Automated protein complex modelling

Chad A. Davis

ISBN 10: 383812376X / ISBN 13: 9783838123769

Published by Südwestdeutscher Verlag Für Hochschulschriften Jun 2015, 2015

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Protein interaction networks provide an increasingly complex picture of the relationships between macromolecules in the cell. Complementing these interactions with structural data provides critical insights into interaction mechanisms. However, structural information is available only for a tiny fraction of protein interactions and complexes currently known. A method is developed here to predict macromolecular complex structures by systematic combination of pairwise interactions of known structure. An efficient algorithm was designed that exploits heuristics to reduce the large search space. This is complemented with an automated scoring system to filter out the exponentially large number of unrealistic complexes, leaving a ranked set of the most plausible models. On a benchmark set of complexes of known structure, many complexes can be re-created with high accuracy and certain models are much larger and more complete than what is possible with traditional modelling techniques. As the rate of structurally resolved interactions grows, the ability to model larger and more diverse complexes will grow exponentially.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch.

Seller Inventory # 9783838123769

Bibliographic Details

• Title: Automated protein complex modelling
• Publisher: Südwestdeutscher Verlag Für Hochschulschriften Jun 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 383812376X
• ISBN 13: 9783838123769
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 167 grams

About this title

Synopsis

Protein interaction networks provide an increasingly complex picture of the relationships between macromolecules in the cell. Complementing these interactions with structural data provides critical insights into interaction mechanisms. However, structural information is available only for a tiny fraction of protein interactions and complexes currently known. A method is developed here to predict macromolecular complex structures by systematic combination of pairwise interactions of known structure. An efficient algorithm was designed that exploits heuristics to reduce the large search space. This is complemented with an automated scoring system to filter out the exponentially large number of unrealistic complexes, leaving a ranked set of the most plausible models. On a benchmark set of complexes of known structure, many complexes can be re-created with high accuracy and certain models are much larger and more complete than what is possible with traditional modelling techniques. As the rate of structurally resolved interactions grows, the ability to model larger and more diverse complexes will grow exponentially.

About the Author

Chad A. Davis, Ph.D., received a B.S.E. in Computer Science and Engineering from Northern Arizona University, an M.Sc. in Bioinformatics from the joint program at the Ludwig-Maximilian University of Munich and the Technical University of Munich. His doctoral work was carried out at the European Molecular Biology Laboratory in Heidelberg.