Structural Bioinformatics: An Algorithmic Approach

by Burkowski; Forbes J.

• ISBN13: 9781584886839
• ISBN10: 1584886838
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-10-30
• Publisher: Chapman & Hall/

Summary

Showcasing the beauty of protein structures, Structural Bioinformatics: An Algorithmic Approach illustrates how to apply key algorithms to solve a range of biological issues.

Table of Contents

Preface	p. xix
Acknowledgments	p. xxiii
The Study of Structural Bioinformatics	p. 1
Motivation	p. 1
Small Beginnings	p. 4
Structural Bioinformatics and the Scientific Method	p. 5
Three Realms: Nature, Science, and Computation	p. 6
Hypothesis, Model, and Theory	p. 8
Laws, Postulates, and Assumptions	p. 12
Model Theory and Computational Theory	p. 13
Different Assumptions for Different Models	p. 14
A More Detailed Problem Analysis: Force Fields	p. 15
Nature	p. 16
Science	p. 16
Energy Terms for Bonded Atoms	p. 16
Energy Terms for Nonbonded Atoms	p. 19
Total Potential Energy	p. 21
Computation	p. 21
Modeling Issues	p. 25
Rashomon	p. 26
Ockham	p. 26
Bellman	p. 27
Interpretability	p. 28
Refutability	p. 29
Complexity and Approximation	p. 29
Sources of Error	p. 32
Summary	p. 33
Exercises	p. 34
References	p. 36
Introduction to Macromolecular Structure	p. 37
Motivation	p. 37
Overview of Protein Structure	p. 38
Amino Acids and Primary Sequence	p. 38
Secondary Structure	p. 44
Alpha Helices	p. 44
Beta Strands	p. 47
Loops	p. 52
Tertiary Structure	p. 53
What Is Tertiary Structure?	p. 54
The Tertiary Structure of Myoglobin	p. 54
Tertiary Structure Beyond the Binding Pocket	p. 58
Quaternary Structure	p. 64
Protein Functionality	p. 67
Protein Domains	p. 68
An Overview of Rna Structure	p. 70
Nucleotides and RNA Primary Sequence	p. 71
RNA Secondary Structure	p. 72
RNA Tertiary Structure	p. 75
Exercises	p. 78
References	p. 80
Data Sources, Formats, and Applications	p. 83
Motivation	p. 83
Sources of Structural Data	p. 84
PDB: The Protein Data Bank	p. 84
PDBsum: The PDB Summary	p. 86
SCOP: Structural Classification of Proteins	p. 86
CATH: The CATH Hierarchy	p. 88
PubChem	p. 92
DrugBank	p. 94
PDB File Format	p. 95
Visualization of Molecular Data	p. 98
Plug-In versus Stand-Alone	p. 99
Change of Viewing Perspective	p. 99
Graphical Representation	p. 99
Visual Effects	p. 101
Selection Abilities	p. 101
Computational Tools	p. 102
Extras	p. 102
Software for Structural Bioinformatics	p. 103
PyMOL	p. 103
Eclipse	p. 103
MarvinSketch	p. 104
ACD/ChemSketch	p. 104
JOELib2	p. 105
Chemistry Development Kit (CDK)	p. 105
BioPython	p. 105
Exercises	p. 106
References	p. 109
Dynamic Programming	p. 111
Motivation	p. 111
Introduction	p. 112
A DP Example: The Al Gore Rhythm For Giving Talks	p. 112
Problem Statement	p. 112
Terminology: Configurations and Scores	p. 113
Analysis of Our Given Problem	p. 113
A Recipe for Dynamic Programming	p. 116
Longest Common Subsequence	p. 116
Problem Statement	p. 117
Prefixes	p. 118
Relations Among Subproblems	p. 118
A Recurrence for the LCS	p. 119
Exercises	p. 123
RNA Secondary Structure Prediction	p. 125
Motivation	p. 126
Introduction to the Problem	p. 128
Nature	p. 129
Where Do Hydrogen Bonds Form?	p. 129
Thermodynamic Issues	p. 130
Consensus Sequence Patterns	p. 132
Complications	p. 133
Science	p. 133
Modeling Secondary Structure	p. 133
Single Base Pairs	p. 134
Stacking Energy Models	p. 134
Computation	p. 138
Display of Secondary Structure	p. 139
Restating the Problem	p. 145
The Nussinov Dynamic Programming Algorithm	p. 146
Execution Time	p. 155
The Mfold Algorithm: Terminology	p. 155
The MFOLD Algorithm: Recursion	p. 160
MFOLD Extensions	p. 162
MFOLD Execution Time	p. 162
Exercises	p. 163
References	p. 164
Protein Sequence Alignment	p. 167
Protein Homology	p. 167
Nature	p. 168
Science	p. 170
Partial Matches	p. 172
Building a BLOSUM Matrix	p. 173
Gaps	p. 179
Summary	p. 180
Computation	p. 180
Subproblem Specification	p. 181
