Book: Protein Structure Prediction

Book Title: Protein Structure Prediction: Method and Algorithms
Wiley Book Series on Bioinformatics

Please email the editors a title and a 250 word abstract if you would like to contribute to this book

Editors:

Huzefa Rangwala	George Karypis
Assistant Professor Computer Science/Bioinformatics George Mason University 4400 University Dr. Fairfax, VA 22031 Email: rangwala at cs dot gmu dot edu Phone: (703) 993-3826 Fax: (703) 993-1710 www.cs.gmu.edu/~hrangwal	Associate Professor Computer Science University of Minnesota 480, DTC 117 Pleasant St. SE Minneapolis, MN 55455 Email: karypis at cs dot umn dot edu Phone: (612) 626-7524 Fax: (612) 625-0572 www.cs.umn.edu/~karypis

Schedule:

11.15.2008: Chapter Title and Abstract Deadline
01.15.2009: First Draft of Chapters
03.15.2009: Send Chapters for Review
05.01.2009: Receive Feedback.
06.01.2009: Submit Revised Chapter.
07.01.2009: Submit Final Version to Editor/Publisher
08.01.2009: Tentative Publication Date

Copyrights and Permissions:

If your chapter includes copyrighted material, please use the form below to obtain permission from the publisher (see the Wiley copyright guidelines on what types of material require permission). Keep copies of signed permission forms, fax a copy to +1-703-993-1710, and mail the originals to Huzefa Rangwala, Department of Computer Science, George Mason University ,4400 University Drive, MS 4A5, Fairfax, VA 22030, USA

Formatting Instructions:

Chapters should be 20-25 pages (including the text, tables and figures) when formatted as the sample chapter (approximately 500 words per page). If the length requirement poses a problem, please contact one of the editors. Please try to use the Wiley style and formatting.

Wiley Style Files and Formatting Instructions

Book Overview:

Proteins have a vast influence on the molecular machinery of life. Stunningly complex networks of proteins perform innumerable functions in every living cell. Knowing the three-dimensional structure of proteins is crucial to advances in biology as this information provides insight into how proteins operate. For example, structural information enables function prediction, the identification of other interacting bio-molecules (e.g., proteins, DNA and RNA), and the rational search for ligands that can be used to enhance or inhibit these interactions.

Currently, production of sequence information far out-paces that of structural and functional information. Consequently, researchers increasingly rely on computational techniques to extract useful information from known structures contained in large databases. The past two decades have seen the development and advancement of a wide array of protein structure prediction methods. The success and diversity of these methods is showcased in a biennial protein structure prediction competition, called "Critical Assessment of Techniques for Protein Structure Prediction" (CASP).

We have planned carefully for this book to become a milestone compilation of recent key research accomplishments in the structure prediction arena. This book will focus on the methods and algorithms that are used to predict protein structure written by experts who participate at CASP. It will also include chapters devoted to the applications of modeled protein structures.

Topics:

No.	Title	Authors and Affliations
1	Introduction
2	CASP: Structure Prediction Competition	Andriy Kryshtafovych (UC Davis)
3	The Protein Structure Initiative	A. Fiser (Albert Einstein College of Medicine) and B. Rost (Columbia University).
4	Prediction of one-dimensional structural properties of proteins by integrated neural networks	Eshel Faraggi and Yaoqi Zhou (Indiana University Purdue University)
5	Local Structure Alphabet	Agnel Joseph, Aurélie Borno, Alexandre de Brevern (French Institute of Medical and Health Research
6	Shedding light on transmembrane topology	Gabor E. Tusnady and Istvan Simon (Institute of Enzymology, BRC, Hungarian Academy of Sciences)
7	Prediction of protein disorder and flexibility	Takashi Ishida and Kengo Kinoshita (Institute of Medical Science, University of Tokyo Human Genome Center)
8	Contact Map Prediction by Machine Learning	Alberto J.M. Martin, Catherine Mooney, Ian Walsh Gianluca Pollastri (University of College Dublin)
9	Survey of Remote Homology Detection and Fold Recognition Methods	Huzefa Rangwala (George Mason University)
10	Integrative Protein Fold Recognition by Alignments and Machine Learning	Allison N. Tegge, Zheng Wang, and Jianlin Cheng (U Missouri)
11	TASSER-based protein structure prediction	Hongyi Zhou, Shashi Pandit, Jeffrey Skolnick (Georgia Tech.)
12	Composite approaches to protein tertiary structure prediction: A case-study by I-TASSER	Ambrish Roy and Yang Zhang (Kansas University)
13	Hybrid Methods for Protein Strutcure Prediction	Thomas Huber (Queensland University)
14	Modeling loops in protein structures	Fernandez-Fuentes, Andras Fiser (Albert Einstein College of Medicine)
15	Model Quality using a statistical Program from a side-chain environment point of view	Genki Terashi, Mayuko Takeda-Shitaka, Kazuhiko Kanou and Hideaki Umeyama (Kitasato University).
16	Model Quality Prediction	L.J. McGuffin (University of Reading)
17	Guidelines for membrane proteins modeling and validation	Maya Schushan and Nir Ben-Tal (The George S Wise Faculty of Life Sciences , Tel Aviv University)
18	Structure to Function Prediction	Gaurav Pandey and Vipin Kumar (University of Minnesota)
19	Mutagenesis Studies with Delanauy-based Tessalation	Iosif Vaisman (George Mason University)
20	Modeling Mutation in Proteins	Nikolay Dokholyan (University of North Carolina)
21	Conformational Search for the Protein Native State	Amarda Shehu (George Mason University)

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