Journal article
Turning high-throughput structural biology into predictive inhibitor design
- Abstract:
- A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein–ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein–ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (MPro), obtaining parallel measurements of over 200 protein–ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, eps, 9.5MB, Terms of use)
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- Publisher copy:
- 10.1073/pnas.2214168120
- Publisher:
- National Academy of Sciences
- Journal:
- Proceedings of the National Academy of Sciences More from this journal
- Volume:
- 120
- Issue:
- 11
- Article number:
- e2214168120
- Place of publication:
- United States
- Publication date:
- 2023-03-06
- Acceptance date:
- 2022-09-19
- DOI:
- EISSN:
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1091-6490
- ISSN:
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0027-8424
- Pmid:
-
36877844
- Language:
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English
- Keywords:
- Pubs id:
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1334244
- Local pid:
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pubs:1334244
- Deposit date:
-
2024-03-14
Terms of use
- Copyright holder:
- Saar et al.
- Copyright date:
- 2023
- Rights statement:
- © 2023 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
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