Skip to main content

Advertisement

SpringerLink
Log in

Calculate protein–ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

We participated in the Cathepsin S (CatS) sub-challenge of the Drug Design Data Resource (D3R) Grand Challenge 3 (GC3) in 2017 to blindly predict the binding poses of 24 CatS-bound ligands, the binding affinity ranking of 136 ligands, and the binding free energies of a subset of 33 ligands in Stage 1A and Stage 2. Our submitted predictions ranked relatively well compared to the submissions from other participants. Here we present our methodologies used in the challenge. For the binding pose prediction, we employed the Glide module in the Schrodinger Suite 2017 and AutoDock Vina. For the binding affinity/free energy prediction, we carried out molecular dynamics simulations of the complexes in explicit water solvent with counter ions, and then estimated the binding free energies with our newly developed model of extended linear interaction energy (ELIE), which is inspired by two other popular end-point approaches: the linear interaction energy (LIE) method, and the molecular mechanics with Poisson–Boltzmann surface area solvation method (MM/PBSA). Our studies suggest that ELIE is a good trade-off between efficiency and accuracy, and it is appropriate for filling the gap between the high-throughput docking and scoring methods and the rigorous but much more computationally demanding methods like free energy perturbation (FEP) or thermodynamics integration (TI) in computer-aided drug design (CADD) projects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Drug Design Data Resource. https://drugdesigndata.org. Accessed 25 Apr 2018

  2. Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB, Carlson HA, Burley SK, Walters WP, Amaro RE, Feher VA, Gilson MK (2016) D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol Des 30:651–668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gaieb Z, Liu S, Gathiaka S, Chiu M, Yang H, Shao C, Feher V, Walters WP, Juhn B, Rudolph MG, Burley SK, Gilson MK, Amaro RE (2018) D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 32:1–20

    Article  CAS  PubMed  Google Scholar 

  4. D3R Grand Challenge 3. https://drugdesigndata.org/about/grand-challenge-3. Accessed 25 Apr 2018

  5. Kramer L, Turk D, Turk B (2017) The future of cysteine cathepsins in disease management. Trends Pharmacol Sci 38:873–898

    Article  CAS  PubMed  Google Scholar 

  6. Turk V, Stoka V, Vasiljeva O, Renko M, Sun T (2012) Cysteine cathepsins: from structure, function and regulation to new frontiers. Biochim Biophys Acta 1824:68–88

    Article  CAS  PubMed  Google Scholar 

  7. Wiener JJM, Sun S, Thurmond RL (2010) Recent advances in the design of cathepsin S inhibitors. Curr Top Med Chem 10:717–732

    Article  CAS  PubMed  Google Scholar 

  8. Gupta S, Singh RK, Dastidar S, Ray A (2008) Cysteine cathepsin S as an immunomodulatory target: present and future trends. Expert Opin Ther Targets 12:291–299

    Article  CAS  PubMed  Google Scholar 

  9. Gustin DJ, Sehon CA, Wei J, Cai H, Meduna SP, Khatuya H, Sun S, Gu Y, Jiang W, Thurmond RL, Karlsson L, Edwards JP (2005) Discovery and SAR studies of a novel series of noncovalent cathepsin S inhibitors. Biorgan Med Chem Lett 15:1687–1691

    Article  CAS  Google Scholar 

  10. Ameriks MK, Axe FU, Bembenek SD, Edwards JP, Gu Y, Karlsson L, Randal M, Sun S, Thurmond RL, Zhu J (2009) Pyrazole-based cathepsin S inhibitors with arylalkynes as P1 binding elements. Biorgan Med Chem Lett 19:6131–6134

    Article  CAS  Google Scholar 

  11. Ameriks MK, Cai H, Edwards JP, Gebauer D, Gleason E, Gu Y, Karlsson L, Nguyen S, Sun S, Thurmond RL, Zhu J (2009) Pyrazole-based arylalkyne cathepsin S inhibitors. Part II: optimization of cellular potency. Biorgan Med Chem Lett 19:6135–6139

    Article  CAS  Google Scholar 

  12. Wiener DK, Lee-Dutra A, Bembenek S, Nguyen S, Thurmond RL, Sun S, Karlsson L, Grice CA, Jones TK, Edwards JP (2010) Thioether acetamides as P3 binding elements for tetradydropyrido-pyrazole cathepsin S inhibitors. Biorgan Med Chem Lett 20:2379–2382

    Article  CAS  Google Scholar 

  13. Wiener JJM, Wickboldt AT, Nguyen S, Sun S, Rynberg R, Rizzolio M, Karlsson L, Edwards JP, Grice CA (2013) Pyrazole-based arylalkyne cathepsin S inhibitors. Part III: modification of P4 region. Biorgan Med Chem Lett 23:1070–1074

