Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier

Author:

Kumar Ambuj12,Khade Pranav M12,Dorman Karin S13ORCID,Jernigan Robert L12ORCID

Affiliation:

1. Bioinformatics and Computational Biology Program, Iowa State University , Ames, IA 50011, USA

2. Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University , Ames, IA 50011, USA

3. Department of Statistics, Iowa State University , Ames, IA 50011, USA

Abstract

Abstract Summary A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/). Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Institutes of Health

NSF

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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