Literature DB >> 33068433

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.

Hongyan Du1,2, Junbo Gao1, Gaoqi Weng1, Junjie Ding3, Xin Chai1, Jinping Pang1, Yu Kang1, Dan Li1, Dongsheng Cao4, Tingjun Hou1,2.   

Abstract

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.
© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entities:  

Year:  2021        PMID: 33068433      PMCID: PMC7778999          DOI: 10.1093/nar/gkaa876

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  27 in total

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2.  cBinderDB: a covalent binding agent database.

Authors:  Jiewen Du; Xin Yan; Zhihong Liu; Lu Cui; Peng Ding; Xiaoqing Tan; Xiuming Li; Huihao Zhou; Qiong Gu; Jun Xu
Journal:  Bioinformatics       Date:  2017-04-15       Impact factor: 6.937

3.  Expanding the Armory: Predicting and Tuning Covalent Warhead Reactivity.

Authors:  Richard Lonsdale; Jonathan Burgess; Nicola Colclough; Nichola L Davies; Eva M Lenz; Alexandra L Orton; Richard A Ward
Journal:  J Chem Inf Model       Date:  2017-11-21       Impact factor: 4.956

4.  ChemDoodle 6.0.

Authors:  William L Todsen
Journal:  J Chem Inf Model       Date:  2014-08-12       Impact factor: 4.956

5.  Can Relative Binding Free Energy Predict Selectivity of Reversible Covalent Inhibitors?

Authors:  Payal Chatterjee; Wesley M Botello-Smith; Han Zhang; Li Qian; Abdelaziz Alsamarah; David Kent; Jerome J Lacroix; Michel Baudry; Yun Luo
Journal:  J Am Chem Soc       Date:  2017-11-29       Impact factor: 15.419

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Journal:  Bioinformatics       Date:  2014-12-12       Impact factor: 6.937

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Authors:  The UniProt Consortium
Journal:  Nucleic Acids Res       Date:  2018-03-16       Impact factor: 16.971

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Authors:  David Mendez; Anna Gaulton; A Patrícia Bento; Jon Chambers; Marleen De Veij; Eloy Félix; María Paula Magariños; Juan F Mosquera; Prudence Mutowo; Michal Nowotka; María Gordillo-Marañón; Fiona Hunter; Laura Junco; Grace Mugumbate; Milagros Rodriguez-Lopez; Francis Atkinson; Nicolas Bosc; Chris J Radoux; Aldo Segura-Cabrera; Anne Hersey; Andrew R Leach
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

10.  PubChem 2019 update: improved access to chemical data.

Authors:  Sunghwan Kim; Jie Chen; Tiejun Cheng; Asta Gindulyte; Jia He; Siqian He; Qingliang Li; Benjamin A Shoemaker; Paul A Thiessen; Bo Yu; Leonid Zaslavsky; Jian Zhang; Evan E Bolton
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

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  3 in total

1.  Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network.

Authors:  Hongyan Du; Dejun Jiang; Junbo Gao; Xujun Zhang; Lingxiao Jiang; Yundian Zeng; Zhenxing Wu; Chao Shen; Lei Xu; Dongsheng Cao; Tingjun Hou; Peichen Pan
Journal:  Research (Wash D C)       Date:  2022-07-21

2.  CovPDB: a high-resolution coverage of the covalent protein-ligand interactome.

Authors:  Mingjie Gao; Aurélien F A Moumbock; Ammar Qaseem; Qianqing Xu; Stefan Günther
Journal:  Nucleic Acids Res       Date:  2022-01-07       Impact factor: 16.971

Review 3.  Mechanism of activation and the rewired network: New drug design concepts.

Authors:  Ruth Nussinov; Mingzhen Zhang; Ryan Maloney; Chung-Jung Tsai; Bengi Ruken Yavuz; Nurcan Tuncbag; Hyunbum Jang
Journal:  Med Res Rev       Date:  2021-10-25       Impact factor: 12.388

  3 in total

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