    Article  CAS  Google Scholar 

  14. Ameriks MK, Bembenek SD, Burdett MT, Choong IC, Edwards JP, Gebauer D, Gu Y, Karlsson L, Purkey HE, Staker BL, Sun S, Thurmond RL, Zhu J (2010) Diazinones as P2 replacements for pyrazole-based cathepsin S inhibitors. Biorgan Med Chem Lett 20:4060–4064

    Article  CAS  Google Scholar 

  15. Wei J, Pio BA, Cai H, Meduna SP, Sun S, Gu Y, Jiang W, Thurmond RL, Karlsson L, Edwards JP (2007) Pyrazole-based cathepsin S inhibitors with improved cellular potency. Biorgan Med Chem Lett 17:5525–5528

    Article  CAS  Google Scholar 

  16. Pauly TA, Sulea T, Ammirati M et al (2003) Specificity determinants of human cathepsin S revealed by crystal structures of complexes. Biochemistry 42:3203–3213

    Article  CAS  PubMed  Google Scholar 

  17. Markt P, McGoohan C, Walker B et al (2008) Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening. J Chem Inf Model 48:1693–1705

    Article  CAS  PubMed  Google Scholar 

  18. Thurmond RL, Sun S, Sehon CA et al (2004) Identification of a potent and selective noncovalent cathepsin S inhibitor. J Pharmacol Exp Ther 308:268–276

    Article  CAS  PubMed  Google Scholar 

  19. D3R Grand Challenge 3 Evaluation Results. https://drugdesigndata.org/about/grand-challenge-3-evaluation-results. Accessed 20 Apr 2018

  20. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. Ι. nonpolar gases. J Chem Phys 22:1420–1426

    Article  CAS  Google Scholar 

  21. Kirkwood JG (1935) Statistical mechanics of fluid mixtures. J Chem Phys 3:300–313

    Article  CAS  Google Scholar 

  22. Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren M, 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 137:2695–2703

    Article  CAS  PubMed  Google Scholar 

  23. Hu Y, Sherborne B, Lee T-S, Case DA, York DM, Guo Z (2016) J Comput Aided Mol Des 30(7):533–539

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gilson MK, Zhou HX (2007) Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct 36:21–42

    Article  CAS  PubMed  Google Scholar 

  26. Aqvist J, Medina C, Samuelsson JE (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385–391

    Article  CAS  PubMed  Google Scholar 

  27. Hansson T, Marelius J, Aqvist J (1998) Ligand binding affinity prediction by linear interaction energy methods. J Comput Aided Mol Des 12:27–35

    Article  CAS  PubMed  Google Scholar 

  28. Vosmeer CR, Pool R, Van Stee MF, Perić-Hassler L, Vermeulen NPE, Geerke DP (2014) Towards automated binding affinity prediction using an iterative linear interaction energy approach. Int J Mol Sci 15:798–816

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Capoferri L, Verkade-Vreeker MCA, Buitenhuis D, Commandeur JNM, Pastor M, Vermeulen NPE, Geerke DP (2015) Linear interaction energy based prediction of cytochrome P450 1A2 binding affinities with reliability estimation. PLoS ONE 10:1–23

    Article  CAS  Google Scholar 

  30. Srinivasan J, Cheatham TE, Cieplak P et al (1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices. J Am Chem Soc 120:9401–9409

    Article  CAS  Google Scholar 

  31. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82

    Article  CAS  PubMed  Google Scholar 

  32. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234

    Article  CAS  PubMed  Google Scholar 

  35. Schrödinger LLC (2017) Schrödinger Release 2017-2: Maestro. Schrödinger LLC, New York

    Google Scholar 

  36. Jacobson MP, Friesner RA, Xiang Z, Honig B (2002) On the role of crystal packing forces in determining protein sidechain conformations. J Mol Biol 320:597–608

    Article  CAS  PubMed  Google Scholar 

  37. Shelley JC, Cholleti A, Frye L, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21:681–691

    Article  CAS  PubMed  Google Scholar 

  38. 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 (2015) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12:281–296

    Article  CAS  PubMed  Google Scholar 

  39. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shaw DE, Shelley M, Perry JK, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  Google Scholar 

  40. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49:6177–6196

    Article  CAS  PubMed  Google Scholar 

  41. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Sanner MF (1999) Python: a programming language for software integration and development. J Mol Graph Model 17:57–61

    CAS  PubMed  Google Scholar 

  43. Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25:247–260

    Article  CAS  PubMed  Google Scholar 

  44. Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  CAS  PubMed  Google Scholar 

