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SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches

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Abstract

In this paper, we compute, by means of a non equilibrium alchemical technique, the water-octanol partition coefficients (LogP) for a series of drug-like compounds in the context of the SAMPL6 challenge initiative. Our blind predictions are based on three of the most popular non-polarizable force fields, CGenFF, GAFF2, and OPLS-AA and are critically compared to other MD-based predictions produced using free energy perturbation or thermodynamic integration approaches with stratification. The proposed non-equilibrium method emerges has a reliable tool for LogP prediction, systematically being among the top performing submissions in all force field classes for at least two among the various indicators such as the Pearson or the Kendall correlation coefficients or the mean unsigned error. Contrarily to the widespread equilibrium approaches, that yielded apparently very disparate results in the SAMPL6 challenge, all our independent prediction sets, irrespective of the adopted force field and of the adopted estimate (unidirectional or bidirectional) are, mutually, from moderately to strongly correlated.

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References

  1. Isik M, Mobley DL, Levorse D, Rhodes T, Chodera J (2019) Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. biorxiv. https://doi.org/10.1101/757393

    Article  Google Scholar 

  2. Source: DrugBank. Description: The DrugBank database is a unique bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. http://www.drugbank.ca/drugs/DB03366. Accessed 6 May 2019

  3. Bannan Caitlin C, Burley Kalistyn H, Chiu Michael, Shirts Michael R, Gilson Michael K, Mobley David L (2016) Blind prediction of cyclohexane-water distribution coefficients from the sampl5 challenge. J Comput-Aided Mol Des 30(11):927–944

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Cheng Tiejun, Zhao Yuan, Xun Li Fu, Lin Yong Xu, Zhang Xinglong, Li Yan, Wang Renxiao, Lai Luhua (2007) Computation of octanol-water partition coefficients by guiding an additive model with knowledge. J Chem Inf Model 47(6):2140–2148

    CAS  PubMed  Google Scholar 

  5. Ghose Arup K, Viswanadhan Vellarkad N, Wendoloski John J (1998) Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of alogp and clogp methods. J Phys Chem A 102(21):3762–3772

    CAS  Google Scholar 

  6. Molinspiration cheminformatics software. https://www.molinspiration.com/. Accessed 6 May 2019

  7. Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL, Gilson MK (2016) Overview of the sampl5 host-guest challenge: are we doing better? J Comput Aided Mol Des 31:1–19

    PubMed  PubMed Central  Google Scholar 

  8. Rizzi Andrea, Murkli Steven, McNeill John N, Yao Wei, Sullivan Matthew, Gilson Michael K, Chiu Michael W, Isaacs Lyle, Gibb Bruce C, Mobley David L, Chodera John D (2018) Overview of the sampl6 host-guest binding affinity prediction challenge. J Comput Aided Mol Des 32(10):937–963

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gapsys Vytautas, Seeliger Daniel, de Groot BL (2012) New soft-core potential function for molecular dynamics based alchemical free energy calculations. J Chem Theor Comput 8:2373–2382

    CAS  Google Scholar 

  10. Procacci Piero, Cardelli Chiara (2014) Fast switching alchemical transformations in molecular dynamics simulations. J Chem Theory Comput 10:2813–2823

    CAS  PubMed  Google Scholar 

  11. Jarzynski C (1997) Nonequilibrium equality for free energy differences. Phys Rev Lett 78:2690–2693

    CAS  Google Scholar 

  12. Crooks GE (1998) Nonequilibrium measurements of free energy differences for microscopically reversible markovian systems. J Stat Phys 90:1481–1487

    Google Scholar 

  13. GAFF and GAFF2 are public domain force fields and are part of the AmberTools16 distribution, available for download at http://amber.org internet address (accessed March 2017). According to the AMBER development team, the improved version of GAFF, GAFF2, is an ongoing poject aimed at “reproducing both the high quality interaction energies and key liquid properties such as density, heat of vaporization and hydration free energy”. GAFF2 is expected “to be an even more successful general purpose force field and that GAFF2-based scoring functions will significantly improve the successful rate of virtual screenings.”

