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Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations

Nature Protocols volume 17, pages 1114–1141 (2022)Cite this article

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Abstract

Designing a reliable computational methodology to calculate protein:ligand standard binding free energies is extremely challenging. The large change in configurational enthalpy and entropy that accompanies the association of ligand and protein is notoriously difficult to capture in naive brute-force simulations. Addressing this issue, the present protocol rests upon a rigorous statistical mechanical framework for the determination of protein:ligand binding affinities together with the comprehensive Binding Free-Energy Estimator 2 (BFEE2) application software. With the knowledge of the bound state, available from experiments or docking, application of the BFEE2 protocol with a reliable force field supplies in a matter of days standard binding free energies within chemical accuracy, for a broad range of protein:ligand complexes. Limiting undesirable human intervention, BFEE2 assists the end user in preparing all the necessary input files and performing the post-treatment of the simulations towards the final estimate of the binding affinity.

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Fig. 1: Illustration of the geometrical and alchemical routes.
Fig. 2: Workflow of our methodology.
Fig. 3: Settings for the generation of inputs for the Abl-SH3:p41 case example following the geometrical route.
Fig. 4: Monitoring the convergence of a PMF calculation.
Fig. 5: Settings for the post-treatment of the Abl-SH3:p41 example following the geometrical route.
Fig. 6: Settings for the generation of inputs for the Abl-SH3:p41 case example following the alchemical route.
Fig. 7: Settings for post-treatment of the Abl-SH3:p41 example for the alchemical route.
Fig. 8: Results of PMF calculations in the geometrical route for the Abl-SH3:p41 example.
Fig. 9: Free-energy changes with respect to λ in the alchemical route.
Fig. 10: Example of the outputs of ParseFEP.
Fig. 11: Analysis of the enthalpic driving force of the association of Factor Xa: quaternary ammonium.

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Data availability

The input and output files of BFEE2 of examples are provided in Supplementary Data. The data shown in Figs. 8–11 were obtained from new simulations, as a way to verify and guarantee the reproducibility of our protocol. Input files for these simulations are available from the corresponding authors upon request.

Code availability

The Python package of BFEE2 can be installed through pip (https://pypi.org/project/BFEE2/) and conda (https://anaconda.org/conda-forge/bfee2). The source code of BFEE2 is available on GitHub (https://github.com/fhh2626/BFEE2)77.

References

  1. Limongelli, V. Ligand binding free energy and kinetics calculation in 2020. WIREs Comput. Mol. Sci. 10, e1455 (2020).

    Article  CAS  Google Scholar 

  2. Chodera, J. D. & Mobley, D. L. Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design. Annu. Rev. Biophys. 42, 121–142 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Li, A. & Gilson, M. K. Protein-ligand binding enthalpies from near-millisecond simulations: analysis of a preorganization paradox. J. Chem. Phys. 149, 72311 (2018).

    Article  Google Scholar 

  4. de Ruiter, A. & Oostenbrink, C. Advances in the calculation of binding free energies. Curr. Opin. Struct. Biol. 61, 207–212 (2020).

    Article  PubMed  Google Scholar 

  5. Chipot, C. Frontiers in free-energy calculations of biological systems. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4, 71–89 (2014).

    Article  CAS  Google Scholar 

  6. Hermans, J. & Shankar, S. The free energy of xenon binding to myoglobin from molecular dynamics simulation. Isr. J. Chem. 27, 225–227 (1986).

    Article  CAS  Google Scholar 

  7. Roux, B., Nina, M., Pomès, R. & Smith, J. C. Thermodynamic stability of water molecules in the bacteriorhodopsin proton channel: a molecular dynamics free energy perturbation study. Biophys. J. 71, 670–681 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hermans, J. & Wang, L. 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, 2707–2714 (1997).

    Article  CAS  Google Scholar 

  9. Mann, G. & Hermans, J. Modeling protein–small molecule interactions: structure and thermodynamics of noble gases binding in a cavity in mutant phage T4 lysozyme L99A. J. Mol. Biol. 302, 979–989 (2000).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Deng, Y. & Roux, B. Calculation of standard binding free energies: aromatic molecules in the T4 lysozyme L99A mutant. J. Chem. Theory Comput. 2, 1255–1273 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Mobley, D. L., Chodera, J. D. & Dill, K. A. On the use of orientational restraints and symmetry corrections in alchemical free energy calculations. J. Chem. Phys. 125, 84902 (2006).

