in the ‘a’ and ‘d’ position more often than Tyr in

both datasets and favors the ‘d’ position.

Aminio Acid Frequency Swiss-Prot

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

A C D E F G H I K L M N P Q R S T V W Y

Amino Acid

No

rma

lize

d C

ou

nt

Heptad Position A Heptad Position D

Figure 4-7 Normalized Amino Acid Distribution Swiss-Prot

72

Amino Acid Frequency SPTR

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

A C D E F G H I K L M N P Q R S T V W Y

Amino Acid

No

rma

lize

d C

ou

nt

Heptad Position A Heptad Position D

Figure 4-8 Normalized Amino Acid Distribution SPTR

Figure 4-7, Normalized Amino Acid Distribution Swiss-Prot, and Figure 4-8,

Normalized Amino Acid Distribution SPTR, shows the relative frequency the amino

acids appear in the A and D positions for both sets of data. For the Swiss-Prot dataset the

A position is dominated by Leu, Ile, Val, Lys, Asn, Arg and Ala and in the ‘d’ position

Leu, Ala, Ile, Val, Lys, Gln, and Met. The SPTR data shows that the ‘a’ position is

dominated by Leu, Ile, Val, Phe, Ala, and Tyr and in the ‘d’ position Leu, Ile, Val, Ala,

Phe, Met, and Tyr. Both of these datasets show that Ala competes with the hydrophobic

amino acids in occurrence frequency. Other studies (Tripet 2000, Wagschal 1999, Lupas

1991) have found that L is most likely to be found in the ‘a’ and ‘d’ positions followed by

the other hydrophobic amino acids with a strong showing of Ala in both the ‘a’ and ‘d’

73

positions. The strongest disagreement was in the frequency in which Met occurred. This

study showed it was consistently one of the least likely hydrophobic amino acid to occur

in the in the ‘a’ and ‘d’ position, but in the other studies, Met was the third most likely

hydrophobic amino acid to appear in the ‘a’ and ‘d’ positions.

The stabilizing effect that clusters have on longer sequence chains has been seen

experimentally. Do long protein chains have more clusters? If they, do how are they

characterized?

To answer this question the clusters found in all the sequences in both datasets are

examined. A minimum sequence length of 42 amino acids or 6 heptads is examined and

compared. The distribution of the normalized cluster length across all sequence lengths is

shown in Figure 4-8, Normalized Cluster Count by Heptad Length. This figure shows the

total number of clusters of length three or greater that appear in the various length

sequences. The Swiss-Prot dataset has 5526 clusters and the SPTR dataset had 102718.

Figure 4-9, Normalized Clusters by Heptad Length, shows that the Swiss-Prot

dataset has a slight propensity for having fewer clusters in shorter sequences than that of

the SPTR dataset. As the sequences get longer the cluster count for both sets of data falls

off, but the SPTR data diminishes more rapidly than that of the Swiss-Prot data. While

the SPTR dataset approaches no clusters counted beyond 12 heptads in length there is a

relative consistent from length 12 through 19 heptads. Since the Swiss-Prot data is comes

from the coiled-coil data set this data seems to suggest that clusters are important in

longer coiled-coils.

74

Total Clusters in Heptad Lengths

-

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Heptads

No

rm

alize

d C

lus

te

rs

Co

un

t

Swiss-Prot SPTR

Figure 4-9 Normalized Cluster by Heptad Length

When considering the number of hydrophobic amino acid in any given length,

how often do the coiled sequences have them in the hydrophobic core positions ‘a’ and

‘d’? Do the coils in nature have a minimum number of hydrophobic amino acids in their

hydrophobic core?

The frequency of hydrophobic amino acids in the coiled sequences is determined

by counting the number of times a hydrophobic amino acid appear in the ‘a’ and ‘d’

positions for all sequence lengths. The results are summarized in Table 4-2, Phe, Ile, Leu,

Met, Val, and Tyr Frequency Swiss-Prot, and Table 4-3, Phe, Ile, Leu, Met, Val, and Tyr

Frequency SPTR. Both tables are based on the number of ‘a’ and ‘d’ positions found in

the coiled sequence. The averages are based on the number of ‘a’ and ‘d’ positions

available in the given heptad length, divided by the average number of hydrophobic

amino acids found in all sequences of that length.

