Skip to main content

Protein–Ligand Binding Free Energy Calculations with FEP+

Part of the Methods in Molecular Biology book series (MIMB,volume 2022)

Abstract

Accurate and reliable calculation of protein–ligand binding free energy is of central importance in computational biophysics and structure-based drug design. Among the various methods to calculate protein–ligand binding affinities, alchemical free energy perturbation (FEP) calculations performed by way of explicitly solvated molecular dynamics simulations (FEP/MD) provide a thermodynamically rigorous and complete description of the binding event and should in turn yield highly accurate predictions. Although the original theory of FEP was proposed more than 60 years ago, subsequent applications of FEP to compute protein–ligand binding free energies in the context of drug discovery projects over much of that time period was sporadic and generally unsuccessful. This was mainly due to the limited accuracy of the available force fields, inadequate sampling of the protein–ligand conformational space, complexity of simulation set up and analysis, and the large computational resources required to pursue such calculations. Over the past few years, there have been advances in computing power, classical force field accuracy, enhanced sampling algorithms, and simulation setup. This has led to newer FEP implementations such as the FEP+ technology developed by Schrödinger Inc., which has enabled accurate and reliable calculations of protein–ligand binding free energies and positioned free energy calculations to play a guiding role in small-molecule drug discovery. In this chapter, we outline the methodological advances in FEP+, including the OPLS3 force fields, the REST2 (Replica Exchange with Solute Tempering) enhanced sampling, the incorporation of REST2 sampling with conventional FEP (Free Energy Perturbation) through FEP/REST, and the advanced simulation setup and data analysis. The validation of FEP+ method in retrospective studies and the prospective applications in drug discovery projects are also discussed. We then present the recent extension of FEP+ method to handle challenging perturbations, including core-hopping transformations, macrocycle modifications, and reversible covalent inhibitor optimization. The limitations and pitfalls of the current FEP+ methodology and the best practices in real applications are also examined.

Key words

  • Protein–ligand binding
  • Free energy perturbation
  • FEP+
  • OPLS3
  • REST2

This is a preview of subscription content, access via your institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-1-4939-9608-7_9
  • Chapter length: 32 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-1-4939-9608-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Kobilka BK (2007) G protein coupled receptor structure and activation. Biochim Biophys Acta 1768(4):794–807. https://doi.org/10.1016/j.bbamem.2006.10.021

    CAS  CrossRef  PubMed  Google Scholar 

  2. Freund TF, Katona I, Piomelli D (2003) Role of endogenous cannabinoids in synaptic signaling. Physiol Rev 83(3):1017–1066. https://doi.org/10.1152/physrev.00004.2003

    CAS  CrossRef  PubMed  Google Scholar 

  3. Dale Purves GJA, Fitzpatrick D, Hall WC, LaMantia A-S, McNamara JO, White LE (2007) Neuroscience. Sinauer Associates, Sunderland, MA

    Google Scholar 

  4. Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42(6):724–733

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  5. Abel R, Wang L, Harder ED, Berne BJ, Friesner RA (2017) Advancing drug discovery through enhanced free energy calculations. Acc Chem Res 50(7):1625–1632. https://doi.org/10.1021/acs.accounts.7b00083

    CAS  CrossRef  PubMed  Google Scholar 

  6. Gallicchio E, Levy RM (2011) Advances in all atom sampling methods for modeling protein-ligand binding affinities. Curr Opin Struct Biol 21(2):161–166

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  7. 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 Struct Biol 21(2):150–160

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  8. Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303(5665):1813–1818

    CAS  CrossRef  PubMed  Google Scholar 

  9. Chipot C, Pohorille A (2007) Free energy calculations: theory and applications in chemistry and biology, vol 86. Springer, Berlin

    CrossRef  Google Scholar 

  10. Perez A, Morrone JA, Simmerling C, Dill KA (2016) Advances in free-energy-based simulations of protein folding and ligand binding. Curr Opin Struct Biol 36:25–31. https://doi.org/10.1016/j.sbi.2015.12.002

