Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking

ACS Omega

Volumn 2, Issue 7, 2017, Pages 4022-4029

  Ajani, Haresh ^a,b   Pecina, Adam ^a   Eyrilmez, Saltuk M ^a,b   Fanfrlík, Jindřich ^a   Haldar, Susanta ^a   Řezáč, Jan ^a   Hobza, Pavel ^a,b   Lepšík, Martin ^a  

^a INSTITUTE OF ORGANIC CHEMISTRY AND BIOCHEMISTRY   (Czech Republic)

^b PALACKÝ UNIVERSITY   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85028929358     PISSN: None     EISSN: 24701343     Source Type: Journal    
DOI: 10.1021/acsomega.7b00503     Document Type: Article

References (74)

1. Schneider, G. Virtual screening: an endless staircase? Nat. Rev. Drug Discovery 2010, 9, 273-276 10.1038/nrd3139
2. Irwin, J. J.; Shoichet, B. K. Docking Screens for Novel Ligands Conferring New Biology J. Med. Chem. 2016, 59, 4103-4120 10.1021/acs.jmedchem.5b02008
3. Cheng, T.; Li, X.; Li, Y.; Liu, Z.; Wang, R. Comparative Assessment of Scoring Functions on a Diverse Test Set J. Chem. Inf. Model. 2009, 49, 1079-1093 10.1021/ci9000053
4. Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. 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. 2016, 18, 12964-12975 10.1039/C6CP01555G
5. Yuriev, E.; Agostino, M.; Ramsland, P. A. Challenges and advances in computational docking: 2009 in review J. Mol. Recognit. 2011, 24, 149-164 10.1002/jmr.1077
6. Jain, A. N.; Nicholls, A. Recommendations for evaluation of computational methods J. Comput.-Aided Mol. Des. 2008, 22, 133-139 10.1007/s10822-008-9196-5
7. Kontoyianni, M.; McClellan, L. M.; Sokol, G. S. Evaluation of docking performance: comparative data on docking algorithms J. Med. Chem. 2004, 47, 558-565 10.1021/jm0302997
8. Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-based scoring function to predict protein-ligand interactions J. Mol. Biol. 2000, 295, 337-356 10.1006/jmbi.1999.3371
9. Warren, G. L.; Andrews, C. W.; Capelli, A.-M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. A critical assessment of docking programs and scoring functions J. Med. Chem. 2006, 49, 5912-5931 10.1021/jm050362n
10. Böhm, H.-J. The development of a simple empirical scoring function to estimate the binding constant for a protein ligand complex of known 3-dimensional structure J. Comput.-Aided Mol. Des. 1994, 8, 243-256 10.1007/BF00126743
11. Wang, R.; Lai, L.; Wang, S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction J. Comput.-Aided Mol. Des. 2002, 16, 11-26 10.1023/A:1016357811882
12. Gohlke, H.; Klebe, G. Statistical potentials and scoring functions applied to protein-ligand binding Curr. Opin. Struct. Biol. 2001, 11, 231-235 10.1016/S0959-440X(00)00195-0
13. Spitzmüller, A.; Velec, H. F. G.; Klebe, G. MiniMuDS: A New Optimizer using Knowledge-Based Potentials Improves Scoring of Docking Solutions J. Chem. Inf. Model. 2011, 51, 1423-1430 10.1021/ci200098v
14. Ishchenko, A. V.; Shakhnovich, E. I. SMall molecule growth 2001 (SMoG2001): An improved knowledge-based scoring function for protein-ligand interactions J. Med. Chem. 2002, 45, 2770-2780 10.1021/jm0105833
15. Ain, Q. U.; Aleksandrova, A.; Roessler, F. D.; Ballester, P. J. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2015, 5, 405-424 10.1002/wcms.1225
