1st Edition

Structural Bioinformatics An Algorithmic Approach

By Forbes J. Burkowski Copyright 2009
442 Pages 24 Color & 124 B/W Illustrations
by Chapman & Hall

429 Pages
by Chapman & Hall

The Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure. Helps Students Go Further in Their Study of Structural Biology Following some introductory material... Read more

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

Biography

Forbes J. Burkowski

… the book presents a number of topics in structural bioinformatics, aiming to emphasize the beauty of the area as well as some of the main problems. It targets advanced undergraduate students and hence the description of more complicated algorithms is avoided. It nevertheless provides an interesting introduction to the area.
—Lucian Ilie, Mathematical Reviews, Issue 2009k