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SAMPL7 blind predictions using nonequilibrium alchemical approaches

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Abstract

In the context of the SAMPL7 challenge, we computed, employing a non-equilibrium (NE) alchemical technique, the standard binding free energy of two series of host-guest systems, involving as a host the Isaac’s TrimerTrip, a Cucurbituril-like open cavitand, and the Gilson’s Cyclodextrin derivatives. The adopted NE alchemy combines enhanced sampling molecular dynamics simulations with driven fast out-of-equilibrium alchemical trajectories to recover the free energy via the Jarzynski and Crooks NE theorems. The GAFF2 non-polarizable force field was used for the parametrization. Performances were acceptable and similar in accuracy to those we submitted for Gibb’s Deep Cavity Cavitands in the previous SAMPL6 host-guest challenge, confirming the reliability of the computational approach and exposing, in some cases, some important deficiencies of the GAFF2 non-polarizable force field.

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References

  1. Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The sampl4 host-guest blind prediction challenge: an overview. J Comput Aided Mol Des 28(4):305–317

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 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, pp 1–19

  3. Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M, Gilson MK, Chiu MW, Isaacs L, Gibb BC, Mobley DL, Chodera JD (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 

  4. Ndendjio SZ, Liu W, Yvanez N, Meng Z, Zavalij PY, Isaacs L (2020) Synthesis and recognition properties Triptycene walled glycoluril trimer. New J Chem 44:338–345

    CAS  PubMed  Google Scholar 

  5. Kellett K, Duggan BM, Gilson MK (2019) Facile synthesis of a diverse library of mono-3-substituted \(\beta\)-cyclodextrin analogues. Supramol Chem 31(4):251–259

    CAS  Google Scholar 

  6. Gibb Corinne LD, Gibb Bruce C (2014) Binding of cyclic carboxylates to octa-acid deep-cavity cavitand. J Comput Aided Mol Des 28(4):319–325

    CAS  PubMed  Google Scholar 

  7. Amezcua Martin, Mobley David (2020) SAMPL7 challenge overview: assessing the reliability of polarizable and non-polarizable methods for host-guest binding free energy calculations. ChemrXiv 8 :12768353.v1

  8. https://samplchallenges.github.io/roadmap/submissions/ , Accessed 23 June 2020

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

    Google Scholar 

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

    CAS  Google Scholar 

  11. Procacci P, Guarrasi M, Guarnieri G (2018) Sampl6 host-guest blind predictions using a non equilibrium alchemical approach. J Comput Aided Mol Des 32(10):965–982

    CAS  PubMed  Google Scholar 

  12. Procacci P (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 

  13. Nerattini F, Chelli R, Procacci P (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 

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

    CAS  PubMed  Google Scholar 

  15. Procacci P (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 

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

    CAS  Google Scholar 

  17. 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 

  18. Procacci P (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 

  19. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminf 3(1):33

    Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  21. Hasel W, Hendrickson TF, Clark SW (1988) A rapid approximation to the solvent accessible surface areas of atoms. Tetrahedron Comput Methodol 1(2):103–116

    CAS  Google Scholar 

  22. Marchi M, Procacci P (1998) Coordinates scaling and multiple time step algorithms for simulation of solvated proteins in the npt ensemble. J Chem Phys 109:5194–5202

    CAS  Google Scholar 

  23. Procacci P (2019) Solvation free energies via alchemical simulations: let’s get honest about sampling, once more. Phys. Chem. Chem Phys 25:13826–13834

    Google Scholar 

  24. Procacci P (2019) Accuracy, precision, and efficiency of nonequilibrium alchemical methods for computing free energies of solvation. i. bidirectional approaches. J Chem Phys 151(14):144113

    PubMed  Google Scholar 

  25. Piero P (2019) Precision and computational efficiency of nonequilibrium alchemical methods for computing free energies of solvation. ii. unidirectional estimates. J Chem Phys 151(14):144115

    Google Scholar 

  26. 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 

  27. Anderson TW, Darling DA (1954) A test of goodness of fit. J Am Stat Assoc 49:765–769

    Google Scholar 

  28. Jarque CM, Bera AK (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ Lett 6(3):255–259

    Google Scholar 

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

    CAS  Google Scholar 

  30. 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 

  31. 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 

  32. Procacci P, Chelli R (2017) Statistical mechanics of ligand-receptor noncovalent association, revisited: binding site and standard state volumes in modern alchemical theories. J Chem Theory Comput 13(5):1924–1933

    CAS  PubMed  Google Scholar 

  33. Zhang C, Chao L, Jing Z, Chuanjie W, Piquemal J-P, Ponder JW, Ren P (2018) Amoeba polarizable atomic multipole force field for nucleic acids. J Chem Theory Comput 14(4):2084–2108