16. Khamis, M. A.; Gomaa, W. Comparative assessment of machine-learning scoring functions on PDBbind 2013 Eng. Appl. Artif. Intell. 2015, 45, 136-151 10.1016/j.engappai.2015.06.021
17. Ewing, T. J. A.; Makino, S.; Skillman, A. G.; Kuntz, I. D. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases J. Comput.-Aided Mol. Des. 2001, 15, 411-428 10.1023/A:1011115820450
18. Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function J. Comput. Chem. 1998, 19, 1639-1662 10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B
19. Li, L.; Wang, B.; Meroueh, S. O. Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries J. Chem. Inf. Model. 2011, 51, 2132-2138 10.1021/ci200078f
20. Řezáč, J.; Hobza, P. Benchmark Calculations of Interaction Energies in Noncovalent Complexes and Their Applications Chem. Rev. 2016, 116, 5038-5071 10.1021/acs.chemrev.5b00526
21. Fanfrlík, J.; Kolář, M.; Kamlar, M.; Hurný, D.; Ruiz, F. X.; Cousido-Siah, A.; Mitschler, A.; Řezáč, J.; Munusamy, E.; Lepšík, M.; Matějíček, P.; Veselý, J.; Podjarny, A.; Hobza, P. Modulation of Aldose Reductase Inhibition by Halogen Bond Tuning ACS Chem. Biol. 2013, 8, 2484-2492 10.1021/cb400526n
22. Fanfrlík, J.; Ruiz, F. X.; Kadlčíková, A.; Řezáč, J.; Cousido-Siah, A.; Mitschler, A.; Haldar, S.; Lepšík, M.; Kolář, M. H.; Majer, P.; Podjarny, A. D.; Hobza, P. The Effect of Halogen-to-Hydrogen Bond Substitution on Human Aldose Reductase Inhibition ACS Chem. Biol. 2015, 10, 1637-1642 10.1021/acschembio.5b00151
23. Pecina, A.; Meier, R.; Fanfrlík, J.; Lepšík, M.; Řezáč, J.; Hobza, P.; Baldauf, C. The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation Chem. Commun. 2016, 52, 3312-3315 10.1039/C5CC09499B
24. Ciancetta, A.; Genheden, S.; Ryde, U. A QM/MM study of the binding of RAPTA ligands to cathepsin B J. Comput.-Aided Mol. Des. 2011, 25, 729-742 10.1007/s10822-011-9448-7
25. Pecina, A.; Lepšík, M.; Řezáč, J.; Brynda, J.; Mader, P.; Řezáčová, P.; Hobza, P.; Fanfrlík, J. QM/MM calculations reveal the different nature of the interaction of two carborane-based sulfamide inhibitors of human carbonic anhydrase II J. Phys. Chem. B 2013, 117, 16096-16104 10.1021/jp410216m
26. Mader, P.; Pecina, A.; Cigler, P.; Lepšík, M.; Šícha, V.; Hobza, P.; Grüner, B.; Fanfrlík, J.; Brynda, J.; Řezáčová, P. Carborane-Based Carbonic Anhydrase Inhibitors: Insight into CAII/CAIX Specificity from a High-Resolution Crystal Structure, Modeling, and Quantum Chemical Calculations BioMed Res. Int. 2014, 389869 10.1155/2014/389869
27. Fanfrlík, J.; Brahmkshatriya, P. S.; Řezáč, J.; Jílková, A.; Horn, M.; Mareš, M.; Hobza, P.; Lepšík, M. Quantum Mechanics-Based Scoring Rationalizes the Irreversible Inactivation of Parasitic Schistosoma mansoni Cysteine Peptidase by Vinyl Sulfone Inhibitors J. Phys. Chem. B 2013, 117, 14973-14982 10.1021/jp409604n
28. Söderhjelm, P.; Ryde, U. How Accurate Can a Force Field Become? A Polarizable Multipole Model Combined with Fragment-wise Quantum-Mechanical Calculations J. Phys. Chem. A 2009, 113, 617-627 10.1021/jp8073514
29. Berg, L.; Mishra, B. K.; Andersson, C. D.; Ekström, F.; Linusson, A. The Nature of Activated Non-classical Hydrogen Bonds: A Case Study on Acetylcholinesterase-Ligand Complexes Chem.-Eur. J. 2016, 22, 2672-2681 10.1002/chem.201503973
