Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor

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Publicado en:Nature Communications vol. 16, no. 1 (2025), p. 6949-6961
Autor principal: Elez, Katarina
Otros Autores: Hempel, Tim, Shrimp, Jonathan H., Moor, Nicole, Raich, Lluís, Rocha, Cheila, Winter, Robin, Le, Tuan, Pöhlmann, Stefan, Hoffmann, Markus, Hall, Matthew D., Noé, Frank
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022 |a 2041-1723 
024 7 |a 10.1038/s41467-025-62139-5  |2 doi 
035 |a 3234542037 
045 2 |b d20250101  |b d20251231 
084 |a 145839  |2 nlm 
100 1 |a Elez, Katarina  |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786) 
245 1 |a Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor 
260 |b Nature Publishing Group  |c 2025 
513 |a Journal Article 
520 3 |a Drug screening resembles finding a needle in a haystack: identifying a few effective inhibitors from a large pool of potential drugs. Large experimental screens are expensive and time-consuming, while virtual screening trades off computational efficiency and experimental correlation. Here we develop a framework that combines molecular dynamics (MD) simulations with active learning. Two components drastically reduce the number of candidates needing experimental testing to less than 20: (1) a target-specific score that evaluates target inhibition and (2) extensive MD simulations to generate a receptor ensemble. The active learning approach reduces the number of compounds requiring experimental testing to less than 10 and cuts computational costs by ∼29-fold. Using this framework, we discovered BMS-262084 as a potent inhibitor of TMPRSS2 (IC50 = 1.82 nM). Cell-based experiments confirmed BMS-262084’s efficacy in blocking entry of various SARS-CoV-2 variants and other coronaviruses. The identified inhibitor holds promise for treating viral and other diseases involving TMPRSS2.Approaches making virtual and experimental screening more resource-efficient are vital for identifying effective inhibitors from a vast pool of potential drugs but remain elusive. Here, the authors address this issue by developing an active learning framework leveraging high-throughput molecular dynamics simulations to identify potential inhibitors for therapeutic applications. 
653 |a Coronaviruses 
653 |a Simulation 
653 |a Datasets 
653 |a Learning 
653 |a Inhibitors 
653 |a Computational efficiency 
653 |a Effectiveness 
653 |a Computing costs 
653 |a Drug development 
653 |a Severe acute respiratory syndrome coronavirus 2 
653 |a Computer applications 
653 |a Drug screening 
653 |a Viral diseases 
653 |a Drugs 
653 |a Libraries 
653 |a Molecular dynamics 
653 |a Candidates 
653 |a COVID-19 
653 |a Proteins 
653 |a Therapeutic applications 
653 |a Environmental 
700 1 |a Hempel, Tim  |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Physics, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Microsoft Research AI for Science, Berlin, Germany (ROR: https://ror.org/04bpb0r34) (GRID: grid.506102.0) 
700 1 |a Shrimp, Jonathan H.  |u National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA (ROR: https://ror.org/01cwqze88) (GRID: grid.94365.3d) (ISNI: 0000 0001 2297 5165) 
700 1 |a Moor, Nicole  |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) 
700 1 |a Raich, Lluís  |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786) 
700 1 |a Rocha, Cheila  |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) 
700 1 |a Winter, Robin  |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Bioinformatics, Bayer AG, Berlin, Germany (ROR: https://ror.org/04hmn8g73) (GRID: grid.420044.6) (ISNI: 0000 0004 0374 4101) 
700 1 |a Le, Tuan  |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Bioinformatics, Bayer AG, Berlin, Germany (ROR: https://ror.org/04hmn8g73) (GRID: grid.420044.6) (ISNI: 0000 0004 0374 4101) 
700 1 |a Pöhlmann, Stefan  |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) 
700 1 |a Hoffmann, Markus  |u Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany (ROR: https://ror.org/02f99v835) (GRID: grid.418215.b) (ISNI: 0000 0000 8502 7018); Faculty of Biology and Psychology, University Göttingen, Göttingen, Germany (ROR: https://ror.org/01y9bpm73) (GRID: grid.7450.6) (ISNI: 0000 0001 2364 4210) 
700 1 |a Hall, Matthew D.  |u National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA (ROR: https://ror.org/01cwqze88) (GRID: grid.94365.3d) (ISNI: 0000 0001 2297 5165) 
700 1 |a Noé, Frank  |u Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Department of Physics, Freie Universität Berlin, Berlin, Germany (ROR: https://ror.org/046ak2485) (GRID: grid.14095.39) (ISNI: 0000 0001 2185 5786); Microsoft Research AI for Science, Berlin, Germany (ROR: https://ror.org/04bpb0r34) (GRID: grid.506102.0); Department of Chemistry, Rice University, Houston, TX, USA (ROR: https://ror.org/008zs3103) (GRID: grid.21940.3e) (ISNI: 0000 0004 1936 8278) 
773 0 |t Nature Communications  |g vol. 16, no. 1 (2025), p. 6949-6961 
786 0 |d ProQuest  |t Health & Medical Collection 
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