2018, Number S1
Molecular docking: current advances and challenges
Language: English
References: 183
Page: 65-87
PDF size: 919.87 Kb.
ABSTRACT
Automated molecular docking aims at predicting the possible interactions between two molecules. This method has proven useful in medicinal chemistry and drug discovery providing atomistic insights into molecular recognition. Over the last 20 years methods for molecular docking have been improved, yielding accurate results on pose prediction. Nonetheless, several aspects of molecular docking need revision due to changes in the paradigm of drug discovery. In the present article, we review the principles, techniques, and algorithms for docking with emphasis on protein-ligand docking for drug discovery. We also discuss current approaches to address major challenges of docking.REFERENCES
Ai, Y., Yu, L., Tan, X., Chai, X., & Liu, S. (2016). Discovery of Covalent Ligands via Noncovalent Docking by Dissecting Covalent Docking Based on a Steric-Clashes Alleviating Receptor (SCAR) Strategy. Journal of Chemical Information and Modeling, 56(8), 1563–1575. https://doi.org/10.1021/acs. jcim.6b00334
Altuntaş, S., Bozkus, Z., & Fraguela, B. B. (2016). GPU accelerated molecular docking simulation with genetic algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9598, pp. 134–146). Springer, Cham. https://doi.org/10.1007/978-3-319-31153-1_10
Ashtawy, H. M., & Mahapatra, N. R. (2014). Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8452 LNBI, pp. 15–32). Springer International Publishing. https://doi.org/10.1007/978-3-319-09042-9_2
Bai, F., Liao, S., Gu, J., Jiang, H., Wang, X., & Li, H. (2015). An accurate metalloprotein-specific scoring function and molecular docking program devised by a dynamic sampling and iteration optimization strategy. Journal of Chemical Information and Modeling, 55(4), 833–847. https://doi.org/10.1021/ci500647f
Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999). Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. Journal of Medicinal Chemistry, 42(25), 5100–5109. https://doi.org/10.1021/jm990352k 25. Chen, D., Menche, G., Power, T. D., Sower, L., Peterson, J. W., & Schein, C. H. (2007). Accounting for ligand-bound metal ions in docking small molecules on adenylyl cyclase toxins. Proteins: Structure, Function, and Bioinformatics, 67(3), 593–605. https://doi.org/10.1002/prot.21249
Clark, A. J., Tiwary, P., Borrelli, K., Feng, S., Miller, E. B., Abel, R., Friesner R. A., Berne, B. J. (2016). Prediction of Protein–Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations. Journal of Chemical Theory and Computation, 12(6), 2990–2998. https://doi.org/10.1021/acs. jctc.6b00201
Corbeil, C. R., & Moitessier, N. (2009). Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. Journal of Chemical Information and Modeling, 49(4), 997–1009. https://doi. org/10.1021/ci8004176
Crawford, T. D., Tsui, V., Flynn, E. M., Wang, S., Taylor, A. M., Côté, A., Audia, J. E., Beresini, M. H., Burdick D. J., Cummings, R., Dakin, L. A., Duplessis, M., Good, A. C., Hewitt M. C., Huang, H., Jayaram, H., Kiefer, J. R., Jiang, Y., Murray, J., Nasveschuk, C. G., Pardo, E., Poy, F., Romero, F. A., Tang, Y., Wang, J., Xu, Z., Zawadzke, L. E., Zhu, X., Albrecht, B. K., Magnuson, S. R., Bellon, S., Cochran, A. G. (2016). Diving into the Water: Inducible Binding Conformations for BRD4, TAF1(2), BRD9, and CECR2 Bromodomains. Journal of Medicinal Chemistry, 59(11), 5391–5402. https:// doi.org/10.1021/acs.jmedchem.6b00264
Du, J., Bleylevens, I. W. M., Bitorina, A. V., Wichapong, K., & Nicolaes, G. A. F. (2014). Optimization of compound ranking for structure-based virtual ligand screening using an established FRED-surflex consensus approach. Chemical Biology and Drug Design, 83(1), 37–51. https://doi.org/10.1111/cbdd.12202
Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V, & Mee, R. P. (1997). Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of Computer- Aided Molecular Design, 11(5), 425–445. https://doi.org/Doi 10.1023/A:1007996124545
Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017). Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs. jcim.7b00153
Fakhouri, L., El-Elimat, T., Hurst, D. P., Reggio, P. H., Pearce, C. J., Oberlies, N. H., & Croatt, M. P. (2015). Isolation, semisynthesis, covalent docking and transforming growth factor beta-activated kinase 1 (TAK1)-inhibitory activities of (5Z)-7-oxozeaenol analogues. Bioorganic and Medicinal Chemistry, 23(21), 6993–6999. https://doi.org/10.1016/j.bmc.2015.09.037
Fani, N., Sattarinezhad, E., & Bordbar, A. K. (2017). Identification of new 2,5-diketopiperazine derivatives as simultaneous effective inhibitors of αβ-tubulin and BCRP proteins: Molecular docking, Structure-Activity Relationships and virtual consensus docking studies. Journal of Molecular Structure, 1137, 362–372. https:// doi.org/10.1016/j.molstruc.2017.02.049
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. (2004). Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. Journal of Medicinal Chemistry, 47(7), 1739–1749. https:// doi.org/10.1021/jm0306430
Harris, J. B., Eldridge, M. L., Sayler, G., Menn, F.-M., Layton, A. C., & Baudry, J. (2014). A computational approach predicting CYP450 metabolism and estrogenic activity of an endocrine disrupting compound (PCB-30). Environmental Toxicology and Chemistry, 33(7), 1615–1623. https://doi.org/10.1002/etc.2595
