State-of-the-Art Protein Secondary-Structure Prediction Using a Novel Two-Stage Alignment and ...

113 Pages·2008·1.63 MB·English

STATE-OF-THE-ART PROTEIN SECONDARY-STRUCTURE PREDICTION USING A
NOVEL TWO-STAGE ALIGNMENT AND MACHINE-LEARNING METHOD

By

AMI M. GATES

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008

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© 2008 Ami M. Gates

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To My Family and Friends

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ACKNOWLEDGMENTS
I would like to dedicate this overwhelming moment to my loving and supportive family,
and to my wonderful friends. I would like to thank my parents, Eileen and Myke, who always
supported my goals and listened endlessly; my brother Josh, who offered continuous
encouragement; and my late brother Chad, whose last words to me were “PhD”. I would like to
thank my dear friends Amos, Karina, Jesse, Neko, and Nathan for standing by me, and I would
like to thank my committee chair, Arunava Banerjee, who always believed in me.
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TABLE OF CONTENTS

page
ACKNOWLEDGMENTS ...............................................................................................................4
LIST OF TABLES ...........................................................................................................................8
LIST OF FIGURES .........................................................................................................................9
ABSTRACT ...................................................................................................................................11
CHAPTER
1 INTRODUCTION ..................................................................................................................13
Introduction .............................................................................................................................13
Proteins ...................................................................................................................................13
Protein Secondary Structure ...................................................................................................14
Machine Learning and Protein Secondary Structure Prediction .............................................15
Protein Secondary Structure Prediction Methods ...................................................................16
Dynamic Alignment-Based Protein Window-SVM Integrated Prediction for Three State
Protein Secondary Structure ................................................................................................17
Overview .................................................................................................................................17
2 REVIEW OF THE BIOLOGY OF PROTEINS .....................................................................19
Brief Biology of Proteins ........................................................................................................19
From DNA to Protein .............................................................................................................19
Protein and Amino Acids ........................................................................................................20
Protein Folding .......................................................................................................................21
Secondary Structure ................................................................................................................22
Protein Evolution and Sequence Conservation .......................................................................23
3 LITERATURE REVIEW .......................................................................................................31
Problem of Secondary Structure Prediction ...........................................................................31
Literature Review of Secondary Structure Prediction ............................................................32
Methods Preceding 1993 .................................................................................................32
Methods Proceeding 1993 ...............................................................................................36
Neural network methods from 1993 – 2007 .............................................................37
Summary of neural network based methods ............................................................40
Support Vector Machine Methods from 2001 – 2007 .....................................................40
Summary of SVM Based Methods ..................................................................................42
Combined or Meta Methods ............................................................................................42
Direct Homology Based Methods ...................................................................................43

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4 MATERIALS AND METHODS ...........................................................................................45
Introduction .............................................................................................................................45
Protein Data and Databanks ....................................................................................................45
Datasets ...................................................................................................................................46
Protein Identity, Similarity, and Homology ...........................................................................48
Multiple Sequence Alignment and PSI-BLAST .....................................................................50
Basic Local Alignment Search Tool (BLAST) Algorithm ..............................................52
BLAST: step 1 ..........................................................................................................53
BLAST: step 2 ..........................................................................................................54
BLAST: step 3 ..........................................................................................................54
Position-Specific Iterative BLAST (PSI-BLAST) Algorithm ........................................54
Creating the PSSM ...................................................................................................55
Summary of PSI-BLAST .........................................................................................57
Input Vectors and Sliding Windows .......................................................................................57
Accuracy Measures .................................................................................................................58
Machine Learning Techniques ...............................................................................................59
Support Vector Machines ................................................................................................59
Using SVMs in Secondary Structure Prediction .............................................................63
Neural Networks .....................................................................................................................63
Information Theory and Prediction ........................................................................................64
5 NEW SECONDARY STRUCTURE PREDICTION METHOD DARWIN .........................72
Dynamic Alignment-Based Protein Window-SVM Integrated Prediction for Three State
Protein Secondary Structure: A New Prediction Server. ....................................................72
Introduction and Motivation of DARWIN .............................................................................73
Methods and Algorithms used in DARWIN ..........................................................................75
Phases of DARWIN: Stage 1 ..........................................................................................76
Phase 1 ......................................................................................................................76
Phase 2a: If at least one viable template is found: ...................................................77
Phase 2b: If no viable template is found: .................................................................78
Phase 3 ......................................................................................................................79
Phases of DARWIN Stage 2: Fixed-Size Fragment Analysis .........................................79
Fragment size selection ............................................................................................80
Step 1 ........................................................................................................................80
Step 2 ........................................................................................................................80
Step 3 ........................................................................................................................81
Ensemble of Support Vector Machines in DARWIN.............................................................82
The SVM Kernel and Equation .......................................................................................82
Training the SVM and Using PSI-BLAST Profiles ........................................................83
Datasets and Measures of Accuracy for DARWIN ................................................................84
Experiments, Measures, and Results ......................................................................................86
Conclusions on DARWIN ......................................................................................................90