  14. Procacci Piero (2017) Primadorac: a free web interface for the assignment of partial charges, chemical topology, and bonded parameters in organic or drug molecules. J Chem Inf Model 57(6):1240–1245

    CAS  PubMed  Google Scholar 

  15. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD (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

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Dodda Leela S, Vilseck Jonah Z, Tirado-Rives Julian, Jorgensen William L (2017) 1.14*cm1a-lbcc: localized bond-charge corrected cm1a charges for condensed-phase simulations. J Phys Chem B 121(15):3864–3870

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Dodda Leela S, de Vaca Israel Cabeza, Tirado-Rives Julian, Jorgensen William L (2017) Ligpargen web server: an automatic opls-aa parameter generator for organic ligands. Nucleic Acids Res 45(W1):W331–W336

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Shirts MR, Bair E, Hooker G, Pande VS (2003) Equilibrium free energies from nonequilibrium measurements using maximum likelihood methods. Phys Rev Lett 91:140601

    PubMed  Google Scholar 

  19. Matos Guilherme Duarte Ramos, Kyu Daisy Y, Loeffler Hannes H, Chodera John D, Shirts Michael R, Mobley David L (2017) Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the freesolv database. J Chem Eng Data 62(5):1559–1569

    PubMed  PubMed Central  Google Scholar 

  20. Bennett CH (1976) Efficient estimation of free energy differences from monte carlo data. J Comp Phys 22:245–268

    Google Scholar 

  21. Procacci Piero (2016) I. dissociation free energies of drug-receptor systems via non-equilibrium alchemical simulations: a theoretical framework. Phys Chem Chem Phys 18:14991–15004

    CAS  PubMed  Google Scholar 

  22. Nerattini Francesca, Chelli Riccardo, Procacci Piero (2016) Ii. dissociation free energies in drug-receptor systems via nonequilibrium alchemical simulations: application to the fk506-related immunophilin ligands. Phys Chem Chem Phys 18:15005–15018

    CAS  PubMed  Google Scholar 

  23. Procacci Piero (2018) Myeloid cell leukemia 1 inhibition: an in silico study using non-equilibrium fast double annihilation technology. J Chem Theor Comput 14(7):3890–3902

    CAS  Google Scholar 

  24. Procacci P, Guarrasi M, Guarnieri G (2018) Sampl6 host-guest blind predictions using a non equilibrium alchemical approach. J Comput Aided Mol Des 32:965–982

    CAS  PubMed  Google Scholar 

  25. Jorgensen WL, Buckner JK, Boudon S, TiradoRives J (1988) Efficient computation of absolute free energies of binding by computer simulations. Application to the methane dimer in water. J Chem Phys 89:3742–3746

    CAS  Google Scholar 

  26. Pohorille A, Jarzynski C, Chipot C (2010) Good practices in free-energy calculations. J Phys Chem B 114(32):10235–10253

    CAS  PubMed  Google Scholar 

  27. Kirkwood JG (1935) Statistical mechanics of fluid mixtures. J Chem Phys 3:300–313

    CAS  Google Scholar 

  28. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. I. Nonpolar gases. J Chem Phys 22:1420–1426

    CAS  Google Scholar 

  29. Shirts Michael R, Mobley David L (2013) An introduction to best practices in free energy calculations. Methods Mol Biol 924:271–311

    CAS  PubMed  Google Scholar 

  30. Procacci Piero (2017) Alchemical determination of drug-receptor binding free energy: where we stand and where we could move to. J Mol Gr Model 71:233–241

    CAS  Google Scholar 

  31. Procacci P (2019) Solvation free energies via alchemical simulations: let’s get honest about sampling, once more. Phys Chem Chem Phys 21:13826–13834

    CAS  PubMed  Google Scholar 

  32. Naden Levi N, Shirts Michael R (2015) Linear basis function approach to efficient alchemical free energy calculations. 2. Inserting and deleting particles with coulombic interactions. J Chem Theor Comput 11:2536–2549

    CAS  Google Scholar 

  33. Marsili S, Signorini GF, Chelli R, Marchi M, Procacci P (2010) Orac: a molecular dynamics simulation program to explore free energy surfaces in biomolecular systems at the atomistic level. J Comput Chem 31:1106–1116

    CAS  PubMed  Google Scholar 

  34. Gore Jeff, Ritort Felix, Bustamante Carlos (2003) Bias and error in estimates of equilibrium free-energy differences from nonequilibrium measurements. Proc Natl Acad Sci USA 100(22):12564–12569

    CAS  PubMed  Google Scholar 

  35. Procacci P, Marsili S, Barducci A, Signorini GF, Chelli R (2006) Crooks equation for steered molecular dynamics using a nosé-hoover thermostat. J Chem Phys 125:164101

    PubMed  Google Scholar 

  36. Hummer G (2001) Fast-growth thermodynamic integration: Error and efficiency analysis. J Chem Phys 114:7330–7337

    CAS  Google Scholar 

  37. Razali NM, Wah YB (2011) Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J Stat Model Anal 2:21–33

    Google Scholar 

  38. Vanommeslaeghe K, MacKerell AD (2012) Automation of the charmm general force field (cgenff) i: bond perception and atom typing. J Chem Inf Model 52(12):3144–3154