75

HeptadLength

AveHydroph

SeqsFound

%Hydroph

HeptadLength

Ave LrgHydroph

SeqsFound

% LrgHydroph

6 7.75 557 0.65 16 19.65 20 0.618.57 311 0.66 21.11 18 0.64

7 9.04 309 0.65 17 21.06 16 0.629.71 232 0.65 21.15 20 0.6

8 10.16 178 0.63 18 23.06 17 0.6410.97 155 0.65 23.27 15 0.63

9 11.27 135 0.63 19 24 17 0.6312.13 112 0.64 25.17 6 0.65

10 12.92 75 0.65 20 25.43 7 0.6413.33 83 0.63 23.56 9 0.57

11 14.04 80 0.64 21 27.4 15 0.6514.52 82 0.63 25.44 9 0.59

12 15.95 44 0.66 22 28.7 10 0.6516.49 41 0.66 27.83 6 0.62

13 17.03 29 0.66 23 29.17 12 0.6317.04 48 0.63 30 1 0.64

14 18.03 35 0.64 24 29 1 0.618.84 37 0.65 30.4 5 0.62

15 18.52 33 0.62 25 32 1 0.6419.52 23 0.63 34.5 2 0.68

Table 4-2 Phe, Ile, Leu, Met, Val, and Tyr Frequency Swiss-Prot

76

HeptadLength

AveHydroph

SeqsFound

%Hydroph

HeptadLength

Ave LrgHydroph

SeqsFound

% LrgHydroph

6 8.2 27039 0.68 16 20.65 51 0.658.89 12137 0.68 22.04 52 0.67

7 9.48 7673 0.68 17 22.34 50 0.6610.1 5204 0.67 22.34 38 0.64

8 10.72 3966 0.67 18 23.48 33 0.6511.26 2719 0.66 24.32 31 0.66

9 11.97 1978 0.66 19 24.4 30 0.6412.69 1500 0.67 25.27 15 0.65

10 13.44 1279 0.67 20 26.64 11 0.6713.99 892 0.67 26.71 21 0.65

11 14.74 736 0.67 21 27.79 19 0.6615.49 452 0.67 26 12 0.6

12 16.7 240 0.7 22 29.38 16 0.6717.16 222 0.69 30.08 12 0.67

13 17.34 160 0.67 23 28.85 13 0.6317.73 175 0.66 31 4 0.66

14 18.74 123 0.67 24 30.67 3 0.6419.51 133 0.67 30.4 5 0.62

15 19.49 136 0.65 25 32 1 0.6420.25 75 0.65 32 1 0.63

Table 4-3 Phe, Ile, Leu, Met, Val, and Tyr Frequency SPTR

An examination of all sequences of all lengths show that on average, the

hydrophobic core of these coiled regions are occupied by hydrophobic amino acids about

66% of the time. The Swiss-Prot dataset had an average of 65% for heptad lengths of 6 to

15. As the sequence length extends and few sequences are found, the average falls to

60%. The SPTR dataset show that in the heptad lengths of 6 to 15 the hydrophobic core

occupancy rate was about 67% and beyond that the average was 65%. This would seem

to suggest that when the Stable Coil algorithm is used to predict coiled-coil regions there

77

is a constant number of hydrophobic amino acid that must reside in the hydrophobic core.

This could prove to be a minimum cutoff for coiled-coil regions.

Knowing that clusters exist in both the Swiss-Prot and SPTR datasets, how many

clusters of any length are in sequences of different length? Are hydrophobic clusters

more numerous that non-hydrophobic clusters? This analysis will provide insight into

what separates hydrophobic clusters. The cluster effect can extend beyond the

hydrophobic cluster if two clusters are separated by a single hydrophobic core position

[TRI 2003].

Hydrophobic core clusters are characterized next. In this analysis a hydrophobic

cluster is consecutive ‘a’ and ‘d’ positions being occupied by the amino acids Phe, Ile,

Leu, Met, Val, and Tyr and a non-hydrophobic cluster is when two or more consecutive

‘a’ and ‘d’ positions are occupied by a non-hydrophobic amino acid. Both datasets are

analyzes and are summarized below in the graphs. The first two graphs Figure 4-10, Total

Clusters and Ratio by Sequence Length Swiss-Prot, and Figure 4-11, Total Clusters and

Ratio by Sequence Length SPTR, show the total number of hydrophobic and non-

hydrophobic clusters that are present for a given sequence length.

78

Total Clusters and Ratio By Length Swiss-Prot

0

100

200

300

400

500

600

700

6 7 8 910

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

Heptads

Clu

ste

r C

ou

nt

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Ra

tio

Hydro Clusters Non-Hydro Clusters Ratio

Figure 4-10 Total Clusters and Ratio by Heptad Length Swiss-Prot

Total Clusters and Ratio by Heptad Length SPTR

0

5000

10000

15000

20000

25000

30000

35000

40000

6 7 8 910

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

Heptads

Clu

ste

r C

ou

nt

0

0.2

0.4

0.6

0.8

1

1.2

Ra

tio

Hydro Clusters Non-Hydro Clusters RatioFigure 4-11 Total Clusters and Ratio by Heptad Length SPTR

79

Both charts show that the number of clusters for both the hydrophobic and non-

hydrophobic amino acids, diminish sharply after sequences grow beyond 12 heptads and

very few are found beyond 28 heptads. Even thought the total numbers diminish, both

dataset show a similar pattern ratio of hydrophobic clusters to non-hydrophobic clusters

as the sequence length goes from 6 heptads to over 25 heptads. This indicates that when

hydrophobic clusters are present they are separated by non-hydrophobic clusters between

60 and 80% of the time.