Scoring Alignments	p. 181
Suitability of the Subproblem	p. 182
A Global Alignment Example	p. 186
Variations in the Global Alignment Algorithm	p. 186
The Significance of a Global Alignment	p. 187
Computer-Assisted Comparison	p. 188
Percentage Identity Comparison	p. 189
Local Alignment	p. 190
Exercises	p. 193
References	p. 195
Protein Geometry	p. 197
Motivation	p. 197
Introduction	p. 198
Calculations Related to Protein Geometry	p. 198
Interatomic Distance	p. 198
Bond Angle	p. 198
Dihedral Angles	p. 199
Defining Dihedral Angles	p. 199
Computation of a Normal	p. 201
Calculating the Phi Dihedral Angle	p. 204
Sign of the Dihedral Angle	p. 204
Calculating the Psi Dihedral Angle	p. 206
Ramachandran Plots	p. 206
Inertial Axes	p. 212
Exercises	p. 216
References	p. 220
Coordinate Transformations	p. 223
Motivation	p. 223
Introduction	p. 224
Translation Transformations	p. 224
Translation to Find Centroid at the Origin	p. 224
Rotation Transformations	p. 225
Rotation Transformations in the Plane	p. 226
Rotations in 3-D Space	p. 227
Isometric Transformations	p. 231
Our Setting Is a Euclidean Vector Space	p. 232
Orthogonality of A Implies Isometry of T	p. 232
Isometry of T Implies Orthogonality of A	p. 233
Preservation of Angles	p. 234
More Isometries	p. 234
Back to Rotations in the Plane	p. 235
Rotations in the 3-D Space: A Summary	p. 238
Exercises	p. 238
References	p. 239
Structure Comparison, Alignment, and Superposition	p. 241
Motivation	p. 242
Introduction	p. 245
Specifying the Problem	p. 245
Techniques for Structural Comparison	p. 246
Scoring Similarities and Optimizing Scores	p. 247
Superposition Algorithms	p. 247
Overview	p. 247
Characterizing the Superposition Algorithm	p. 249
Formal Problem Description	p. 249
Computations to Achieve Maximal Overlap	p. 251
Summary	p. 257
Measuring Overlap	p. 259
Calculation of the Root Mean Square Deviation (RMSD)	p. 259
RMSD Issues	p. 259
Dealing with Weaker Sequence Similarity	p. 260
Strategies Based on a Distance Matrix	p. 261
Algorithms Comparing Relationships within Proteins	p. 263
Dali	p. 263
SSAP	p. 267
Motivation	p. 267
Introduction to SSAP	p. 269
Overview of SSAP	p. 271
Calculating the Views	p. 272
Building the Consensus Matrix	p. 272
Compute the Optimal Path in the Consensus Matrix	p. 278
Exercises	p. 279
References	p. 282
Machine Learning	p. 285
Motivation	p. 285
Issues of Complexity	p. 287
Computational Scalability	p. 287
Intrinsic Complexity	p. 287
Inadequate Knowledge	p. 288
Prediction Via Machine Learning	p. 289
Training and Testing	p. 291
Types of Learning	p. 292
Types of Supervised Learning	p. 293
Supervised Learning: Notation and Formal Definitions	p. 293
Objectives of the Learning Algorithm	p. 294
Linear Regression	p. 295
Ridge Regression	p. 297
Predictors and Data Recording	p. 299
Underfitting and Overfitting	p. 300
Preamble for Kernel Methods	p. 300
Kernel Functions	p. 303
The "Kernel Trick"	p. 304
Design Issues	p. 305
Validation Data Sets	p. 306
Holdout Validation	p. 307
N-Fold Cross Validation	p. 307
Classification	p. 308
Classification as Machine Learning	p. 309
Ad Hoc Classification	p. 310
Heuristics for Classification	p. 311
Feature Weighting	p. 311
Nearest Neighbor Classification	p. 312
Delaunay and Voronoi	p. 313
Nearest Neighbor Time and Space Issues	p. 315
Support Vector Machines	p. 315
Linear Discrimination	p. 315
Margin of Separation	p. 318
Support Vectors	p. 319
The SVM as an Optimization Problem	p. 320
The Karush-Kuhn-Tucker Condition	p. 322
Evaluation of w[subscript 0]	p. 322
Linearly Nonseparable Data	p. 323
Parameter Values	p. 326
Evaluation of w[subscript 0] (Soft Margin Case)	p. 327
Classification with Soft Margin	p. 327
Support Vector Machines and Kernels	p. 328
Expected Test Error	p. 328
Transparency	p. 329
Exercises	p. 331
References	p. 334
Appendices	p. 337
Index	p. 385
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