DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening

IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
{"title":"DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening","authors":"","doi":"10.1016/j.jmb.2024.168548","DOIUrl":null,"url":null,"abstract":"<div><p>The DockThor-VS platform (<span><span>https://dockthor.lncc.br/v2/</span><svg><path></path></svg></span>) is a free protein–ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein–ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.</p></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0022283624001438/pdfft?md5=57349e8fba1907bce8b7894215619c89&pid=1-s2.0-S0022283624001438-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022283624001438","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein–ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein–ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

Abstract Image

DockThor-VS:受体配体虚拟筛选的免费平台
DockThor-VS平台(https://dockthor.lncc.br/v2/)是一个免费的蛋白质配体对接服务器,旨在促进和协助药物发现项目利用高性能计算准确地进行基于对接的虚拟筛选实验。DockThor对接引擎是一种基于网格的方法,设计用于柔性配体和刚性受体对接。它采用多解遗传算法和 MMFF94S 分子力场评分函数进行姿势预测。该引擎专为处理肽等高柔性配体而设计。蛋白质配体复合物的亲和预测和排序使用线性经验评分函数 DockTScore 进行。配体和蛋白质制备的主要步骤可在 DockThor 门户网站上查看,因此可以改变氨基酸残基的质子化状态,并将辅助因子作为刚性实体纳入其中。用户还可以自定义和可视化网格框的主要参数。对接实验的结果会自动聚类和排序,为用户提供各种有意义的结合模式。DockThor-VS 平台提供友好的用户界面和强大的算法,使研究人员能够高效、准确地进行虚拟筛选实验。DockThor Portal 利用巴西高性能平台 SDumont 的计算能力,进一步提高了对接实验的效率和速度。此外,该网络服务器还通过提供潜在靶标和化合物数据集(如与 COVID-19 相关的蛋白质和用于再利用研究的经 FDA 批准的药物)的策划结构来促进和加强虚拟筛选实验。总之,DockThor-VS 是一种动态的、不断发展的基于对接的虚拟筛选解决方案,可应用于药物发现项目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Molecular Biology
Journal of Molecular Biology 生物-生化与分子生物学
CiteScore
11.30
自引率
1.80%
发文量
412
审稿时长
28 days
期刊介绍: Journal of Molecular Biology (JMB) provides high quality, comprehensive and broad coverage in all areas of molecular biology. The journal publishes original scientific research papers that provide mechanistic and functional insights and report a significant advance to the field. The journal encourages the submission of multidisciplinary studies that use complementary experimental and computational approaches to address challenging biological questions. Research areas include but are not limited to: Biomolecular interactions, signaling networks, systems biology; Cell cycle, cell growth, cell differentiation; Cell death, autophagy; Cell signaling and regulation; Chemical biology; Computational biology, in combination with experimental studies; DNA replication, repair, and recombination; Development, regenerative biology, mechanistic and functional studies of stem cells; Epigenetics, chromatin structure and function; Gene expression; Membrane processes, cell surface proteins and cell-cell interactions; Methodological advances, both experimental and theoretical, including databases; Microbiology, virology, and interactions with the host or environment; Microbiota mechanistic and functional studies; Nuclear organization; Post-translational modifications, proteomics; Processing and function of biologically important macromolecules and complexes; Molecular basis of disease; RNA processing, structure and functions of non-coding RNAs, transcription; Sorting, spatiotemporal organization, trafficking; Structural biology; Synthetic biology; Translation, protein folding, chaperones, protein degradation and quality control.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信