    Article  Google Scholar 

  13. Gilson, M. K., Given, J. A., Bush, B. L. & McCammon, J. A. The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys. J. 72, 1047–1069 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fu, H., Shao, X., Chipot, C. & Cai, W. Extended adaptive biasing force algorithm. An on-the-fly implementation for accurate free-energy calculations. J. Chem. Theory Comput. 12, 3506–3513 (2016).

    Article  CAS  PubMed  Google Scholar 

  15. Fu, H. et al. Zooming across the free-energy landscape: shaving barriers, and flooding valleys. J. Phys. Chem. Lett. 9, 4738–4745 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Fu, H., Shao, X., Cai, W. & Chipot, C. Taming rugged free energy landscapes using an average force. Acc. Chem. Res. 52, 3254–3264 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Fu, H. et al. Finding an optimal pathway on a multidimensional free-energy landscape. J. Chem. Inf. Model. 60, 5366–5374 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Woo, H.-J. & Roux, B. Calculation of absolute protein–ligand binding free energy from computer simulations. Proc. Natl Acad. Sci. USA 102, 6825–6830 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gumbart, J. C., Roux, B. & Chipot, C. Standard binding free energies from computer simulations: what is the best strategy? J. Chem. Theory Comput. 9, 794–802 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Fu, H., Cai, W., Hénin, J., Roux, B. & Chipot, C. New coarse variables for the accurate determination of standard binding free energies. J. Chem. Theory Comput. 13, 5173–5178 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, J., Deng, Y. & Roux, B. Absolute binding free energy calculations using molecular dynamics simulations with restraining potentials. Biophys. J. 91, 2798–2814 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fu, H. et al. BFEE: a user-friendly graphical interface facilitating absolute binding free-energy calculations. J. Chem. Inf. Model. 58, 556–560 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fu, H., Chen, H., Cai, W., Shao, X. & Chipot, C. BFEE2: automated, streamlined, and accurate absolute binding free-energy calculations. J. Chem. Inf. Model. 61, 2116–2123 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).

    Article  CAS  PubMed  Google Scholar 

  25. Comer, J. et al. The adaptive biasing force method: everything you always wanted to know but were afraid to ask. J. Phys. Chem. B 119, 1129–1151 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Zwanzig, R. W. High-temperature equation of state by a perturbation method. I. Nonpolar gases. J. Chem. Phys. 22, 1420–1426 (1954).

    Article  CAS  Google Scholar 

  27. Chen, H. et al. Boosting free-energy perturbation calculations with GPU-accelerated namd. J. Chem. Inf. Model. 60, 5301–5307 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kirkwood, J. G. Statistical mechanics of fluid mixtures. J. Chem. Phys. 3, 300–313 (1935).

    Article  CAS  Google Scholar 

  29. Fiorin, G., Klein, M. L. & Hénin, J. Using collective variables to drive molecular dynamics simulations. Mol. Phys. 111, 3345–3362 (2013).

    Article  CAS  Google Scholar 

  30. Zhang, H. et al. Accurate estimation of the standard binding free energy of netropsin with DNA. Molecules 23, 228 (2018).

    Article  PubMed Central  Google Scholar 

  31. Du, S. et al. Curvature of buckybowl corannulene enhances its binding to proteins. J. Phys. Chem. C 123, 922–930 (2019).

