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Application of the alchemical transfer and potential of mean force methods to the SAMPL8 host-guest blinded challenge

Abstract

We report the results of our participation in the SAMPL8 GDCC Blind Challenge for host-guest binding affinity predictions. Absolute binding affinity prediction is of central importance to the biophysics of molecular association and pharmaceutical discovery. The blinded SAMPL series have provided an important forum for assessing the reliability of binding free energy methods in an objective way. In this challenge, we employed two binding free energy methods, the newly developed alchemical transfer method (ATM) and the well-established potential of mean force (PMF) physical pathway method, using the same setup and force field model. The calculated binding free energies from the two methods are in excellent quantitative agreement. Importantly, the results from the two methods were also found to agree well with the experimental binding affinities released subsequently, with R values of 0.89 (ATM) and 0.83 (PMF). These results were ranked among the best of the SAMPL8 GDCC challenge and second only to those obtained with the more accurate AMOEBA force field. Interestingly, the two host molecules included in the challenge (TEMOA and TEETOA) displayed distinct binding mechanisms, with TEMOA undergoing a dehydration transition whereas guest binding to TEETOA resulted in the opening of the binding cavity that remains essentially dry during the process. The coupled reorganization and hydration equilibria observed in these systems is a useful prototype for the study of these phenomena often observed in the formation of protein-ligand complexes. Given that the two free energy methods employed here are based on entirely different thermodynamic pathways, the close agreement between the two and their general agreement with the experimental binding free energies are a testament to the high quality and precision achieved by theory and methods. The study provides further validation of the novel ATM binding free energy estimation protocol and paves the way to further extensions of the method to more complex systems.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. http://www.github.com/samplchallenges/SAMPL8/tree/master/host_guest/GDCC.

  2. http://www.github.com/samplchallenges/SAMPL8/blob/master/host_guest/Analysis/ExperimentalMeasurements/Final-Data-Table-031621-SAMPL8.docx.

References

  1. Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) The SAMPL2 blind prediction challenge: introduction and overview. J Comput Aided Mol Des 24(4):259–279

    Article  CAS  Google Scholar 

  2. Mobley DL, Liu S, Lim NM, Wymer KL, Perryman AL, Forli S, Deng N, Su J, Branson K, Olson AJ (2014) Blind prediction of hiv integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des 28:327

    Article  CAS  Google Scholar 

  3. Amezcua M, El Khoury L, Mobley DL (2021) SAMPL7 host-guest challenge overview: assessing the reliability of polarizable and non-polarizable methods for binding free energy calculations. J Comput Aided Mol Des 35(1):1–35

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42:724–733

    Article  CAS  Google Scholar 

  6. Armacost KA, Riniker S, Cournia Z (2020) Novel directions in free energy methods and applications. J Chem Inf Model 60:1–5

    Article  CAS  Google Scholar 

  7. Gallicchio E, Levy RM (2012) Prediction of SAMPL3 host-guest affinities with the binding energy distribution analysis method (BEDAM). J Comput Aided Mol Des 25:505–516

    Article  Google Scholar 

  8. Emilio G, Haoyuan C, He C, Michael F, Yang G, Peng H, Malathi K, Kao Chuan L, Beidi NY, Manasi P, Jie Z, Levy RM (2015) BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge. J Comput Aided Mol Des 29:315–325

    Article  Google Scholar 

  9. Emilio G, Nanjie D, Peng H, Perryman AL, Santiago DN, Stefano F, Olson AJ, Levy RM (2014) Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge. J Comput Aided Mol Des 28:475–490

    Article  Google Scholar 

  10. Deng N, Flynn WF, Xia J, Vijayan RSK, Zhang B, Peng H, Mentes A, Gallicchio E, Levy RM (2016) Large scale free energy calculations for blind predictions of protein-ligand binding: the d3r grand challenge 2015. J Comput Aided Mol Des 30(9):743–751

    Article  CAS  Google Scholar 

  11. Pal RK, Haider K, Kaur D, Flynn W, Xia J, Levy RM, Taran T, Wickstrom L, Kurtzman T, Gallicchio E (2016) A combined treatment of hydration and dynamical effects for the modeling of host-guest binding thermodynamics: the SAMPL5 blinded challenge. J Comput Aided Mol Des 31:29–44

    Article  Google Scholar 

  12. Wu JZ, Azimi S, Khuttan S, Deng N, Gallicchio E (2021) Alchemical transfer approach to absolute binding free energy estimation. J Chem Theory Comput 17:3309

    Article  CAS  Google Scholar 

  13. Deng N, Cui D, Zhang BW, Xia J, Cruz J, Levy RM (2018) Comparing alchemical and physical pathway methods for computing the absolute binding free energy of charged ligands. Phys Chem Chem Phys 20(25):17081–17092

    Article  CAS  Google Scholar 

  14. Suating P, Nguyen TT, Ernst NE, Wang Y, Jordan JH, Gibb CL, Ashbaugh HS, Gibb BC (2020) Proximal charge effects on guest binding to a non-polar pocket. Chem Sci 11(14):3656–3663

