Effect of preprocessing and simulation parameters on the performance of molecular docking studies

Stanzione F, Giangreco I, Cole JC (2021) Use of molecular docking computational tools in drug discovery. Prog Med Chem 60:273–343

Tao X, Huang Y, Wang C et al (2020) Recent developments in molecular docking technology applied in food science: a review. Int J Food Sci Technol 55(1):33–45

Trisciuzzi D, Alberga D, Leonetti F et al (2018) Molecular docking for predictive toxicology. In: Nicolotti O (eds) Computational Toxicology. Methods in Molecular Biology, vol 1800. Humana Press, New York, NY. Springer, p 181–197

Campbell AJ, Lamb ML, Joseph-McCarthy D (2014) Ensemble-based docking using biased molecular dynamics. J Chem Inf Model 54(7):2127–2138

Santos KB, Guedes IA, Karl AL et al (2020) Highly flexible ligand docking: benchmarking of the DockThor program on the LEADS-PEP protein–peptide data set. J Chem Inf Model 60(2):667–683

Madhavi Sastry G, Adzhigirey M, Day T et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234

Zhang D, Li H, Wang H et al (2016) Docking accuracy enhanced by QM-derived protein charges. Mol Phys 114(20):3015–3025

Cho AE, Guallar V, Berne BJ et al (2005) Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach. J Comput Chem 26(9):915–931

Wang Z, Sun H, Yao X 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(18):12,964-12,975

Trott O, Olson AJ (2010) Autodock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461

Santos-Martins D, Solis-Vasquez L, Tillack AF et al (2021) Accelerating autodock4 with GPUs and gradient-based local search. J Chem Theory Comput 17(2):1060–1073

Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53(8):1893–1904

Andersson CD, Thysell E, Lindström A et al (2007) A multivariate approach to investigate docking parameters’ effects on docking performance. J Chem Inf Model 47(4):1673–1687

Su M, Yang Q, Du Y et al (2018) Comparative assessment of scoring functions: the CASF-2016 update. J Chem Inf Model 59(2):895–913

Guedes IA, Barreto A, Marinho D et al (2021) New machine learning and physics-based scoring functions for drug discovery. Sci Rep 11(1):1–19

Rappe AK, Goddard WA III (1991) Charge equilibration for molecular dynamics simulations. J Phys Chem 95(8):3358–3363

Chen J, Martínez TJ (2007) Qtpie: Charge transfer with polarization current equalization. a fluctuating charge model with correct asymptotics. Chem Phys Lett 438(4–6):315–320

Bultinck P, Langenaeker W, Lahorte P et al (2002) The electronegativity equalization method i: parametrization and validation for atomic charge calculations. J Phys Chem A 106(34):7887–7894

Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17(5–6):490–519

Gasteiger J, Marsili M (1978) A new model for calculating atomic charges in molecules. Tetrahedron Lett 19(34):3181–3184

O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3(1):1–14

Perilla JR, Schulten K (2017) Physical properties of the HIV-1 capsid from all-atom molecular dynamics simulations. Nat Commun 8(1):1–10

Sanches K, Dias RVR, da Silva PH et al (2019) Grb2 dimer interacts with Coumarin through SH2 domains: a combined experimental and molecular modeling study. Heliyon 5(11):e02,869

Formoso E, Matxain JM, Lopez X et al (2010) Molecular dynamics simulation of bovine pancreatic ribonuclease A- CpA and transition state-like complexes. J Phys Chem B 114(21):7371–7382

Best RB, Zhu X, Shim J et al (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J Chem Theory Comput 8(9):3257–3273

Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935

Zhan F, Ding S, Xie W et al (2020) Towards understanding the inter-action of β-lactoglobulin with capsaicin: multi-spectroscopic, thermodynamic, molecular docking and molecular dynamics simulation approaches. Food Hydrocolloids 105(105):767

Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713

Hornak V, Abel R, Okur A et al (2006) Comparison of multiple amber force fields and development of improved protein back bone parameters. Proteins: Struct, Function, Bioinform 65(3):712–725

Vettoretti G, Moroni E, Sattin S et al (2016) Molecular dynamics simulations reveal the mechanisms of allosteric activation of Hsp90 by designed ligands. Sci Rep 6(1):1–13

Oostenbrink C, Villa A, Mark AE et al (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25(13):1656–1676

Berendsen HJ, Postma JP, van Gunsteren WF et al (1981) Interaction models for water in relation to protein hydration. In: Pullman B (eds) Intermolecular Forces. The Jerusalem Symposia on Quantum Chemistry and Biochemistry, vol 14. Springer, Dordrecht, pp 331–342

Vanommeslaeghe K, Hatcher E, Acharya C et al (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31(4):671–690

Yu W, He X, Vanommeslaeghe K et al (2012) Extension of the CHARMM general force field to sulfonyl-containing compounds and its utility in biomolecular simulations. J Comput Chem 33(31):2451–2468

Zoete V, Cuendet MA, Grosdidier A et al (2011) Swiss-Param: A fast force field generation tool for small organic molecules. J Comput Chem 32(11):2359–2368. https://doi.org/10.1002/jcc.21816

Case D, Aktulga H, Belfon K et al (2022) Amber 2022. University of California, San Francisco

Sousa da Silva AW, Vranken WF (2012) Acpype-antechamber python parser interface. BMC Res Notes 5(1):1–8

Wang J, Wang W, Kollman PA et al (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260

Schüttelkopf AW, van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D60:1355–1363

Berendsen HJ, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1–3):43–56

Berendsen HJ, Jv P, Van Gunsteren WF et al (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690

Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52(12):7182–7190

The PyMOL Molecular Graphics System, Version 1.8 Schrödinger, LLC

Virtanen P, Gommers R, Oliphant TE et al (2020) SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat Methods 17:261–272. https://doi.org/10.1038/s41592-019-0686-2

Torres PH, Sodero AC, Jofily P et al (2019) Key topics in molecular docking for drug design. Int J Mol Sci 20(18):4574

Feinstein WP, Brylinski M (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminformatics 7(1):1–10

Hassan NM, Alhossary AA, Mu Y et al (2017) Protein-ligand blind docking using Quickvina-W with inter-process spatio-temporal integration. Sci Rep 7(1):1–13

Liu Y, Grimm M, Wt D et al (2020) CB-Dock: a web server for cavity detection-guided protein–ligand blind docking. Acta Pharmacol Sin 41(1):138–144

Brylinski M, Feinstein WP (2013) eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des 27(6):551–567

Jo S, Kim T, Iyer VG et al (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29(11):1859–1865

Singh S, Baker QB, Singh DB (2022) Molecular docking and molecular dynamics simulation. In: Singh DB, Pathak RK (eds) Bioinformatics. Elsevier, pp 291–304

Liu K, Watanabe E, Kokubo H (2017) Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. J Comput Aided Mol Des 31(2):201–211

Murray CW, Baxter CA, Frenkel AD (1999) The sensitivity of the results of molecular docking to induced fit effects: application to thrombin, thermolysin and neuraminidase. J Comput Aided Mol Des 13:547–562

Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDock-Tools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

de Oliveira OV, Rocha GB, Paluch AS et al (2021) Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening. J Biomol Struct Dyn 39(11):3924–3933

Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508