Journal of Computer-Aided Molecular Design

, Volume 27, Issue 12, pp 989–1007 | Cite as

Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics

  • Kai Wang
  • John D. Chodera
  • Yanzhi Yang
  • Michael R. Shirts


We present a method to identify small molecule ligand binding sites and poses within a given protein crystal structure using GPU-accelerated Hamiltonian replica exchange molecular dynamics simulations. The Hamiltonians used vary from the physical end state of protein interacting with the ligand to an unphysical end state where the ligand does not interact with the protein. As replicas explore the space of Hamiltonians interpolating between these states, the ligand can rapidly escape local minima and explore potential binding sites. Geometric restraints keep the ligands from leaving the vicinity of the protein and an alchemical pathway designed to increase phase space overlap between intermediates ensures good mixing. Because of the rigorous statistical mechanical nature of the Hamiltonian exchange framework, we can also extract binding free energy estimates for all putative binding sites. We present results of this methodology applied to the T4 lysozyme L99A model system for three known ligands and one non-binder as a control, using an implicit solvent. We find that our methodology identifies known crystallographic binding sites consistently and accurately for the small number of ligands considered here and gives free energies consistent with experiment. We are also able to analyze the contribution of individual binding sites to the overall binding affinity. Our methodology points to near term potential applications in early-stage structure-guided drug discovery.


Ligand binding Binding site identification Binding mode prediction GPU-accelerated molecular dynamics Hamiltonian replica exchange Free energy calculation 



We would like to acknowledge support from Teragrid/XSEDE Grant TG-MCB100015 for allocations on the Lincoln and Forge GPU computing clusters, both housed at NCSA at University of Illinois, Urbana-Champaign, as well as partial support from NSF-CBET 1134256. We would also like to thank Peter Eastman, Mark Friedrichs, Randy Radmer, Chris Bruns, and Vijay Pande (Stanford University) for help with OpenMM implementation details within YANK. We would also like to acknowledge David Molbey (UC-Irvine) and Brian Schoichet (University of Toronto) for feedback on aspects of the study.

