Protein Structure Prediction

. Ed(s): Zaki, Mohammed J. (Rensselaer Polytechnic Institute); Bystroff, Chris

ISBN 10: 1588297527 / ISBN 13: 9781588297525

Published by Humana Press Inc., 2007

About this Item

This book covers elements of both the data-driven comparative modeling approach to structure prediction and also recent attempts to simulate folding using explicit or simplified models. It focuses on the various computational methods for prediction. Editor(s): Zaki, Mohammed J. (Rensselaer Polytechnic Institute); Bystroff, Chris. Series: Methods in Molecular Biology. Num Pages: 337 pages, 69 black & white illustrations, biography. BIC Classification: PSBC. Category: (P) Professional & Vocational. Dimension: 231 x 158 x 27. Weight in Grams: 672. . 2007. 2nd ed. 2008. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9781588297525

Bibliographic Details

• Title: Protein Structure Prediction
• Publisher: Humana Press Inc.
• Publication Date: 2007
• Language: English
• ISBN 10: 1588297527
• ISBN 13: 9781588297525
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

This book covers elements of both the data-driven comparative modeling approach to structure prediction and also recent attempts to simulate folding using explicit or simplified models. Despite the unsolved mystery of how a protein folds, advances are being made in predicting the interactions of proteins with other molecules, such as small ligands, nucleic acids or other proteins. Also rapidly advancing are the methods for solving the inverse folding problem, the problem of finding a sequence to fit a structure. This book focuses on the various computational methods for prediction, their successes and their limitations, from the perspective of their most well known practitioners.

From the Back Cover

For forty years we have known the essential ingredients for protein folding – an amino acid sequence, and water. But the problem of predicting the three-dimensional structure from its sequence has eluded computational biologists even in the age of supercomputers and high throughput structural genomics. Despite the unsolved mystery of how a protein folds, advances are being made in predicting the interactions of proteins with other molecules, such as small ligands, nucleic acids or other proteins. Protein Structure Prediction focuses on the various computational methods for prediction, their successes and their limitations, from the perspective of their most well-known practitioners. Leaders in the field provide insights into template-based methods of prediction, structure alignment and indexing, protein features prediction, and methods for de novo structure prediction. Protein Structure Prediction is a cutting-edge text that all researchers in the field should have in their libraries.