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E-raamat: Introduction to Protein Structure Prediction: Methods and Algorithms

Edited by (University of Minnesota), Series edited by (Department of Computer Science, Georgia State University), Series edited by (University of Western Australia), Edited by (George Mason University)
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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.

Arvustused

Preface vii
Contributors xi
1 Introduction to Protein Structure Prediction
1(14)
Huzefa Rangwala
George Karypis
2 Casp: A Driving Force in Protein Structure Modeling
15(18)
Andriy Kryshtafovych
Krzysztof Fidelis
John Moult
3 The Protein Structure Initiative
33(12)
Andras Fiser
Adam Godzik
Christine Orengo
Burkhard Rost
4 Prediction of One-Dimensional Structural Properties of Proteins by Integrated Neural Networks
45(30)
Yaoqi Zhou
Eshel Faraggi
5 Local Structure Alphabets
75(32)
Agnel Praveen Joseph
Aurelie Bomot
Alexandre G. de Brevern
6 Shedding Light on Transmembrane Topology
107(30)
Gabor E. Tusnady
Istvan Simon
7 Contact Map Prediction by Machine Learning
137(28)
Alberto J.M. Martin
Catherine Mooney
Ian Walsh
Gianluca Pollastri
8 A Survey of Remote Homology Detection and fold Recognition Methods
165(30)
Huzefa Rangwala
9 Integrative Protein fold Recognition by Alignments and Machine Learning
195(24)
Allison N. Tegge
Zheng Wang
Jianlin Cheng
10 Tasser-Based Protein Structure Prediction
219(24)
Shashi Bhushan Pandit
Hongyi Zhou
Jeffrey Skolnick
11 Composite Approaches to Protein Tertiary Structure Prediction: A Case-Study by I-Tasser
243(22)
Ambrish Roy
Sitao Wu
Yang Zhang
12 Hybrid Methods for Protein Structure Prediction
265(14)
Dmitri Mourado
Bostjan Kobe
Nicholas E. Dixon
Thomas Huber
13 Modeling Loops in Protein Structures
279(20)
Narcis Femandez-Fuentes
Andras Fiser
14 Model Quality Assessment using a Statistical Program that Adopts a Side Chain Environment Viewpoint
299(24)
Genki Terashi
Mayuko Takeda-Shitaka
Kazuhiko Kanou
Hideaki Umeyama
15 Model Quality Prediction
323(20)
Liam J. McGuffin
16 Ligand-Binding Residue Prediction
343(26)
Chris Kauffman
George Karypis
17 Modeling and Validation of Transmembrane Protein Structures
369(34)
Maya Schushan
Nir Ben-Tal
18 Structure-Based Machine Learning Models for Computational Mutagenesis
403(28)
Majid Masso
Iosif I. Vaisman
19 Conformational Search for the Protein Native State
431(22)
Amarda Shehu
20 Modeling Mutations in Proteins using Medusa and Discrete Molecule Dynamics
453(24)
Shuangye Yin
Feng Ding
Nikolay V. Dokholyan
Index 477
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.