Supplementary material

10822_2013_9689_MOESM1_ESM.pdf (320 kb)
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  1. 1.
    Schneider G (2010) Virtual screening: an endless staircase? Nat Rev Drug Discov 9(4):273–276CrossRefGoogle Scholar
  2. 2.
    B-Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7–8):394–400CrossRefGoogle Scholar
  3. 3.
    Lie MA, Thomsen R, Pedersen CNS, Schiøtt B, Christensen MH (2011) Molecular docking with ligand attached water molecules. J Chem Inf Model 51(4):909–917CrossRefGoogle Scholar
  4. 4.
    Thompson DC, Humblet C, Joseph-McCarthy D (2008) Investigation of MM-PBSA rescoring of docking poses. J Chem Inf Model 48(5):1081–1091CrossRefGoogle Scholar
  5. 5.
    Graves AP, Shivakumar DM, Boyce SE, Jacobson MP, Case DA, Shoichet BK (2008) Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J Mol Biol 377(3):914–934CrossRefGoogle Scholar
  6. 6.
    Kellenberger E, Rodrigo J, Muller P, Rognan D (2004) Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57(2):225–242CrossRefGoogle Scholar
  7. 7.
    Warren GL, Andrews CW, Capelli A-M, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931CrossRefGoogle Scholar
  8. 8.
    Deng W, Verlinde CLMJ (2008) Evaluation of different virtual screening programs for docking in a charged binding pocket. J Chem Inf Model 48(10):2010–2020CrossRefGoogle Scholar
  9. 9.
    Levitt DG, Banaszak LJ (1992) POCKET: A computer graphies method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph 10(4):229–234CrossRefGoogle Scholar
  10. 10.
    Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15(6):359–363CrossRefGoogle Scholar
  11. 11.
    Patrick Brady Jr G, Stouten PFW (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aid Mol Des 14(4):383–401CrossRefGoogle Scholar
  12. 12.
    Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49(2):377–389CrossRefGoogle Scholar
  13. 13.
    Verdonk ML, Cole JC, Watson P, Gillet V, Willett P (2001) SuperStar: improved knowledge-based interaction fields for protein binding sites. J Mol Biol 307(3):841–859CrossRefGoogle Scholar
  14. 14.
    Bliznyuk AA, Gready JE (1999) Simple method for locating possible ligand binding sites on protein surfaces. J Comput Chem 20(9):983–988CrossRefGoogle Scholar
  15. 15.
    Mobley DL, Graves AP, Chodera JD, McReynolds AC, Shoichet BK, Dill KA (2007) Predicting absolute ligand binding free energies to a simple model site. J Mol Biol 371(4):1118–1134CrossRefGoogle Scholar
  16. 16.
    Jiang W, Roux B (2010) Free energy perturbation Hamiltonian replica-exchange molecular dynamics (FEP/H-REMD) for absolute ligand binding free energy calculations. J Chem Theory Comput 6(9):2559–2565CrossRefGoogle Scholar
  17. 17.
    Deng Y, Roux B (2009) Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem B 113(8):2234–2246CrossRefGoogle Scholar
  18. 18.
    Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS (2011) Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struc Biol 21(2):150–160CrossRefGoogle Scholar
  19. 19.
    Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, Legrand S, Beberg AL, Ensign DL, Bruns CM, Pande VS (2009) Accelerating molecular dynamic simulation on graphics processing units. J Comput Chem 30(6):864–872CrossRefGoogle Scholar
  20. 20.
    Eastman P, Pande V (2010) OpenMM: a hardware-independent framework for molecular simulations. Comput Sci Eng 12(4):34–39CrossRefGoogle Scholar
  21. 21.
    Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187–217CrossRefGoogle Scholar
  22. 22.
    Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1–3):43–56CrossRefGoogle Scholar
  23. 23.
    Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham TE, DeBolt S, Ferguson D, Seibel G, Kollman P (1995) AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput Phys Commun 91(1–3):1–41CrossRefGoogle Scholar
  24. 24.
    Clark SW, Tempczyk A, Hawley RC, Hendrickson T (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112(16):6127–6129CrossRefGoogle Scholar
  25. 25.
    Onufriev A, Bashford D, Case DA (2000) Modification of the Generalized Born Model Suitable for Macromolecules. J Phys Chem B 104(15):3712–3720CrossRefGoogle Scholar
  26. 26.
    Michel J, Verdonk ML, Essex JW (2006) Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization? J Med Chem 49(25):7427–7439CrossRefGoogle Scholar
  27. 27.
    Shaw DE, Chao JC, Eastwood MP, Joseph G, Grossman JP, Richard HC, Lerardi DJ, István K, Klepeis JL, Layman T, McLeavey C, Deneroff MM, Moraes MA, Mueller R, Priest EC, Shan Y, Spengler J, Theobald M, Towles B, Wang SC, Dror RO, Kuskin JS, Larson RH, Salmon JK, Young C, Batson B, Bowers KJ (2008) Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM 51(7):91CrossRefGoogle Scholar
  28. 28.
    Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W (2010) Atomic-level characterization of the structural dynamics of proteins. Science 330(6002):341–346CrossRefGoogle Scholar
  29. 29.
    Mobley DL (2012) Let’s get honest about sampling. J Comput Aid Mol Des 26(1):93–95CrossRefGoogle Scholar
  30. 30.
    Purisima EO, Hogues H (2012) Protein-ligand binding free energies from exhaustive docking. J Phys Chem B 116(23):6872–6879CrossRefGoogle Scholar
  31. 31.
    Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314(1–2):141–151CrossRefGoogle Scholar
  32. 32.
    Fukunishi H, Watanabe O, Takada S (2002) On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J Chem Phys 116(20):9058CrossRefGoogle Scholar
  33. 33.
    Hamelberg D, Mongan J, Andrew MJ (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120(24):11919–11929CrossRefGoogle Scholar
  34. 34.
    Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 23(2):187–199CrossRefGoogle Scholar
