1st Edition

Structural Bioinformatics
An Algorithmic Approach




ISBN 9781584886839
Published October 30, 2008 by Chapman and Hall/CRC
429 Pages 24 Color & 124 B/W Illustrations

USD $115.00

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Book Description

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 in the first few chapters, the text solves the longest common subsequence problem using dynamic programming and explains the science models for the Nussinov and MFOLD algorithms. It then reviews sequence alignment, along with the basic mathematical calculations needed for measuring the geometric properties of macromolecules. After looking at how coordinate transformations facilitate the translation and rotation of molecules in a 3D space, the author introduces structural comparison techniques, superposition algorithms, and algorithms that compare relationships within a protein. The final chapter explores how regression and classification are becoming more useful in protein analysis and drug design.

At the Crossroads of Biology, Mathematics, and Computer Science
Connecting biology, mathematics, and computer science, this practical text presents various bioinformatics topics and problems within a scientific methodology that emphasizes nature (the source of empirical observations), science (the mathematical modeling of the natural process), and computation (the science of calculating predictions and mathematical objects based on mathematical models).

Table of Contents

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

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Reviews

… 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