Progress in molecular docking

doi:10.1007/s40484-019-0172-y

Quantitative Biology

2019, Vol. 7

Issue (2): 83-89 https://doi.org/10.1007/s40484-019-0172-y

本期目录

Progress in molecular docking

Jiyu Fan¹, Ailing Fu², Le Zhang^1,^3,⁴(

)

¹. School of Computer Science, Sichuan University, Sichuan 610065, China
². College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
³. Medical Big Data Center of Sichuan University, Chengdu 610065, China
⁴. Chongqqing Zhongdi Medical Information Technology Co., Ltd., Chongqing 401320, China

全文: PDF(1100 KB) HTML

Abstract：

Background: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode.

Results: This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning.

Conclusion: Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.

Key words： molecular docking numerical analysis optimization data mining

收稿日期: 2018-11-23 出版日期: 2019-05-30

Corresponding Author(s): Le Zhang

引用本文:

. [J]. Quantitative Biology, 2019, 7(2): 83-89.
Jiyu Fan, Ailing Fu, Le Zhang. Progress in molecular docking. Quant. Biol., 2019, 7(2): 83-89.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-019-0172-y
https://academic.hep.com.cn/qb/CN/Y2019/V7/I2/83

Fig.1

Fig.2

Fig.3

Fig.4

Name	Search algorithm	Evaluation method	Speed	Features & Application areas
Flex X [³³]	Fragmentation algorithm	Semi-empirical calculation on free energy	Fast	Flexible-rigid docking. It can be used for virtual screening of small molecule databases by using incremental construction strategy
Gold [³⁴]	GA (genetic algorithm)	Semi-empirical calculation on free energy	Fast	Flexible docking. It is a GA-based docking program. The accuracy and reliability of this software have been highly evaluated
Glide [³⁵]	Exhaustive systematic search	Semi-empirical calculation on free energy	Medium	Flexible docking. This software uses domain knowledge to narrow the searching range and has XP(extra precision), SP(standard precision) and high throughout virtual screen modes
AutoDock [³⁶]	GA (genetic algorithm) LGA (lamarckian genetic algorithm)	Semi-empirical calculation on free energy	Medium	Flexible-rigid docking. This software is always used with Autodock-tools and it is free for academic use
ZDOCK [³⁷]	Geometric complement-arity and molecular dynamics	Molecular force field	Medium	Rigid docking. Chen et al. [³⁷] propose a new scoring function which combines pairwise shape complementarity(PSC) with desolvation and electrostatic and develop the ZDOCK server [³⁸]
RDOCK [³⁹]	GA(genetic algorithm) MC (monte carlo) MIN (Simplex minimization)	Molecular force field	Medium	Rigid docking. The CHARMm-based procedure for refinement and scoring. Besides predicting the binding mode, it is especially designed for high throughput virtual screening (HTVS) campaigns
LeDOCK [⁴⁰]	Simulated annealing (SA) Genetic algorithm (GA)	Molecular force field	Fast	Flexible docking. LeDock is a new molecular docking program. From the results of the present study [⁴¹], since it is fast and exhibits a high accuracy, it is recommended for the virtual screen task
Dock [⁴²]	Fragmentation algorithm	Molecular force field	Fast	Flexible docking. It is widely applicable and is always used in docking between flexible proteins and flexible ligands
Autodock Vina [⁶]	GA(genetic algorithm)	Semi-empirical calculation on free energy	Fast	Flexible-rigid docking. AutoDock Vina employs an iterated local search global optimizer and it is faster than the AutoDock 4

