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Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo

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Abstract

Water molecules can be found interacting with the surface and within cavities in proteins. However, water exchange between bulk and buried hydration sites can be slow compared to simulation timescales, thus leading to the inefficient sampling of the locations of water. This can pose problems for free energy calculations for computer-aided drug design. Here, we apply a hybrid method that combines nonequilibrium candidate Monte Carlo (NCMC) simulations and molecular dynamics (MD) to enhance sampling of water in specific areas of a system, such as the binding site of a protein. Our approach uses NCMC to gradually remove interactions between a selected water molecule and its environment, then translates the water to a new region, before turning the interactions back on. This approach of gradual removal of interactions, followed by a move and then reintroduction of interactions, allows the environment to relax in response to the proposed water translation, improving acceptance of moves and thereby accelerating water exchange and sampling. We validate this approach on several test systems including the ligand-bound MUP-1 and HSP90 proteins with buried crystallographic waters removed. We show that our BLUES (NCMC/MD) method enhances water sampling relative to normal MD when applied to these systems. Thus, this approach provides a strategy to improve water sampling in molecular simulations which may be useful in practical applications in drug discovery and biomolecular design.

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Data availability

The Supporting Information is available free of charge on https://github.com/MobleyLab/blues-water-hopping-paper and includes the code, scripts and input files used in this work.

Abbreviations

BLUES:

Binding modes of Ligands Using Enhanced Sampling

MD:

Molecular Dynamics

NCMC:

Nonequilibrium Candidate Monte Carlo

MUP-1:

Major Urinary Protein

HSP90:

Heat Shock Protein 90

References

  1. Abel R, Salam NK, Shelley J, Farid R, Friesner RA, Sherman W (2011) Contribution of explicit solvent effects to the binding affinity of small-molecule inhibitors in blood coagulation factor serine proteases. ChemMedChem 6(6):1049–1066

    CAS  PubMed  Google Scholar 

  2. Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent in the thermodynamics of Factor Xa ligand binding. J Am Chem Soc 130(9):2817–2831

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Adams D (1974) Chemical potential of hard-sphere fluids by Monte Carlo methods. Mol Phys 28(5):1241–1252

    CAS  Google Scholar 

  4. Adams D (1975) Grand canonical ensemble Monte Carlo for a Lennard–Jones fluid. Mol Phys 29(1):307–311

    CAS  Google Scholar 

  5. Amaral M, Kokh DB, Bomke J, Wegener A, Buchstaller HP, Eggenweiler HM, Matias P, Sirrenberg C, Wade RC, Frech M (2017) Protein conformational flexibility modulates kinetics and thermodynamics of drug binding. Nat Commun 8(1):2276

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ball P (2008) Water as an active constituent in cell biology. Chem Rev 108(1):74–108

    CAS  PubMed  Google Scholar 

  7. Baron R, Setny P, McCammon JA (2010) Water in cavity-ligand recognition. J Am Chem Soc 132(34):12091–12097

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Bayden AS, Moustakas DT, Joseph-McCarthy D, Lamb ML (2015) Evaluating free energies of binding and conservation of crystallographic waters Using SZMAP. J Chem Inf Model 55(8):1552–1565

    CAS  PubMed  Google Scholar 

  9. Bellissent-Funel M-C, Hassanali A, Havenith M, Henchman R, Pohl P, Sterpone F, van der Spoel D, Xu Y, Garcia AE (2016) Water determines the structure and dynamics of proteins. Chem Rev 116(13):7673–7697

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Ben-Shalom IY, Lin C, Kurtzman T, Walker RC, Gilson MK (2019) Simulating water exchange to buried binding sites. J Chem Theory Comput 15(4):2684–2691

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Burley KH, Gill SC, Lim NM, Mobley DL (2019) Enhancing side chain rotamer sampling using nonequilibrium candidate Monte Carlo. J Chem Theory Comput 15(3):1848–1862

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cournia Z, Allen B, Sherman W (2017) Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J Chem Inf Model 57(12):2911–2937

    CAS  PubMed  Google Scholar 

  14. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N \(\cdot\)log( N ) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092