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Bannwarth C, Ehlert S, Grimme S (2019) Gfn2-xtb–an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J Chem Theory Comput 15(3):1652–1671

    CAS  PubMed  Google Scholar 

  35. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and autodocktools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Vassetti D, Pagliai M, Procacci P (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 Theory Comput 15(3):1983–1995

    CAS  PubMed  Google Scholar 

  37. Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) Am 1: a new general purpose quantum mechanical model. J Am Chem Soc 107:3902–3909

    CAS  Google Scholar 

  38. See comments on OVERLAP and INDENT host conformation in the AMOEBA submission file Clip-ponder.txt at https://github.com/samplchallenges/SAMPL7/tree/master/host_guest/Analysis/Submissions/TrimerTrip. Accessed 23 June 2020

  39. Gapsys V, Michielssens S, Peters JH, de Groot BL, Leonov H (2015) Calculation of binding free energies. In: Molecular modeling of protein. Humana Press, pp 73–209

  40. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comp Chem 25:1157–1174

    CAS  Google Scholar 

  41. 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 

  42. Mobley DL, Bannan CC, Rizzi A, Bayly CI, Chodera JD, Lim VT, Lim NM, Beauchamp KA, Slochower DR, Shirts MR, Gilson MK, Eastman PK (2018) Escaping atom types in force fields using direct chemical perception. J Chem Theory Comput 14(11):6076–6092 PMID: 30351006

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Gore J, Ritort F, Bustamante C (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  PubMed Central  Google Scholar 

  44. Procacci P (2015) Unbiased free energy estimates in fast nonequilibrium transformations using gaussian mixtures. J Chem Phys 142(15):154117

    PubMed  Google Scholar 

  45. Procacci P (2020) A remark on the efficiency of the double-system/single-box nonequilibrium approach in the sampl6 sampling challenge. J Comput Aided Mol Des 34(6):635–639

    CAS  PubMed  Google Scholar 

  46. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25

    Google Scholar 

  47. 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 Theory Comput 11:2536–2549

    CAS  PubMed  Google Scholar 

  48. Sun ZX, Wang XH, Zhang JZH (2017) Bar-based optimum adaptive sampling regime for variance minimization in alchemical transformation. Phys Chem Chem Phys 19:15005–15020

    CAS  PubMed  Google Scholar 

  49. Yildirim A, Wassenaar TA, van der Spoel D (2018) Statistical efficiency of methods for computing free energy of hydration. J Chem Phys 149(14):144111

    PubMed  Google Scholar 

  50. Khalak Y, Tresadern G, de Groot BL, Gapsys V (2020) Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-020-00359-1

    Article  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  52. 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 

  53. Heinzelmann G, Gilson MK (2020) Automated docking refinement and virtual compound screening with absolute binding free energy calculations. bioRxiv. https://doi.org/10.1101/2020.04.15.043240

  54. Boresch S, Tettinger F, Leitgeb M, Karplus M (2003) Absolute binding free energies: a quantitative approach for their calculation. J Phys Chem B 107(35):9535–9551

    CAS  Google Scholar 

  55. Tanweer Ul Islam (2017) Stringency-based ranking of normality tests. Commun Stat Simul Comput 46(1):655–668

    Google Scholar 

  56. Gilson MK, Given JA, Bush BL, McCammon JA (1997) The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys J 72:1047–1069

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Deng Y, Roux B (2006) Calculation of standard binding free energies: aromatic molecules in the t4 lysozyme l99a mutant. J Chem Theory Comput 2(5):1255–1273

    CAS  PubMed  Google Scholar 

  58. Shi Y, Laury ML, Wang Z, Ponder JW (2020) Amoeba binding free energies for the sampl7 trimertrip host-guest challenge. J Comput Aided Mol Des 1–15

  59. Deng Y, Roux B (2009) Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem B 113:2234–2246

    CAS  PubMed  Google Scholar 

  60. Hermans J, Wang L (1997) Inclusion of loss of translational and rotational freedom in theoretical estimates of free energies of binding. Application to a complex of benzene and mutant t4 lysozyme. J Am Chem Soc 119(11):2707–2714

    CAS  Google Scholar 

  61. Zhou H-X, Gilson MK (2009) Theory of free energy and entropy in noncovalent binding. Chem Rev 109:4092–4107

    CAS  PubMed  PubMed Central  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. University of Florence, Department of Chemistry, 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. SAMPL7 blind predictions using nonequilibrium alchemical approaches. J Comput Aided Mol Des 35, 37–47 (2021). https://doi.org/10.1007/s10822-020-00365-3

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