Tab.1

Fig.5

Fig.6

1	G. M.Morris, and M.Lim-Wilby, (2008) Molecular docking. Methods Mol. Biol., 443, 365–382 https://doi.org/10.1007/978-1-59745-177-2_19 pmid: 18446297
2	Y. Z.Chen, and D. G.Zhi, (2001) Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins, 43, 217–226 https://doi.org/10.1002/1097-0134(20010501)43:2<217::AID-PROT1032>3.0.CO;2-G pmid: 11276090
3	J. L.Morrison, , R.Breitling, , D. J.Higham, and D. R.Gilbert, (2006) A lock-and-key model for protein-protein interactions. Bioinformatics, 22, 2012–2019 https://doi.org/10.1093/bioinformatics/btl338 pmid: 16787977
4	Koshland Jr, D. E. (2010) The key–lock theory and the induced fit theory. Angew. Chem. Int. Ed., 33, 2375–2378
5	J.Audie, and S. Scarlata, (2007) A novel empirical free energy function that explains and predicts protein-protein binding affinities. Biophys. Chem., 129, 198–211 https://doi.org/10.1016/j.bpc.2007.05.021 pmid: 17600612
6	O.Trott, and A. J. Olson, (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 31, 455–461 pmid: 19499576
7	W. L.DeLano, (2002) Pymol: an open-source molecular graphics tool. Ccp4 Newslett. Protein Crystallogr., 40,11
8	H.Berman, , J. Westbrook, , Z.Feng, , G.Gilliland, , T.Bhat, , H.Weissig, , I.Shindyalov, and P.Bourne, (2000) The Protein Data Bank, 1999 –. In: International Tables for Crystallography, Eds. Rossmann, M. G. and Arnold, E. 67, 675–684
9	S.Kim, , P. A. Thiessen, , E. E.Bolton, , J.Chen, , G.Fu, , A. Gindulyte, , L.Han, , J.He, , S. He, , B. A.Shoemaker, , et al. (2016) PubChem substance and compound databases. Nucleic Acids Res., 44, D1202–D1213 https://doi.org/10.1093/nar/gkv951. pmid: 26400175
10	J. J.Irwin, and B. K.Shoichet, (2005) ZINC–a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 45, 177–182 https://doi.org/10.1021/ci049714+ pmid: 15667143
11	G. E.Martin, , C. E.Hadden, , D. J.Russell, , B. D.Kaluzny, , J. E.Guido, , W. K.Duholke, , B. A.Stiemsma, , T. J.Thamann, , R. C.Crouch, , K.Blinov, , et al. (2010) Identification of degradants of a complex alkaloid using NMR cryoprobe technology and ACD/structure elucidator. J. Heterocycl. Chem., 39, 1241–1250. https://doi.org/10.1002/jhet.5570390619
12	C. R.Groom, , I. J. Bruno, , M. P.Lightfoot, and S. C.Ward, (2016) The Cambridge Structural Database. Acta Crystallogr. B Struct. Sci. Cryst. Eng. Mater., 72, 171–179 https://doi.org/10.1107/S2052520616003954 pmid: 27048719
13	D. B.Kitchen, , H.Decornez, , J. R.Furr, and J.Bajorath, (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov., 3, 935–949 https://doi.org/10.1038/nrd1549 pmid: 15520816
14	Joseph-McCarthy, D., Baber, J. C., Feyfant, E., Thompson, D. C. and Humblet, C. (2007) Lead optimization via high-throughput molecular docking. Curr. Opin. Drug Discov. Devel., 10, 264–274
15	H.Ge, , Y. Wang, , C.Li, , N.Chen, , Y.Xie, , M.Xu, , Y. He, , X.Gu, , R.Wu, , Q. Gu, , et al. (2013) Molecular dynamics-based virtual screening: accelerating the drug discovery process by high-performance computing. J. Chem. Inf. Model., 53, 2757–2764 https://doi.org/10.1021/ci400391s pmid: 24001302
16	J. L.Melville, , E. K.Burke, and J. D.Hirst, (2009) Machine learning in virtual screening. Comb. Chem. High Throughput Screen., 12, 332–343 https://doi.org/10.2174/138620709788167980 pmid: 19442063
17	E.Gawehn, , J. A. Hiss, and G.Schneider, (2016) Deep learning in drug discovery. Mol. Inform., 35, 3–14 https://doi.org/10.1002/minf.201501008 pmid: 27491648
18	J. C.Pereira, , E. R.Caffarena, and C. N.Dos Santos, (2016) Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 56, 2495–2506 https://doi.org/10.1021/acs.jcim.6b00355 pmid: 28024405
19	E. O.Pyzerknapp, , C.Suh, , R.Gómezbombarelli, , J.Aguileraiparraguirre, and A.Aspuruguzik, (2015) What is high-throughput virtual screening? A perspective from organic materials discovery. Annu. Rev. Mater. Res., 45, 45:195–216
20	S. Z.Grinter, , Y.Liang, , S. Y.Huang, , S. M.Hyder, and X.Zou, (2011) An inverse docking approach for identifying new potential anti-cancer targets. J. Mol. Graph. Model., 29, 795–799 https://doi.org/10.1016/j.jmgm.2011.01.002 pmid: 21315634
21	F.Chen, , Z. Wang, , C.Wang, , Q.Xu, , J. Liang, , X.Xu, , J.Yang, , C.Wang, , T.Jiang, and R.Yu, (2017) Application of reverse docking for target prediction of marine compounds with anti-tumor activity. J. Mol. Graph. Model., 77, 372–377 https://doi.org/10.1016/j.jmgm.2017.09.015 pmid: 28950183
22	H.Xie, , M. H. Lee, , F.Zhu, , K.Reddy, , Z.Huang, , D. J.Kim, , Y.Li, , C. Peng, , D. Y.Lim, , S.Kang, , et al. (2013) Discovery of the novel mTOR inhibitor and its antitumor activities in vitro and in vivo. Mol. Cancer Ther., 12, 950–958 https://doi.org/10.1158/1535-7163.MCT-12-1241 pmid: 23536724