    Article  CAS  Google Scholar 

  32. Sun, H., Li, Y., Tian, S., Wang, J. & Hou, T. P-loop conformation governed crizotinib resistance in G2032R-mutated ROS1 tyrosine kinase: clues from free energy landscape. PLOS Comput. Biol. 10, e1003729 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Deng, N. et al. Comparing alchemical and physical pathway methods for computing the absolute binding free energy of charged ligands. Phys. Chem. Chem. Phys. 20, 17081–17092 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kuusk, A. et al. Adoption of a turn conformation drives the binding affinity of p53 C-terminal domain peptides to 14-3-3σ. ACS Chem. Biol. 15, 262–271 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. Qian, Y. et al. Absolute free energy of binding calculations for macrophage migration inhibitory factor in complex with a druglike inhibitor. J. Phys. Chem. B 123, 8675–8685 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Comer, J. et al. Beta-1,3 oligoglucans specifically bind to immune receptor CD28 and may enhance T cell activation. Int. J. Mol. Sci. 22, 3124 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Velez-Vega, C. & Gilson, M. K. Overcoming dissipation in the calculation of standard binding free energies by ligand extraction. J. Comput. Chem. 34, 2360–2371 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Liu, H., Fu, H., Chipot, C., Shao, X. & Cai, W. Accuracy of alternate nonpolarizable force fields for the determination of protein–ligand binding affinities dominated by cation−π interactions. J. Chem. Theory Comput. 17, 3908–3915 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. Srinivasan, J., Cheatham, T. E., Cieplak, P., Kollman, P. A. & Case, D. A. Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate–DNA helices. J. Am. Chem. Soc. 120, 9401–9409 (1998).

    Article  CAS  Google Scholar 

  40. Limongelli, V., Bonomi, M. & Parrinello, M. Funnel metadynamics as accurate binding free-energy method. Proc. Natl Acad. Sci. USA 110, 6358–6363 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Laio, A. & Parrinello, M. Escaping free-energy minima. Proc. Natl Acad. Sci. USA 99, 12562–12566 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Raniolo, S. & Limongelli, V. Ligand binding free-energy calculations with funnel metadynamics. Nat. Protoc. 15, 2837–2866 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Mobley, D. L., Chodera, J. D. & Dill, K. A. Confine-and-release method: obtaining correct binding free energies in the presence of protein conformational change. J. Chem. Theory Comput. 3, 1231–1235 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Miao, Y., Bhattarai, A. & Wang, J. Ligand Gaussian accelerated molecular dynamics (LiGaMD): characterization of ligand binding thermodynamics and kinetics. J. Chem. Theory Comput. 16, 5526–5547 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wang, L., Friesner, R. A. & Berne, B. J. Replica exchange with solute scaling: a more efficient version of replica exchange with solute tempering (REST2). J. Phys. Chem. B 115, 9431–9438 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kofke, D. A. & Cummings, P. T. Precision and accuracy of staged free-energy perturbation methods for computing the chemical potential by molecular simulation. Fluid Phase Equilib 150–151, 41–49 (1998).

    Article  Google Scholar 

  47. Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Tian, C. et al. ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 16, 528–552 (2020).

    Article  PubMed  Google Scholar 

  49. Lemkul, J. A., Huang, J., Roux, B. & MacKerell, A. D. An empirical polarizable force field based on the classical drude oscillator model: development history and recent applications. Chem. Rev. 116, 4983–5013 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ponder, J. W. et al. Current status of the AMOEBA polarizable force field. J. Phys. Chem. B 114, 2549–2564 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jo, S. & Jiang, W. A generic implementation of replica exchange with solute tempering (REST2) algorithm in NAMD for complex biophysical simulations. Comput. Phys. Commun. 197, 304–311 (2015).

    Article  CAS  Google Scholar 

  52. Deng, Y. & Roux, B. Computation of binding free energy with molecular dynamics and grand canonical monte carlo simulations. J. Chem. Phys. 128, 115103 (2008).

    Article  PubMed  Google Scholar 

  53. Ben-Shalom, I. Y., Lin, C., Kurtzman, T., Walker, R. C. & Gilson, M. K. Simulating water exchange to buried binding sites. J. Chem. Theory Comput. 15, 2684–2691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).

    Article  CAS  PubMed  Google Scholar 

  55. Case, D. A. et al. Amber 2021 (University of California, San Francisco, 2021).

  56. Liu, P., Dehez, F., Cai, W. & Chipot, C. A toolkit for the analysis of free-energy perturbation calculations. J. Chem. Theory Comput. 8, 2606–2616 (2012).

    Article  CAS  PubMed  Google Scholar 

  57. Phillips, J. C. et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 153, 44130 (2020).

    Article  CAS  Google Scholar 

  58. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).

