TarFisDock: a web server for identifying drug targets with docking approach

By Honglin Li, Zhenting Gao, L. Kang, Hailei Zhang, Kun Yang, Kunqian Yu, Xiaomin Luo, Weiliang Zhu, Kaixian Chen, Jianhua Shen and 2 more. Nucleic Acids Res., 2006

377 citations recorded by the source corpus.

TarFisDock is a web-based tool for automating the procedure of searching for small molecule–protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand–protein docking program. In contrast to conventional ligand–protein docking, reverse ligand–protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand–protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at .

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/efdc1116503bcd3bebfb56c668d742126fd99bbe

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Citations in the corpus

PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach (2010, methodology): “Recently, we have developed an in silico target prediction method for a given small molecule by ‘probing’ the potential ligand binding sites stored in potential drug target database (PDTD) via ligand–protein reverse docking strategy (17,18).”
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (2018, background): “Once the 3D structural information is obtained, docking can be applied to find interactions between a compound and a target, which predicts compound conformations in the binding site of the target using search algorithms and ranks them via scoring functions representing estimated binding affinities ”
Molecular Docking: Shifting Paradigms in Drug Discovery (2019, methodology): “In particular, TarFisDock [54] and idTarget [147] enable the screening of a given ligand to a set of proteins available within the PDTD and PDB by using the DOCK [79] and MEDock [153] docking programs, respectively.”
DrugBank: a knowledgebase for drugs, drug actions and drug targets (2007, background): “Since its initial release, DrugBank has been used in a wide range of applications including in silico drug discovery (9), drug ‘rejuvenation’ (10), drug docking or screening (11), drug metabolism prediction (12), drug target prediction (13) and general pharmaceutical education.”
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference (2012, methodology): “Therefore, NBI performed better than DBSI, TBSI and other reverse docking methods [10,11].”
HIT: linking herbal active ingredients to targets (2010, methodology): “Such information has lead to integration of further resources and computational methods, such as PDTD (3), TarFisDock (4), STITCH (5) and others (6–9) which have served as valuable platforms for target identification, validation and drug actions.”
Computational approaches in target identification and drug discovery (2016, background): “To name a few, chemical structure similarity searching [8], data mining/ machine learning [9], panel docking [10], and bioactivity spectra based algorithms [11] have been routinely and successfully implemented [12,13].”
In silico methods and tools for drug discovery (2021, methodology): “Alterna-tively, a reverse docking technique can be used to identify potential protein targets based on the concept that ligands with similar structures may bind to similar proteins with similar binding affinities, displaying similar biological effects [42 – 45].”
Computational drug discovery (2012, background): “TarFisDock is a web server that identifies drug targets using a reverse docking strategy to seek all possible binding proteins for a given small molecule [18] .”
In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling (2007, methodology): “A web server for inverse docking was recently reported that allows automatic screening of small molecules over a target database of 698 protein structures covering 15 therapeutic areas (Li et al ., 2006).”