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Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

ABSTRACT: To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

SUBMITTER: Nguyen TH

PROVIDER: S-EPMC9950021 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

Nguyen Trung Hai TH Thai Quynh Mai QM Pham Minh Quan MQ Minh Pham Thi Hong PTH Phung Huong Thi Thu HTT

Molecular diversity 20230224 2

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Da ...[more]

PMID: 36823394

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Project description:Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a dreaded pandemic in lack of specific therapeutic agent. SARS-CoV-2 Mpro, an essential factor in viral pathogenesis, is recognized as a prospective therapeutic target in drug discovery against SARS-CoV-2. To tackle this pandemic, Food and Drug Administration-approved drugs are being screened against SARS-CoV-2 Mpro via in silico and in vitro methods to detect the best conceivable drug candidates. However, identification of natural compounds with anti-SARS-CoV-2 Mpro potential have been recommended as rapid and effective alternative for anti-SARS-CoV-2 therapeutic development. Thereof, a total of 653 natural compounds were identified against SARS-CoV-2 Mpro from NP-lib database at MTi-OpenScreen webserver using virtual screening approach. Subsequently, top four potential compounds, i.e. 2,3-Dihydroamentoflavone (ZINC000043552589), Podocarpusflavon-B (ZINC000003594862), Rutin (ZINC000003947429) and Quercimeritrin 6"-O-L-arabinopyranoside (ZINC000070691536), and co-crystallized N3 inhibitor as reference ligand were considered for stringent molecular docking after geometry optimization by DFT method. Each compound exhibited substantial docking energy >-12 kcal/mol and molecular contacts with essential residues, including catalytic dyad (His41 and Cys145) and substrate binding residues, in the active pocket of SARS-CoV-2 Mpro against N3 inhibitor. The screened compounds were further scrutinized via absorption, distribution, metabolism, and excretion - toxicity (ADMET), quantum chemical calculations, combinatorial molecular simulations and hybrid QM/MM approaches. Convincingly, collected results support the potent compounds for druglikeness and strong binding affinity with the catalytic pocket of SARS-CoV-2 Mpro. Hence, selected compounds are advocated as potential inhibitors of SARS-CoV-2 Mpro and can be utilized in drug development against SARS-CoV-2 infection.

Dataset Information

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

Publications

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

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