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6 DARWIN WEB SERVER ......................................................................................................94
Introduction .............................................................................................................................94
Using the Server .....................................................................................................................94
Design of the DARWIN Web Service ....................................................................................96
7 DISCUSSION AND CONCLUSION ..................................................................................102
Introduction ...........................................................................................................................102
Protein Secondary Structure Prediction Progress .................................................................102
Strength of DARWIN ...........................................................................................................104
Future Work and Improvements ...........................................................................................105
LIST OF REFERENCES .............................................................................................................106
BIOGRAPHICAL SKETCH .......................................................................................................113

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LIST OF TABLES
Table page

5-1 Detailed average prediction results for DARWIN. ............................................................92
5-2 Average prediction results for dataset EVA5 for DARWIN compared to top
published indirect homology method results. ....................................................................92
5-3. Average prediction results for dataset EVA6 for DARWIN compared to top
published indirect homology method results. ....................................................................92

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LIST OF FIGURES
Figure page

2-1 Simplification of the processes of transcription and translation.. ......................................24
2-2 Once a polypeptide is created through the process of translation, it is released into
the cytosol and is known as the primary or linear sequence.. ............................................25
2-3 The 20 known amino acids. Adapted from Voet and Voet, 2005. .....................................26
2-4 Torsion angles phi and psi that offer rotational flexibility between amino acid
peptide bonds. Adapted from Voet and Voet, 2005. ..........................................................27
2-5. Ramachandran Plot for a set of three alanine amino acids joined as a tripeptide. .............28
2-6 Example of a helical protein secondary structure. The hydrogen bonds are denoted
with dashed lines ................................................................................................................29
2-7 Sheet protein secondary structure, with hydrogen bonds noted with dashed lines.
Adapted from Voet and Voet, 2005. ..................................................................................30
3-1 Example of a linear sequence of amino acids, each accompanied by a secondary
structure label of H, C, or E. ..............................................................................................44
4-1 Protein Data Bank (PDB) website. This area is a repository for known protein
structures and related protein information. ........................................................................66
4-2 Matrix known as BLOSUM 62, a similarity matrix derived from small local blocks
of aligned sequences that share at least 62% identity ........................................................67
4-3 Example of a PSI-BLAST generated alignment between a query protein and a
subject protein.. ..................................................................................................................67
4-4 Example of a PSI-BLAST generated position specific scoring matrix (PSSM). ..............68
4-5 Example of the BLAST algorithm. A given query protein is analyzed by looking at
all three amino acid word sets ............................................................................................69
4-6 Visual example of the production of input vectors that can be used to train and test
machine learning constructs.. .............................................................................................70
4-7 Visual example of decision boundary between two classes and the margin that is
maximized. .........................................................................................................................71
5-1 The PSI-BLAST example alignment portion. Several areas in a given alignment can
result in missing information.. ...........................................................................................93
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5-2 Histogram for each dataset, EVA5 and EVA6 displays the percentage of proteins
predicted by DARWIN with given accuracy.. ...................................................................93
6-1 Image of the DARWIN Web page that allows Internet based graphical user interface
with the DARWIN service. ..............................................................................................101
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