The next set of charts, Figure 4-12, Total Clusters by Cluster Size Swiss-Prot and

Figure 4-13, Total Clusters by Cluster Size SPTR, show the size of the clusters of both

types found in both datasets. The non-hydrophobic clusters are counted starting at length

two while the hydrophobic clusters are counted starting at length 3. Non-hydrophobic

clusters never exceed 6 in length, while the hydrophobic clusters had a diminished

presence beyond length 12.

80

Total Clusters by Cluster Size Swiss-Prot

0

500

1000

1500

2000

2500

3000

3500

2 3 4 5 6 7 8 9 10 11 12 13 14

Cluster Size

Co

un

t

Hydro Clusters Non-Hydro Clusters

Figure 4-12 Total Clusters by Cluster size Swiss-Prot

Total Clusters by Cluster Size SPTR

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

2 3 4 5 6 7 8 9 10 11 12 13 14

Cluster Size

Co

un

t

Hydro Clusters Non-Hydro Clusters

Figure 4-13 Total Clusters by Cluster size SPTR

81

Tables 4-4 though 4-15, detail the distribution of the hydrophobic clusters and

non-hydrophobic clusters for 6 specific heptad lengths found in the two datasets. The

first 6 tables show the analysis for the Swiss-Prot data and the second set of six is for the

SPTR data. Each table represents a different sequence length. The hydrophobic and non-

hydrophobic cluster lengths range from 3 to 10. The first column in the table is the cluster

length, the second and third column, Hydro and Non-Hydro respectively. These columns

have the number of clusters of each length and type that are found for the sequence length

the table represents.

82

ClusterLength

Hydro NonHydro

3 343 1124 164 235 84 46 357 58 49 110Total 636 139

Table 4-4 Clusters 6 Heptad S-P

ClusterLength

Hydro NonHydro

3 211 484 82 105 75 76 397 158 89 210Total 432 65

Table 4-5 Clusters 6 Heptad+1 S-P

ClusterLength

Hydro NonHydro

3 214 514 109 125 76 16 467 108 79 210Total 464 73

Table 4-6 Clusters 7 Heptad S-P

ClusterLength

Hydro NonHydro

3 139 494 85 55 69 16 337 178 79 410 1Total 355 55

Table 4-7 Cluster 7+1 Heptad S-P

ClusterLength

Hydro NonHydro

3 135 514 59 45 45 16 267 78 99 110 2Total 284 56

Table 4-8 Clusters 8 Heptad S-P

ClusterLength

Hydro NonHydro

3 124 354 64 45 41 26 187 128 89 010 1Total 268 41

Table 4-9 Clusters 8+1 Heptad S-P

83

ClusterLength

Hydro NonHydro

3 15346 47814 9453 9215 5156 96 27787 12828 5749 21310 91Total 34939 5711

Table 4-10 Clusters 6 Heptad SPTR

ClusterLength

Hydro NonHydro

3 7214 17484 4500 3035 2981 786 1642 27 842 18 8429 18510 185Total 17959 2132

Table 4-11 Clusters 6 Heptad+1 SPTR

ClusterLength

Hydro NonHydro

3 4041 10304 2947 2125 1959 456 1220 27 7268 3699 19510 19Total 11571 1289

Table 4-12 Clusters 7 Heptad SPTR

ClusterLength

Hydro NonHydro

3 2756 9544 1905 1195 1444 126 9107 5458 2479 14710 60Total 8071 1085

Table 4-13 Clusters 7+1 Heptad SPTR

ClusterLength

Hydro NonHydro

3 2242 9244 1237 1535 1115 146 7517 4178 2579 14910 72Total 6318 1091

Table 4-14 Clusters 8 Heptad SPTR

ClusterLength

Hydro NonHydro

3 1695 6804 958 1385 729 116 4817 3318 1979 9010 52Total 4582 829

Table 4-15 Clusters 8+1 Heptad SPTR

84

These tables show that for the Swiss-Prot dataset, as the size of the cluster

increases by one from 3 to 4 the number found in the sequences declines by 60%,

whereas the SPTR dataset the number found declines by 38%. This would indicate that

clusters of three are favored over the clusters of four in the Swiss-Prot dataset, whereas in

the SPTR dataset the clusters of appear more often than clusters of four these are not as

strongly favored. The most dramatic declines are found in the non-hydrophobic clusters

in both datasets. In the SPTR dataset, when the cluster size is increased from 3 to 4, the

number of clusters found declines by 80%. Similarly, the drop for the Swiss-Prot dataset

is 85%. This data seems to suggest that the presence of small clusters is favored over

large clusters in both the hydrophobic and non-hydrophobic cases. This could suggest

that nature uses small hydrophobic clusters in combination with many small non-

hydrophobic clusters to form longer stable regions in coiled sequences. Nature seems to

favor small stable regions to long stable regions. This may allow flexibility in protein

folding and performance.