  45. 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 21:132–146

    Article  CAS  Google Scholar 

  46. Bayly CI, Cieplak P, Cornell W, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97:10269–10280

    Article  CAS  Google Scholar 

  47. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE et al (2016) Gaussian 16, Revision A.03. Gaussian, Inc., Wallingford CT

  48. 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 11:3696–3713

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  50. Case DA, Betz RM, Cerutti DS et al (2016) AMBER 2016. University of California, San Francisco

    Google Scholar 

  51. Goetz AW, Williamson MJ, Xu D, Poole D, Grand SL, Walker RC (2012) Routine microsecond molecular dynamics simulations with AMBER—part I: generalized born. J Chem Theory Comput 8:1542–1555

    Article  CAS  Google Scholar 

  52. Salomon-Ferrer R, Goetz AW, Poole D, Grand SL, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER—part II: particle mesh Ewald. J Chem Theory Comput 9:3878–3888

    Article  CAS  PubMed  Google Scholar 

  53. Loncharich RJ, Brooks BR, Pastor RW (1992) Langevin dynamics of peptides: the frictional dependence of isomerization rates of N-acetylalanyl-N′-methylamide. Biopolymers 32:523–535

    Article  CAS  PubMed  Google Scholar 

  54. Izaguirre J, Catarello D, Wozniak J, Skeel R (2001) Langevin stabilization of molecular dynamics. J Chem Phys 114:2090–2098

    Article  CAS  Google Scholar 

  55. Darden T, York D, Pedersen L (1993) Particle Mesh Ewald: an N.Log (N) method for Ewald Sums in large systems. J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  56. Essmann U, Perera L, Berkowitz ML (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8593

    Article  CAS  Google Scholar 

  57. Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of N-alkanes. J Comput Phys 23:327–341

    Article  CAS  Google Scholar 

  58. Rocchia W, Alexov E, Honig B (2001) Extending the applicability of the nonlinear Poisson-Boltzmann equation: multiple dielectric constants and multivalent ions. J Phys Chem B 105:6507–6514

    Article  CAS  Google Scholar 

  59. Pearlman DA, Charifson PS (2001) Are free energy calculations useful in practice? A comparision with rapid scoring functions for the p38 MAP kinase protein system. J Med Chem 44:3417–3423

    Article  CAS  PubMed  Google Scholar 

  60. Luccarelli J, Michel J, Tirado-Rives J, Jorgensen WL (2010) Effects of water placement on predictions of binding affinities for p38α MAP kinase inhibitors. J Chem Theory Comput 6:3850–3856

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. The evaluation results for the pose prediction of Cathepsin Stage 1A. https://drugdesigndata.org/php/d3r/gc3/combined/pose/index.php?component=968&results=rmsd&chart=pose&partial=0&ligand=Mean. Accessed 20 Apr 2018

  62. The evaluation results for the Ranking Set of Cathepsin Stage 1A. https://drugdesigndata.org/php/d3r/gc3/combined/scoring/index.php?component=968&method=ligand&partial=0&group=noties. Accessed 20 Apr 2018

  63. The evaluation results for the Free Energy Set of Cathepsin Stage 1A. https://drugdesigndata.org/php/d3r/gc3/combined/free-energy/index.php?component=968&partial=0&group=noties. Accessed 20 Apr 2018

Download references

Acknowledgements

This work was supported by the research grants from the National Institutes of Health of USA (R01-GM079383, R21-GM097617, P30 DA035778-01A1). Computational support from the Center for Research Computing of University of Pittsburgh, and the Extreme Science and Engineering Discovery Environment (CHE090098), is acknowledged.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Xibing He, Viet H. Man, Beihong Ji, Xiang-Qun Xie & Junmei Wang

Authors
  1. Xibing He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Viet H. Man
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beihong Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang-Qun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junmei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junmei Wang.

Electronic supplementary material

Below is the link to the electronic supplementary material.

10822_2018_162_MOESM1_ESM.pdf

Supporting Information. Supplementary Material is provided with this manuscript. Figure S1, 2D structures of the tetrahydropyrido-pyrazole core and the pyridinone-like core in the Cathepsin S ligands. Figure S2, 2D structure of CatS-5. Figure S3, Performance of ranking predictions in Stage 2 of CatS set. Figure S4, Performance of submissions for the CatS Free Energy set in Stage 2. (PDF 694 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, X., Man, V.H., Ji, B. et al. Calculate protein–ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3. J Comput Aided Mol Des 33, 105–117 (2019). https://doi.org/10.1007/s10822-018-0162-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-018-0162-6

Keywords

Search

Navigation