Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations
© 2022 Elsevier B.V. All rights reserved..
Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:564 |
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| Enthalten in: |
Chemical physics - 564(2023) vom: 01. Jan., Seite 111709 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nguyen, Trung Hai [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 21.12.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.chemphys.2022.111709 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM347039782 |
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| 500 | |a Date Revised 21.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2022 Elsevier B.V. All rights reserved. | ||
| 520 | |a Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Docking, Simulation | |
| 650 | 4 | |a FEP | |
| 650 | 4 | |a FEP, Free Energy Perturbation | |
| 650 | 4 | |a LIE | |
| 650 | 4 | |a LIE, Linear Interaction Energy | |
| 650 | 4 | |a ML, Machine Learning | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Mpro, SARS-CoV-2 Mpro | |
| 650 | 4 | |a SARS-CoV-2 Mpro | |
| 650 | 4 | |a SL, Supervised Learning | |
| 650 | 4 | |a Supervised learning | |
| 700 | 1 | |a Tam, Nguyen Minh |e verfasserin |4 aut | |
| 700 | 1 | |a Tuan, Mai Van |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Vu, Van V |e verfasserin |4 aut | |
| 700 | 1 | |a Quang, Duong Tuan |e verfasserin |4 aut | |
| 700 | 1 | |a Ngo, Son Tung |e verfasserin |4 aut | |
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