30. Wichapong, K.; Rohe, A.; Platzer, C.; Slynko, I.; Erdmann, F.; Schmidt, M.; Sippl, W. Application of Docking and QM/MM-GBSA Rescoring to Screen for Novel Myt1 Kinase Inhibitors J. Chem. Inf. Model. 2014, 54, 881-893 10.1021/ci4007326
31. Chaskar, P.; Zoete, V.; Röhrig, U. F. Toward On-The-Fly Quantum Mechanical/Molecular Mechanical (QM/MM) Docking: Development and Benchmark of a Scoring Function J. Chem. Inf. Model. 2014, 54, 3137-3152 10.1021/ci5004152
32. Chaskar, P.; Zoete, V.; Röhrig, U. F. On-the-Fly QM/MM Docking with Attracting Cavities J. Chem. Inf. Model. 2017, 57, 73-84 10.1021/acs.jcim.6b00406
33. Burger, S. K.; Thompson, D. C.; Ayers, P. W. Quantum Mechanics/Molecular Mechanics Strategies for Docking Pose Refinement: Distinguishing between Binders and Decoys in Cytochrome c Peroxidase J. Chem. Inf. Model. 2011, 51, 93-101 10.1021/ci100329z
34. Antony, J.; Grimme, S.; Liakos, D. G.; Neese, F. Protein-Ligand Interaction Energies with Dispersion Corrected Density Functional Theory and High-Level Wave Function Based Methods J. Phys. Chem. A 2011, 115, 11210-11220 10.1021/jp203963f
35. Raha, K.; Merz, K. M. A quantum mechanics-based scoring function: Study of zinc ion-mediated ligand binding J. Am. Chem. Soc. 2004, 126, 1020-1021 10.1021/ja038496i
36. Raha, K.; Merz, K. M. 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. 2005, 48, 4558-4575 10.1021/jm048973n
37. Řezáč, J.; Hobza, P. Advanced Corrections of Hydrogen Bonding and Dispersion for Semiempirical Quantum Mechanical Methods J. Chem. Theory Comput. 2012, 8, 141-151 10.1021/ct200751e
38. Kolář, M.; Fanfrlík, J.; Lepšík, M.; Forti, F.; Luque, F. J.; Hobza, P. Assessing the Accuracy and Performance of Implicit Solvent Models for Drug Molecules: Conformational Ensemble Approaches J. Phys. Chem. B 2013, 117, 5950-5962 10.1021/jp402117c
39. Stewart, J. J. P. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters J. Mol. Model. 2013, 19, 1-32 10.1007/s00894-012-1667-x
40. Klamt, A.; Schüürmann, G. 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 1993, 799-805 10.1039/P29930000799
41. Sulimov, A. V.; Kutov, D. C.; Katkova, E. V.; Sulimov, V. B. Combined Docking with Classical Force Field and Quantum Chemical Semiempirical Method PM7 Adv. Bioinf. 2017, 7167691 10.1155/2017/7167691
42. Oferkin, I. V.; Katkova, E. V.; Sulimov, A. V.; Kutov, D. C.; Sobolev, S. I.; Voevodin, V. V.; Sulimov, V. B. Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima Adv. Bioinf. 2015, 126858 10.1155/2015/126858
43. Hostaš, J.; Řezáč, J.; Hobza, P. On the performance of the semiempirical quantum mechanical PM6 and PM7 methods for noncovalent interactions Chem. Phys. Lett. 2013, 568-569, 161-166 10.1016/j.cplett.2013.02.069
44. Řezáč, J.; Hobza, P. A halogen-bonding correction for the semiempirical PM6 method Chem. Phys. Lett. 2011, 506, 286-289 10.1016/j.cplett.2011.03.009
45. Fanfrlík, J.; Bronowska, A. K.; Řezáč, J.; Přenosil, O.; Konvalinka, J.; Hobza, P. A Reliable Docking/Scoring Scheme Based on the Semiempirical Quantum Mechanical PM6-DH2 Method Accurately Covering Dispersion and H-Bonding: HIV-1 Protease with 22 Ligands J. Phys. Chem. B 2010, 114, 12666-12678 10.1021/jp1032965
