Introduction to protein structure prediction: methods and algorithms

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.

Author Notes

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.

Huzefa Rangwala and George KarypisAndriy Kryshtafovych and Krzysztof Fidelis and John MoultAndras Fiser and Adam Godzik and Christine Orengo and Burkhard RostYaoqi Zhou and Eshel FaraggiAgnel Praveen Joseph and Aurélie Bornot and Alexandre G. de BrevernGábor E. Tusnády and István SimonAlberto J. M. Martin and Catherine Mooney and Ian Walsh and Gianluca PollastriHuzefa RangwalaAllison N. Tegge and Zheng Wang and Jianlin ChengShashi Bhushan Pandit and Hongyi Zhou and Jeffrey SkolnickAmbrish Roy and Sitao Wu and Yang ZhangDmitri Mourado and Bostjan Kobe and Nicholas E. Dixon and Thomas HuberNarcis Fernandez-Fuentes and Andras FiserGenki Terashi and Mayuko Takeda-Shitaka and Kazuhiko Kanou and Hideaki UmeyamaLiam J. McGuffinChris Kauffman and George KarypisMaya Schushan and Nir Ben-TalMajid Masso and Iosif I. VaismanAmarda ShehuShuangye Yin and Feng Ding and Nikolay V. Dokholyan

Preface	p. vii
Contributors	p. xi
1 Introduction to Protein Structure Prediction	p. 1
2 CASP: A Driving Force In Protein Structure Modeling	p. 15
3 The Protein Structure Initiative	p. 33
4 Prediction of One-Dimensional Structural Properties of Proteins By Integrated Neural Networks	p. 45
5 Local Structure Alphabets	p. 75
6 Shedding Light on Transmembrane Topology	p. 107
7 Contact Map Prediction by Machine Learning	p. 137
8 A Survey of Remote Homology Detection and Fold Recognition Methods	p. 165
9 Integrative Protein Fold Recognition by Alignments and Machine Learning	p. 195
10 Tasser-Based Protein Structure Prediction	p. 219
11 Composite Approaches to Protein Tertiary Structure Prediction: A Case-Study by I-Tasser	p. 243
12 Hybrid Methods for Protein Structure Prediction	p. 265
13 Modeling Loops in Protein Structures	p. 279
14 Model Quality Assessment using a Statistical Program that Adopts a Side Chain Environment Viewpoint	p. 299
15 Model Quality Prediction	p. 323
16 Ligand-Binding Residue Prediction	p. 343
17 Modeling and Validation of Transmembrane Protein Structures	p. 369
18 Structure-Based Machine Learning Models for Computational Mutagenesis	p. 403
19 Conformational Search for the Protein Native State	p. 431
20 Modeling Mutations in Proteins Using Medusa and Discrete Molecule Dynamics	p. 453
Index	p. 477

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