General and reliable description of structures and energetics in protein-ligand (PL) binding using the docking/scoring methodology has until now been elusive. We address this urgent deficiency of scoring functions (SFs) by the systematic development of corrected semiempirical quantum mechanical (SQM) methods, which correctly describe all types of noncovalent interactions and are fast enough to treat systems of thousands of atoms. Two most accurate SQM methods, PM6-D3H4X and SCC-DFTB3-D3H4X, are coupled with the conductor-like screening model (COSMO) implicit solvation model in so-called "SQM/COSMO" SFs and have shown unique recognition of native ligand poses in cognate docking in four challenging PL systems, including metalloprotein. Here, we apply the two SQM/COSMO SFs to 17 diverse PL complexes and compare their performance with four widely used classical SFs (Glide XP, AutoDock4, AutoDock Vina, and UCSF Dock). We observe superior performance of the SQM/COSMO SFs and identify challenging systems. This method, due to its generality, comparability across the chemical space, and lack of need for any system-specific parameters, gives promise of becoming, after comprehensive large-scale testing in the near future, a useful computational tool in structure-based drug design and serving as a reference method for the development of other SFs.

Department of Computational Chemistry Institute of Organic Chemistry and Biochemistry Czech Academy of Sciences v v i Flemingovo nam 2 16610 Praha 6 Czech Republic

Department of Physical Chemistry Palacký University tř 17 listopadu 1192 12 77146 Olomouc Czech Republic

Department of Physical Chemistry Regional Centre of Advanced Technologies and Materials Palacký University 77146 Olomouc Czech Republic

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Schneider G. Virtual screening: an endless staircase?. Nat. Rev. Drug Discovery 2010, 9, 273–276. 10.1038/nrd3139. PubMed DOI

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. PubMed DOI PMC

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI PMC

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI PMC

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. DOI

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. PubMed DOI

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. DOI

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. PubMed DOI PMC

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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, 38986910.1155/2014/389869. PubMed DOI PMC

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI PMC

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. DOI

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, 716769110.1155/2017/7167691. PubMed DOI PMC

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, 12685810.1155/2015/126858. PubMed DOI PMC

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. DOI

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. DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

Hylsová M.; Carbain B.; Fanfrlík J.; Musilová L.; Haldar S.; Köprülüoğlu 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. PubMed DOI

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. PubMed DOI

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. DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI PMC

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. PubMed DOI PMC

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. PubMed DOI PMC

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

Small-Molecule Drug Discovery Suite 2016-1; Schrödinger, LLC: New York, NY, 2016.

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.

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI

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. PubMed DOI PMC

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. PubMed DOI

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. PubMed DOI PMC

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. PubMed DOI PMC

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. PubMed DOI