Counting the clusters found in the different length sequences gives an

appreciation for the difference found in sequences of different lengths, but how are the

various amino acids distributed in various cluster lengths?

Having determined the frequency of the various cluster lengths, the next step is to

attempt to describe the amino acids that participate in the dominant cluster lengths for

both the hydrophobic and non-hydrophobic amino acids. From the tabulated data above

most hydrophobic clusters are 3 to 4 amino acids in length and appear in sequence length

85

of 6 and 7 heptads. The non-hydrophobic clusters are more selective. These occur in

clusters of two and diminish quickly and are rare beyond length 6.

Figure 4-14, Hydrophobic Amino Acids in Clusters, is a normalized count of the

hydrophobic amino acids that occur in clusters. Both datasets show a similar trend in that

Leu appears most often and Tyr the least. The only discrepancy is in the appearance of

Phe. Phe appear much more often in cluster in the total SPTR dataset than in that of the

Swiss-Prot dataset. The Non-hydrophobic clusters are not as easily characterized. Figure

4-15, Non-Hydrophobic Amino Acids in Clusters, shows that while the Swiss-Prot

dataset favors Ala, Asn, Thr, Ser, and Gln, the SPTR data set favors Ala, Lys, Gln, Glu,

and Arg. The only area of agreement between the two datasets is in what does not appear

in non-hydrophobic clusters. Gly, His, Cys, Asp, and Trp do not appear in either datasets.

Of these Cys, His, and, Asp are hydrophilic amino acids.

Hydrophobic Cluster

0

0.1

0.2

0.3

0.4

0.5

0.6

L I V M Y F

Amino Acid

No

rm

alize

d C

ou

nt

Swiss-Prot SPTR

Figure 4-14 Hydrophobic Amino Acids in Clusters

86

Non-Hydrophobic Cluster

0

0.05

0.1

0.15

0.2

0.25

a n t s q k r e g h c d w p

Amino Acid

No

rm

alize

d C

ou

nt

Swiss-Prot SPTR

Figure 4-15 Non-Hydrophobic Amino Acids in Clusters

The next 4 tables list the specific amino acids that appear in hydrophobic and non-

hydrophobic clusters of various lengths. Table 4-16, Cluster Type Count Swiss-Prot, and

Table 4-17, Non-hydrophobic cluster, Swiss-Prot show the sequences that occur more

than 6 times in the Swiss-Prot dataset. Table 4-18, Cluster Type Count, SPTR, and Table

4-19, Non-hydrophobic cluster, SPTR show the sequences that occur more than 100

times. These cutoff values were chosen first to cut down on the infrequent data and

second, include specific cluster types and their occurrence in the different length

sequences. In this analysis non-hydrophobic cluster length of two amino acids are

considered. The tables have the sequence length the exact cluster sequence and the

number of this type of cluster found.

87

Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num12 IIL 7 13 ILL 16 14 LVL 15 17 ILL 1712 ILI 10 13 LIL 12 14 LVLL 6 17 LIL 612 ILIL 7 13 LLF 7 14 VLL 15 17 LLI 612 ILL 40 13 LLI 6 15 ILL 10 17 LLL 1612 ILLL 9 13 LLL 21 15 ILV 10 17 LVL 712 IML 8 13 LLLL 7 15 LIL 21 18 ILI 612 IVL 8 13 LLV 13 15 LILL 7 18 ILL 912 LIL 21 13 LML 7 15 LLL 16 18 LLL 1812 LLFV 6 13 LVL 8 15 LLLL 8 18 LVL 812 LLI 8 13 VLL 18 15 LLV 6 18 LYL 712 LLL 38 13 VLV 8 15 LLY 7 19 ILL 1212 LLLL 9 13 VLVVV 6 15 LVL 6 19 LILL 612 LLM 6 14 ILL 17 15 LVLL 6 19 LLL 1412 LLV 8 14 LIL 16 15 VLL 8 19 LVL 1212 LLY 10 14 LIV 8 16 ILI 6 20 LLL 712 LML 12 14 LLI 8 16 ILL 12 21 LILL 712 LVL 26 14 LLL 42 16 LIL 10 21 LLL 1712 LYL 6 14 LLLL 10 16 LLI 6 21 LML 712 VLL 13 14 LML 10 16 LLL 25 21 LVL 913 ILI 11 14 LMLLL 6 16 LVL 16 21 LVLL 8

Table 4-16 Hydrophobic Cluster Count, Swiss-Prot

Table 4-16, Hydrophobic Cluster Count, Swiss-Prot, shows that Leu, Ile and Val are the

dominant amino acids in the clusters from the Swiss-Prot dataset and the majority of the

clusters are 3 and 4 amino acids long. Table 4-17, Non-Hydrophobic Cluster Count,

Swiss-Prot, shows that the amino acid Ala is dominant in clusters of 2 and 3.

