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LigTMap:基于配体和结构的小分子化合物靶点识别与活性预测

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds.

作者信息

Shaikh Faraz, Tai Hio Kuan, Desai Nirali, Siu Shirley W I

机构信息

Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida da Universidade, Taipa, Macau, China.

Division of Biological and Life Sciences, Ahmedabad University, Ahmedabad, India.

出版信息

J Cheminform. 2021 Jun 10;13(1):44. doi: 10.1186/s13321-021-00523-1.

DOI:10.1186/s13321-021-00523-1
PMID:34112240
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8194164/
Abstract

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap . The source code is released on GitHub ( https://github.com/ShirleyWISiu/LigTMap ) under the BSD 3-Clause License to encourage re-use and further developments.

摘要

靶点预测是现代药物发现中的关键一步。然而,现有的靶点预测实验方法既耗时又昂贵。在此,我们介绍LigTMap,这是一个具有全自动工作流程的在线服务器,它可以从PDBbind数据库中提取的17类治疗性蛋白质中识别化合物的蛋白质靶点。它将配体相似性搜索与对接和结合相似性分析相结合,以预测潜在靶点。在对1251种化合物的验证实验中,在前10个列表中,超过70%的化合物成功预测到了靶点。LigTMap的性能与当前最好的服务器SwissTargetPrediction和SEA相当。当用我们从近期文献中新编译的化合物进行测试时,我们获得了更高的前10成功率(我们的为66%,而SwissTargetPrediction为60%,SEA为64%)和相似的前1成功率(我们的为45%,而SwissTargetPrediction为51%,SEA为41%)。LigTMap直接以PDB格式提供配体对接结构,以便结果可直接用于计算机辅助药物设计和药物再利用项目中的进一步结构研究。LigTMap网络服务器可通过https://cbbio.online/LigTMap免费访问。源代码在BSD 3条款许可下发布在GitHub(https://github.com/ShirleyWISiu/LigTMap)上,以鼓励重复使用和进一步开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/b1708ad61cfc/13321_2021_523_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/f63346a80eb9/13321_2021_523_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/9dc1c5c00b5c/13321_2021_523_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/c910af98cffa/13321_2021_523_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/b1708ad61cfc/13321_2021_523_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/f63346a80eb9/13321_2021_523_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/9dc1c5c00b5c/13321_2021_523_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/c910af98cffa/13321_2021_523_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2b1/8194164/b1708ad61cfc/13321_2021_523_Fig4_HTML.jpg

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