Evaluating reverse docking on general and selective inhibitors: a case study about glide

doi:10.5246/jcps.2020.10.063

Journal of Chinese Pharmaceutical Sciences ›› 2020, Vol. 29 ›› Issue (10): 679-688.DOI: 10.5246/jcps.2020.10.063

• Original articles • Next Articles

Evaluating reverse docking on general and selective inhibitors: a case study about glide

Mingna Li¹, Xing Wu², Liangren Zhang^1*, Zhenming Liu^1*

1. State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China
2. Hengdian Group Holdings Limited, Hangzhou 310007, China

Received:2020-04-16 Revised:2020-07-15 Online:2020-10-31 Published:2020-08-20
Contact: Tel.: +86-10-82805281, E-mail: zmliu@bjmu.edu.cn
Supported by:
National Key Research and Development Project (Grant No. 2019YFC1708900), the National Natural Science Foundation of China (Grant No. 81872730, 81673279, 21772005), National Major Scientific and Technological Special Project for Significant New Drugs Development (Grant No. 2018ZX09735001-003, 2019ZX09201005-001, 2019ZX09204-001) and Beijing Natural Science Foundation (Grant No. 7202088, 7172118).

Abstract

Abstract:

As a powerful tool for target prediction, reverse docking remains largely unexplored. The objective evaluation of reversedocking software can help us know better about the strength and weakness of these tools, hence guiding us in target prediction. In the present study, we evaluated the target prediction power of Glide (SP) against general inhibitors and selective inhibitors. The results showed that the scoring tendency could be different for each ligand, and overall scoring sampling was necessary for a better understanding of the docking score for a certain protein-ligand pair. Besides, the input conformation of the binding pocket could affect the docking result. Glide (SP) showed a preferable performance on the target prediction of the general inhibitors. However, the accuracy of the target prediction of the selective inhibitors was relatively low, indicating that Glide (SP) might not be capable for this task. The case study about COVID-19 proved that coagulation factor Xa might be a potential target of chloroquine. Therefore, we recommend the further development of reverse docking tools and rectification of inter-target scoring bias.

Key words: Reverse docking, Target prediction, Software evaluation

CLC Number:

R916

Supporting:

Mingna Li, Xing Wu, Liangren Zhang, Zhenming Liu. Evaluating reverse docking on general and selective inhibitors: a case study about glide[J]. Journal of Chinese Pharmaceutical Sciences, 2020, 29(10): 679-688.

References

[1] Sydow, D.; Burggraaff, L.; Szengel, A.; van Vlijmen, H.W.T.; IJzerman, A.P.; van Westen, G.J.P.; Volkamer, A. Advances and challenges in computational target prediction. J. Chem. Inf. Model. 2019, 59, 1728-1742.

[2] da Matta, C.B.B.; de Queiroz, A.C.; Santos, M.S.; Alexandre-Moreira, M.S.; Gonçalves, V.T.; de Nigris del Cistia, C.; Sant’Anna, C.M.R.; DaCosta, J.B.N. Novel dialkylphosphorylhydrazones: Synthesis, leishmanicidal evaluation and theoretical investigation of the proposed mechanism of action. Eur. J. Med. Chem. 2015, 101, 1-12.

[3] Lim, T.G.; Lee, S.Y.; Huang, Z.N.; Lim, D.Y.; Chen, H.Y.; Jung, S.K.; Bode, A.M.; Lee, K.W.; Dong, Z.G. Curcumin suppresses proliferation of colon cancer cells by targeting CDK2. Cancer Prev. Res.( Phila). 2014, 7, 466-474.

[4] Wang, Z.Y.; Luo, S.Q.; Wan, Z.; Chen, C.Y.; Zhang, X.N.; Li, B.B.; Huang, G.L.; Chen, L.Y.; He, Z.W.; Huang, Z.N. Glabridin arrests cell cycle and inhibits proliferation of hepatocellular carcinoma by suppressing braf/MEK signaling pathway. Tumor Biol. 2016, 37, 5837-5846.

[5] Lapillo, M.; Tuccinardi, T.; Martinelli, A.; Macchia, M.; Giordano, A.; Poli, G. Extensive reliability evaluation of docking-based target-fishing strategies. Int. J. Mol. Sci. 2019, 20, 1023.

[6] Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 2004, 47, 1739-1749.

[7] Halgren, T.A.; Murphy, R.B.; Friesner, R.A.; Beard, H.S.; Frye, L.L.; Pollard, W.T.; Banks, J.L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 2004, 47, 1750-1759.

[8] Schomburg, K.T.; Rarey, M. Benchmark data sets for structure-based computational target prediction. J. Chem. Inf. Model. 2014, 54, 2261-2274.

[9] Liu, Z.H.; Li, Y.; Han, L.; Li, J.; Liu, J.; Zhao, Z.X.; Nie, W.; Liu, Y.C.; Wang, R.X. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics. 2015, 31, 405-412.

[10] Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235-242.

[11] Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.F.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074-D1082.

[12] Kim, S.; Chen, J.; Cheng, T.J.; Gindulyte, A.; He, J.; He, S.Q.; Li, Q.L.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019, 47, D1102-D1109.

[13] Madhavi Sastry, G.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 2013, 27, 221-234.

[14] Harder, E.; Damm, W.; Maple, J.; Wu, C.J.; Reboul, M.; Xiang, J.Y.; Wang, L.L.; Lupyan, D.; Dahlgren, M.K.; Knight, J.L.; Kaus, J.W.; Cerutti, D.S.; Krilov, G.; Jorgensen, W.L.; Abel, R.; Friesner, R.A. OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 2016, 12, 281-296.

[15] Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 2001, 46, 3-26.

[16] Wang, W.; Zhou, X.; He, W.L.; Fan, Y.; Chen, Y.Z.; Chen, X. The interprotein scoring noises in glide docking scores. Proteins: Struct. Funct. Bioinform. 2012, 80, 169-183

[17] Luo, Q.Y.; Zhao, L.; Hu, J.X.; Jin, H.W.; Liu, Z.M.; Zhang, L.R. The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing. PLoS One. 2017, 12, e0171433.

[18] Kakodkar, P.; Kaka, N.; Baig, M.N. A comprehensive literature review on the clinical presentation, and management of the pandemic coronavirus disease 2019 (COVID-19). Cureus. 2020, 12, e7560.

[19] Ewing, W.R.; Becker, M.R.; Manetta, V.E.; Davis, R.S.; Pauls, H.W.; Mason, H.; Choi-Sledeski, Y.M.; Green, D.; Cha, D.; Spada, A.P.; Cheney, D.L.; Mason, J.S.; Maignan, S.; Guilloteau, J.P.; Brown, K.; Colussi, D.; Bentley, R.; Bostwick, J.; Kasiewski, C.J.; Morgan, S.R.; Leadley, R.J.; Dunwiddie, C.T.; Perrone, M.H.; Chu, V. Design and structure-activity relationships of potent and selective inhibitors of blood coagulation factor Xa. J. Med. Chem. 1999, 42, 3557-3571.

[20] Li, H.C.; Ma, J.; Zhang, H.; Cheng, Y.; Wang, X.; Hu, Z.W.; Li, N.; Deng, X.R.; Zhang, Y.; Zheng, X.Z.; Yang, F.; Weng, H.Y.; Dong, J.P.; Liu, J.W.; Wang, Y.Y.; Liu, X.M. Thoughts and practice on the treatment of severe and critical new coronavirus pneumonia. Chin. J.Tuberculosis Res. Dis. 2020, 43, E038.

Evaluating reverse docking on general and selective inhibitors: a case study about glide

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