{"title":"Thermal Titration Molecular Dynamics: The Revenge of the Fragments.","authors":"Matteo Pavan,Silvia Menin,Andrea Dodaro,Gianluca Novello,Chiara Cavastracci Strascia,Mattia Sturlese,Veronica Salmaso,Stefano Moro","doi":"10.1021/acs.jcim.4c01681","DOIUrl":null,"url":null,"abstract":"During the last 20 years, the fragment-based drug discovery approach gained popularity in both industrial and academic settings due to its efficient exploration of the chemical space. This bottom-up approach relies on identifying high-efficiency small ligands (fragments) that bind to a target binding site and then rationally evolve them into mature druglike compounds. To achieve such a task, researchers rely on accurate information about the ligand binding mode, usually obtained through experimental techniques, such as X-ray crystallography or computer simulations. However, the physicochemical characteristics of fragments limit the accuracy and reliability of computational predictions of their binding mode. This article presents a new Thermal Titration Molecular Dynamics (TTMD) protocol, a recently developed enhanced sampling method for qualitatively estimating protein-ligand-binding stability, specifically tuned for the refinement of fragment docking results. The protocol has been applied to eight pharmaceutically relevant targets on 12 different test cases, including ligands with very low molecular weight and structural complexity (MiniFrag/FragLites). In more than 80% of cases, TTMD successfully identified the native fragment binding mode among a set of docking poses, outperforming docking alone and proving to be a useful tool to assist the fragment screening and optimization process.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"41 1","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.4c01681","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
During the last 20 years, the fragment-based drug discovery approach gained popularity in both industrial and academic settings due to its efficient exploration of the chemical space. This bottom-up approach relies on identifying high-efficiency small ligands (fragments) that bind to a target binding site and then rationally evolve them into mature druglike compounds. To achieve such a task, researchers rely on accurate information about the ligand binding mode, usually obtained through experimental techniques, such as X-ray crystallography or computer simulations. However, the physicochemical characteristics of fragments limit the accuracy and reliability of computational predictions of their binding mode. This article presents a new Thermal Titration Molecular Dynamics (TTMD) protocol, a recently developed enhanced sampling method for qualitatively estimating protein-ligand-binding stability, specifically tuned for the refinement of fragment docking results. The protocol has been applied to eight pharmaceutically relevant targets on 12 different test cases, including ligands with very low molecular weight and structural complexity (MiniFrag/FragLites). In more than 80% of cases, TTMD successfully identified the native fragment binding mode among a set of docking poses, outperforming docking alone and proving to be a useful tool to assist the fragment screening and optimization process.
期刊介绍:
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
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