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  11. Kollman P (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93(7):2395–2417. https://doi.org/10.1021/cr00023a004

    CAS  CrossRef  Google Scholar 

  12. Durrant J, McCammon J (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9(1):1–9

    CrossRef  Google Scholar 

  13. Deng Y, Roux B (2009) Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem B 113(8):2234–2346

    CAS  CrossRef  PubMed  Google Scholar 

  14. Riniker S, Christ C, Hansen H, Hünenberger P, Oostenbrink C, Steiner D, van Gunsteren W (2011) Calculation of relative free energies for ligand-protein binding, solvation, and conformational transitions using the GROMOS software. J Phys Chem B 115(46):13570–13577. https://doi.org/10.1021/jp204303a

    CAS  CrossRef  PubMed  Google Scholar 

  15. Hansen N, van Gunsteren WF (2014) Practical aspects of free-energy calculations: a review. J Chem Theory Comput 10(7):2632–2647. https://doi.org/10.1021/ct500161f

    CAS  CrossRef  PubMed  Google Scholar 

  16. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. I. Nonpolar gases. J Chem Phys 22(8):1420–1426. https://doi.org/10.1063/1.1740409

    CAS  CrossRef  Google Scholar 

  17. Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J, Romero DL, Masse C, Knight JL, Steinbrecher T, Beuming T, Damm W, Harder E, Sherman W, Brewer M, Wester R, Murcko M, Frye L, Farid R, Lin T, Mobley DL, Jorgensen WL, Berne BJ, Friesner RA, Abel R (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137(7):2695–2703

    CAS  CrossRef  PubMed  Google Scholar 

  18. Schrodinger Suite 2016 FEP+ (2016) Schrodinger Suite 2016 FEP+. Schrodinger L. L. C., New York, NY

    Google Scholar 

  19. Ford MC, Babaoglu K (2017) Examining the feasibility of using free energy perturbation (FEP+) in predicting protein stability. J Chem Inf Model 57(6):1276–1285. https://doi.org/10.1021/acs.jcim.7b00002

    CAS  CrossRef  PubMed  Google Scholar 

  20. Rombouts FJR, Tresadern G, Buijnsters P, Langlois X, Tovar F, Steinbrecher TB, Vanhoof G, Somers M, Andrés J-I, Trabanco AA (2015) Pyrido[4,3-e][1,2,4]triazolo[4,3-a]pyrazines as selective, brain penetrant phosphodiesterase 2 (PDE2) inhibitors. ACS Med Chem Lett 6(3):282–286. https://doi.org/10.1021/ml500463t

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  21. van Vlijmen H, Desjarlais RL, Mirzadegan T (2017) Computational chemistry at Janssen. J Comput Aided Mol Des 31(3):267–273. https://doi.org/10.1007/s10822-016-9998-9

    CAS  CrossRef  PubMed  Google Scholar 

  22. Keränen H, Pérez-Benito L, Ciordia M, Delgado F, Steinbrecher TB, Oehlrich D, van Vlijmen HWT, Trabanco AA, Tresadern G (2017) Acylguanidine beta secretase 1 inhibitors: a combined experimental and free energy perturbation study. J Chem Theory Comput 13(3):1439–1453. https://doi.org/10.1021/acs.jctc.6b01141

    CAS  CrossRef  PubMed  Google Scholar 

  23. Ciordia M, Pérez-Benito L, Delgado F, Trabanco AA, Tresadern G (2016) Application of free energy perturbation for the design of BACE1 inhibitors. J Chem Inf Model 56(9):1856–1871. https://doi.org/10.1021/acs.jcim.6b00220

    CAS  CrossRef  PubMed  Google Scholar 

  24. Wagner V, Jantz L, Briem H, Sommer K, Rarey M, Christ CD (2017) Computational macrocyclization: from de novo macrocycle generation to binding affinity estimation. ChemMedChem 12(22):1866–1872. https://doi.org/10.1002/cmdc.201700478

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  25. Abel R, Mondal S, Masse C, Greenwood J, Harriman G, Ashwell MA, Bhat S, Wester R, Frye L, Kapeller R, Friesner RA (2017) Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr Opin Struct Biol 43:38–44. https://doi.org/10.1016/j.sbi.2016.10.007

