JOURNAL BROWSE
Search
Advanced SearchSearch Tips
In silico target identification of biologically active compounds using an inverse docking simulation
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : TANG [HUMANITAS MEDICINE]
  • Volume 3, Issue 2,  2013, pp.12.1-12.4
  • Publisher : Association of Humanitas Medicine
  • DOI : 10.5667/tang.2013.0008
 Title & Authors
In silico target identification of biologically active compounds using an inverse docking simulation
Choi, Youngjin;
  PDF(new window)
 Abstract
Identification of target protein is an important procedure in the course of drug discovery. Because of complexity, action mechanisms of herbal medicine are rather obscure, unlike small-molecular drugs. Inverse docking simulation is a reverse use of molecular docking involving multiple target searches for known chemical structure. This methodology can be applied in the field of target fishing and toxicity prediction for herbal compounds as well as known drug molecules. The aim of this review is to introduce a series of in silico works for predicting potential drug targets and side-effects based on inverse docking simulations.
 Keywords
inverse docking;herbal medicine;target prediction;computer simulation;
 Language
English
 Cited by
 References
1.
Barlow DJ, Buriani A, Ehrman T, Bosisio E, Eberini I, Hylands PJ. In-silico studies in Chinese herbal medicines' research: evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date. J Ethnopharmacol. 2012;140:526-534. crossref(new window)

2.
Bohm HJ. LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des. 1992;6:593-606. crossref(new window)

3.
Cai J, Han C, Hu T, Zhang J, Wu D, Wang F, Liu Y, Ding J, Chen K, Yue J, Shen X, Jiang H. Peptide deformylase is a potential target for anti-Helicobacter pylori drugs: reverse docking, enzymatic assay, and x-ray crystallography validation. Protein Sci. 2006;15:2071-2081. crossref(new window)

4.
Canduri F, de Azevedo WF. Protein crystallography in drug discovery. Curr Drug Targets. 2008;9:1048-1053. crossref(new window)

5.
Chen CYC. TCM database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico. PloS one. 2011;6:e15939. crossref(new window)

6.
Chen YZ, Ung CY. Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. J Mol Graph Model. 2001;20:199-218. crossref(new window)

7.
Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins. 2001;43:217-226. crossref(new window)

8.
Chrysina ED, Chajistamatiou A, Chegkazi M. From structure--based to knowledge--based drug design through x-ray protein crystallography: sketching glycogen phosphorylase binding sites. Curr Med Chem. 2011;18:2620-2629. crossref(new window)

9.
Cramer RD 3rd, Patterson DE, Bunce JD. Recent advances in comparative molecular field analysis (CoMFA). Prog Clin Biol Res. 1989;291:161-165.

10.
Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol. 2007;152:9-20. crossref(new window)

11.
Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des. 1997;11:425-445. crossref(new window)

12.
Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H. PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics. 2008;9:104-110. crossref(new window)

13.
Jenkins JL, Bender A, Davies JW. In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today: Technologies. 2006;3:413-421. crossref(new window)

14.
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3:935-949. crossref(new window)

15.
Lee M, Kim D. Large-scale reverse docking profiles and their applications. BMC Bioinformatics. 2012;13:S6.

16.
Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H. TarFisDock: a web server for identifying drug targets with docking approach. Nucl Acids Res. 2006;34:W219-224. crossref(new window)

17.
Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR. Towards the development of universal, fast and highly accurate docking/scoring methods: a long ways to go. Brit J Pharmacol. 2008;153:S7-S26.

18.
Muegge I, Martin YC. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem. 1999;42:791-804. crossref(new window)

19.
Nonell-Canals A, Mestres J. In silico target profiling of one billion molecules. Mol Inf. 2011;30:405-409. crossref(new window)

20.
Oshiro CM, Kuntz ID, Dixon JS. Flexible ligand docking using a genetic algorithm. J Comput Aided Mol Des. 1995;9:113-130. crossref(new window)

21.
Paul N, Kellenberger E, Bret G, Muller P, Rognan D. Recovering the true targets of specific ligands by virtual screening of the protein data bank. Proteins. 2004;54:671-680. crossref(new window)

22.
Rognan D. Structure-based approaches to target fishing and ligand profiling. Mol Inf. 2010;29:176-187. crossref(new window)

23.
Shoichet BK, McGovern S, Wei B, Irwin JJ. Lead discovery using molecular docking. Curr Opin Chem Biol. 2002;6:439-446. crossref(new window)

24.
Sinko W, Lindert S, McCammon JA. Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design. Chem Biol Drug Des. 2013;81:41-49. crossref(new window)

25.
Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J, Ritter O, Abola EE. Protein Data Bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallogr D Biol Crystallog r. 1998;1:1078-1084.

26.
Taylor RD, Jewsbury PJ, Essex JW. A review of protein-small molecule docking methods. J Comput Aided Mol Des. 2002;16:151-166. crossref(new window)

27.
Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD Jr. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem. 2010;31:671-690.

28.
Velec HF, Gohlke H, Klebe G. DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem. 2005;48:6296-6303. crossref(new window)

29.
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins. 200 3;52:609-623. crossref(new window)

30.
Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general amber force field. J Comput Chem. 2004;25:1157-1174. crossref(new window)