Houston, D. R., & Walkinshaw, M. D. (2013). Consensus docking: Improving the reliability of docking in a virtual screening context. Journal of Chemical Information and Modeling, 53(2), 384–390. https://doi.org/10.1021/ci300399w Huang, N., Shoichet, B. K., & Irwin, J. J. (2006). Benchmarking Sets for Molecular Docking. Journal of Medicinal Chemistry, 49(23), 6789–6801. https://doi.org/10.1021/jm0608356
Li, A., Sun, H., Du, L., Wu, X., Cao, J., You, Q., & Li, Y. (2014). Discovery of novel covalent proteasome inhibitors through a combination of pharmacophore screening, covalent docking, and molecular dynamics simulations. Journal of Molecular Modeling, 20(11), 2515. https://doi.org/10.1007/s00894-014-2515-y
London, N., Farelli, J. D., Brown, S. D., Liu, C., Huang, H., Korczynska, M., Al-Obaidi, N. F., Babbitt, P. C., Almo, S. C., Allen, K. N., Shoichet, B. K. (2015). Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases. Biochemistry, 54(2), 528–537. https://doi.org/10.1021/ bi501140k
Marzaro, G., Guiotto, A., Borgatti, M., Finotti, A., Gambari, R., Breveglieri, G., & Chilin, A. (2013). Psoralen derivatives as inhibitors of NF-κB/DNA interaction: Synthesis, molecular modeling, 3D-QSAR, and biological evaluation. Journal of Medicinal Chemistry, 56(5), 1830–1842. https://doi. org/10.1021/jm3009647
Medina-Franco, J. L., Méndez-Lucio, O., & Martinez-Mayorga, K. (2014). The interplay between molecular modeling and chemoinformatics to characterize protein-ligand and proteinprotein interactions landscapes for drug discovery. Advances in Protein Chemistry and Structural Biology, 96, 1–37. https:// doi.org/10.1016/bs.apcsb.2014.06.001
Mendonça, E., Barreto, M., Guimarães, V., Santos, N., Pita, S., & Boratto, M. (2017). Accelerating Docking Simulation Using Multicore and GPU Systems. In O. Gervasi, B. Murgante, S. Misra, G. Borruso, C. M. Torre, A. M. A. C. Rocha, … A. Cuzzocrea (Eds.), International Conference on Computational Science and Its Applications (Vol. 10404, pp. 439–451). Cham: Springer International Publishing. https://doi.org/10.1007/978- 3-319-62392-4_32
Morris, G. M., Ruth, H., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/ jcc.21256
Murphy, R. B., Repasky, M. P., Greenwood, J. R., Tubert-Brohman, I., Jerome, S., Annabhimoju, R., Boyles, N. A., Schmitz, C. D., Abel, R., Farid, R., Friesner, R. A. (2016). WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand- Receptor Docking. Journal of Medicinal Chemistry, 59(9), 4364–4384. https://doi.org/10.1021/acs.jmedchem.6b00131
Murray, C. W., Auton, T. R., & Eldridge, M. D. (1998). Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model. Journal of Computer-Aided Molecular Design, 12(5), 503–19.
Oferkin, I. V., Katkova, E. V., Sulimov, A. V., Kutov, D. C., Sobolev, S. I., Voevodin, V. V., & Sulimov, V. B. (2015). Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima. Advances in Bioinformatics, 2015, 1–12. https://doi.org/10.1155/2015/126858
Ran, T., Zhang, Z., Liu, K., Lu, Y., Li, H., Xu, J., Xiong, X., Zhang, Y., Xu, A., Lu, S., Liu, H., Lu, T., Chen, Y. (2015). Insight into the key interactions of bromodomain inhibitors based on molecular docking, interaction fingerprinting, molecular dynamics and binding free energy calculation. Molecular bioSystems, 11(5), 1295–1304. https://doi.org/10.1039/c4mb00723a
Ruiz-Carmona, S., Alvarez-Garcia, D., Foloppe, N., Garmendia- Doval, A. B., Juhos, S., Schmidtke, P., Barril, X., Hubbard, R. E., Morley, S. D. (2014). rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids. PLoS Computational Biology, 10(4). e100357. https:// doi.org/10.1371/journal.pcbi.1003571
Samanta, P. N., & Das, K. K. (2017). Inhibition activities of catechol diether based non-nucleoside inhibitors against the HIV reverse transcriptase variants: Insights from molecular docking and ONIOM calculations. Journal of Molecular Graphics and Modelling, 75, 294–305. https://doi.org/10.1016/j. jmgm.2017.06.011
Sulimov, A. V., Kutov, D. C., Katkova, E. V., Ilin, I. S., & Sulimov, V. B. (2017). New generation of docking programs: Supercomputer validation of force fields and quantum-chemical methods for docking. Journal of Molecular Graphics and Modelling, 78, 139–147. https://doi.org/10.1016/j.jmgm.2017.10.007
Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., Bonvin, A. M. M. J. J., Weng, Z. (2015). Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. Journal of Molecular Biology, 427(19), 3031–3041. https://doi.org/10.1016/j.jmb.2015.07.016
Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Xu, L., Li., Y., Tian, S., 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. Physical Chemistry Chemical Physics : PCCP, 18, 12964– 12975. https://doi.org/10.1039/c6cp01555g
Yang, H., Zhou, Q., Li, B., Wang, Y., Luan, Z., Qian, D., & Li, H. (2010). GPU Acceleration of Dock6’s Amber Scoring Computation. In Advances in experimental medicine and biology (Vol. 680, pp. 497–511). Springer International Publishing. https://doi. org/10.1007/978-1-4419-5913-3_56 Yang, J. M., & Chen, C. C. (2004). GEMDOCK: A Generic Evolutionary Method for Molecular Docking. Proteins: Structure, Function and Genetics, 55(2), 288–304. https://doi.org/10.1002/ prot.20035