    CAS  CrossRef  PubMed  Google Scholar 

  26. Kuhn B, Tichý M, Wang L, Robinson S, Martin RE, Kuglstatter A, Benz J, Giroud M, Schirmeister T, Abel R, Diederich F, Hert J (2017) Prospective evaluation of free energy calculations for the prioritization of cathepsin L inhibitors. J Med Chem 60(6):2485–2497. https://doi.org/10.1021/acs.jmedchem.6b01881

    CAS  CrossRef  PubMed  Google Scholar 

  27. Hauser K, Negron C, Albanese SK, Ray S, Steinbrecher T, Abel R, Chodera JD, Wang L (2018) Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations. Commun Biol 1(1):70. https://doi.org/10.1038/s42003-018-0075-x

    CrossRef  PubMed  PubMed Central  Google Scholar 

  28. Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL, Kaus JW, Cerutti DS, Krilov G, Jorgensen WL, Abel R, Friesner RA (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12(1):281–296

    CAS  CrossRef  PubMed  Google Scholar 

  29. Liu P, Kim B, Friesner RA, Berne BJ (2005) Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proc Natl Acad Sci U S A 102(39):13749–13754

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  30. Wang L, Friesner RA, Berne BJ (2011) Replica exchange with solute scaling: a more efficient version of replica exchange with solute tempering (REST2). J Phys Chem B 115(30):9431–9438

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  31. Wang L, Berne BJ, Friesner RA (2012) On achieving high accuracy and reliability in the calculation of relative protein-ligand binding affinities. Proc Natl Acad Sci U S A 109(6):1937–1942

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  32. Wang L, Deng Y, Wu Y, Kim B, LeBard DN, Wandschneider D, Beachy M, Friesner RA, Abel R (2017) Accurate modeling of scaffold hopping transformations in drug discovery. J Chem Theory Comput 13(1):42–54. https://doi.org/10.1021/acs.jctc.6b00991

    CAS  CrossRef  PubMed  Google Scholar 

  33. Yu HS, Deng Y, Wu Y, Sindhikara D, Rask AR, Kimura T, Abel R, Wang L (2017) Accurate and reliable prediction of the binding affinities of macrocycles to their protein targets. J Chem Theory Comput 13(12):6290–6300. https://doi.org/10.1021/acs.jctc.7b00885

    CAS  CrossRef  PubMed  Google Scholar 

  34. Knight JL, Brooks CL (2009) λ-Dynamics free energy simulation methods. J Comput Chem 30(11):1692–1700. https://doi.org/10.1002/jcc.21295

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  35. Jarzynski C (2007) Comparison of far-from-equilibrium work relations. C R Phys 8(5):495–506. https://doi.org/10.1016/j.crhy.2007.04.010

    CAS  CrossRef  Google Scholar 

  36. Shobana S, Roux B, Andersen OS (2000) Free energy simulations: thermodynamic reversibility and variability. J Phys Chem B 104(21):5179–5190

    CAS  CrossRef  Google Scholar 

  37. Bennett CH (1976) Efficient estimation of free energy differences from Monte Carlo data. J Comput Phys 22(2):245–268

    CrossRef  Google Scholar 

  38. Paliwal H, Shirts MR (2011) A benchmark test set for alchemical free energy transformations and its use to quantify error in common free energy methods. J Chem Theory Comput 7(12):4115–4134. https://doi.org/10.1021/ct2003995

    CAS  CrossRef  PubMed  Google Scholar 

  39. Lovering F, Aevazelis C, Chang J, Dehnhardt C, Fitz L, Han S, Janz K, Lee J, Kaila N, McDonald J, Moore W, Moretto A, Papaioannou N, Richard D, Ryan MS, Wan Z-K, Thorarensen A (2016) Imidazotriazines: spleen tyrosine kinase (Syk) inhibitors identified by free-energy perturbation (FEP). ChemMedChem 11(2):217–233. https://doi.org/10.1002/cmdc.201500333