88

Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num12 AA 9 13 RA 6 16 AQ 8 42 AAA 1012 AK 8 14 AA 11 16 QN 6 46 EK 612 AN 8 14 AE 6 17 AT 8 52 EK 1112 AQ 13 14 AK 7 17 HE 6 62 ER 1312 AR 10 14 AQ 7 17 TN 6 62 KT 612 EK 11 14 AR 6 18 HK 6 76 ER 1112 ER 12 14 ER 10 19 AN 10 77 EK 812 HA 7 14 KE 6 20 AAA 6 86 ER 612 KA 8 14 QK 6 21 AC 612 KQ 7 14 TA 6 21 EK 812 NA 10 14 TK 8 22 EK 612 NK 6 14 TS 6 23 AA 612 QA 11 15 AK 11 23 SA 812 QKE 7 15 AN 7 23 TK 712 QT 7 15 AS 12 24 AK 712 RQ 8 15 KA 6 27 AAAN 812 SA 7 15 NA 7 27 ENS 1112 TK 9 15 QK 6 27 TD 913 AK 7 15 TE 7 28 TK 713 QA 7 16 AK 9 29 AASN 6

Table 4-17 Non-Hydrophobic Cluster Count, Swiss-Prot

Table 4-18, Hydrophobic Cluster Count, SPTR, shows the SPTR dataset is

dominated by 3 and 4 length clusters with Leu, Ile, and Val, but there are more Phe than

in the Swiss-Prot dataset. Table 4-19, Non-Hydrophobic Cluster Count, SPTR, shows

that most of the clusters are 2 amino acids long composed main of Ala and Asn.

89

Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num12 FIL 113 12 LLF 189 12 VVL 170 14 LIL 12812 FLI 123 12 LLI 422 12 YLL 115 14 LLI 12712 FLL 277 12 LLL 823 12 YYL 118 14 LLL 22212 III 251 12 LLLL 186 13 FLL 122 14 LLV 10212 IIL 293 12 LLM 169 13 IIL 146 14 LVL 11412 IIV 125 12 LLV 282 13 ILI 163 15 LLL 18912 ILF 225 12 LLY 131 13 ILL 193 16 LLL 14612 ILI 323 12 LML 196 13 LII 135 17 LLL 11612 ILL 514 12 LVF 103 13 LIL 20712 ILLL 128 12 LVI 158 13 LIV 10412 ILV 230 12 LVL 399 13 LLF 17812 IVI 133 12 LVV 140 13 LLI 18712 IVL 184 12 LYL 126 13 LLL 45212 LFI 116 12 MLL 183 13 LLLL 11912 LFL 217 12 VII 129 13 LLV 15712 LIF 113 12 VIL 173 13 LML 10512 LII 250 12 VLI 220 13 LVL 18412 LIL 408 12 VLL 372 13 VLI 10512 LILL 122 12 VLM 107 13 VLL 21612 LIV 192 12 VLV 166 14 ILL 168

Table 4-18 Hydrophobic Cluster Count, SPTR

Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num Lngth Cluster Num12 AA 516 12 KQ 133 12 SA 251 13 TN 10512 AE 134 12 KS 147 12 SK 141 14 AA 19812 AG 147 12 KT 127 12 SN 201 14 AT 11312 AK 241 12 NA 263 12 SQ 171 14 TA 11112 AN 225 12 ND 115 12 SS 158 15 AA 11712 AQ 208 12 NK 153 12 ST 22212 AR 155 12 NN 247 12 TA 31512 AS 235 12 NQ 162 12 TE 11112 AT 328 12 NR 132 12 TK 15112 CA 120 12 NS 221 12 TN 20012 EA 125 12 NT 184 12 TQ 18112 EK 118 12 QA 217 12 TS 21512 EN 103 12 QH 122 12 TT 20412 GA 173 12 QK 126 13 AA 25112 GN 106 12 QN 167 13 AK 11312 HA 126 12 QQ 163 13 AN 10412 HT 124 12 QS 122 13 AT 13312 KA 224 12 QT 150 13 NA 11912 KK 183 12 RA 171 13 NN 16912 KN 214 12 RN 113 13 SA 118

Table 4-19 Non-Hydrophobic Cluster Count, SPTR

90

Finally, do stabilizing clusters exist and if so, how can they be characterized? It is

thought that each coiled-coil begins with a stabilizing cluster. Each of the coil sequences

in the two datasets are examined, first to find if there is a cluster beginning each

sequence, and then which amino acids populate these clusters. For this part of the

analysis, a convention of 0’s and _’s are used to signify the hydrophobic amino acids and

non-hydrophobic amino acids respectively. Table 4-20, Stabilizing Cluster Swiss-Prot,

shows that only about 33% of the Swiss-Prot sequences begin with a cluster and Table 4-

21, Stabilizing Cluster SPTR shows that 38% of the SPTR begin with clusters.