  35. 35.
    Barducci A, Bonomi M, Parrinello M (2011) Metadynamics. WIREs Comput Mol Sci 1(5):826–843CrossRefGoogle Scholar
  36. 36.
    Deighan M, Bonomi M, Pfaendtner J (2012) Efficient Simulation of Explicitly solvated proteins in the well-tempered ensemble. J Chem Theory Comput 8(7):2189–2192CrossRefGoogle Scholar
  37. 37.
    Kokubo H, Tanaka T, Okamoto Y (September 2013) Two-dimensional replica-exchange method for predicting protein-ligand binding structures. J Comput Chem 34(30):2601–2614Google Scholar
  38. 38.
    Chodera JD, Shirts MR (2011) Replica exchange and expanded ensemble simulations as Gibbs sampling: simple improvements for enhanced mixing. J Chem Phys 135(19):194110CrossRefGoogle Scholar
  39. 39.
    Peter KE, 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):461–469Google Scholar
  40. 40.
    Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129(12):124105CrossRefGoogle Scholar
  41. 41.
    Gallicchio E, Lapelosa M, Levy RM (2010) Binding energy distribution analysis method (bedam) for estimation of protein ligand binding affinities. J Chem Theory Comput 6(9):2961–2977CrossRefGoogle Scholar
  42. 42.
    Boyce SE, Mobley DL, Rocklin GJ, Graves AP, Dill KA, Shoichet BK (2009) Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site. J Mol Biol 394(4):747–763CrossRefGoogle Scholar
  43. 43.
    Wei BQ, Baase WA, Weaver LH, Matthews BW, Shoichet BK (2002) A model binding site for testing scoring functions in molecular docking. J Mol Biol 322(2):339–355CrossRefGoogle Scholar
  44. 44.
    Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004) Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol 337(5):1161–1182CrossRefGoogle Scholar
  45. 45.
    Ferrari AM, Wei BQ, Costantino L, Shoichet BK (2004) Soft docking and multiple receptor conformations in virtual screening. J Med Chem 47(21):5076–5084CrossRefGoogle Scholar
  46. 46.
    Graves AP, Brenk R, Shoichet BK (2005) Decoys for docking. J Med Chem 48(11):3714–3728CrossRefGoogle Scholar
  47. 47.
    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):1668–1688CrossRefGoogle Scholar
  48. 48.
    Mobley DL, Dumont E, Chodera JD, Dill KA (2007) Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J Phys Chem B 111(9):2242–2254CrossRefGoogle Scholar
  49. 49.
    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J Comput Chem 21(2):132–146CrossRefGoogle Scholar
  50. 50.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23(16):1623–1641CrossRefGoogle Scholar
  51. 51.
    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):1157–1174CrossRefGoogle Scholar
  52. 52.
    Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260CrossRefGoogle Scholar
  53. 53.
    Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8(3):195–202CrossRefGoogle Scholar
  54. 54.
    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):2785–2791CrossRefGoogle Scholar
  55. 55.
    Eastman P, Pande VS (2010) CCMA: a robust, parallelizable constraint method for molecular simulations. J Chem Theory Comput 6(2):434–437CrossRefGoogle Scholar
  56. 56.
    Mobley DL, Chodera JD, Dill KA (2006) On the use of orientational restraints and symmetry corrections in alchemical free energy calculations. J Chem Phys 125(8):084902CrossRefGoogle Scholar
  57. 57.
    Boresch S, Tettinger F, Leitgeb M, Karplus M (2003) Absolute binding free energies: a quantitative approach for their calculation. J Phys Chem A 107(35)Google Scholar
  58. 58.
    Shan Y, Kim ET, Eastwood MP, Dror RO, Seeliger MA, Shaw DE (2011) How does a drug molecule find its target binding site? J Am Chem Soc 133(24):9181–9183CrossRefGoogle Scholar
  59. 59.
    Harvey MJ, Giupponi G, De Fabritiis G (2009) ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J Chem Theory Comput 5(6):1632–1639CrossRefGoogle Scholar
  60. 60.
    Zacharias M, Straatsma TP, McCammon JA (1994) Separation-shifted scaling, a new scaling method for Lennard–Jones interactions in thermodynamic integration. J Chem Phys 100(12):9025CrossRefGoogle Scholar
  61. 61.
    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(6):529–539CrossRefGoogle Scholar
  62. 62.
    Shirts MR, Pande VS (2005) Solvation free energies of amino acid side chains for common molecular mechanics water models. J Chem Phys 122:134508CrossRefGoogle Scholar
  63. 63.
    Pham TT, Shirts MR (2011) Identifying low variance pathways for free energy calculations of molecular transformations in solution phase. J Chem Phys 135(3):034114CrossRefGoogle Scholar
  64. 64.
    Sindhikara D, Emerson DJ, Roitberg AE (2010) Exchange often and properly in replica exchange molecular dynamics. J Chem Theory Comput 6:2804–2808CrossRefGoogle Scholar
  65. 65.
    Kabsch W (1976) A solution for the best rotation to relate two sets of vectors. Acta Crystallogr A 32(5):922–923CrossRefGoogle Scholar
  66. 66.
    Kabsch W (1978) A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallogr A 34(5):827–828CrossRefGoogle Scholar
  67. 67.
  68. 68.
    Sander J, Ester M, Kriegel H-P, Xu X (1998) Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Discov 2(2):169–194CrossRefGoogle Scholar
  69. 69.
    Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 8(2):127–134CrossRefGoogle Scholar
  70. 70.
    Shirts MR, Chodera JD pymbar,

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Kai Wang
    • 1
  • John D. Chodera
    • 2
  • Yanzhi Yang
    • 1
  • Michael R. Shirts
    • 1
  1. 1.Department of Chemical EngineeringUniversity of VirginiaCharlottesvilleUSA
  2. 2.Computational Biology ProgramMemorial Sloan-Kettering Cancer CenterNew YorkUSA

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