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  Protein Structure Prediction: Bioinformatic Approach.

IUL Biotechnology Series, 3

by Igor F. Tsigelny (Editor) 

Edition: First




 

 


Book Details:
  • Series: IUL Biotechnology Series

  • Volume: 3

  • Binding: Hardcover 

  • Pages: 306

  • Dimensions (in inches): 1.75 x 9.50 x 6.50

  • Publisher: International University Line 

  • Publication Date: May 5, 2002

  • ISBN: 0-9636817-7-X

  • List Price: $129.95

  • Our Price: $116.96


Editorial Reviews
From Book News, Inc.
Scientists developing prediction methods will find ideas here in descriptions of successful methods and programs in protein structure prediction. Chapters on concepts of protein structure prediction discuss areas such as a Bayesian approach to protein fold recognition, three-dimensional structure prediction using simplified structure models and Bayesian block fragments, and protein structure prediction using hidden Markov model structural libraries. Chapters on methods of structure and sequence alignment outline methods such as the PCONS consensus approach, and discuss new insights into protein fold space and sequence-structure relationships. Book News, Inc.®, Portland, OR


 

 

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Abstract:

The book, written by the best experts in the field, is a compendium of protein structure prediction methods and strategies. The information is presented in such a manner that any of developers can use it in his/her work and users would have deep understanding of the programs they use.
  One of the reviewers called this book “Encyclopedia of Protein Structure Prediction” and the book corresponds to this name. It covers a majority of methods and strategies of theoretical protein structure prediction.

    This book is used in a number of universities as a textbook for bioinformatics courses.
   Edited by Igor F. Tsigelny, University of California, San Diego.

 

 

 

Reviews


  Reviewer: Russel F. Doolittle, Professor of the University of California, San Diego, Member of National Academy of Sciences

 

Moore’s Law, which predicts the rapidity with which the speed and capacity of computer chips are increasing, is a well-known concept. Less well appreciated is how fast new generations of software follow in its wake to harness all the expanded power. A good example occurs in the field of protein structure prediction, where new approaches to fold recognition, structural alignment and threading continue to appear at a rate that leaves the individual investigator at a loss of which way to turn to solve any particular problem.

       Now there is a very timely book on the subject, edited by Igor Tsigelny, that serves as an excellent guide to the very newest approaches. A wide variety of programs and strategies is discussed, including new applications of hidden Markov models and novel Bayesian approaches for building up models from block fragments. In one way or other, the theme that holds throughout has to do with determining three-dimensional structures for the plethora of newly determined amino acid sequences.

       The 20 chapters are from groups around the world. They are well chosen and present sufficient perspective that the interested reader can seek out an appropriate path for his own needs. Having said that, I must add that these are very meaty and detailed renderings.

       The reader who immerses himself heavily in them is bound to emerge an experienced modeler.

       The advent of structural genomics—aka proteomics—demands resources like this volume.