ClusterPattern

NumberFound

PercentOf total

000 591 20.44%_000 358 12.38%__000 164 5.67%___000 65 2.25%____000 17 0.59%000__ 95 3.29%000_0 172 5.95%0000_ 153 5.29%00000 171 5.91%000___ 37 1.28%000__0 58 2.01%

Table 4-20 Stabilizing Cluster, Swiss Prot

91

ClusterPattern

NumberFound

PercentOf total

000 18527 27.51%_000 7537 11.19%__000 2776 4.12%

___000 906 1.35%____000 255 0.38%

000__ 2355 3.50%000_0 4832 7.17%0000_ 4479 6.65%00000 6861 10.19%

000___ 763 1.13%000__0 1592 2.36%

Table 4-21 Stabilizing Cluster, SPTR

In an attempt to find starting stabilizing clusters other beginning sequences

patterns were examined. Offsetting the starting sequences by _ heptad at a time, shows

that a beginning cluster does not appear even after offsetting 2 full heptads. The Swiss-

Prot analysis examined 2891 sequences and the SPTR analysis examined 67358

sequences.

The majority of the sequences used in this analysis do not begin with a stabilizing

cluster. To search the beginning of the sequence for a starting cluster, the assumed

beginning of the sequence was advanced by increments of _ a heptad. This attempt still

revealed that a majority of the sequences contain no starting stabilizing cluster. At best

between the two data sets 35% of the sequences used began with a cluster.

Of the sequences that did begin with a stabilizing cluster, a closer look at the

cluster patterns 000, 0000, and 00000 shows that the dominant amino acids found in these

clusters are Leu and Ile. Most of the clusters appear in the 12 heptads long sequences.

The cluster sequence Leu-Leu-Leu appears most often. Table 4-22, Cluster Amino Acids

92

Swiss-Prot, shows all the starting cluster combinations that occurred more than once. The

table is sorted on sequence length and has the clusters that appear and the number that are

found for each sequence length. This table contains 274 entries out of the 591 sequences

that begin with clusters of three or more. There are a total of 2891 sequences in this

dataset. When a sequence begins with a stabilizing cluster, the amino acids that appear at

the beginning of those clusters most often are Leu occurring 46%, Ile occurs 19% and

Val 13.5 %.

93

Len Cluster Num Len Cluster Num Len Cluster Num Len Cluster Num12 ILIL 5 13 FYF 2 15 ILV 2 25 YLI 312 ILL 6 13 ILILL 3 15 VLL 3 26 LLL 412 ILLL 4 13 ILL 4 16 ILI 4 27 ILILL 512 IVLVLL 2 13 ILVLIM 2 16 LLI 2 27 ILVLL 212 LFL 2 13 LLFYFL 2 16 LVL 4 27 LLVLL 812 LIL 4 13 LLI 2 16 LVLLLL 2 27 VLVLF 212 LILL 2 13 LLL 4 17 LLI 2 28 ILVLL 212 LIV 2 13 LLLL 2 18 LLL 7 32 LVL 212 LIVVL 2 13 LLV 3 19 ILL 2 34 FILILL 412 LLFV 6 13 MVL 2 19 ILLV 2 42 MLL 512 LLI 3 13 VLI 2 19 LLVL 2 86 MMFVL 312 LLILL 3 13 VLILL 2 19 VLL 2 87 MMFVL 312 LLL 7 13 VLILLV 2 20 FLL 2 95 MMF 212 LLLL 6 13 VLL 2 20 MLL 412 LLLM 2 13 VLLL 2 21 LLILL 312 LLLY 2 13 VLV 2 21 LLL 212 LLM 3 14 FILILV 2 21 MMF 612 LLMLL 2 14 FLIV 3 22 LLL 412 LLV 3 14 IFILM 2 22 LML 212 LLVL 2 14 LFLL 2 22 VLIL 212 LML 3 14 LIL 2 23 LIVL 212 LVL 5 14 LLL 2 24 ILLI 412 LYL 2 14 LVLVLL 2 24 LVL 212 LYV 3 14 VLL 212 MIMM 2 14 VLV 212 VLFL 2 14 YILILL 412 VLI 3 14 YILL 312 VLL 212 VLLL 212 VVL 312 YLIY 3

98 64 67 45

Table 4-22 Cluster Amino Acids Swiss-Prot

The SPTR database results are shown in Table 4-23, Cluster Amino Acids SPTR,

which lists the number of times the different cluster combinations occur as long as it

appears over 15 times.

