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IUL Biotechnology Series, 3 |
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by Igor F. Tsigelny (Editor) |
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Edition: First |
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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
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.