Introduction to Protein Structure Prediction Methods and Algorithms

by Rangwala, Huzefa; Karypis, George

• ISBN13: 9780470470596
• ISBN10: 0470470593
• Edition: 1st
• Format: Hardcover
• Copyright: 2010-12-28
• Publisher: Wiley

Summary

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.

Author Biography

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.

Table of Contents

Introduction to Protein Structure Prediction

CASP: a driving force in protein structure modeling

The Protein Structure Initiative

Prediction of one-dimensional structural properties of proteins by integrated neural networks

Local Structure Alphabets

Shedding light on transmembrane topology

Contact Map Prediction by Machine Learning

A survey of remote homology detection and fold recognition methods

Integrative Protein Fold Recognition by Alignments and Machine Learning

TASSER-based protein structure prediction

Composite approaches to protein tertiary structure prediction: A case-study by I-TASSER

Hybrid Methods for Protein Structure Prediction

Modeling loops in protein structures

Model quality assessment using a statistical program that adopts a side-chain environment viewpoint

Model Quality Prediction

Ligand-binding Residue

Modeling and validation of transmembrane protein structures

Structure-based machine learning models for computational mutagenesis

Conformational Search the protein native-state

Modeling Mutations in Proteins Using Medusa and Discrete Molecule Dynamics

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