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

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...

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Autores principales: Nigam, AkshatKumar, Hurley, Matthew F. D., Li, Fengling, Konkoǐová, Eva, Klíma, Martin, Trylčová, Jana, Pollice, Robert, Çinaroǧlu, Süleyman Selim, Levin-Konigsberg, Roni, Handjaya, Jasemine, Schapira, Matthieu, Chau, Irene, Perveen, Sumera, Ng, Ho-Leung, Ümit Kaniskan, H., Han, Yulin, Singh, Sukrit, Gorgulla, Christoph, Kundaje, Anshul, Jin, Jian, Voelz, Vincent A., Weber, Jan, Nencka, Radim, Boura, Evzen, Vedadi, Masoud, Aspuru-Guzik, Alán
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592886/
https://www.ncbi.nlm.nih.gov/pubmed/37873443
http://dx.doi.org/10.1101/2023.10.03.560722
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author Nigam, AkshatKumar
Hurley, Matthew F. D.
Li, Fengling
Konkoǐová, Eva
Klíma, Martin
Trylčová, Jana
Pollice, Robert
Çinaroǧlu, Süleyman Selim
Levin-Konigsberg, Roni
Handjaya, Jasemine
Schapira, Matthieu
Chau, Irene
Perveen, Sumera
Ng, Ho-Leung
Ümit Kaniskan, H.
Han, Yulin
Singh, Sukrit
Gorgulla, Christoph
Kundaje, Anshul
Jin, Jian
Voelz, Vincent A.
Weber, Jan
Nencka, Radim
Boura, Evzen
Vedadi, Masoud
Aspuru-Guzik, Alán
author_facet Nigam, AkshatKumar
Hurley, Matthew F. D.
Li, Fengling
Konkoǐová, Eva
Klíma, Martin
Trylčová, Jana
Pollice, Robert
Çinaroǧlu, Süleyman Selim
Levin-Konigsberg, Roni
Handjaya, Jasemine
Schapira, Matthieu
Chau, Irene
Perveen, Sumera
Ng, Ho-Leung
Ümit Kaniskan, H.
Han, Yulin
Singh, Sukrit
Gorgulla, Christoph
Kundaje, Anshul
Jin, Jian
Voelz, Vincent A.
Weber, Jan
Nencka, Radim
Boura, Evzen
Vedadi, Masoud
Aspuru-Guzik, Alán
author_sort Nigam, AkshatKumar
collection PubMed
description 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 (IC(50) 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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spelling pubmed-105928862023-11-20 Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors Nigam, AkshatKumar Hurley, Matthew F. D. Li, Fengling Konkoǐová, Eva Klíma, Martin Trylčová, Jana Pollice, Robert Çinaroǧlu, Süleyman Selim Levin-Konigsberg, Roni Handjaya, Jasemine Schapira, Matthieu Chau, Irene Perveen, Sumera Ng, Ho-Leung Ümit Kaniskan, H. Han, Yulin Singh, Sukrit Gorgulla, Christoph Kundaje, Anshul Jin, Jian Voelz, Vincent A. Weber, Jan Nencka, Radim Boura, Evzen Vedadi, Masoud Aspuru-Guzik, Alán bioRxiv Article 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 (IC(50) 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. Cold Spring Harbor Laboratory 2023-11-13 /pmc/articles/PMC10592886/ /pubmed/37873443 http://dx.doi.org/10.1101/2023.10.03.560722 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Nigam, AkshatKumar
Hurley, Matthew F. D.
Li, Fengling
Konkoǐová, Eva
Klíma, Martin
Trylčová, Jana
Pollice, Robert
Çinaroǧlu, Süleyman Selim
Levin-Konigsberg, Roni
Handjaya, Jasemine
Schapira, Matthieu
Chau, Irene
Perveen, Sumera
Ng, Ho-Leung
Ümit Kaniskan, H.
Han, Yulin
Singh, Sukrit
Gorgulla, Christoph
Kundaje, Anshul
Jin, Jian
Voelz, Vincent A.
Weber, Jan
Nencka, Radim
Boura, Evzen
Vedadi, Masoud
Aspuru-Guzik, Alán
Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
title Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
title_full Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
title_fullStr Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
title_full_unstemmed Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
title_short Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
title_sort drug discovery in low data regimes: leveraging a computational pipeline for the discovery of novel sars-cov-2 nsp14-mtase inhibitors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592886/
https://www.ncbi.nlm.nih.gov/pubmed/37873443
http://dx.doi.org/10.1101/2023.10.03.560722
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