46. Lepšík, M.; Řezáč, J.; Kolář, M.; Pecina, A.; Hobza, P.; Fanfrlík, J. The Semiempirical Quantum Mechanical Scoring Function for In Silico Drug Design ChemPlusChem 2013, 78, 921-931 10.1002/cplu.201300199
47. Vorlová, B.; Nachtigallová, D.; Jirásková-Vaníčková, J.; Ajani, H.; Jansa, P.; Řezáč, J.; Fanfrlík, J.; Otyepka, M.; Hobza, P.; Konvalinka, J.; Lepšík, M. Malonate-based inhibitors of mammalian serine racemase: kinetic characterization and structure-based computational study Eur. J. Med. Chem. 2015, 89, 189-197 10.1016/j.ejmech.2014.10.043
48. Cousido-Siah, A.; Ruiz, F. X.; Fanfrlík, J.; Giménez-Dejoz, J.; Mitschler, A.; Kamlar, M.; Veselý, J.; Ajani, H.; Parés, X.; Farrés, J.; Hobza, P.; Podjarny, A. D. IDD388 Polyhalogenated Derivatives as Probes for an Improved Structure-Based Selectivity of AKR1B10 Inhibitors ACS Chem. Biol. 2016, 11, 2693-2705 10.1021/acschembio.6b00382
49. Dostál, J.; Pecina, A.; Hrušková-Heidingsfeldová, O.; Marečková, L.; Pichová, I.; Řezáčová, P.; Lepšík, M.; Brynda, J. Atomic resolution crystal structure of Sapp2p, a secreted aspartic protease from Candida parapsilosis Acta Crystallogr., Sect. D: Biol. Crystallogr. 2015, 71, 2494-2504 10.1107/S1399004715019392
50. Nekardová, M.; Vymětalová, L.; Khirsariya, P.; Kováčová, S.; Hylsová, M.; Jorda, R.; Kryštof, V.; Fanfrlík, J.; Hobza, P.; Paruch, K. Structural Basis of the Interaction of Cyclin-Dependent Kinase 2 with Roscovitine and Its Analogues Having Bioisosteric Central Heterocycles ChemPhysChem 2017, 785 10.1002/cphc.201601319
51. Hylsová, M.; Carbain, B.; Fanfrlík, J.; Musilová, L.; Haldar, S.; Köprülüoǧlu, C.; Ajani, H.; Brahmkshatriya, P. S.; Jorda, R.; Kryštof, V.; Hobza, P.; Echalier, A.; Paruch, K.; Lepšík, M. Explicit treatment of active-site waters enhances quantum mechanical/implicit solvent scoring: Inhibition of CDK2 by new pyrazolo[1,5-a]pyrimidines Eur. J. Med. Chem. 2017, 126, 1118-1128 10.1016/j.ejmech.2016.12.023
52. Pecina, A.; Haldar, S.; Fanfrlík, J.; Meier, R.; Řezáč, J.; Lepšík, M.; Hobza, P. SQM/COSMO Scoring Function at the DFTB3-D3H4 Level: Unique Identification of Native Protein-Ligand Poses J. Chem. Inf. Model. 2017, 57, 127-132 10.1021/acs.jcim.6b00513
53. Elstner, M.; Hobza, P.; Frauenheim, T.; Suhai, S.; Kaxiras, E. Hydrogen bonding and stacking interactions of nucleic acid base pairs: A density-functional-theory based treatment J. Chem. Phys. 2001, 114, 5149-5155 10.1063/1.1329889
54. Miriyala, V. M.; Řezáč, J. Description of non-covalent interactions in SCC-DFTB methods J. Comput. Chem. 2017, 38, 688-697 10.1002/jcc.24725
55. 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 10.1021/jm0306430
56. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility J. Comput. Chem. 2009, 30, 2785-2791 10.1002/jcc.21256
57. Trott, O.; Olson, A. J. Software News and Update AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading J. Comput. Chem. 2010, 31, 455-461 10.1002/jcc.21334
58. Allen, W. J.; Balius, T. E.; Mukherjee, S.; Brozell, S. R.; Moustakas, D. T.; Lang, P. T.; Case, D. A.; Kuntz, I. D.; Rizzo, R. C. DOCK 6: Impact of New Features and Current Docking Performance J. Comput. Chem. 2015, 36, 1132-1156 10.1002/jcc.23905
