Quantum chemical approaches in structure-based virtual screening and lead optimization

Frontiers in Chemistry

Volumn 6, Issue MAY, 2018, Pages

  Cavasotto, Claudio N ^a   Adler, Natalia S ^a   Aucar, Maria G ^a  

^a Facultad de Ciencias Naturales e Instituto M Lillo   (Argentina)

Author keywords

Binding free energy; Drug lead optimization; Molecular docking; Molecular dynamics; Quantum mechanics; Semi empirical methods; Structure based drug design

Indexed keywords

EID: 85048220166     PISSN: None     EISSN: 22962646     Source Type: Journal    
DOI: 10.3389/fchem.2018.00188     Document Type: Short Survey

References (70)

1. Ajani, H., Pecina, A., Eyrilmez, S. M., Fanfrlík, J., Haldar, S., Rezác, J., et al. (2017). Superior performance of the SQM/COSMO scoring functions in native pose recognition of diverse protein-ligand complexes in cognate docking. ACS Omega 2, 4022-4029. doi: 10.1021/acsomega.7b00503
2. Alvarez, L. D., Veleiro, A. S., and Burton, G. (2015). Exploring the molecular basis of action of ring D aromatic steroidal antiestrogens. Proteins 83, 1297-1306. doi: 10.1002/prot.24820
3. 2 domain. J. Comput. Chem. 32, 2254-2263. doi: 10.1002/jcc.21808
4. Anisimov, V. M., Ziemys, A., Kizhake, S., Yuan, Z., Natarajan, A., and Cavasotto, C. N. (2011). Computational and experimental studies of the interaction between phospho-peptides and the C-terminal domain of BRCA1. J. Comput. Aided Mol. Des. 25, 1071-1084. doi: 10.1007/s10822-011-9484-3
5. Aqvist, J., Medina, C., and Samuelsson, J. E. (1994). A new method for predicting binding affinity in computer-aided drug design. Protein Eng. 7, 385-391. doi: 10.1093/protein/7.3.385
6. Blomberg, M. R., Borowski, T., Himo, F., Liao, R. Z., and Siegbahn, P. E. (2014). Quantum chemical studies of mechanisms for metalloenzymes. Chem. Rev. 114, 3601-3658. doi: 10.1021/cr400388t
7. Brandenburg, J. G., and Grimme, S. (2014). Accurate modeling of organic molecular crystals by dispersion-corrected density functional tight binding (DFTB). J. Phys. Chem. Lett. 5, 1785-1789. doi: 10.1021/jz500755u
8. Brooks, B. R., Brooks, C. L., Mackerell, A. D., Nilsson, L., Petrella, R. J., Roux, B., et al. (2009). CHARMM: the biomolecular simulation program. J. Comput. Chem. 30, 1545-1614. doi: 10.1002/jcc.21287
9. Cavasotto, C. N. (2011). "Handling protein flexibility in docking and high-throughput docking," in Virtual Screening. Principles, Challenges and Practical Guidelines, ed C. Sotriffer (Weinheim: Wiley-VCH Verlag), 245-262
10. Cavasotto, C. N. (2012a). "Binding free energy calculations and scoring in small-molecule docking," in Physico-Chemical and Computational Approaches to Drug Discovery, eds F. J. Luque and X. Barril (London: Royal Society of Chemistry), 195-222
11. Cavasotto, C. N. (2012b). Normal mode-based approaches in receptor ensemble docking. Methods Mol. Biol. 819, 157-168. doi: 10.1007/978-1-61779-465-0_11
12. Cavasotto, C. N., and Orry, A. J. (2007). Ligand docking and structure-based virtual screening in drug discovery. Curr. Top. Med. Chem. 7, 1006-1014. doi: 10.2174/156802607780906753
13. Cavasotto, C. N., and Palomba, D. (2015). Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models. Chem. Commun. 51, 13576-13594. doi: 10.1039/C5CC05050B
14. Chaskar, P., Zoete, V., and Röhrig, U. F. (2014). Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function. J. Chem. Inf. Model. 54, 3137-3152. doi: 10.1021/ci5004152
15. Chaskar, P., Zoete, V., and Röhrig, U. F. (2017). On-the-fly QM/MM docking with attracting cavities. J. Chem. Inf. Model. 57, 73-84. doi: 10.1021/acs.jcim.6b00406
