{"title":"蛋白质配体对接程序是否足以预测与 SARS-CoV-2 主蛋白酶结合的非共价配体的实验位置?","authors":"Ariadna Llop-Peiró , Guillem Macip , Santiago Garcia-Vallvé , Gerard Pujadas","doi":"10.1016/j.drudis.2024.104137","DOIUrl":null,"url":null,"abstract":"<div><p>Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.</p></div>","PeriodicalId":301,"journal":{"name":"Drug Discovery Today","volume":"29 10","pages":"Article 104137"},"PeriodicalIF":6.5000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Are protein–ligand docking programs good enough to predict experimental poses of noncovalent ligands bound to the SARS-CoV-2 main protease?\",\"authors\":\"Ariadna Llop-Peiró , Guillem Macip , Santiago Garcia-Vallvé , Gerard Pujadas\",\"doi\":\"10.1016/j.drudis.2024.104137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.</p></div>\",\"PeriodicalId\":301,\"journal\":{\"name\":\"Drug Discovery Today\",\"volume\":\"29 10\",\"pages\":\"Article 104137\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drug Discovery Today\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1359644624002629\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Discovery Today","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359644624002629","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
摘要
数以百计的虚拟筛选(VS)研究都以严重急性呼吸系统综合征-冠状病毒 2(SARS-CoV-2)的主要蛋白酶(M-pro)为目标,以鉴定抑制其蛋白水解作用的小分子化合物。大多数研究使用 AutoDock Vina 或 Glide 方法[高通量 VS (HTVS)、标准精度 (SP) 或额外精度 (XP)],独立或在 VS 工作流程中使用。此外,蛋白质数据库(PDB)包含了 M-pro 与各种非共价配体之间的多个复合物,为评估对接程序的预测能力提供了一个极好的基准。在这里,我们通过使用各种目标结构/制剂来预测这些复合物的实验姿势,分析了三种 Glide 方法和 AutoDock Vina 的能力。我们的目的是优化目标设置和对接方法,尽量减少假阳性,并在 SARS-CoV-2 M-pro 非共价抑制剂 VS 活动中最大限度地识别各种化学类型。
Are protein–ligand docking programs good enough to predict experimental poses of noncovalent ligands bound to the SARS-CoV-2 main protease?
Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.
期刊介绍:
Drug Discovery Today delivers informed and highly current reviews for the discovery community. The magazine addresses not only the rapid scientific developments in drug discovery associated technologies but also the management, commercial and regulatory issues that increasingly play a part in how R&D is planned, structured and executed.
Features include comment by international experts, news and analysis of important developments, reviews of key scientific and strategic issues, overviews of recent progress in specific therapeutic areas and conference reports.