    CAS  CrossRef  PubMed  Google Scholar 

  40. Christ CD, Fox T (2013) Accuracy assessment and automation of free energy calculations for drug design. J Chem Inf Model 54(1):108–120. https://doi.org/10.1021/ci4004199

    CAS  CrossRef  PubMed  Google Scholar 

  41. Steinbrecher TB, Dahlgren M, Cappel D, Lin T, Wang L, Krilov G, Abel R, Friesner R, Sherman W (2015) Accurate binding free energy predictions in fragment optimization. J Chem Inf Model 55(11):2411–2420. https://doi.org/10.1021/acs.jcim.5b00538

    CAS  CrossRef  PubMed  Google Scholar 

  42. Lenselink EB, Louvel J, Forti AF, van Veldhoven JPD, de Vries H, Mulder-Krieger T, McRobb FM, Negri A, Goose J, Abel R, van Vlijmen HWT, Wang L, Harder E, Sherman W, Ijzerman AP, Beuming T (2016) Predicting binding affinities for GPCR ligands using free-energy perturbation. ACS Omega 1(2):293–304. https://doi.org/10.1021/acsomega.6b00086

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  43. Goldfeld DA, Murphy R, Kim B, Wang L, Beuming T, Abel R, Friesner RA (2015) Docking and free energy perturbation studies of ligand binding in the kappa opioid receptor. J Phys Chem B 119(3):824–835. https://doi.org/10.1021/jp5053612

    CAS  CrossRef  PubMed  Google Scholar 

  44. Kaus JW, Harder E, Lin T, Abel R, McCammon JA, Wang L (2015) How to deal with multiple binding poses in alchemical relative protein–ligand binding free energy calculations. J Chem Theory Comput 11(6):2670–2679. https://doi.org/10.1021/acs.jctc.5b00214

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  45. Mikulskis P, Genheden S, Ryde U (2014) A large-scale test of free-energy simulation estimates of protein–ligand binding affinities. J Chem Inf Model 54(10):2794–2806. https://doi.org/10.1021/ci5004027

    CAS  CrossRef  PubMed  Google Scholar 

  46. Clark AJ, Gindin T, Zhang B, Wang L, Abel R, Murret CS, Xu F, Bao A, Lu NJ, Zhou T, Kwong PD, Shapiro L, Honig B, Friesner RA (2017) Free energy perturbation calculation of relative binding free energy between broadly neutralizing antibodies and the gp120 glycoprotein of HIV-1. J Mol Biol 429(7):930–947. https://doi.org/10.1016/j.jmb.2016.11.021

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  47. Steinbrecher T, Zhu C, Wang L, Abel R, Negron C, Pearlman D, Feyfant E, Duan J, Sherman W (2017) Predicting the effect of amino acid single-point mutations on protein stability—large-scale validation of MD-based relative free energy calculations. J Mol Biol 429(7):948–963. https://doi.org/10.1016/j.jmb.2016.12.007

    CAS  CrossRef  PubMed  Google Scholar 

  48. Jorgensen WL, Tirado-Rives J (1988) The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110(6):1657–1666. https://doi.org/10.1021/ja00214a001

    CAS  CrossRef  PubMed  Google Scholar 

  49. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118(45):11225–11236. https://doi.org/10.1021/ja9621760

    CAS  CrossRef  Google Scholar 

  50. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117(19):5179–5197. https://doi.org/10.1021/ja00124a002

    CAS  CrossRef  Google Scholar 

  51. Cerutti DS, Swope WC, Rice JE, Case DA (2014) ff14ipq: a self-consistent force field for condensed-phase simulations of proteins. J Chem Theory Comput 10(10):4515–4534. https://doi.org/10.1021/ct500643c

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  52. Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78(8):1950–1958. https://doi.org/10.1002/prot.22711

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  53. 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. https://doi.org/10.1002/jcc.20035

    CAS  CrossRef  PubMed  Google Scholar 

  54. Best RB, Zhu X, Shim J, Lopes PEM, Mittal J, Feig M, MacKerell AD (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J Chem Theory Comput 8(9):3257–3273. https://doi.org/10.1021/ct300400x

