Structural Bioinformatics

Preface


The Study of Structural Bioinformatics
Motivation
Small Beginnings
Structural Bioinformatics and the Scientific Method
A More Detailed Problem Analysis: Force Fields
Modeling Issues
Sources of Error
Summary


Introduction to Macromolecular Structure
Motivation
Overview of Protein Structure
Overview of RNA Structure


Data Sources, Formats, and Applications
Motivation
Sources of Structural Data
PDB File Format
Visualization of Molecular Data
Software for Structural Bioinformatics


Dynamic Programming
Motivation
Introduction
A DP Example: The Al Gore Rhythm for Giving Talks
A Recipe for Dynamic Programming
Longest Common Subsequence


RNA Secondary Structure Prediction
Motivation
Introduction to the Problem
The Nussinov Dynamic Programming
The MFOLD Algorithm: Terminology


Protein Sequence Alignment
Protein Homology
Variations in the Global Alignment Algorithm
The Significance of a Global Alignment
Local Alignment


Protein Geometry
Introduction
Calculations Related to Protein Geometry
Ramachandran Plots
Inertial Axes


Coordinate Transformations
Motivation
Introduction
Translation Transformations
Rotation Transformations
Isometric Transformations


Structure Comparison, Alignment, and Superposition
Motivation
Introduction
Techniques for Structural Comparison
Scoring Similarities and Optimizing Scores
Superposition Algorithms
Algorithms Comparing Relationships within a Protein


Machine Learning
Motivation
Issues of Complexity
Prediction via Machine Learning
Data Used during Training and Testing
Objectives of the Learning Algorithm
Linear Regression
Ridge Regression
Preamble for Kernel Methods
Kernel Functions
Classification
Heuristics for Classification
Nearest Neighbor Classification
Support Vector Machines
Linearly Nonseparable Data
Support Vector Machines and Kernels
Expected Test Error
Transparency


Overview of the Appendices
Index