16. Cho, A. E., Guallar, V., Berne, B. J., and Friesner, R. (2005). Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach. J. Comp. Chem. 26, 915-931. doi: 10.1002/jcc.20222
17. Cross, J. B., Thompson, D. C., Rai, B. K., Baber, J. C., Fan, K. Y., Hu, Y., et al. (2009). Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J. Chem. Inf. Model. 49, 1455-1474. doi: 10.1021/ci900056c
18. De Benedetti, P. G., and Fanelli, F. (2014). Multiscale quantum chemical approaches to QSAR modeling and drug design. Drug Discov. Today 19, 1921-1927. doi: 10.1016/j.drudis.2014.09.024
19. Dewar, M. J. S., Zoebisch, E. G., Healy, E. F., and Stewart, J. J. P. (1985). Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 107, 3902-3909. doi: 10.1021/ja00299a024
20. Dixon, S. L., and Merz, K. M. Jr. (1996). Semiempirical molecular orbital calculations with linear system size scaling. J. Chem. Phys. 104, 6643-6649. doi: 10.1063/1.471382
21. Ehrlich, S., Göller, A. H., and Grimme, S. (2017). Towards full quantum-mechanics-based protein-ligand binding affinities. ChemPhysChem 18, 898-905. doi: 10.1002/cphc.201700082
22. Elstner, M., Hobza, P., Frauenheim, T., Suhai, S., and Kaxiras, E. (2001). Hydrogen bonding and stacking interactions of nucleic acid base pairs: a density-functional-theory based treatment. J. Chem. Phys. 114, 5149-5155. doi: 10.1063/1.1329889
23. Fong, P., Mcnamara, J. P., Hillier, I. H., and Bryce, R. A. (2009). Assessment of QM/MM scoring functions for molecular docking to HIV-1 protease. J. Chem. Inf. Model. 49, 913-924. doi: 10.1021/ci800432s
24. Forti, F., Cavasotto, C. N., Orozco, M., Barril, X., and Luque, F. J. (2012). A multilevel strategy for the exploration of the conformational flexibility of small molecules. J. Chem. Theory Comput. 8, 1808-1819. doi: 10.1021/ct300097s
25. Frush, E. H., Sekharan, S., and Keinan, S. (2017). In silico prediction of ligand binding energies in multiple therapeutic targets and diverse ligand sets-a case study on BACE1, TYK2, HSP90, and PERK proteins. J. Phys. Chem. B 121, 8142-8148. doi: 10.1021/acs.jpcb.7b07224
26. Gaieb, Z., Liu, S., Gathiaka, S., Chiu, M., Yang, H., Shao, C., et al. (2018). D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J. Comput Aided Mol. Des. 32, 1-20. doi: 10.1007/s10822-017-0088-4
27. Gathiaka, S., Liu, S., Chiu, M., Yang, H., Stuckey, J. A., Kang, Y. N., et al. (2016). D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions. J. Comput Aided Mol. Des. 30, 651-668. doi: 10.1007/s10822-016-9946-8
28. Gaus, M., Cui, Q., and Elstner, M. (2011). DFTB3: extension of the self-consistent-charge density-functional tight-binding method (SCC-DFTB). J. Chem. Theory Comput. 7, 931-948. doi: 10.1021/ct100684s
29. Gilson, M. K., and Zhou, H. X. (2007). Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 36, 21-42. doi: 10.1146/annurev.biophys.36.040306.132550
30. Gohlke, H., and Klebe, G. (2002). Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem. Int. Ed. Engl. 41, 2644-2676. doi: 10.1002/1521-3773(20020802)41:15<2644::AID-ANIE2644>3.0.CO;2-O
31. Grimme, S., Antony, J., Ehrlich, S., and Krieg, H. (2010). A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132:154104. doi: 10.1063/1.3382344
32. Grimme, S., Brandenburg, J. G., Bannwarth, C., and Hansen, A. (2015). Consistent structures and interactions by density functional theory with small atomic orbital basis sets. J. Chem. Phys. 143:054107. doi: 10.1063/1.4927476