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  55. MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiórkiewicz-Kuczera J, Yin D, Karplus M (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616. https://doi.org/10.1021/jp973084f

    CAS  CrossRef  PubMed  Google Scholar 

  56. Schuler LD, Daura X, van Gunsteren WF (2001) An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. J Comput Chem 22(11):1205–1218. https://doi.org/10.1002/jcc.1078

    CAS  CrossRef  Google Scholar 

  57. Wang L, Deng Y, Knight JL, Wu Y, Kim B, Sherman W, Shelley JC, Lin T, Abel R (2013) Modeling local structural rearrangements using FEP/REST: application to relative binding affinity predictions of CDK2 inhibitors. J Chem Theory Comput 9(2):1282–1293

    CAS  CrossRef  PubMed  Google Scholar 

  58. Lim NM, Wang L, Abel R, Mobley DL (2016) Sensitivity in binding free energies due to protein reorganization. J Chem Theory Comput 12(9):4620–4631. https://doi.org/10.1021/acs.jctc.6b00532

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  59. Wang L, Berne BJ (2018) Efficient sampling of puckering states of monosaccharides through replica exchange with solute tempering and bond softening. J Chem Phys 149(7):072306. https://doi.org/10.1063/1.5024389

    CAS  CrossRef  PubMed  Google Scholar 

  60. Pohorille A, Jarzynski C, Chipot C (2010) Good practices in free-energy calculations. J Phys Chem B 114(32):10253

    CrossRef  Google Scholar 

  61. Liu S, Wu Y, Lin T, Abel R, Redmann JP, Summa CM, Jaber VR, Lim NM, Mobley DL (2013) Lead optimization mapper: automating free energy calculations for lead optimization. J Comput Aided Mol Des 27(9):755–770. https://doi.org/10.1007/s10822-013-9678-y

    CAS  CrossRef  PubMed  Google Scholar 

  62. Jorgensen WL, Schyman P (2012) Treatment of halogen bonding in the OPLS-AA force field: application to potent anti-HIV agents. J Chem Theory Comput 8(10):3895–3901. https://doi.org/10.1021/ct300180w

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  63. Halgren TA, Nachbar RB (1996) Merck molecular force field. IV. Conformational energies and geometries for MMFF94. J Comput Chem 17(5–6):587–615. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<587::AID-JCC4>3.0.CO;2-Q

    CAS  CrossRef  Google Scholar 

  64. Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle Mesh Ewald. J Chem Theory Comput 9(9):3878–3888. https://doi.org/10.1021/ct400314y

    CAS  CrossRef  PubMed  Google Scholar 

  65. Michael Bergdorf SB, Rendleman CA, Shaw DE (2015) Desmond/GPU performance as of October 2015. D E Shaw Research Technical Report DESRES/TR--2015-01

    Google Scholar 

  66. Wang L, Lin T, Abel R (2014) Cycle closure estimation of relative binding affinities and errors. Patents

    Google Scholar 

  67. Brown SP, Muchmore SW, Hajduk PJ (2009) Healthy skepticism: assessing realistic model performance. Drug Discov Today 14(7–8):420–427. https://doi.org/10.1016/j.drudis.2009.01.012

    CrossRef  PubMed  Google Scholar 

  68. Liu S, Wang L, Mobley DL (2015) Is ring breaking feasible in relative binding free energy calculations? J Chem Inf Model 55(4):727–735

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  69. Abel R, Wang L (2015) Methods and systems for calculating free energy differences using a modified bond stretch potential. United States Patent

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lingle Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Verify currency and authenticity via CrossMark

Cite this protocol

Wang, L., Chambers, J., Abel, R. (2019). Protein–Ligand Binding Free Energy Calculations with FEP+. In: Bonomi, M., Camilloni, C. (eds) Biomolecular Simulations. Methods in Molecular Biology, vol 2022. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9608-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9608-7_9

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9607-0

  • Online ISBN: 978-1-4939-9608-7

  • eBook Packages: Springer Protocols