94

Len Seq Num Len Seq Num Len Seq Num Len Seq Num12 FII 23 12 LFV 24 12 MFL 20 13 FLL 2712 FIL 21 12 LFY 26 12 MII 17 13 FLV 1612 FLF 17 12 LIF 43 12 MIL 34 13 III 2812 FLI 31 12 LII 64 12 MLI 26 13 IIL 1912 FLL 42 12 LIL 114 12 MLL 40 13 ILF 2612 FLV 16 12 LILL 21 12 MVII 20 13 ILI 4112 FLY 20 12 LIM 21 12 MVL 30 13 ILL 2712 FVL 18 12 LIV 71 12 VFL 17 13 ILV 1712 IFL 19 12 LIY 17 12 VII 31 13 IVF 2212 IFLFML 37 12 LLF 75 12 VIL 40 13 LFL 1912 IFLL 16 12 LLFL 16 12 VIV 27 13 LIF 1712 IFLLMV 25 12 LLI 126 12 VLF 31 13 LII 3212 III 92 12 LLIL 26 12 VLI 69 13 LIL 3012 IIL 61 12 LLIV 20 12 VLIL 17 13 LIMII 1612 IILL 25 12 LLL 192 12 VLL 83 13 LIV 1612 IIV 37 12 LLLI 23 12 VLLL 17 13 LLF 8012 IIY 17 12 LLLL 47 12 VLM 30 13 LLI 3312 ILF 107 12 LLLLL 18 12 VLV 40 13 LLL 4812 ILI 91 12 LLLV 25 12 VVF 19 13 LLLL 2212 ILIL 16 12 LLM 50 12 VVL 34 13 LLV 4212 ILL 130 12 LLMIM 23 12 YFM 21 13 LLY 1712 ILLL 21 12 LLML 25 12 YIL 24 13 LVI 1612 ILV 48 12 LLV 72 12 YLI 17 13 LVL 3412 ILY 18 12 LLVL 20 13 LVV 2112 IMI 28 12 LLY 33 13 MLF 2112 IVF 22 12 LMI 37 13 VIL 1812 IVI 20 12 LML 51 13 VLI 3312 IVL 33 12 LMV 16 13 VLL 3812 IVLL 17 12 LVF 27 13 VLLIML 1612 IVV 27 12 LVI 34 13 YLMYLL 2112 IYL 16 12 LVL 122 14 IIL 1612 LFF 23 12 LVLY 18 14 ILL 1612 LFI 32 12 LVV 37 14 LILV 2012 LFL 74 12 LVY 22 16 VLL 2112 LFLL 32 12 LYL 25 22 YFL 18

1272 1581 3557 904

Table 4-23 Cluster Amino Acids SPTR

When a sequence begins with a stabilizing cluster, the amino acids that appear at

the beginning of those clusters most often are Leu occurring 49.5%, Ile occurring 25%

95

and Val 13 %. This is a similar rate at which the Swiss-Prot clusters began. The SPTR

analysis is based on the 4461 clusters found out of the 18527 clusters found in all the

sequences. There are a total of 67358 sequences in the SPTR dataset.

These data show that between 30 and 40% of the sequences used in this analysis

do start with a stabilizing cluster. When a sequence does begin with a stabilizing cluster,

those clusters have a 90% chance of beginning with either Leu, Ile, of Val. This may be a

broad marker that signifies the beginning of a coiled sequence. But the definition offered

by Stable Coil may be too broad. Since the Stable Coil is using a windowing algorithm to

define the coiled region, it my not define the starting and ending point of the coil well

enough to define a starting stabilizing cluster.

Summary of Findings

In this chapter, an analysis of the hydrophobic core of the coiled regions in coiled

coil sequences was preformed. The hydrophobic core of the sequences was first

characterized to find which amino acids were present and how often they occurred. This

was followed by an analysis of clusters of hydrophobic amino acids that are found in the

hydrophobic core of adjacent heptads. The important findings are listed below.

∞ Hydrophobic amino acids occupy the hydrophobic ‘a’ and ‘d’ core positions on

average 65% for the Swiss-Prot dataset and 67% for the SPTR dataset.

∞ The number of hydrophobic clusters decreases by a factor of 2 for each

hydrophobic core position added to the sequence length.

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∞ The number of non-hydrophobic clusters decreases by a factor of 8 as the each

hydrophobic core position added to the sequence length.

∞ Ala is the most likely non-hydrophobic amino acid to appear in the hydrophobic

core in both the Swiss-Prot and SPTR datasets.

∞ The ratio of hydrophobic clusters to non-hydrophobic clusters is .6 to .8 for

sequences from 6 heptads to 22 heptads in length.

∞ Cluster frequency decreases sharply for sequences 6 heptads to 9 heptads in

length.

∞ In both the Swiss-Prot and SPTR datasets Leu is favored in the ‘d’ hydrophobic

core position and Val and Ile is favored in the ‘a’ hydrophobic core position.

∞ Stabilizing Clusters are found in 39% of the of the SPTR sequences and 33% of

the Swiss-Prot sequences.

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Chapter 5

CHAPTER 5

CONCLUSION

This thesis would not have been possible without the direct support and guidance

from Dr. Robert Hodges and Dr. Brian Tripet of the UCHSC and their research on coiled-

coils. Their willingness to help was far beyond anything expected.

