2. Academic Validation
  3. Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

  • bioRxiv. 2023 Nov 13:2023.10.03.560722. doi: 10.1101/2023.10.03.560722.
AkshatKumar Nigam 1 2,  Matthew F D Hurley 3,  Fengling Li 4,  Eva Konkoǐová 5,  Martin Klíma 5,  Jana Trylčová 5,  Robert Pollice 6 7 8,  Süleyman Selim Çinaroǧlu 9,  Roni Levin-Konigsberg 2,  Jasemine Handjaya 6 7,  Matthieu Schapira 4 10,  Irene Chau 4,  Sumera Perveen 4,  Ho-Leung Ng 11,  H Ümit Kaniskan 12,  Yulin Han 12,  Sukrit Singh 13,  Christoph Gorgulla 14 15 16,  Anshul Kundaje 1 2,  Jian Jin 12,  Vincent A Voelz 3,  Jan Weber 5,  Radim Nencka 5,  Evzen Boura 5,  Masoud Vedadi 4 10 17 18,  Alán Aspuru-Guzik 6 7 19 20 21 22 23
Affiliations

Affiliations

  • 1 Department of Computer Science, Stanford University.
  • 2 Department of Genetics, Stanford University.
  • 3 Department of Chemistry, Temple University, Philadelphia, PA 19122, USA.
  • 4 Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
  • 5 Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
  • 6 Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada.
  • 7 Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada.
  • 8 Current affiliation: Stratingh Institute for Chemistry, University of Groningen, The Netherlands.
  • 9 Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
  • 10 Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
  • 11 Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, KS 66506, USA.
  • 12 Department of Pharmacological Sciences and Oncological Sciences, Mount Sinai Center for Therapeutics Discovery, Tisch Cancer Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA.
  • 13 Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center.
  • 14 St. Jude Children's Research Hospital, Department of Structural Biology, Memphis, TN, USA.
  • 15 Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, USA.
  • 16 Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, USA.
  • 17 QBI COVID-19 Research Group (QCRG), San Francisco, CA, USA.
  • 18 Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
  • 19 Department of Chemical Engineering & Applied Chemistry, University of Toronto, Canada.
  • 20 Department of Materials Science & Engineering, University of Toronto, Canada.
  • 21 Vector Institute for Artificial Intelligence, Toronto, Canada.
  • 22 Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada.
  • 23 Acceleration Consortium, University of Toronto, Toronto, ON, Canada.
PMID: 37873443 DOI: 10.1101/2023.10.03.560722
Abstract

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and Other emerging viruses.

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