23	Z. P.Liu, , S. Liu, , R.Chen, , X.Huang, and L. Y.Wu, (2017) Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces. BMC Bioinformatics, 18, 27 https://doi.org/10.1186/s12859-016-1410-1 pmid: 28077065
24	R. S.Ferreira, , A.Simeonov, , A.Jadhav, , O.Eidam, , B. T.Mott, , M. J.Keiser, , J. H.McKerrow, , D. J.Maloney, , J. J.Irwin, and B. K.Shoichet, (2010) Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J. Med. Chem., 53, 4891–4905 https://doi.org/10.1021/jm100488w pmid: 20540517
25	L.Zhang, , M. Qiao, , H.Gao, , B.Hu, , H. Tan, , X.Zhou, and C. M.Li, (2016) Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation. Nanoscale, 8, 14877–14887 https://doi.org/10.1039/C6NR01637E pmid: 27460959
26	L.,Zhang, C. Zheng, , T.Li, , L.Xing, , H.Zeng, , T.Li, , H. Yang, , J.Cao, , B.Chen, and Z. Zhou, (2017) Building up a robust risk mathematical platform to predict colorectal cancer. Complexity, 8917258
27	I. J.Enyedy, and W. J.Egan, (2008) Can we use docking and scoring for hit-to-lead optimization? J. Comput. Aided Mol. Des., 22, 161–168 https://doi.org/10.1007/s10822-007-9165-4 pmid: 18183356
28	J. R.Tame, (1999) Scoring functions: a view from the bench. J. Comput. Aided Mol. Des., 13, 99–108 https://doi.org/10.1023/A:1008068903544 pmid: 10091117
29	H.Yabuuchi, , S. Niijima, , H.Takematsu, , T.Ida, , T.Hirokawa, , T.Hara, , T.Ogawa, , Y.Minowa, , G.Tsujimoto, and Y.Okuno, (2011) Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol. Syst. Biol., 7, 472 https://doi.org/10.1038/msb.2011.5 pmid: 21364574
30	L.Zhang, , Y. Liu, , M.Wang, , Z.Wu, , N. Li, , J.Zhang, and C.Yang, (2017) EZH2-, CHD4-, and IDH-linked epigenetic perturbation and its association with survival in glioma patients. J. Mol. Cell Biol., 9, 477–488 https://doi.org/10.1093/jmcb/mjx056 pmid: 29272522
31	L.Zhang, , M. Xiao, , J.Zhou, and J.Yu, (2018) Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a Jellyfish-based LAUPs analysis application (JBLA). Bioinformatics, 34, 3624–3630 https://doi.org/10.1093/bioinformatics/bty392 pmid: 29762634
32	L.Zhang, and S. Zhang, (2017) Using game theory to investigate the epigenetic control mechanisms of embryo development: Comment on: “Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition” by Qian Wang et al. Phys. Life Rev., 20, 140–142 https://doi.org/10.1016/j.plrev.2017.01.007 pmid: 28109753
33	B.Kramer, , M. Rarey, and T.Lengauer, (1999) Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins, 37, 228–241 https://doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228::AID-PROT8>3.0.CO;2-8 pmid: 10584068
34	M. L.Verdonk, , J. C.Cole, , M. J.Hartshorn, , C. W.Murray, and R. D.Taylor, (2003) Improved protein-ligand docking using GOLD. Proteins, 52, 609–623 https://doi.org/10.1002/prot.10465 pmid: 12910460
35	T. A.Halgren, , R. B.Murphy, , R. A.Friesner, , H. S.Beard, , L. L.Frye, , W. T.Pollard, and J. L.Banks, (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 47, 1750–1759 https://doi.org/10.1021/jm030644s pmid: 15027866
36	G. M.Morris, , R.Huey, , W.Lindstrom, , M. F.Sanner, , R. K.Belew, , D. S.Goodsell, and A. J.Olson, (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem., 30, 2785–2791 https://doi.org/10.1002/jcc.21256 pmid: 19399780
37	R.Chen, , L. Li, and Z.Weng, (2003) ZDOCK: an initial‒stage protein‒docking algorithm. Proteins, 52, 80–87. https://doi.org/10.1002/prot.10389 pmid: 12784371
38	B. G.Pierce, , K.Wiehe, , H.Hwang, , B. H.Kim, , T.Vreven, and Z.Weng, (2014) ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics, 30, 1771–1773 https://doi.org/10.1093/bioinformatics/btu097 pmid: 24532726
39	L.Li, , R. Chen, and Z.Weng, (2010) RDOCK: refinement of rigid-body protein docking predictions. Proteins, 53, 693–707 https://doi.org/10.1002/prot.10460
40	H.Zhao, and A. Caflisch, (2013) Discovery of ZAP70 inhibitors by high-throughput docking into a conformation of its kinase domain generated by molecular dynamics. Bioorg. Med. Chem. Lett., 23, 5721–5726 https://doi.org/10.1016/j.bmcl.2013.08.009 pmid: 23993776
41	Z.Wang, , H. Sun, , X.Yao, , D.Li, , L. Xu, , Y.Li, , S.Tian, and T.Hou, (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys., 18, 12964–12975 https://doi.org/10.1039/C6CP01555G pmid: 27108770
42	P.Therese, L., Brozell, S. R., Sudipto, M., Pettersen, E. F., Meng, E. C., Veena, T., Rizzo, R. C., Case, D. A., James, T. L., Kuntz, I. D. (2009) DOCK 6: combining techniques to model RNA-small molecule complexes. Rna, 15, 1219–1230

Viewed

Full text

Abstract

Cited

Shared

Discussed