Quantitative method for the assignment of hinge and shear mechanism in protein domain movements

Taylor, D., Cawley, G. ORCID: https://orcid.org/0000-0002-4118-9095 and Hayward, S. ORCID: https://orcid.org/0000-0001-6959-2604 (2014) Quantitative method for the assignment of hinge and shear mechanism in protein domain movements. Bioinformatics, 30 (22). pp. 3189-3196. ISSN 1367-4803

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Abstract

Motivation: A popular method for classification of protein domain movements apportions them into two main types: those with a ‘hinge’ mechanism and those with a ‘shear’ mechanism. The intuitive assignment of domain movements to these classes has limited the number of domain movements that can be classified in this way. Furthermore, whether intended or not, the term ‘shear’ is often interpreted to mean a relative translation of the domains. Results: Numbers of occurrences of four different types of residue contact changes between domains were optimally combined by logistic regression using the training set of domain movements intuitively classified as hinge and shear to produce a predictor for hinge and shear. This predictor was applied to give a 10-fold increase in the number of examples over the number previously available with a high degree of precision. It is shown that overall a relative translation of domains is rare, and that there is no difference between hinge and shear mechanisms in this respect. However, the shear set contains significantly more examples of domains having a relative twisting movement than the hinge set. The angle of rotation is also shown to be a good discriminator between the two mechanisms. Availability and implementation: Results are free to browse at http:// www.cmp.uea.ac.uk/dyndom/interface/. Supplementary information: Supplementary data are available at Bioinformatics online.

Item Type:	Article
Additional Information:	© The Author 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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:	08 Oct 2014 08:48
Last Modified:	19 Apr 2023 00:20
URI:	https://ueaeprints.uea.ac.uk/id/eprint/50224
DOI:	10.1093/bioinformatics/btu506

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