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the charmm general force field (cgenff) ii: Assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52(12):3155–3168

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Procacci Piero (2016) Hybrid MPI/OpenMP implementation of the ORAC molecular dynamics program for generalized ensemble and fast switching alchemical simulations. J Chem Inf Model 56(6):1117–1121

    CAS  PubMed  Google Scholar 

  41. Izadi S, Onufriev AV (2016) Accuracy limit of rigid 3-point water models. J Chem Phys 145(7):074501

    PubMed  PubMed Central  Google Scholar 

  42. Marchi M, Procacci P (1998) Coordinates scaling and multiple time step algorithms for simulation of solvated proteins in the npt ensemble. J Chem Phys 109:5194–520g2

    CAS  Google Scholar 

  43. Tuckerman M, Berne BJ (1992) Reversible multiple time scale molecular dynamics. J Chem Phys 97:1990–2001

    CAS  Google Scholar 

  44. Procacci P, Paci E, Darden T, Marchi M (1997) Orac: a molecular dynamics program to simulate complex molecular systems with realistic electrostatic interactions. J Comput Chem 18:1848–1862

    CAS  Google Scholar 

  45. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh ewald method. J Chem Phys 103:8577–8593

    CAS  Google Scholar 

  46. Kim Sunghwan, Thiessen Paul A, Bolton Evan E, Chen Jie, Gang Fu, Gindulyte Asta, Han Lianyi, He Jane, He Siqian, Shoemaker Benjamin A, Wang Jiyao, Bo Yu, Zhang Jian, Bryant Stephen H (2016) Pubchem substance and compound databases. Nucleic Acids Res 44(D1):D1202–D1213

    CAS  PubMed  Google Scholar 

  47. Liu P, Kim B, Friesner RA, Berne BJ (2005) Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proc Natl Acad Sci 102:13749–13754

    CAS  PubMed  Google Scholar 

  48. Beutler TC, Mark AE, van Schaik RC, Gerber PR, van Gunsteren WF (1994) Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations. Chem Phys Lett 222:5229–539

    Google Scholar 

  49. Gapsys Vytautas, Seeliger Daniel, de Groot BL (2012) New soft-core potential function for molecular dynamics based alchemical free energy calculations. J Chem Theor Comput 8:2373–2382

    CAS  Google Scholar 

  50. Yildirim Ahmet, Wassenaar Tsjerk A, van der Spoel David (2018) Statistical efficiency of methods for computing free energy of hydration. J Chem Phys 149(14):144111

    PubMed  Google Scholar 

  51. Vassetti Dario, Pagliai Marco, Procacci Piero (2019) Assessment of gaff2 and opls-aa general force fields in combination with the water models tip3p, spce, and opc3 for the solvation free energy of druglike organic molecules. J Chem Theor Comput 15(3):1983–1995

    CAS  Google Scholar 

  52. Stephens MA (1979) Test of fit for the logistic distribution based on the empirical distribution function. Biometrika 66:591–595

    Google Scholar 

  53. Vanommeslaeghe Kenno, Yang Mingjun, MacKerell Alexander D Jr (2015) Robustness in the fitting of molecular mechanics parameters. J Comput Chem 36(14):1083–1101

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935

    CAS  Google Scholar 

  55. Kusalik PG, Svishchev IM (1994) The spatial structure in liquid water. Science 265:1219–1221

    CAS  PubMed  Google Scholar 

  56. Gestblom B, Sjöblom GA (1984) Dielectric relaxation studies of aqueous long-chain alcohol solutions. Acta Chem Scand A38:47–56

    CAS  Google Scholar 

  57. Cresco: Centro computazionale di ricerca sui sistemi complessi. Italian National Agency for New Technologies, Energy (ENEA). See https://www.cresco.enea.it. Accessed 24 June 2015

  58. Politzer Peter, Murray Jane S, Clark Timothy (2013) Halogen bonding and other \(\sigma\)-hole interactions: a perspective. Phys Chem Chem Phys 15:11178–11189

    CAS  PubMed  Google Scholar 

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Acknowledgements

The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes (see www.cresco.enea.it for information).

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Authors and Affiliations

  1. Department of Chemistry, University of Florence, Via Lastruccia n. 3, 50019, Sesto Fiorentino, FI, Italy

    Piero Procacci

  2. ENEA, Portici Research Centre, DTE-ICT-HPC, P.le E. Fermi, 1, 80055, Portici, NA, Italy

    Guido Guarnieri

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  1. Piero Procacci
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Correspondence to Piero Procacci.

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Procacci, P., Guarnieri, G. SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches. J Comput Aided Mol Des 34, 371–384 (2020). https://doi.org/10.1007/s10822-019-00233-9

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