    CAS  Google Scholar 

  15. Deng Y, Roux B (2008) Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations. J Chem Phys 128(11):115103

    PubMed  Google Scholar 

  16. Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang L-P, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS (2013) OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J Chem Theory Comput 9(1):461–469

    CAS  PubMed  Google Scholar 

  17. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang L-P, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659

    PubMed  PubMed Central  Google Scholar 

  18. Ernst J, Clubb R, Zhou H, Gronenborn A, Clore G (1995) Demonstration of positionally disordered water within a protein hydrophobic cavity by NMR. Science 267(5205):1813–1817

    CAS  PubMed  Google Scholar 

  19. Gill SC, Lim NM, Grinaway PB, Rustenburg AS, Fass J, Ross GA, Chodera JD, Mobley DL (2018a) Binding modes of ligands using enhanced sampling (BLUES): rapid decorrelation of ligand binding modes via nonequilibrium candidate Monte Carlo. J Phys Chem B 122(21):5579–5598

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Gill SC, Lim NM, Grinaway PB, Rustenburg AS, Fass J, Ross GA, Chodera JD, Mobley DL (2018b) Binding modes of ligands using enhanced sampling (BLUES): rapid decorrelation of ligand binding modes via nonequilibrium candidate Monte Carlo. J Phys Chem B 122:21

    Google Scholar 

  21. Hastings WK (1970) Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 57:97

    Google Scholar 

  22. Hopkins CW, Le Grand S, Walker RC, Roitberg AE (2015) Long-time-step molecular dynamics through hydrogen mass repartitioning. J Chem Theory Comput 11(4):1864–1874

    CAS  PubMed  Google Scholar 

  23. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65(3):712–725

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  Google Scholar 

  25. Lakkaraju SK, Raman EP, Yu W, MacKerell AD (2014) Sampling of organic solutes in aqueous and heterogeneous environments using oscillating excess chemical potentials in grand canonical-like Monte Carlo-molecular dynamics simulations. J Chem Theory Comput 10(6):2281–2290

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Levy Y, Onuchic JN (2006) Water mediation in protein folding and molecular recognition. Annu Rev Biophys Biomol Struct 35(1):389–415

    CAS  PubMed  Google Scholar 

  27. Li Z, Lazaridis T (2012) Computing the thermodynamic contributions of interfacial water. In: Baron R (ed) Computational drug discovery and design. Methods in molecular biology, vol 819. Springer, New York, pp 393–404

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Maurer M, de Beer S, Oostenbrink C (2016) Calculation of relative binding free energy in the water-filled active site of oligopeptide-binding protein A. Molecules 21(4):499

    PubMed  PubMed Central  Google Scholar 

  30. Meyer E (1992) Internal water molecules and H-bonding in biological macromolecules: a review of structural features with functional implications. Protein Sci 1(12):1543–1562

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Mezei M (1980) A cavity-biased (T, V, \(\mu\)) Monte Carlo method for the computer simulation of fluids. Mol Phys 40(4):901–906

    CAS  Google Scholar 

  32. Michel J, Tirado-Rives J, Jorgensen WL (2009) Prediction of the water content in protein binding sites. J Phys Chem B 113(40):13337–13346

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Mobley DL, Gilson MK (2017) Predicting binding free energies: frontiers and benchmarks. Annu Rev Biophys 46(1):531–558

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Nguyen CN, Cruz A, Gilson MK, Kurtzman T (2014) Thermodynamics of water in an enzyme active site: grid-based hydration analysis of coagulation Factor Xa. J Chem Theory Comput 10(7):2769–2780

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Nguyen CN, Kurtzman Young T, Gilson MK (2012) Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. J Chem Phys 137(4):044101

    PubMed  PubMed Central  Google Scholar 

  36. Nilmeier JP, Crooks GE, Minh DDL, Chodera JD (2011) Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation. Proc Natl Acad Sci USA 108(45):E1009–E1018

    PubMed  Google Scholar 

  37. Nittinger E, Schneider N, Lange G, Rarey M (2015) Evidence of water molecules—a statistical evaluation of water molecules based on electron density. J Chem Inf Model 55(4):771–783

    CAS  PubMed  Google Scholar 

  38. Park S, Saven JG (2005) Statistical and molecular dynamics studies of buried waters in globular proteins. Proteins 60(3):450–463