33. Grosdidier, A., Zoete, V., and Michielin, O. (2007). EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins 67, 1010-1025. doi: 10.1002/prot.21367
34. Hansen, N., and Van Gunsteren, W. F. (2014). Practical aspects of free-energy calculations: a review. J. Chem. Theory Comput. 10, 2632-2647. doi: 10.1021/ct500161f
35. Jorgensen, W. L. (2009). Efficient drug lead discovery and optimization. Acc. Chem. Res. 42, 724-733. doi: 10.1021/ar800236t
36. Juárez-Jiménez, J., Barril, X., Orozco, M., Pouplana, R., and Luque, F. J. (2015). Assessing the suitability of the multilevel strategy for the conformational analysis of small ligands. J. Phys. Chem. B 119, 1164-1172. doi: 10.1021/jp506779y
37. Kim, M., and Cho, A. E. (2016). Incorporating QM and solvation into docking for applications to GPCR targets. Phys. Chem. Chem. Phys. 18, 28281-28289. doi: 10.1039/C6CP04742D
38. 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. doi: 10.1038/nrd1549
39. Klamt, A. (1995). Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J. Phys. Chem. 99, 2224-2235. doi: 10.1021/j100007a062
40. Klamt, A. (2011). The COSMO and COSMO-RS solvation models. Wiley Interdiscip. Rev. Comput. Mol. Sci. 1, 699-709. doi: 10.1002/wcms.56
41. Klamt, A., and Schüürmann, G. (1993). COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J. Chem. Soc. Perkin Trans. 2, 799-805. doi: 10.1039/P29930000799
42. Korth, M. (2010). Third-generation hydrogen-bonding corrections for semiempirical QM methods and force fields. J. Chem. Theory Comput. 6, 3808-3816. doi: 10.1021/ct100408b
43. Lepšík, M., Rezác, J., Kolár, M., Pecina, A., Hobza, P., and Fanfrlík, J. (2013). The semiempirical quantum mechanical scoring function for in silico drug design. ChemPlusChem 78, 921-931. doi: 10.1002/cplu.201300199
44. Misini Ignjatovic, M., Caldararu, O., Dong, G., Munoz-Gutierrez, C., Adasme-Carreno, F., and Ryde, U. (2016). Binding-affinity predictions of HSP90 in the D3R grand challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. J. Comput Aided Mol. Des. 30, 707-730. doi: 10.1007/s10822-016-9942-z
45. Mobley, D. L., and Gilson, M. K. (2017). Predicting binding free energies: frontiers and benchmarks. Annu. Rev. Biophys. 46, 531-558. doi: 10.1146/annurev-biophys-070816-033654
46. Mobley, D. L., and Klimovich, P. V. (2012). Perspective: alchemical free energy calculations for drug discovery. J. Chem. Phys. 137:230901. doi: 10.1063/1.4769292
47. Mucs, D., and Bryce, R. A. (2013). The application of quantum mechanics in structure-based drug design. Expert Opin. Drug. Discov. 8, 263-276. doi: 10.1517/17460441.2013.752812
48. Nascimento, é. C. M., Oliva, M., Swiderek, K., Martins, J. B. L., and Andrés, J. (2017). Binding analysis of some classical acetylcholinesterase inhibitors: insights for a rational design using free energy perturbation method calculations with QM/MM MD simulations. J. Chem. Inf. Model. 57, 958-976. doi: 10.1021/acs.jcim.7b00037
49. Olsson, M. A., and Ryde, U. (2017). Comparison of QM/MM methods to obtain ligand-binding free energies. J. Chem. Theory Comput. 13, 2245-2253. doi: 10.1021/acs.jctc.6b01217
50. Pecina, A., Haldar, S., Fanfrlík, J., Meier, R., Rezác, J., Lepšík, M., et al. (2017). SQM/COSMO scoring function at the DFTB3-D3H4 level: unique identification of native protein-ligand poses. J. Chem. Inf. Model. 57, 127-132. doi: 10.1021/acs.jcim.6b00513
51. Pecina, A., Meier, R., Fanfrlík, J., Lepšík, M., Rezác, J., Hobza, P., et al. (2016). The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation. Chem. Commun. 52, 3312-3315. doi: 10.1039/C5CC09499B