59. Li, Y.; Liu, Z.; Li, J.; Han, L.; Liu, J.; Zhao, Z.; Wang, R. Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set J. Chem. Inf. Model. 2014, 54, 1700-1716 10.1021/ci500080q
60. Kirchmair, J.; Wolber, G.; Laggner, C.; Langer, T. Comparative performance assessment of the conformational model generators omega and catalyst: A large-scale survey on the retrieval of protein-bound ligand conformations J. Chem. Inf. Model. 2006, 46, 1848-1861 10.1021/ci060084g
61. Warren, G. L.; Do, T. D.; Kelley, B. P.; Nicholls, A.; Warren, S. D. Essential considerations for using protein-ligand structures in drug discovery Drug Discovery Today 2012, 17, 1270-1281 10.1016/j.drudis.2012.06.011
62. Zilian, D.; Sotriffer, C. A. SFCscore(RF): A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes J. Chem. Inf. Model. 2013, 53, 1923-1933 10.1021/ci400120b
63. Zhao, H.; Caflisch, A. Discovery of ZAP70 inhibitors by high-throughput docking into a conformation of its kinase domain generated by molecular dynamics Bioorg. Med. Chem. Lett. 2013, 23, 5721-5726 10.1016/j.bmcl.2013.08.009
64. Jain, A. N. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine J. Med. Chem. 2003, 46, 499-511 10.1021/jm020406h
65. Small-Molecule Drug Discovery Suite 2016-1; Schrödinger, LLC: New York, NY, 2016.
66. Case, D. A.; Babin, V.; Berryman, J. T.; Betz, R. M.; Cai, Q.; Cerutti, D. S.; Cheatham, T. E.; Darden, T. A.; Duke, R. E.; Gohlke, H.; Goetz, A. W.; Gusarov, S.; Homeyer, N.; Janowski, P.; Kaus, J.; Kolossváry, I.; Kovalenko, A.; Lee, T. S.; LeGrand, S.; Luchko, T.; Luo, R.; Madej, B.; Merz, K. M.; Paesani, F.; Roe, D. R.; Roitberg, A.; Sagui, C.; Salomon-Ferrer, R.; Seabra, G.; Simmerling, C. L.; Smith, W.; Swails, J.; Walker; Wang, J.; Wolf, R. M.; Wu, X.; Kollman, P. A. AMBER 14; University of California: San Francisco, 2014.
67. Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations J. Mol. Graphics Modell. 2006, 25, 247-260 10.1016/j.jmgm.2005.12.005
68. Jakalian, A.; Bush, B. L.; Jack, D. B.; Bayly, C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method J. Comput. Chem. 2000, 21, 132-146 10.1002/(SICI)1096-987X(20000130)21:2<132::AID-JCC5>3.0.CO;2-P
69. Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation J. Comput. Chem. 2002, 23, 1623-1641 10.1002/jcc.10128
70. Gaus, M.; Cui, Q.; Elstner, M. DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB) J. Chem. Theory Comput. 2011, 7, 931-948 10.1021/ct100684s
71. Gaus, M.; Goez, A.; Elstner, M. Parametrization and Benchmark of DFTB3 for Organic Molecules J. Chem. Theory Comput. 2013, 9, 338-354 10.1021/ct300849w
72. Gaus, M.; Lu, X.; Elstner, M.; Cui, Q. Parameterization of DFTB3/3OB for Sulfur and Phosphorus for Chemical and Biological Applications J. Chem. Theory Comput. 2014, 10, 1518-1537 10.1021/ct401002w
73. Lu, X.; Gaus, M.; Elstner, M.; Cui, Q. Parametrization of DFTB3/3OB for magnesium and zinc for chemical and biological applications J. Phys. Chem. B 2015, 119, 1062-1082 10.1021/jp506557r
74. Kubillus, M.; Kubař, T.; Gaus, M.; Řezáč, J.; Elstner, M. Parameterization of the DFTB3 method for Br, Ca, Cl, F, I, K, and Na in organic and biological systems J. Chem. Theory Comput. 2015, 11, 332-342 10.1021/ct5009137