    Article  Google Scholar 

  59. Hénin, J. & Chipot, C. Overcoming free energy barriers using unconstrained molecular dynamics simulations. J. Chem. Phys. 121, 2904–2914 (2004).

    Article  PubMed  Google Scholar 

  60. Pisabarro, M. T. & Serrano, L. Rational design of specific high-affinity peptide ligands for the Abl-SH3 domain. Biochemistry 35, 10634–10640 (1996).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  62. Hahn, A. M. & Then, H. Characteristic of Bennett’s acceptance ratio method. Phys. Rev. E 80, 031111 (2009).

    Article  Google Scholar 

  63. Bennett, C. H. Efficient estimation of free energy differences from Monte Carlo data. J. Comput. Phys. 22, 245–268 (1976).

    Article  Google Scholar 

  64. Brown, S. P. & Muchmore, S. W. Large-scale application of high-throughput molecular mechanics with Poisson–Boltzmann surface area for routine physics-based scoring of protein–ligand complexes. J. Med. Chem. 52, 3159–3165 (2009).

    Article  CAS  PubMed  Google Scholar 

  65. Morton, A. & Matthews, B. W. Specificity of ligand binding in a buried nonpolar cavity of T4 lysozyme: linkage of dynamics and structural plasticity. Biochemistry 34, 8576–8588 (1995).

    Article  CAS  PubMed  Google Scholar 

  66. Mares-Guia, M., Nelson, D. L. & Rogana, E. Electronic effects in the interaction of para-substituted benzamidines with trypsin: the involvement of the π-electronic density at the central atom of the substituent in binding. J. Am. Chem. Soc. 99, 2331–2336 (1977).

    Article  CAS  PubMed  Google Scholar 

  67. Katz, B. A. et al. Structural basis for selectivity of a small molecule, S1-binding, submicromolar inhibitor of urokinase-type plasminogen activator. Chem. Biol. 7, 299–312 (2000).

    Article  CAS  PubMed  Google Scholar 

  68. Schwarzl, S. M., Tschopp, T. B., Smith, J. C. & Fischer, S. Can the calculation of ligand binding free energies be improved with continuum solvent electrostatics and an ideal-gas entropy correction? J. Comput. Chem. 23, 1143–1149 (2002).

    Article  CAS  PubMed  Google Scholar 

  69. Schärer, K. et al. Quantification of cation–π interactions in protein–ligand complexes: crystal-structure analysis of Factor Xa bound to a quaternary ammonium ion ligand. Angew. Chemie Int. Ed. 44, 4400–4404 (2005).

    Article  Google Scholar 

  70. Khan, H. M., MacKerell, A. D. & Reuter, N. Cation–π interactions between methylated ammonium groups and tryptophan in the CHARMM36 additive force field. J. Chem. Theory Comput. 15, 7–12 (2019).

    Article  CAS  PubMed  Google Scholar 

  71. Liu, H., Fu, H., Shao, X., Cai, W. & Chipot, C. Accurate description of cation–π interactions in proteins with a nonpolarizable force field at no additional cost. J. Chem. Theory Comput. 16, 6397–6407 (2020).

    Article  CAS  PubMed  Google Scholar 

  72. Bingham, R. J. et al. Thermodynamics of binding of 2-methoxy-3-isopropylpyrazine and 2-methoxy-3-isobutylpyrazine to the major urinary protein. J. Am. Chem. Soc. 126, 1675–1681 (2004).

    Article  CAS  PubMed  Google Scholar 

  73. Timm, D. E., Baker, L. J., Mueller, H., Zidek, L. & Novotny, M. V. Structural basis of pheromone binding to mouse major urinary protein (MUP-I). Protein Sci 10, 997–1004 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Christopher, J. A. et al. Biophysical fragment screening of the β1-adrenergic receptor: identification of high affinity arylpiperazine leads using structure-based drug design. J. Med. Chem. 56, 3446–3455 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Singharoy, A., Chipot, C., Moradi, M. & Schulten, K. Chemomechanical coupling in hexameric protein–protein interfaces harnesses energy within V-type atpases. J. Am. Chem. Soc. 139, 293–310 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Adachi, K., Oiwa, K., Yoshida, M., Nishizaka, T. & Kinosita, K. Controlled rotation of the F1-ATPase reveals differential and continuous binding changes for ATP synthesis. Nat. Commun. 3, 1022 (2012).