    Article  CAS  Google Scholar 

  15. Śledź P, Caflisch A (2018) Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Struct Biol 48:93–102

    Article  Google Scholar 

  16. Seidel T, Wieder O, Garon A, Langer T (2020) Applications of the pharmacophore concept in natural product inspired drug design. Mol Inform 39(11):2000059

    Article  CAS  Google Scholar 

  17. 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

    Article  CAS  Google Scholar 

  18. Gallicchio E, Levy RM (2011) Recent theoretical and computational advances for modeling protein-ligand binding affinities. Adv Protein Chem Struct Biol 85:27–80

    Article  CAS  Google Scholar 

  19. Cournia Z, Allen BK, Beuming T, Pearlman DA, Radak BK, Sherman W (2020) Rigorous free energy simulations in virtual screening. J Chem Inf Model 60:4153

    Article  CAS  Google Scholar 

  20. Gallicchio E (2021) Free energy-based computational methods for the study of protein-peptide binding equilibria. In: Thomas S (ed) Computational peptide science: methods and protocols. Springer, Berlin

    Google Scholar 

  21. He P, Sarkar S, Gallicchio E, Kurtzman T, Wickstrom L (2019) Role of displacing confined solvent in the conformational equilibrium of \(\beta\)-cyclodextrin. J Phys Chem B 123(40):8378–8386

    Article  CAS  Google Scholar 

  22. Tan Z, Gallicchio E, Lapelosa M, Levy RM (2012) Theory of binless multi-state free energy estimation with applications to protein-ligand binding. J Chem Phys 136:144102

    Article  Google Scholar 

  23. He X, Liu S, Lee TS, Ji B, Man VH, York DM, Wang J (2020) Fast, accurate, and reliable protocols for routine calculations of protein-ligand binding affinities in drug design projects using AMBER GPU-TI with ff14SB/GAFF. ACS Omega 5(9):4611–4619

    Article  CAS  Google Scholar 

  24. Shirts MR, Klein C, Swails JM, Yin J, Gilson MK, Mobley DL, Case DA, Zhong ED (2017) Lessons learned from comparing molecular dynamics engines on the sampl5 dataset. J Comput Aided Mol Des 31(1):147–161

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  26. Pronk S, Páll S, Shulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Berk H, Erik L (2013) Gromacs 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854

    Article  CAS  Google Scholar 

  27. Khuttan S, Azimi S, Wu JZ, Gallicchio E (2021) Alchemical transformations for concerted hydration free energy estimation with explicit solvation. J Chem Phys 154:054103

    Article  CAS  Google Scholar 

  28. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang LP, Simmonett AC, Harrigan MP, Stern CD et al (2017) Openmm 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659

    Article  Google Scholar 

  29. Gallicchio E, Xia J, Flynn WF, Zhang B, Samlalsingh S, Mentes A, Levy RM (2015) Asynchronous replica exchange software for grid and heterogeneous computing. Comput Phys Commun 196:236–246

    Article  CAS  Google Scholar 

  30. Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecular dynamics. J Mol Gr 14:33–38

    Article  CAS  Google Scholar 

  31. Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M, Gilson MK, Chiu MW, Isaacs L, Gibb BC, Mobley DL et al (2018) Overview of the SAMPL6 host-guest binding affinity prediction challenge. J Comput Aided Mol Des 32(10):937–963

    Article  CAS  Google Scholar 

  32. Shi Y, Laury ML, Wang Z, Ponder JW (2021) Amoeba binding free energies for the SAMPL7 trimertrip host-guest challenge. J Comput Aided Mol Des 35(1):79–93

    Article  CAS  Google Scholar 

  33. Azimi S, Sheenam K, Wu JZ, Pal R, Gallicchio E (2021) Relative binding free energy calculations for ligands with diverse scaffolds with the alchemical transfer method. ArXiv Preprint http://arxiv.org/2107:05153

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Acknowledgements

We acknowledge support from the National Science Foundation (NSF CAREER 1750511 to E.G.) and National Institutes of Health (R01GM100946 to T.K.). Molecular simulations were conducted on the Comet and Expanse GPU clusters at the San Diego Supercomputing Center supported by NSF XSEDE award TG-MCB150001. We appreciate the National Institutes of Health for its support of the SAMPL project via R01GM124270 to David L. Mobley.

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Correspondence to Emilio Gallicchio.

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Azimi, S., Wu, J.Z., Khuttan, S. et al. Application of the alchemical transfer and potential of mean force methods to the SAMPL8 host-guest blinded challenge. J Comput Aided Mol Des 36, 63–76 (2022). https://doi.org/10.1007/s10822-021-00437-y

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  • DOI: https://doi.org/10.1007/s10822-021-00437-y

Keywords

  • Binding free energy estimation
  • Computational alchemy
  • Potential of mean force
  • SAMPL
  • Host-guest