Predicting allosteric pockets in protein biological assemblages

Author:

Kumar Ambuj12,Kaynak Burak T34,Dorman Karin S15ORCID,Doruker Pemra4,Jernigan Robert L12ORCID

Affiliation:

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

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

3. Computational Neurobiology Laboratory, Salk Institute for Biological Studies , La Jolla, CA 92037, United States

4. Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh , Pittsburgh, PA 15232, United States

5. Department of Statistics, Iowa State University , Ames, IA 50011, United States

Abstract

Abstract Motivation Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages. Results Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein’s global dynamics. Availability and implementation APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.

Funder

NIH

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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