Rangwala / Karypis Introduction to Protein Structure Prediction

Methods and Algorithms
1. Auflage 2011
ISBN: 978-1-118-09946-9
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, Band 1, 520 Seiten, E-Book

Reihe: Wiley Series in Bioinformatics

A look at the methods and algorithms used to predict proteinstructure
A thorough knowledge of the function and structure of proteinsis critical for the advancement of biology and the life sciences aswell as the development of better drugs, higher-yield crops, andeven synthetic bio-fuels. To that end, this reference sheds lighton the methods used for protein structure prediction and revealsthe key applications of modeled structures. This indispensable bookcovers the applications of modeled protein structures and unravelsthe relationship between pure sequence information andthree-dimensional structure, which continues to be one of thegreatest challenges in molecular biology.
With this resource, readers will find an all-encompassingexamination of the problems, methods, tools, servers, databases,and applications of protein structure prediction and they willacquire unique insight into the future applications of the modeledprotein structures. The book begins with a thorough introduction tothe protein structure prediction problem and is divided into fourthemes: a background on structure prediction, the prediction ofstructural elements, tertiary structure prediction, and functionalinsights. Within those four sections, the following topics arecovered:
* Databases and resources that are commonly used for proteinstructure prediction
* The structure prediction flagship assessment (CASP) and theprotein structure initiative (PSI)
* Definitions of recurring substructures and the computationalapproaches used for solving sequence problems
* Difficulties with contact map prediction and how sophisticatedmachine learning methods can solve those problems
* Structure prediction methods that rely on homology modeling,threading, and fragment assembly
* Hybrid methods that achieve high-resolution proteinstructures
* Parts of the protein structure that may be conserved and usedto interact with other biomolecules
* How the loop prediction problem can be used for refinement ofthe modeled structures
* The computational model that detects the differences betweenprotein structure and its modeled mutant
Whether working in the field of bioinformatics or molecularbiology research or taking courses in protein modeling, readerswill find the content in this book invaluable.

Autoren/Hrsg.

Rangwala, Huzefa

Karypis, George

Inhaltsverzeichnis

Preface.
Contributors.
1 Introduction to Protein Structure Prediction (HuzefaRangwala and George Karypis).
2 CASP: A Driving Force in Protein Structure Modeling(Andriy Kryshtafovych, Krzysztof Fidelis, and JohnMoult).
3 The Protein Structure Initiative (Andras Fiser, AdamGodzik, Christine Orengo, and Burkhard Rost).
4 Prediction of One-Dimensional Structural Properties ofProteins by Integrated Neural Networks (Yaoqi Zhou and EshelFaraggi).
5 Local Structure Alphabets (Agnel Praveen Joseph,Aurélie Bornot, and Alexandre G. de Brevern).
6 Shedding Light on Transmembrane Topology (GáborE. Tusnády and István Simon).
7 Contact Map Prediction by Machine Learning (AlbertoJ.M. Martin, Catherine Mooney, Ian Walsh, and GianlucaPollastri).
8 A Survey of Remote Homology Detection and Fold RecognitionMethods (Huzefa Rangwala).
9 Interactive Protein Fold Recognition by Alignments andMachine Learning (Allison N. Tegge, Zheng Wang, and JianlinCheng).
10 Tasser-Based Protein Structure Prediction (ShashiBhushan Pandit, Hongyi Zhou, and Jeffrey Skolnick).
11 Composite Approaches to Protein Tertiary StructurePrediction: A Case-Study by I-Tasser (Ambrish Roy, Sitao Wu,and Yang Zhang).
12 Hybrid Methods for Protein Structure Prediction(Dmitri Mourado, Bostjan Kobe, Nicholas E. Dixon, and ThomasHuber).
13 Modeling Loops in Protein Structures (NarcisFernandez-Fuentes, Andras Fiser).
14 Model Quality Assessment Using A Statistical Program thatAdopts A Side Chain Environment Viewpoint (Genki Terashi,Mayuko Takeda-Shitaka, Kazuhiko Kanou and Hideaki Umeyama).
15 Model Quality Prediction (Liam J.McGuffin).
16 Ligand-Binding Residue Prediction (Chris Kauffmanand George Karypis).
17 Modeling and Validation of Transmembrane ProteinStructures (Maya Schushan and Nir Ben-Tal).
18 Structure-Based Machine Learning Models for ComputationalMutagenesis (Majid Masso and Iosif I. Vaisman).
19 Conformational Search for the Protein Native State(Amarda Shehu).
20 Modeling Mutations in Proteins Using MEDUSA and DiscreteMolecule Dynamics (Shuangye Yin, Feng Ding, and Nikolay V.Dokholyan).
Index.

Über Autor(innen)

DR. HUZEFA RANGWALA is an assistant professor in computerscience and bioengineering at George Mason University. He haspublished in various conferences and journals on the topic ofbioinformatics.
DR. GEORGE KARYPIS is a professor in computer science andengineering at the University of Minnesota. He has authored morethan one hundred journal and conference papers and also serves onthe editorial board of the International Journal of Data Miningand Bioinformatics.