Computational and Visualization Techniques for Structural Bioinformatics Using Chimera

Forbes J. Burkowski

VitalSource eBook access code and instructions will be provided within the print book.

July 29, 2014 by Chapman and Hall/CRC
Reference - 461 Pages - 40 Color & 189 B/W Illustrations
ISBN 9781439836613 - CAT# K11654
Series: Chapman & Hall/CRC Mathematical and Computational Biology


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  • Takes a learning-by-doing approach that builds your confidence in solving challenging structural biology problems
  • Demonstrates the applications of linear algebra, such as the use of eigenvectors and eigenvalues to build visual objects that are displayed in the Chimera window
  • Provides a toolkit of Python classes that you can use directly in your applications or as starting scripts
  • Offers over 140 modular scripts and solutions to the end-of-chapter exercises at


A Step-by-Step Guide to Describing Biomolecular Structure

Computational and Visualization Techniques for Structural Bioinformatics Using Chimera shows how to perform computations with Python scripts in the Chimera environment. It focuses on the three core areas needed to study structural bioinformatics: biochemistry, mathematics, and computation.

Understand Important Concepts of Structural Bioinformatics

The book covers topics that deal primarily with protein structure and includes many exercises that are grounded in biological problems at the molecular level. The text encourages mathematical analysis by providing a firm foundation for computations. It analyzes numerous Python scripts for the Chimera environment, with the scripts and other material available on a supplementary website.

Build Python Scripts to Extend the Capabilities of Chimera

Through more than 60 exercises that involve the development of Python scripts, the book gives you concrete guidance on using the scripting capabilities of Chimera. You’ll gain experience in solving real problems as well as understand the various applications of linear algebra. You can also use the scripts as starting points for the development of similar applications and use classes from the StructBio toolkit for computations, such as structure overlap, data plotting, scenographics, and display of residue networks.