Campus Units
Biochemistry, Biophysics and Molecular Biology, Roy J. Carver Department of, Bioinformatics and Computational Biology
Document Type
Article
Publication Version
Published Version
Publication Date
6-20-2018
Journal or Book Title
PloS ONE
Volume
13
Issue
6
First Page
e0199225
DOI
10.1371/journal.pone.0199225
Abstract
Dynamic communities in proteins comprise the cohesive structural units that individually exhibit rigid body motions. These can correspond to structural domains, but are usually smaller parts that move with respect to one another in a protein’s internal motions, key to its functional dynamics. Previous studies emphasized their importance to understand the nature of ligand-induced allosteric regulation. These studies reported that mutations to key community residues can hinder transmission of allosteric signals among the communities. Usually molecular dynamic (MD) simulations (~ 100 ns or longer) have been used to identify the communities—a demanding task for larger proteins. In the present study, we propose that dynamic communities obtained from MD simulations can also be obtained alternatively with simpler models–the elastic network models (ENMs). To verify this premise, we compare the specific communities obtained from MD and ENMs for 44 proteins. We evaluate the correspondence in communities from the two methods and compute the extent of agreement in the dynamic cross-correlation data used for community detection. Our study reveals a strong correspondence between the communities from MD and ENM and also good agreement for the residue cross-correlations. Importantly, we observe that the dynamic communities from MD can be closely reproduced with ENMs. With ENMs, we also compare the community structures of stable and unstable mutant forms of T4 Lysozyme with its wild-type. We find that communities for unstable mutants show substantially poorer agreement with the wild-type communities than do stable mutants, suggesting such ENM-based community structures can serve as a means to rapidly identify deleterious mutants.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright Owner
Mishra, Jernigan
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Mishra, Sambit Kumar and Jernigan, Robert L., "Protein dynamic communities from elastic network models align closely to the communities defined by molecular dynamics" (2018). Biochemistry, Biophysics and Molecular Biology Publications. 196.
https://lib.dr.iastate.edu/bbmb_ag_pubs/196
Comments
This article is published as Mishra SK, Jernigan RL (2018) Protein dynamic communities from elastic network models align closely to the communities defined by molecular dynamics. PLoS ONE 13(6): e0199225. doi: 10.1371/journal.pone.0199225.