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Hayward, Steven 
ORCID: https://orcid.org/0000-0001-6959-2604
(2026)
Method for the Determination of Dynamic Domains in Proteins From Structural Pairs and Conformational Ensembles.
Proteins-Structure Function and Bioinformatics.
ISSN 0887-3585
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Abstract
A straightforward method for the determination of dynamic domains from pairs of conformations or general conformational ensembles of proteins is presented. The method applies metric multi-dimensional scaling (MDS) to distance-differences or root-mean square-fluctuations of inter-atomic distances (RMSFIDs). The approach determines points in a low-dimensional space, each representing an amino-acid residue, where distances between the points give an approximation to the distance-differences, in the case of a pair of conformations, or RMSFIDs for an ensemble. This point-based representation enables top-down clustering methods to be used to determine dynamic domains. The two implementations, Pair-DD and Ensemble-DD, are demonstrated on idealized examples where domains move as perfect rigid bodies, on conformational pairs and ensembles derived from X-ray structures both monomeric and multimeric, and on trajectories derived from simulation methods. A parameter is proposed which can be used as a threshold for acceptance of dynamic domains to enable automatic assignment. The results show excellent correspondence with a well-established approach, but the method has the added advantage of being versatile in that it is applicable to both a pair of structures and an ensemble of conformations. Furthermore, for a pair of conformations, a one-dimensional MDS coordinate seems to be sufficient, meaning that the degree of association of a residue with a dynamic domain can be visualized in a simple plot.
| Item Type: | Article |
|---|---|
| Additional Information: | Data Availability Statement: Pair-DD and Ensemble-DD (Python Jupyter Notebooks) are available at: https://zenodo.org/records/21378357. |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Health Computing Faculty of Science > Research Groups > Computational Biology |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 25 Sep 2026 10:42 |
| Last Modified: | 27 Sep 2026 05:36 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104639 |
| DOI: | 10.1002/prot.70176Digital |
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