A Computational Pipeline for Accurate Prioritization of Protein‐Protein Binding Candidates in High‐Throughput Protein Libraries

Author:

Mondal Arup1ORCID,Singh Bhumika1,Felkner Roland H.2,De Falco Anna3,Swapna GVT3,Montelione Gaetano T.3ORCID,Roth Monica J.2ORCID,Perez Alberto1ORCID

Affiliation:

1. Department of Chemistry and Quantum Theory Project University of Florida Leigh Hall 240 Gainesville FL USA

2. Department of Pharmacology Rutgers-Robert Wood Johnson Medical School 675 Hoes Lane Rm 636 Piscataway NJ 08854 USA

3. Department of Chemistry and Chemical Biology Center for Biotechnology and Interdisciplinary Sciences Rensselaer Polytechnic Institute Troy New York 12180 USA

Abstract

AbstractIdentifying the interactome for a protein of interest is challenging due to the large number of possible binders. High‐throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF‐CBA) to identify proteins that bind a target of interest from a pull‐down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide‐protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF‐CBA to help users identify scenarios where the approach will be most useful. Given the method‘s speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.

Funder

National Institute of General Medical Sciences

Publisher

Wiley

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