Coiled-coil protein domain research has lead to better understanding of this

structure domain in kinesin, myosin, and more recently the SARS virus. Biologists’

ability to create experiments and interpret experimental rapidly will only benefit this

research. However, if insight into the experiment can be gained prior to the lengthy

experiment, time and resources can be saved. In this thesis, the Stable Input program was

written to give biologists at the UCHSC the ability to determine the relative stability of

the coiled-coil protein domain. This program has the capacity to incorporate 6 different

stability factors which produces output that is easily interpreted and portable to other

platforms. The most important aspect of this work is the research biologists now have the

ability to create theoretical protein sequences and draw initial stability conclusions at the

click of a button.

98

In addition to the Stable Input tool, the coiled-coil hydrophobic clustering theory

was explored and quantified. Kwok showed that clustering of hydrophobic amino acids in

the hydrophobic core of consecutive heptad leads to greater stability in the overall coiled-

coil. In an attempt to provide more information about clusters in nature, an exhaustive

search was initiated to quantify clusters in the Swiss-Prot database. This research has

lead to a better understanding to which amino acids frequent the hydrophobic core in

clustered regions.

The analysis between the Swiss-Prot annotated coiled-coils to the protein database

as a whole could be improved by using different coiled-coil prediction algorithms. To

perform this task a method needed to be found that would determine where in the

sequence a coiled-coil might be located and at which heptad registry offset they begin.

This was done using the Stable Coil algorithm because, first, it is used was based on the

experimentally determine stability and helical propensity values and not statistics and

second, both datasets were primarily analyzed using the criteria. The problem with this

approach is in the way the windowing function in Stable Coil may include many more

heptads at the beginning and end of each sequence. Once the windowing function

evaluated the first and last 42 positions they were not evaluated as strictly as the

intermediate positions. This may overestimate the starting and ending point of the

suspected coils. The evaluation of the stabilizing cluster shows in this analysis that the

coils selected showed it missing in over 70% of the time. The true remedy to this problem

is to define with greater precision the starting heptads of a coiled-coil. It seemed apparent

from this analysis that treating the starting and ending heptads of a coiled-coil my not be

the best approach.

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In future analysis a more selective coiled-coil prediction method could be used to

better identify the coiled-coil regions thereby eliminating the start heptad and end heptad

problem. However, the same problem may arise when attempting to determine the heptad

registry. One suggestion would be to used that ‘a’ and ‘d’ positioned amino acids found

in this analysis to a help determine the offset value. An algorithm that would continue to

move the heptad offset until the strongest correlation between the ‘a’ and ‘d’ position and

the presence of the hydrophobic amino acids is found.

Another possible improvement to this thesis and to truly gain an appreciation of

the variety of cluster combinations, a relational database could be created. The data

shown in the tables have been distilled to the point where only the most repetitive

sequences are displayed. A database could allow for absolute queries if particular

sequences are found to be interesting.

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GLOSSARY

Alpha helix: a repetitive secondary structure that gets its name because the relationshipof one amino acid to the next is the same. See Figure 1-3

Beta strand: an amino acid string that does not form a coil. It zigzags in a more extendedway than a helix. See Figure 1-4.

Coiled-coil: a tertiary oligomerization domain that is formed when two or more _-helices wrap around each other in a left-handed super coil

Heptad: the specific repeated 7 positions a, b, c, d, e, f, g that identifies the sevenpositions that characterize the coiled-coil sequences.

Hydrophobic amino acids: for the purpose of this analysis, the amino acids Phe, Ile,Leu, Met, Val, and Tyr. Some references do not include Tyr among the hydrophobicamino acids. A complete list of amino acids can be found in Tables1-2 through 1-5

Hydrophobic cluster: a sequence of 3 or more consecutive hydrophobic core positionsthat have hydrophobic amino acids in them.

Hydrophobic core: the ‘a’ and ‘d’ heptad position in a coiled coil.

Kinesin: Similar to myosin, a family of microtubule-associated motor proteins.

Myosin: a mechanoenzyme protein that supports the movement of cellular componentswith a characteristic actin binding domain head, a neck and tail.

Non-hydrophobic cluster: a sequence of 2 or more consecutive hydrophobic corepositions that have non-hydrophobic amino acids in them

Oligomer: A polymer that consists of two, three, or four monomers.

Oligomerization: The process of converting a monomer or a mixture of monomers intoan oligomer.

SPTR dataset: this is that data that was derived from the entire Swiss-Prot and TrEMBLdatabase.

Swiss-Prot dataset: the dataset used in the analysis that came from the Swiss-Protannotated

Tropomyosin: is a long, rod-like molecule, similar to myosin, that fits in the groove ofthe actin helix

101

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APPENDIX A STABLE INPUT GUI

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APPENDIX B TABULATED OUTPUT