TY - UNPB
T1 - Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
AU - Nigam, A.
AU - Hurley, M. F. D.
AU - Li, F.
AU - Konkoĭová, E.
AU - Klíma, M.
AU - Trylčová, J.
AU - Pollice, R.
AU - Çinaroǧlu, S. S.
AU - Levin-Konigsberg, R.
AU - Handjaya, J.
AU - Schapira, M.
AU - Chau, I.
AU - Perveen, S.
AU - Ng, H. L.
AU - Ümit Kaniskan, H.
AU - Han, Y.
AU - Singh, S.
AU - Gorgulla, C.
AU - Kundaje, A.
AU - Jin, J.
AU - Voelz, V. A.
AU - Weber, J.
AU - Nencka, R.
AU - Boura, E.
AU - Vedadi, M.
AU - Aspuru-Guzik, A.
N1 - Nigam, AkshatKumar Hurley, Matthew F D Li, Fengling Konkoĭová, 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 R01 GM123296/GM/NIGMS NIH HHS/United States S10 OD020095/OD/NIH HHS/United States Preprint United States 2023/10/24 bioRxiv. 2023 Oct 4:2023.10.03.560722. doi: 10.1101/2023.10.03.560722. Preprint.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1101/2023.10.03.560722
DO - 10.1101/2023.10.03.560722
M3 - Preprint
C2 - 37873443
T3 - bioRxiv : the preprint server for biology
BT - Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors
ER -