Mapping allosteric communications within individual proteins

Jian Wang, Abha Jain, Leanna R. McDonald, Craig Gambogi, Andrew L. Lee and Nikolay V. Dokholyan ()
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Jian Wang: Penn State College of Medicine
Abha Jain: Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
Leanna R. McDonald: Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
Craig Gambogi: Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
Andrew L. Lee: Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill
Nikolay V. Dokholyan: Penn State College of Medicine

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.

Date: 2020
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DOI: 10.1038/s41467-020-17618-2

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