Identifying drug-like inhibitors of Mycobacterium tuberculosis H37Rv Seryl tRNA synthetase based on bioassay dataset: homology modelling, docking and molecular dynamics simulation

V. Adarsh, A. Santhiagu
{"title":"Identifying drug-like inhibitors of Mycobacterium tuberculosis H37Rv Seryl tRNA synthetase based on bioassay dataset: homology modelling, docking and molecular dynamics simulation","authors":"V. Adarsh, A. Santhiagu","doi":"10.1504/ijcbdd.2019.10025251","DOIUrl":null,"url":null,"abstract":"Resistance to existing drugs of tuberculosis bacteria demands an immediate requirement to develop effective new chemical entities acting on emerging targets. Seryl-tRNA synthetase (SerRS) is essential for the viability of Mycobacterium tuberculosis (MTB). In this study, we have attempted to develop the tertiary structure of SerRS through homology modelling and to elucidate the active site interactions of inhibitor compounds aided by docking. Homology modelling using PDB ID: '2DQ3: A' chain as template and validation of the model was carried out with Modeller V9.13 and SAVES online server respectively. About 1248 compounds from a putative kinase compound library of PubChem database found active in whole cell bioassay (AID2842) on MTB - H37Rv was used in docking studies using 'AutoDock'. Out of the tested molecules, nine showed docking scores ≤-10 kcal/mol with good drug-like properties were further subjected to molecular dynamics (MD) simulations and found three of the compounds have stable interactions.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"42 1","pages":"373-402"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Biol. Drug Des.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijcbdd.2019.10025251","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Resistance to existing drugs of tuberculosis bacteria demands an immediate requirement to develop effective new chemical entities acting on emerging targets. Seryl-tRNA synthetase (SerRS) is essential for the viability of Mycobacterium tuberculosis (MTB). In this study, we have attempted to develop the tertiary structure of SerRS through homology modelling and to elucidate the active site interactions of inhibitor compounds aided by docking. Homology modelling using PDB ID: '2DQ3: A' chain as template and validation of the model was carried out with Modeller V9.13 and SAVES online server respectively. About 1248 compounds from a putative kinase compound library of PubChem database found active in whole cell bioassay (AID2842) on MTB - H37Rv was used in docking studies using 'AutoDock'. Out of the tested molecules, nine showed docking scores ≤-10 kcal/mol with good drug-like properties were further subjected to molecular dynamics (MD) simulations and found three of the compounds have stable interactions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于生物测定数据集鉴定结核分枝杆菌H37Rv Seryl tRNA合成酶药物样抑制剂:同源性建模、对接和分子动力学模拟
结核病细菌对现有药物的耐药性迫切需要开发有效的新化学实体,作用于新出现的靶点。Seryl-tRNA合成酶(SerRS)对结核分枝杆菌(MTB)的生存至关重要。在这项研究中,我们试图通过同源性建模来开发SerRS的三级结构,并通过对接来阐明抑制剂化合物的活性位点相互作用。以PDB ID: '2DQ3: A'链为模板进行同源性建模,并分别在modelmodelv9.13和SAVES在线服务器上对模型进行验证。从PubChem数据库中发现的在MTB - H37Rv全细胞生物测定中具有活性的激酶化合物库(AID2842)中,约有1248种化合物被用于AutoDock对接研究。在测试的分子中,9个分子的对接分数≤-10 kcal/mol,具有良好的药物性质,进一步进行分子动力学(MD)模拟,发现其中3个化合物具有稳定的相互作用。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Random forest with SMOTE and ensemble feature selection for cervical cancer diagnosis A review on speech organ diseases and cancer detection using artificial intelligence In silico phytochemical repurposing of natural molecules as entry inhibitors against RBD of the spike protein of SARS-CoV-2 using molecular docking studies Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics Computational identification of personal genetic variants in an identical twin sisters' family
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1