    Article  PubMed  Google Scholar 

  77. Fu, H. et al. Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations. BFEE2: Binding free energy estimator 2. https://doi.org/10.5281/zenodo.5501842 (2021).

  78. Liu, H., Okazaki, S. & Shinoda, W. Heteroaryldihydropyrimidines alter capsid assembly by adjusting the binding affinity and pattern of the hepatitis B virus core protein. J. Chem. Inf. Model. 59, 5104–5110 (2019).

    Article  CAS  PubMed  Google Scholar 

  79. Miao, M. et al. Avoiding non-equilibrium effects in adaptive biasing force calculations. Mol. Simul. 47, 390–394 (2021).

    Article  CAS  Google Scholar 

  80. Samways, M. L., Bruce Macdonald, H. E. & Essex, J. W. Grand: a Python module for grand canonical water sampling in OpenMM. J. Chem. Inf. Model. 60, 4436–4441 (2020).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (22073050, 22174075 and 22103041), the China Post-doctoral Science Foundation (bs6619012), Frontiers Science Center for New Organic Matter, Nankai University (63181206), the US National Institutes of Health (R01-AI148740), the National Science Foundation (NSF) through grant no. MCB-1517221, the France and Chicago Collaborating in The Sciences (FACCTS) program, and the Agence Nationale de la Recherche (ProteaseInAction). J.C.G. acknowledges computational resources provided through the Extreme Science and Engineering Discovery Environment (XSEDE; TG-MCB130173). The paper is dedicated to the 100th anniversary of Chemistry at Nankai University.

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Authors and Affiliations

  1. Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Tianjin, China

    Haohao Fu, Haochuan Chen, Xueguang Shao & Wensheng Cai

  2. Laboratoire International Associé CNRS and University of Illinois at Urbana-Champaign, UMR 7019, Université de Lorraine, Vandœuvre-lès-Nancy, France

    Marharyta Blazhynska, Emma Goulard Coderc de Lacam, Florence Szczepaniak, François Dehez & Christophe Chipot

  3. Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA

    Florence Szczepaniak & Benoît Roux

  4. School of Physics, Georgia Institute of Technology, Atlanta, GA, USA

    Anna Pavlova & James C. Gumbart

  5. Department of Chemistry, University of Chicago, Chicago, IL, USA

    Benoît Roux

  6. Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, USA

    Benoît Roux

  7. Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Christophe Chipot

  8. Theoretical and Computational Biophysics Group, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Christophe Chipot

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Contributions

H.F., X.S., W.S. and C.C. conceived the project. H.F. designed the BFEE2 software and implemented the workflow of binding free-energy calculations. H.C. implemented the Gromacs support of BFEE2. H.F., M.B., F.S., E.G.C.D, A.P., F.D. and J.C.G. tested the software. H.F., B.R., W.S. and C.C. wrote the manuscript.

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Correspondence to Wensheng Cai or Christophe Chipot.

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Nature Protocols thanks Nanjie Deng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Woo, H. et al. Proc. Natl Acad. Sci. USA 102, 6825–6830 (2005): https://doi.org/10.1073/pnas.0409005102

Gumbart, J. C. et al. J. Chem. Theory Comput. 9, 794–802 (2013): https://doi.org/10.1021/ct3008099

Fu, H. et al. J. Chem. Theory Comput. 13, 5173–5178 (2017): https://doi.org/10.1021/acs.jctc.7b00791

Fu, H. et al. Acc. Chem. Res. 52, 3254–3264 (2019): https://doi.org/10.1021/acs.accounts.9b00473

Fu, H. et al. J. Chem. Inf. Model. 61, 2116–2123 (2021): https://doi.org/10.1021/acs.jcim.1c00269

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Fu, H., Chen, H., Blazhynska, M. et al. Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations. Nat Protoc 17, 1114–1141 (2022). https://doi.org/10.1038/s41596-021-00676-1

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