Catalogue


Introduction to protein structure prediction : methods and algorithms /
Huzefa Rangwala, George Karypis.
imprint
Hoboken, N.J. : Wiley, c2010.
description
xiv, 500 p.
ISBN
0470470593 (hardback), 9780470470596 (hardback)
format(s)
Book
Holdings
More Details
added author
imprint
Hoboken, N.J. : Wiley, c2010.
isbn
0470470593 (hardback)
9780470470596 (hardback)
abstract
"Focuses on methods for protein prediction. Delivers applications used for predicted models in other studies. Researchers rely on computational techniques to extract useful information from known structures in large databases.
"This book helps unravel the relationship of pure sequence information and three-dimensional structure, which remains one of the great fundamental problems in molecular biology and bioinformatics. It describes key applications of modeled structures, focusing on the methods and algorithms that are used to predict protein structure written by experts who participate in the structure prediction competition. The book also delivers applications used for predicted models in other studies. Researchers in bioinformatics and molecular biology will find this text highly useful, as will students in graduate courses in protein prediction"--
catalogue key
7384894
 
Includes bibliographical references and index.
A Look Inside
About the Author
Author Affiliation
Dr. Huzefa Rangwala is an assistant professor in computer science and bioengineering at George Mason University. He has published in various conferences and journals on the topic of bioinformatics. Dr. George Karypis is a professor in computer science and engineering at the University of Minnesota. He has authored more than one hundred journal and conference papers and also serves on the editorial board of the International Journal of Data Mining and Bioinformatics.
Reviews
Review Quotes
"A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels." (O Six Media, 8 March 2011)
This item was reviewed in:
Reference & Research Book News, February 2011
To find out how to look for other reviews, please see our guides to finding book reviews in the Sciences or Social Sciences and Humanities.
Summaries
Main Description
A look at the methods and algorithms used to predict protein structureA thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology.With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered:Databases and resources that are commonly used for protein structure predictionThe structure prediction flagship assessment (CASP) and the protein structure initiative (PSI)Definitions of recurring substructures and the computational approaches used for solving sequence problemsDifficulties with contact map prediction and how sophisticated machine learning methods can solve those problemsStructure prediction methods that rely on homology modeling, threading, and fragment assemblyHybrid methods that achieve high-resolution protein structuresParts of the protein structure that may be conserved and used to interact with other biomoleculesHow the loop prediction problem can be used for refinement of the modeled structuresThe computational model that detects the differences between protein structure and its modeled mutantWhether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Main Description
This book helps unravel the relationship of pure sequence information and three-dimensional structure, which remains one of the great fundamental problems in molecular biology and bioinformatics. It describes key applications of modeled structures, focusing on the methods and algorithms that are used to predict protein structure written by experts who participate in the structure prediction competition. The book also delivers applications used for predicted models in other studies. Researchers in bioinformatics and molecular biology will find this text highly useful, as will students in graduate courses in protein prediction.
Long Description
A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Table of Contents
Prefacep. vii
Contributorsp. xi
Introduction to Protein Structure Predictionp. 1
CASP: A Driving Force In Protein Structure Modelingp. 15
The Protein Structure Initiativep. 33
Prediction of One-Dimensional Structural Properties of Proteins By Integrated Neural Networksp. 45
Local Structure Alphabetsp. 75
Shedding Light on Transmembrane Topologyp. 107
Contact Map Prediction by Machine Learningp. 137
A Survey of Remote Homology Detection and Fold Recognition Methodsp. 165
Integrative Protein Fold Recognition by Alignments and Machine Learningp. 195
Tasser-Based Protein Structure Predictionp. 219
Composite Approaches to Protein Tertiary Structure Prediction: A Case-Study by I-Tasserp. 243
Hybrid Methods for Protein Structure Predictionp. 265
Modeling Loops in Protein Structuresp. 279
Model Quality Assessment using a Statistical Program that Adopts a Side Chain Environment Viewpointp. 299
Model Quality Predictionp. 323
Ligand-Binding Residue Predictionp. 343
Modeling and Validation of Transmembrane Protein Structuresp. 369
Structure-Based Machine Learning Models for Computational Mutagenesisp. 403
Conformational Search for the Protein Native Statep. 431
Modeling Mutations in Proteins Using Medusa and Discrete Molecule Dynamicsp. 453
Indexp. 477
Table of Contents provided by Ingram. All Rights Reserved.

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