Suppr超能文献

D3Targets - 2019 - 新型冠状病毒:一个用于预测药物靶点以及针对2019冠状病毒病进行基于多靶点和多位点的虚拟筛选的网络服务器。

D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19.

作者信息

Shi Yulong, Zhang Xinben, Mu Kaijie, Peng Cheng, Zhu Zhengdan, Wang Xiaoyu, Yang Yanqing, Xu Zhijian, Zhu Weiliang

机构信息

CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.

School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Acta Pharm Sin B. 2020 Jul;10(7):1239-1248. doi: 10.1016/j.apsb.2020.04.006. Epub 2020 Apr 20.

Abstract

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or / studies, the other is for identifying lead compounds against potential drug targets docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.

摘要

迫切需要一种高效药物来治疗2019冠状病毒病(COVID-19)。为此,我们开发了一种基于分子对接的网络服务器,即D3Targets-2019-nCoV,它具有两项功能,一是预测临床观察到的药物或活性化合物的药物靶点,另一是针对潜在药物靶点识别先导化合物进行对接。该服务器具有其独特之处:(1)尽可能多地纳入了从病毒感染到复制和释放整个过程中涉及的潜在靶蛋白及其不同构象;(2)在蛋白质结构上识别出所有体积大于200 Å的潜在配体结合位点用于对接;(3)对一些构象或结合位点之间的相关信息进行了注释;(4)易于更新,并且公众可免费访问(https://www.d3pharma.com/D3Targets-2019-nCoV/index.php)。目前,该网络服务器总共包含42种蛋白质[20种严重急性呼吸综合征冠状病毒2(SARS-CoV-2)编码蛋白和22种参与病毒感染、复制和释放的人类蛋白],具有69种不同的构象/结构以及557个潜在配体结合口袋。通过6个实例,我们证明该网络服务器对于药物化学家、药理学家和临床医生有效发现或开发针对SARS-CoV-2治疗COVID-19的有效药物应是有用的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/932c/7452032/96674bcf36cd/fx1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验