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. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Synopsis:

Tis is a most comprehensive book on theoretical protein structure prediction. It describes the main concepts in the field and brings concrete algorithms, programs and their web adresses. The book describes diverse methods and algorithms of protein structure prediction including globally optimal threading alignment based on a neural network; and protein fold-recognition method using various libraries of structural and sequential hidden Markov models (HMMs). The book discusses numerous methods of extraction of information from multiple sequence alignments and using them as a primary source of information and application of the rarely used features such as sequence conservation, the variations between sub-families, and the correlation between the patterns of mutation. Generation of sequence-structure alignments and model building is explicitly discussed. New concepts for sequence homology assessment are described including the periodicity analysis of the physicochemical properties of the residues constituting proteins primary structures. A number of chapters describe building block protein folding models and use of small 3D protein blocks for protein structure prediction. One of the chapters discusses the important link between genomic information and protein structure. The chapter describes the clues that could be used to help to infer the evolutionary relationship via structural similarity and improve the ability to predict the biochemical function. Another chapter describes strategy and algorithm, which predicts putative protein targets based on a set of peptides shown to bind a drug molecule from combinatorial libraries. A number of examples show how the use of fold recognition helped biologists in planning and devising experiments and in generating verifiable hypotheses. Meta Server program that collects prediction models from many high quality services and translates them into standard formats enabling convenient analysis of the results is also described. New methods for fold recognition also are described. A structural alignment is the basic part of protein structure prediction. The book introduces several new views of protein fold space, which will help to further understand protein evolution and interpret structural similarities. Differences between the manual and automated approaches to the structural classification problem are described. The book discusses design principles of structure alignment systems that can be used for structure prediction assessments.

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