Classification of protein domain movements using Dynamic Contact Graphs

Taylor, Daniel, Cawley, Gavin ORCID: https://orcid.org/0000-0002-4118-9095 and Hayward, Steven ORCID: https://orcid.org/0000-0001-6959-2604 (2013) Classification of protein domain movements using Dynamic Contact Graphs. PLoS One, 8 (11). ISSN 1932-6203

Preview

PDF (Taylor_etal_2013_plosone) - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

A new method for the classification of domain movements in proteins is described and applied to 1822 pairs of structures from the Protein Data Bank that represent a domain movement in two-domain proteins. The method is based on changes in contacts between residues from the two domains in moving from one conformation to the other. We argue that there are five types of elemental contact changes and that these relate to five model domain movements called: ‘‘free’’, ‘‘openclosed’’, ‘‘anchored’’, ‘‘sliding-twist’’, and ‘‘see-saw.’’ A directed graph is introduced called the ‘‘Dynamic Contact Graph’’ which represents the contact changes in a domain movement. In many cases a graph, or part of a graph, provides a clear visual metaphor for the movement it represents and is a motif that can be easily recognised. The Dynamic Contact Graphs are often comprised of disconnected subgraphs indicating independent regions which may play different roles in the domain movement. The Dynamic Contact Graph for each domain movement is decomposed into elemental Dynamic Contact Graphs, those that represent elemental contact changes, allowing us to count the number of instances of each type of elemental contact change in the domain movement. This naturally leads to sixteen classes into which the 1822 domain movements are classified.

Item Type:	Article
Additional Information:	© 2013 Taylor et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User:	Pure Connector
Date Deposited:	11 Feb 2014 11:20
Last Modified:	19 Apr 2023 00:03
URI:	https://ueaeprints.uea.ac.uk/id/eprint/47488
DOI:	10.1371/journal.pone.0081224

Downloads

Actions (login required)

View Item