Methodological improvements for the analysis of domain movements in large biomolecular complexes

Veevers, Ruth and Hayward, Steven ORCID: https://orcid.org/0000-0001-6959-2604 (2019) Methodological improvements for the analysis of domain movements in large biomolecular complexes. Biophysics and Physicobiology, 16. pp. 328-336. ISSN 2189-4779

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Abstract

Domain movements play a prominent role in the function of many biomolecules such as the ribosome and F0F1-ATP synthase. As more structures of large biomolecules in different functional states become available as experimental techniques for structure determination advance, there is a need to develop methods to understand the conformational changes that occur. DynDom and DynDom3D were developed to analyse two structures of a biomolecule for domain movements. They both used an original method for domain recognition based on clustering of “rotation vectors”. Here we introduce significant improvements in both the methodology and implementation of a tool for the analysis of domain movements in large multimeric biomolecules. The main improvement is in the recognition of domains by using all six degrees of freedom required to describe the movement of a rigid body. This is achieved by way of Chasles’ theorem in which a rigid-body movement can be described as a screw movement about a unique axis. Thus clustering now includes, in addition to rotation vector data, screw-axis location data and axial climb data. This improves both the sensitivity of domain recognition and performance. A further improvement is the recognition and annotation of interdomain bending regions, something not done for multimeric biomolecules in DynDom3D. This is significant as it is these regions that collectively control the domain movement. The new stand-alone, platform-independent implementation, DynDom6D, can analyse biomolecules comprising protein, DNA and RNA, and employs an alignment method to automatically achieve the required equivalence of atoms in the two structures.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology
Depositing User:	LivePure Connector
Date Deposited:	05 Aug 2019 10:31
Last Modified:	21 Apr 2023 00:06
URI:	https://ueaeprints.uea.ac.uk/id/eprint/71879
DOI:	10.2142/biophysico.16.0_328

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