1st Edition

Structural Bioinformatics An Algorithmic Approach

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


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

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

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

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

    RNA Secondary Structure Prediction
    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
    Calculations Related to Protein Geometry
    Ramachandran Plots
    Inertial Axes

    Coordinate Transformations
    Translation Transformations
    Rotation Transformations
    Isometric Transformations

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

    Machine Learning
    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
    Heuristics for Classification
    Nearest Neighbor Classification
    Support Vector Machines
    Linearly Nonseparable Data
    Support Vector Machines and Kernels
    Expected Test Error

    Overview of the Appendices


    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