    CAS  PubMed  Google Scholar 

  39. Pearlstein RA, Sherman W, Abel R (2013) Contributions of water transfer energy to protein–ligand association and dissociation barriers: watermap analysis of a series of p38\(\alpha\) MAP kinase inhibitors: water Transfer in Structure–Kinetic Relationships. Proteins 81(9):1509–1526

    CAS  PubMed  Google Scholar 

  40. Ross GA, Bodnarchuk MS, Essex JW (2015) Water sites, networks, and free energies with grand canonical Monte Carlo. J Am Chem Soc 137(47):14930–14943

    CAS  PubMed  Google Scholar 

  41. Ross GA, Bruce Macdonald HE, Cave-Ayland C, Cabedo Martinez AI, Essex JW (2017) Replica-exchange and standard state binding free energies with grand canonical Monte Carlo. J Chem Theory Comput 13(12):6373–6381

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Sasmal S, Gill SC, Lim NM, Mobley DL (2020) Sampling conformational changes of bound ligands using Nonequilibrium Candidate Monte Carlo. J Chem Theory Comput. https://doi.org/10.1021/acs.jctc.9b01066

    Article  PubMed  Google Scholar 

  43. Schlessman JL, Abe C, Gittis A, Karp DA, Dolan MA, García-Moreno EB (2008) Crystallographic study of hydration of an internal cavity in engineered proteins with buried polar or ionizable groups. Biophys J 94(8):3208–3216

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Sivak DA, Chodera JD, Crooks GE (2013) Using nonequilibrium fluctuation theorems to understand and correct errors in equilibrium and nonequilibrium simulations of discrete Langevin dynamics. Phys Rev X 3:011007

    Google Scholar 

  45. Stöckmann H, Bronowska A, Syme NR, Thompson GS, Kalverda AP, Warriner SL, Homans SW (2008) Residual ligand entropy in the binding of p-substituted benzenesulfonamide ligands to bovine carbonic anhydrase II. J Am Chem Soc 130(37):12420–12426

    PubMed  Google Scholar 

  46. Takano K, Yamagata Y, Yutani K (2003) Buried water molecules contribute to the conformational stability of a protein. Protein Eng Des Sel 16(1):5–9

    CAS  Google Scholar 

  47. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general Amber force field. J Comput Chem 25(9):1157–1174

    CAS  PubMed  Google Scholar 

  48. Woo H-J, Dinner AR, Roux B (2004) Grand canonical Monte Carlo simulations of water in protein environments. J Chem Phys 121(13):6392–6400

    CAS  PubMed  Google Scholar 

  49. Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding. Proc Natl Acad Sci USA 104(3):808–813

    CAS  PubMed  Google Scholar 

  50. Yu B, Blaber M, Gronenborn AM, Clore GM, Caspar DLD (1999) Disordered water within a hydrophobic protein cavity visualized by X-ray crystallography. Proc Natl Acad Sci USA 96(1):103–108

    CAS  PubMed  Google Scholar 

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Acknowledgements

TDB acknowledges support from the ACM SIGHPC/Intel Fellowship. DLM appreciates financial support from the National Institutes of Health (1R01GM108889-01) and the National Science Foundation (CHE 1352608). MKG acknowledges funding from the National Institute of General Medical Sciences (GM61300). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA

    Teresa Danielle Bergazin, Nathan M. Lim & David L. Mobley

  2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA

    Ido Y. Ben-Shalom & Michael K. Gilson

  3. Department of Chemistry, University of California, Irvine, Irvine, CA, 92697, USA

    Sam C. Gill & David L. Mobley

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  1. Teresa Danielle Bergazin
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Correspondence to David L. Mobley.

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DLM is a Member of the Scientific Advisory Board of OpenEye Scientific Software and an Open Science Fellow with Silicon Therapeutics. MKG has an equity interest in and is a Cofounder and Scientific Advisor of VeraChem LLC.

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Bergazin, T.D., Ben-Shalom, I.Y., Lim, N.M. et al. Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo. J Comput Aided Mol Des 35, 167–177 (2021). https://doi.org/10.1007/s10822-020-00344-8

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