Volume 63, Issue 24 e202405767
Research Article

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

Dr. Arup Mondal

Dr. Arup Mondal

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

Contribution: Conceptualization (equal), Data curation (lead), Formal analysis (lead), ​Investigation (lead), Methodology (lead), Validation (lead), Visualization (lead), Writing - original draft (equal), Writing - review & editing (equal)

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Bhumika Singh

Bhumika Singh

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

Contribution: Conceptualization (supporting), Data curation (supporting), Formal analysis (supporting), ​Investigation (supporting), Visualization (supporting)

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Roland H. Felkner

Roland H. Felkner

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

Contribution: Formal analysis (supporting), ​Investigation (supporting), Validation (supporting), Visualization (supporting)

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Dr. Anna De Falco

Dr. Anna De Falco

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

Contribution: Formal analysis (supporting), ​Investigation (supporting), Validation (supporting), Visualization (supporting), Writing - original draft (supporting)

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Dr. GVT Swapna

Dr. GVT Swapna

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

Contribution: Formal analysis (supporting), ​Investigation (supporting), Validation (supporting), Visualization (supporting)

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Prof. Gaetano T. Montelione

Corresponding Author

Prof. Gaetano T. Montelione

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

Contribution: Conceptualization (equal), Funding acquisition (equal), Project administration (equal), Resources (supporting), Supervision (equal), Writing - original draft (equal), Writing - review & editing (equal)

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Prof. Monica J. Roth

Corresponding Author

Prof. Monica J. Roth

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

Contribution: Conceptualization (equal), Funding acquisition (equal), ​Investigation (equal), Project administration (equal), Resources (equal), Supervision (equal), Validation (equal), Writing - original draft (equal), Writing - review & editing (equal)

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Prof. Alberto Perez

Corresponding Author

Prof. Alberto Perez

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

Contribution: Conceptualization (equal), Methodology (equal), Project administration (equal), Resources (equal), Software (equal), Supervision (equal), Writing - original draft (equal), Writing - review & editing (equal)

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First published: 08 April 2024
Citations: 2

Graphical Abstract

We introduce a computational pipeline that identifies peptide epitopes that bind a particular receptor from a library of possible protein sequences (e.g., coming from high-throughput experiments). The pipeline uses an AlphaFold-Competitive Binding Assay (AF-CBA) to rank order different plausible epitopes and select the most likely one.

Abstract

Identifying 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.

Conflict of interests

The authors declare no conflict of interest.

Data Availability Statement

A README file, and example system, and the scripts used in this work can be downloaded from https://github.com/PDNALab/AF-CBA-pipeline. The complete results for the BRD3-MLV system can be freely downloaded from Zenodo https://doi.org/10.5281/zenodo.10801197.

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