52. Phatak, S. S., Stephan, C. C., and Cavasotto, C. N. (2009). High-throughput and in silico screenings in drug discovery. Exp. Opin. Drug Discov. 4, 947-959. doi: 10.1517/17460440903190961
53. Raha, K., and Merz, K. M. Jr. (2004). A quantum mechanics-based scoring function: study of zinc ion-mediated ligand binding. J. Am. Chem. Soc. 126, 1020-1021. doi: 10.1021/ja038496i
54. Raha, K., and Merz, K. M. Jr. (2005). Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes. J. Med. Chem. 48, 4558-4575. doi: 10.1021/jm048973n
55. Rezác, J., and Hobza, P. (2011). A halogen-bonding correction for the semiempirical PM6 method. Chem. Phys. Lett. 506, 286-289. doi: 10.1016/j.cplett.2011.03.009
56. Rezác, J., and Hobza, P. (2012). Advanced corrections of hydrogen bonding and dispersion for semiempirical quantum mechanical methods. J. Chem. Theory Comput. 8, 141-151. doi: 10.1021/ct200751e
57. Richards, W. G. (1977). Quantum Pharmacology. London; Boston, MA: Butterworths
58. Rod, T. H., and Ryde, U. (2005). Quantum mechanical free energy barrier for an enzymatic reaction. Phys. Rev. Lett. 94:138302. doi: 10.1103/PhysRevLett.94.138302
59. Ryde, U., and Söderhjelm, P. (2016). Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev. 116, 5520-5566. doi: 10.1021/acs.chemrev.5b00630
60. Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A., and Farid, R. (2006). Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem. 49, 534-553. doi: 10.1021/jm050540c
61. Spyrakis, F., Bidon-Chanal, A., Barril, X., and Luque, F. J. (2011). Protein flexibility and ligand recognition: challenges for molecular modeling. Curr. Top. Med. Chem. 11, 192-210. doi: 10.2174/156802611794863571
62. Spyrakis, F., and Cavasotto, C. N. (2015). Open challenges in structure-based virtual screening: receptor modeling, target flexibility consideration and active site water molecules description. Arch. Biochem. Biophys. 583, 105-119. doi: 10.1016/j.abb.2015.08.002
63. Stewart, J. J. P. (2007). Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements. J. Mol. Model. 13, 1173-1213. doi: 10.1007/s00894-007-0233-4
64. Su, Y., Gallicchio, E., Das, K., Arnold, E., and Levy, R. M. (2007). Linear Interaction Energy (LIE) models for ligand binding in implicit solvent: theory and application to the binding of NNRTIs to HIV-1 reverse transcriptase. J. Chem. Theory Comput. 3, 256-277. doi: 10.1021/ct600258e
65. Sure, R., and Grimme, S. (2013). Corrected small basis set Hartree-Fock method for large systems. J. Comput. Chem. 34, 1672-1685. doi: 10.1002/jcc.23317
66. Swiderek, K., Ruiz-Pernía, J. J., Moliner, V., and Tuñón, I. (2014). Heavy enzymes-experimental and computational insights in enzyme dynamics. Curr. Opin. Chem. Biol. 21, 11-18. doi: 10.1016/j.cbpa.2014.03.005
67. Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., et al. (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. doi: 10.1039/C6CP01555G
68. Warren, G. L., Andrews, C. W., Capelli, A. M., Clarke, B., Lalonde, J., Lambert, M. H., et al. (2006). A critical assessment of docking programs and scoring functions. J. Med. Chem. 49, 5912-5931. doi: 10.1021/jm050362n
69. Yilmazer, N. D., and Korth, M. (2016). Prospects of applying enhanced semi-empirical QM methods for 2101 virtual drug design. Curr. Med. Chem. 23, 2101-2111. doi: 10.2174/0929867323666160517120005
70. Zoete, V., Schuepbach, T., Bovigny, C., Chaskar, P., Daina, A., Rohrig, U. F., et al. (2016). Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape. J. Comput. Chem. 37, 437-447. doi: 10.1002/jcc.24249