{"title":"靶向SARS-CoV-2主要蛋白酶:药效团和分子建模方法","authors":"Nitchakan Darai, Piyatida Pojtanadithee, Kamonpan Sanachai, Thierry Langer, Peter Wolschann, Thanyada Rungrotmongkol","doi":"10.1007/s00894-025-06441-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Context: </strong>The COVID-19 pandemic, driven by SARS-CoV-2, has had a profound impact on global health, with severe respiratory complications being a primary concern. The main protease (Mpro) of SARS-CoV-2 plays a critical role in viral replication, making it an attractive target for therapeutic intervention. This study aimed to identify potential Mpro inhibitors using an integrated computational approach. From an initial pool of 89,200 compounds in the ChemDiv database, a systematic screening process reduced the candidates to 735 through drug-like property predictions and pharmacophore-based virtual screening. Molecular docking against four co-crystal structures of the inhibitor/Mpro complex, followed by molecular dynamics (MD) simulations and binding free energy calculations, identified E912-0363 and G740-1003 as promising candidates with binding affinities comparable to nirmatrelvir. Extended 500-ns MD simulations further established E912-0363 as a highly promising Mpro inhibitor, supporting its potential for therapeutic development as a complementary or alternative treatment to nirmatrelvir.</p><p><strong>Methods: </strong>Pharmacophore modeling and virtual screening were conducted using the ChemDiv database, reducing 89,200 compounds to 735 candidates based on drug-like property predictions. Molecular docking was performed against four SARS-CoV-2 Mpro co-crystal structures using AutoDock VinaXB and GOLD docking programs. The top five candidates (E912-0363, P635-0261, G740-1003, G069-0804, and 8602-0428) were subjected to 100-ns molecular dynamics (MD) simulations using the AMBER force field. Binding free energy calculations were performed using the MM/GBSA method. Extended 500-ns MD simulations were carried out for the most promising candidate, E912-0363, to evaluate its long-term stability and interaction with the Mpro binding site.</p>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 8","pages":"222"},"PeriodicalIF":2.5000,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Targeting SARS-CoV-2 main protease: a pharmacophore and molecular modeling approach.\",\"authors\":\"Nitchakan Darai, Piyatida Pojtanadithee, Kamonpan Sanachai, Thierry Langer, Peter Wolschann, Thanyada Rungrotmongkol\",\"doi\":\"10.1007/s00894-025-06441-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Context: </strong>The COVID-19 pandemic, driven by SARS-CoV-2, has had a profound impact on global health, with severe respiratory complications being a primary concern. The main protease (Mpro) of SARS-CoV-2 plays a critical role in viral replication, making it an attractive target for therapeutic intervention. This study aimed to identify potential Mpro inhibitors using an integrated computational approach. From an initial pool of 89,200 compounds in the ChemDiv database, a systematic screening process reduced the candidates to 735 through drug-like property predictions and pharmacophore-based virtual screening. Molecular docking against four co-crystal structures of the inhibitor/Mpro complex, followed by molecular dynamics (MD) simulations and binding free energy calculations, identified E912-0363 and G740-1003 as promising candidates with binding affinities comparable to nirmatrelvir. Extended 500-ns MD simulations further established E912-0363 as a highly promising Mpro inhibitor, supporting its potential for therapeutic development as a complementary or alternative treatment to nirmatrelvir.</p><p><strong>Methods: </strong>Pharmacophore modeling and virtual screening were conducted using the ChemDiv database, reducing 89,200 compounds to 735 candidates based on drug-like property predictions. Molecular docking was performed against four SARS-CoV-2 Mpro co-crystal structures using AutoDock VinaXB and GOLD docking programs. The top five candidates (E912-0363, P635-0261, G740-1003, G069-0804, and 8602-0428) were subjected to 100-ns molecular dynamics (MD) simulations using the AMBER force field. Binding free energy calculations were performed using the MM/GBSA method. Extended 500-ns MD simulations were carried out for the most promising candidate, E912-0363, to evaluate its long-term stability and interaction with the Mpro binding site.</p>\",\"PeriodicalId\":651,\"journal\":{\"name\":\"Journal of Molecular Modeling\",\"volume\":\"31 8\",\"pages\":\"222\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Molecular Modeling\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1007/s00894-025-06441-5\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Modeling","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s00894-025-06441-5","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Targeting SARS-CoV-2 main protease: a pharmacophore and molecular modeling approach.
Context: The COVID-19 pandemic, driven by SARS-CoV-2, has had a profound impact on global health, with severe respiratory complications being a primary concern. The main protease (Mpro) of SARS-CoV-2 plays a critical role in viral replication, making it an attractive target for therapeutic intervention. This study aimed to identify potential Mpro inhibitors using an integrated computational approach. From an initial pool of 89,200 compounds in the ChemDiv database, a systematic screening process reduced the candidates to 735 through drug-like property predictions and pharmacophore-based virtual screening. Molecular docking against four co-crystal structures of the inhibitor/Mpro complex, followed by molecular dynamics (MD) simulations and binding free energy calculations, identified E912-0363 and G740-1003 as promising candidates with binding affinities comparable to nirmatrelvir. Extended 500-ns MD simulations further established E912-0363 as a highly promising Mpro inhibitor, supporting its potential for therapeutic development as a complementary or alternative treatment to nirmatrelvir.
Methods: Pharmacophore modeling and virtual screening were conducted using the ChemDiv database, reducing 89,200 compounds to 735 candidates based on drug-like property predictions. Molecular docking was performed against four SARS-CoV-2 Mpro co-crystal structures using AutoDock VinaXB and GOLD docking programs. The top five candidates (E912-0363, P635-0261, G740-1003, G069-0804, and 8602-0428) were subjected to 100-ns molecular dynamics (MD) simulations using the AMBER force field. Binding free energy calculations were performed using the MM/GBSA method. Extended 500-ns MD simulations were carried out for the most promising candidate, E912-0363, to evaluate its long-term stability and interaction with the Mpro binding site.
期刊介绍:
The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling.
Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry.
Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.