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Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here,...

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Autores principales: Wang, Liying, Yu, Zhongtian, Wang, Shiwei, Guo, Zheng, Sun, Qi, Lai, Luhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Masson SAS. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9528019/
https://www.ncbi.nlm.nih.gov/pubmed/36209629
http://dx.doi.org/10.1016/j.ejmech.2022.114803
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author Wang, Liying
Yu, Zhongtian
Wang, Shiwei
Guo, Zheng
Sun, Qi
Lai, Luhua
author_facet Wang, Liying
Yu, Zhongtian
Wang, Shiwei
Guo, Zheng
Sun, Qi
Lai, Luhua
author_sort Wang, Liying
collection PubMed
description SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC(50) values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC(50) of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.
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spelling pubmed-95280192022-10-03 Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen Wang, Liying Yu, Zhongtian Wang, Shiwei Guo, Zheng Sun, Qi Lai, Luhua Eur J Med Chem Research Paper SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC(50) values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC(50) of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors. Elsevier Masson SAS. 2022-12-15 2022-10-03 /pmc/articles/PMC9528019/ /pubmed/36209629 http://dx.doi.org/10.1016/j.ejmech.2022.114803 Text en © 2022 Elsevier Masson SAS. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Research Paper
Wang, Liying
Yu, Zhongtian
Wang, Shiwei
Guo, Zheng
Sun, Qi
Lai, Luhua
Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen
title Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen
title_full Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen
title_fullStr Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen
title_full_unstemmed Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen
title_short Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen
title_sort discovery of novel sars-cov-2 3cl protease covalent inhibitors using deep learning-based screen
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9528019/
https://www.ncbi.nlm.nih.gov/pubmed/36209629
http://dx.doi.org/10.1016/j.ejmech.2022.114803
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