Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

Lindy Astl; Gennady M Verkhivker

doi:10.1016/j.bbagen.2019.07.008

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

Biochim Biophys Acta Gen Subj. 2019 Jul 19:S0304-4165(19)30179-5. doi: 10.1016/j.bbagen.2019.07.008. Online ahead of print.

Authors

Lindy Astl¹, Gennady M Verkhivker²

Affiliations

¹ Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, United States of America.
² Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, United States of America; Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States of America. Electronic address: verkhivk@chapman.edu.

PMID: 31330173
DOI: 10.1016/j.bbagen.2019.07.008

Abstract

Background: Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

Methods: In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

Results: The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

Conclusions: We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

General significance: This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

Keywords: Allosteric modulators; Allosteric proteins; Coevolutionary networks; Functional dynamics; Protein kinases; Residue coevolution; Residue interaction networks.