Protein Structure Prediction: Bioinformatic Approach(IUL Biotechnology S.)

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.

Table of Contents:
1. Computational Studies of Protein Structure and Function Using Threading Program PROSPECT Dong Xu and Ying Xu 2. Bayesian Approach to Protein Fold Recognition: Building Protein Structural Models from Bits and Pieces Jadwiga Bienkowska, Hongxian He, Robert G. Rogers, Lihua Yu 3. Three-Dimensional Structure Prediction Using Simplified Structure Models and Bayesian Block Fragments Jun, ZhuRoland Luthy 4. Protein Structure Prediction Using Hidden Markov Model Structural Libraries Igor Tsigelny, Yuriy Sharikov, Lynn F. Ten Eyck 5. The Role of Sequence Information in Protein Structure Prediction Damien Devos, Florencio Pazos, Osvaldo Olmea, David de Juan, Osvaldo Grana, Jose M. Fernandez, Alfonso Valencia 6. Protein Fold Recognition and Comparative Modeling Using HOMSTRAD, JOY, and FUGUE Ricardo Nunez Miguel, Jiye Shi, Kenji Mizuguchi 7. Fully Automated Protein Tertiary Structure Prediction Using Fourier Transform Spectral Methods Carlos Adriel Del Carpio Munoz and Atsushi Yoshimori 8. From the Building Blocks Folding Model to Protein Structure Prediction Nurit Haspel, C-J Tsai, Haim Wolfson, Ruth Nussinov 9. Protein Threading Statistics: An Attempt to Assess the Significance of a Fold Assignment to a Sequence Antoine Marin, Joel Pothier, Karel Zimmermann, and Jean-Francois Gibrat 10. Protein Structure Prediction by Threading: Force Field Philosophy, Approaches to Alignment Thomas Huber, Andrew Torda 11. Predicting Protein Structure Using SAM, UCSC's Hidden Markov Model Tools Kevin Karplus 12. Local Genome Organization, Gene Expression, and Structural Genomics: Evolution at Work Wayne Volkmuth, Nickolai Alexandrov 13. Protein Structure Prediction on the Basis of Combinatorial Peptide Library Screening I Tsigelny, Y Sharikov, M Kelner, L Ten Eyck 14. A User's Guide to Fold Recognition Naomi Siew, Daniel Fischer 15. Structure Prediction Meta Server Leszek Rychlewski 16. Improved Fold Recognition by Using the PCONS Consensus Approach Huisheng Fang, Bjorn Wallin, Jesper Lundstrom, Christer von Wowern, and Arne Elofsson 17. New Insights into Protein Fold Space and Sequence-Structure Relationships Ilya N. Shindyalov and Philip E. Bourne 18. A Flexible Method for Structural Alignment in Structure Prediction Assessments Vladimir Kotlovyi, Igor Tsigelny, Lynn Ten Eyck 19. Comparative Analysis of Protein Structure: New Concepts and Approaches for Multiple Structure Alignment Chittibabu Guda, Eric D. Scheeff, Philip E. Bourne, and Ilya N. Shindyalov 20. Comparative Analysis of Protein Structure: Automated vs. Manual Alignment of the Protein Kinase Family Eric D. Scheeff, Philip E. Bourne, and Ilya N. Shindyalov

About the Author :
Igor F. Tsigelny, Ph.D., is well-known scientist working for many years in the fields of bioinfomatics, molecular modeling, and theoretical drug design. He works in the University of California at San Diego and San Diego Supercomputer Center. His index of citations is about 1000.