2019-nCoV (SARS-COV-2) was first reported at the end of 2019. The virus induced disease, COVID-19, has caused >3,000 deaths worldwide as of the first day of March 2020. With the aims to develop drugs against all the potential target proteins involved in the whole process of the virus infection, replication and release, and to predict target proteins for the target-unknown active compounds or efficient drugs, we developed the webserver, namely D3Targets-2019-nCoV, that was first published on Feb 3, 2020. D3Targets-2019-nCoV provides two approaches for target prediction and virtual screening, one is protein structure based and the other is ligand based. The protein structure based approach is accessible via “D3Docking”, while the ligand based approach is accessible via “D3Similarity”.
Updated on 27-05-2021: 56 proteins, 97 conformations, 912 ligand-binding sites, 754 ligands.. detailUpdated on 15-03-2021: 53 proteins, 94 conformations, 847 ligand-binding sites, 754 ligands. detailUpdated on 30-01-2021: 51 proteins, 92 conformations, 835 ligand-binding sites, 672 ligands.. detailUpdated on 08-01-2021: 46 proteins, 86 conformations, 797 ligand-binding sites, 672 ligands.. detailUpdated on 31-08-2020: 46 proteins, 86 conformations, 797 ligand-binding sites, 604 ligands.. detail
1. Shi, Yulong; Zhang, Xinben; Mu, Kaijie; Peng, Cheng; Zhu, Zhengdan; Wang, Xiaoyu; Xu, Zhijian; Zhu, Weiliang. D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19. Acta Pharmaceutica Sinica B. 2020, 10(7), 1239-1248. DOI: https://doi.org/10.1016/j.apsb.2020.04.006
2. Zhaoqiang Chen, Xinben Zhang, Cheng Peng, Jinan Wang, Zhijian Xu, Kaixian Chen, Jiye Shi, Weiliang Zhu, D3Pockets: A Method and Web Server for Systematic Analysis of Protein Pocket Dynamics. J. Chem. Inf. Model. 2019, 59, 8, 3353-3358. DOI: https://doi.org/10.1021/acs.jcim.9b00332
3. Yanqing Yang, Zhengdan Zhu, Xiaoyu Wang, Xinben Zhang, Kaijie Mu, Yulong Shi, Cheng Peng, Zhijian Xu, Weiliang Zhu Ligand-based approach for predicting drug targets and for virtual screening against COVID-19. Briefings in Bioinformatics. 2021, 22(2), 1053-1064. DOI: https://doi.org/10.1093/bib/bbaa422
D3Docking was developed with two functions based on protein structure based docking, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/vivo studies, the other is for screening lead compounds against drug targets via molecular docking. For improving success rate, we collected and constructed the three-dimensional structures of different conformations of the potential target proteins, and predicted their druggable conformations as many as reasonable by using NUMD and vsREMD developed by our lab. D3Pockets, which was also developed by our lab, was then applied to predict potential ligand-binding sites for each protein conformation. “TargetPrediction” is for predicting target proteins, while “VirtualScreening” is for molecular docking against multiple ligand-binding sites of a specific or multiple target proteins.
D3Similarity was developed with two purposes based on the two-dimensional and three-dimensional similarity of molecular structure, one is for predicting target proteins for active compounds observed from experimental studies, and another is for virtual screening via 2D and 3D similarity evaluation. To this end, we developed a database composed of the bioactive molecules with known targets or/and well-explored mechanism related to the whole process of coronavirus infection, replication and release. “TargetPrediction” is for predicting target proteins, while “VirtualScreening” is for virtual screening against target proteins based on the 2D or 3D similarity evaluation.
Identification of protein binding pockets is of importance in structure-based drug design. Protein movement may affect the geometric and physicochemical properties of protein pockets. This server (D3Pockets) is developed to detect and analyze the dynamic properties of ligand binding pockets on drug target protein. It can not only detect all potential ligand binding pockets on protein surface based on a pdb file, but also analyze the dynamic properties of the pockets, viz., stability, continuity and correlation, based on a MD trajectory or a conformation ensemble. The results from the server could be used for designing ligands on novel pockets and studying the functional mechanism of a target protein.