Computational Methods for Protein Structure Prediction and Modeling

This book provides systematic technical expositions of the computational methods for all major aspects of protein structure analysis, prediction and modeling. The chapters have been designed to address comprehensively the main topics of the field. In addition, chapters will be connected seamlessly through a careful design of the overall structure of the book. Topics have been selected carefully so that the book would be useful to a broad readership, including students, postdoctoral fellows, professional practitioners, as well as bioinformatic experts who want to brush up topics related to their own research areas. The book can be used as a textbook for upper undergraduate-level or beginning graduate-level bioinformatics courses. To facilitate learning, four appendices describing the prerequisites for reading this book have been included, in (1) biology, (2) computer science, (3) physics and chemistry, and (4) mathematics and statistics. Each chapter in the book is a self-contained review of a specific subject, designed to cover the following material: (1) the problem definition and a historical perspective, (2) mathematical or computational formulation of the problem, (3) computational methods and algorithms, (4) performance results, (5) existing software packages, and (6) the strengths, pitfalls, challenges, and future research directions.

Addresses a broad interdisciplinary audience in biophysics and biochemistry, molecular and cell biology, computational biology, and bioinformatics Provides a comprehensive overview of protein biophysics for both professionals and graduate students Presents computational methods for all major aspects of protein structure analysis

Auteur
Dr. Ying Xu is Regents-GRA Eminent Scholar and Professor at the University of Georgia. Dr. Dong Xu is the Director of the Digital Biology Laboratory at the University of Missouri-Columbia. Dr. Jie Liang is the Director for the Center for Bioinformatics at the University of Illinois at Chicago.

Texte du rabat

Volume one of this two volume sequence focuses on the basic characterization of known protein structures as well as structure prediction from protein sequence information. The 11 chapters provide an overview of the field, covering key topics in modeling, force fields, classification, computational methods, and struture prediction. Each chapter is a self contained review designed to cover (1) definition of the problem and an historical perspective, (2) mathematical or computational formulation of the problem, (3) computational methods and algorithms, (4) performance results, (5) existing software packages, and (6) strengths, pitfalls, challenges, and future research directions.

Contenu
A Historical Perspective and Overview of Protein Structure Prediction.- Empirical Force Fields.- Knowledge-Based Energy Functions for Computational Studies of Proteins.- Computational Methods for Domain Partitioning of Protein Structures.- Protein Structure Comparison and Classification.- Computation of Protein Geometry and Its Applications: Packing and Function Prediction.- Local Structure Prediction of Proteins.- Protein Contact Map Prediction.- Modeling Protein Aggregate Assembly and Structure.- Homology-Based Modeling of Protein Structure.- Modeling Protein Structures Based on Density Maps at Intermediate Resolutions.