Geometric, Feature- and Graph-based Analysis of Protein Structures | Novel Methods and Computational Analysis

Thomas Fober

ISBN 10: 3838134796 / ISBN 13: 9783838134796

Published by Südwestdeutscher Verlag für Hochschulschriften AG Co. KG, 2015

About this Item

Geometric, Feature- and Graph-based Analysis of Protein Structures | Novel Methods and Computational Analysis | Thomas Fober | Taschenbuch | Paperback | 404 S. | Englisch | 2015 | Südwestdeutscher Verlag für Hochschulschriften AG Co. KG | EAN 9783838134796 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 105689820

Bibliographic Details

• Title: Geometric, Feature- and Graph-based Analysis of Protein Structures | Novel Methods and Computational Analysis
• Publisher: Südwestdeutscher Verlag für Hochschulschriften AG Co. KG
• Publication Date: 2015
• Language: English
• ISBN 10: 3838134796
• ISBN 13: 9783838134796
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 25D millimeters 220 millimeters width by 150 millimeters height by 25 millimeters depth
• Weight: 620 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The structural comparison of whole proteins or protein binding sites is usually performed by making use of graphs. Even though graphs exhibit a lot of interesting and useful properties, the usage of other representations can come along with several benefits. In this book the representation of a protein binding site is tackled. Three representations are considered namely the geometric, the feature-based and the graph-based representation. For all three types, some completely new algorithms are developed and presented. Moreover, existing methods are adapted to the application of protein binding sites' comparison. In addition, algorithms for the construction of multiple structural alignments are presented as a structural counterpart to multiple sequence alignment. By considering four different types of experiments, namely classification, similarity retrieval, clustering and construction of structural alignments, strengths and weaknesses of the proposed methods are detected and discussed. In addition a hybrid approach is presented which combines the benefits of a feature-based and a geometric method leading to a significant speed-up while still holding very good results.

About the Author

holds an M.Sc. degree in Computer Science (Dipl.-Inf.) from the Universität Dortmund, Germany, and a Ph.D. degree in the Natural Sciences (Dr. rer. nat.) from the Philipps-Universität Marburg, Germany. Since 2007 he is a research staff member in the Computational Intelligence group at Philipps-Universität Marburg.