Progress in molecular docking

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.

Graphical abstract

Keywords

molecular docking / numerical analysis / optimization / data mining

References

[1] Morris, G. M. and Lim-Wilby, M. (2008) Molecular docking. Methods Mol. Biol., 443, 365–382

[2] Chen, Y. Z. and Zhi, D. G. (2001) Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins, 43, 217–226

[3] Morrison, J. L., Breitling, R., Higham, D. J. and Gilbert, D. R. (2006) A lock-and-key model for protein-protein interactions. Bioinformatics, 22, 2012–2019

[4] Koshland Jr, D. E. (2010) The key–lock theory and the induced fit theory. Angew. Chem. Int. Ed., 33, 2375–2378

[5] Audie, J. and Scarlata, S. (2007) A novel empirical free energy function that explains and predicts protein-protein binding affinities. Biophys. Chem., 129, 198–211

[6] Trott, O. and Olson, A. J. (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

[7] DeLano, W. L. (2002) Pymol: an open-source molecular graphics tool. Ccp4 Newslett. Protein Crystallogr., 40,11

[8] Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I. and Bourne, P. (2000) The Protein Data Bank, 1999 –. In: International Tables for Crystallography, Eds. Rossmann, M. G. and Arnold, E. 67, 675–684

[9] Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., (2016) PubChem substance and compound databases. Nucleic Acids Res., 44, D1202–D1213

[10] Irwin, J. J. and Shoichet, B. K. (2005) ZINC–a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 45, 177–182

[11] Martin, G. E., Hadden, C. E., Russell, D. J., Kaluzny, B. D., Guido, J. E., Duholke, W. K., Stiemsma, B. A., Thamann, T. J., Crouch, R. C., Blinov, K., (2010) Identification of degradants of a complex alkaloid using NMR cryoprobe technology and ACD/structure elucidator. J. Heterocycl. Chem., 39, 1241–1250

[12] Groom, C. R., Bruno, I. J., Lightfoot, M. P. and Ward, S. C. (2016) The Cambridge Structural Database. Acta Crystallogr. B Struct. Sci. Cryst. Eng. Mater., 72, 171–179

[13] Kitchen, D. B., Decornez, H., Furr, J. R. and Bajorath, J. (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov., 3, 935–949

[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] Ge, H., Wang, Y., Li, C., Chen, N., Xie, Y., Xu, M., He, Y., Gu, X., Wu, R., Gu, Q., (2013) Molecular dynamics-based virtual screening: accelerating the drug discovery process by high-performance computing. J. Chem. Inf. Model., 53, 2757–2764

[16] Melville, J. L., Burke, E. K. and Hirst, J. D. (2009) Machine learning in virtual screening. Comb. Chem. High Throughput Screen., 12, 332–343

[17] Gawehn, E., Hiss, J. A. and Schneider, G. (2016) Deep learning in drug discovery. Mol. Inform., 35, 3–14

[18] Pereira, J. C., Caffarena, E. R. and Dos Santos, C. N. (2016) Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 56, 2495–2506

[19] Pyzerknapp, E. O., Suh, C., Gómezbombarelli, R., Aguileraiparraguirre, J. and Aspuruguzik, A. (2015) What is high-throughput virtual screening? A perspective from organic materials discovery. Annu. Rev. Mater. Res., 45, 45:195–216

[20] Grinter, S. Z., Liang, Y., Huang, S. Y., Hyder, S. M. and Zou, X. (2011) An inverse docking approach for identifying new potential anti-cancer targets. J. Mol. Graph. Model., 29, 795–799

[21] Chen, F., Wang, Z., Wang, C., Xu, Q., Liang, J., Xu, X., Yang, J., Wang, C., Jiang, T. and Yu, R. (2017) Application of reverse docking for target prediction of marine compounds with anti-tumor activity. J. Mol. Graph. Model., 77, 372–377

[22] Xie, H., Lee, M. H., Zhu, F., Reddy, K., Huang, Z., Kim, D. J., Li, Y., Peng, C., Lim, D. Y., Kang, S., (2013) Discovery of the novel mTOR inhibitor and its antitumor activities in vitro and in vivo. Mol. Cancer Ther., 12, 950–958

[23] Liu, Z. P., Liu, S., Chen, R., Huang, X. and Wu, L. Y. (2017) Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces. BMC Bioinformatics, 18, 27

[24] Ferreira, R. S., Simeonov, A., Jadhav, A., Eidam, O., Mott, B. T., Keiser, M. J., McKerrow, J. H., Maloney, D. J., Irwin, J. J. and Shoichet, B. K. (2010) Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J. Med. Chem., 53, 4891–4905

[25] Zhang, L., Qiao, M., Gao, H., Hu, B., Tan, H., Zhou, X. and Li, C. M. (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

[26] Zhang, L.,Zheng, C., Li, T., Xing, L., Zeng, H., Li, T., Yang, H., Cao, J., Chen, B. and Zhou, Z. (2017) Building up a robust risk mathematical platform to predict colorectal cancer. Complexity, 8917258

[27] Enyedy, I. J. and Egan, W. J. (2008) Can we use docking and scoring for hit-to-lead optimization? J. Comput. Aided Mol. Des., 22, 161–168

[28] Tame, J. R. (1999) Scoring functions: a view from the bench. J. Comput. Aided Mol. Des., 13, 99–108

[29] Yabuuchi, H., Niijima, S., Takematsu, H., Ida, T., Hirokawa, T., Hara, T., Ogawa, T., Minowa, Y., Tsujimoto, G. and Okuno, Y. (2011) Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol. Syst. Biol., 7, 472

[30] Zhang, L., Liu, Y., Wang, M., Wu, Z., Li, N., Zhang, J. and Yang, C. (2017) EZH2-, CHD4-, and IDH-linked epigenetic perturbation and its association with survival in glioma patients. J. Mol. Cell Biol., 9, 477–488

[31] Zhang, L., Xiao, M., Zhou, J. and Yu, J. (2018) Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a Jellyfish-based LAUPs analysis application (JBLA). Bioinformatics, 34, 3624–3630

[32] Zhang, L. and Zhang, S. (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

[33] Kramer, B., Rarey, M. and Lengauer, T. (1999) Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins, 37, 228–241

[34] Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W. and Taylor, R. D. (2003) Improved protein-ligand docking using GOLD. Proteins, 52, 609–623

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

[36] Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S. and Olson, A. J. (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem., 30, 2785–2791

[37] Chen, R., Li, L. and Weng, Z. (2003) ZDOCK: an initial‒stage protein‒docking algorithm. Proteins, 52, 80–87

[38] Pierce, B. G., Wiehe, K., Hwang, H., Kim, B. H., Vreven, T. and Weng, Z. (2014) ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics, 30, 1771–1773

[39] Li, L., Chen, R. and Weng, Z. (2010) RDOCK: refinement of rigid-body protein docking predictions. Proteins, 53, 693–707

[40] Zhao, H. and Caflisch, A. (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

[41] Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S. and Hou, T. (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

[42] Therese, P.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

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature