Journal of Computer-Aided Molecular Design

, Volume 26, Issue 5, pp 647–659 | Cite as

Prediction of trypsin/molecular fragment binding affinities by free energy decomposition and empirical scores

  • Mark L. Benson
  • John C. Faver
  • Melek N. Ucisik
  • Danial S. Dashti
  • Zheng Zheng
  • Kenneth M. MerzJr.Email author


Two families of binding affinity estimation methodologies are described which were utilized in the SAMPL3 trypsin/fragment binding affinity challenge. The first is a free energy decomposition scheme based on a thermodynamic cycle, which included separate contributions from enthalpy and entropy of binding as well as a solvent contribution. Enthalpic contributions were estimated with PM6-DH2 semiempirical quantum mechanical interaction energies, which were modified with a statistical error correction procedure. Entropic contributions were estimated with the rigid-rotor harmonic approximation, and solvent contributions to the free energy were estimated with several different methods. The second general methodology is the empirical score LISA, which contains several physics-based terms trained with the large PDBBind database of protein/ligand complexes. Here we also introduce LISA+, an updated version of LISA which, prior to scoring, classifies systems into one of four classes based on a ligand’s hydrophobicity and molecular weight. Each version of the two methodologies (a total of 11 methods) was trained against a compiled set of known trypsin binders available in the Protein Data Bank to yield scaling parameters for linear regression models. Both raw and scaled scores were submitted to SAMPL3. Variants of LISA showed relatively low absolute errors but also low correlation with experiment, while the free energy decomposition methods had modest success when scaling factors were included. Nonetheless, re-scaled LISA yielded the best predictions in the challenge in terms of RMS error, and six of these models placed in the top ten best predictions by RMS error. This work highlights some of the difficulties of predicting binding affinities of small molecular fragments to protein receptors as well as the benefit of using training data.


SAMPL Docking and scoring Error analysis Protein–ligand interactions 

Supplementary material

10822_2012_9567_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (PDF 1991 kb)


  1. 1.
    Andrusier N, Mashiach E, Nussinov R, Wolfson HJ (2008) Proteins Struct Func Bioinfo 73(2):271CrossRefGoogle Scholar
  2. 2.
    Halperin I, Ma BY, Wolfson H, Nussinov R (2002) Proteins Struct Func Genet 47(4):409CrossRefGoogle Scholar
  3. 3.
    Leach AR, Shoichet BK, Peishoff CE (2006) J Med Chem 49(20):5851CrossRefGoogle Scholar
  4. 4.
    Warren GL, Andrews CW, Capelli AM, 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) J Med Chem 49(20):5912CrossRefGoogle Scholar
  5. 5.
    Kolb P, Irwin JJ (2009) Curr Top Med Chem 9(9):755CrossRefGoogle Scholar
  6. 6.
    Deng YQ, Roux B (2009) J Phys Chem B 113(8):2234CrossRefGoogle Scholar
  7. 7.
    Faver JC, Benson ML, He X, Roberts BP, Wang B, Marshall MS, Sherrill CD, Merz KM (2011) PLoS ONE 6(4):e18868Google Scholar
  8. 8.
    Faver JC, Benson ML, He X, Roberts BP, Wang B, Marshall MS, Kennedy MR, Sherrill DC, Merz KM (2011) J Chem Theor Comput 7(3):790CrossRefGoogle Scholar
  9. 9.
    Merz KM (2010) J Chem Theor Comput 6(5):1769CrossRefGoogle Scholar
  10. 10.
    Zheng Z, Merz KM (2011) J Chem Inf Model 51(6):1296CrossRefGoogle Scholar
  11. 11.
    Benson ML, Smith RD, Khazanov NA, Dimcheff B, Beaver J, Dresslar P, Nerothin J, Carlson HA (2008) Nucleic Acids Res 36:D674CrossRefGoogle Scholar
  12. 12.
    Hu LG, Benson ML, Smith RD, Lerner MG, Carlson HA (2005) Proteins Struct Func Bioinf 60(3):333CrossRefGoogle Scholar
  13. 13.
    Glide. Version 5.7. New York, NY: Schrödinger, LLC; 2011Google Scholar
  14. 14.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) J Med Chem 47(7):1739CrossRefGoogle Scholar
  15. 15.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) J Med Chem 49(21):6177CrossRefGoogle Scholar
  16. 16.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) J Med Chem 47(7):1750CrossRefGoogle Scholar
  17. 17.
    Park MS, Gao C, Stern HA (2011) Proteins Struct Func Bioinf 79(1):304CrossRefGoogle Scholar
  18. 18.
    Stewart JJP (2008) MOPAC2009. Colorado. Stewart Computational Chemistry, Springs, CO, USAGoogle Scholar
  19. 19.
    Korth M, Pitonak M, Rezac J, Hobza P (2010) J Chem Theor Comput 6(1):344CrossRefGoogle Scholar
  20. 20.
    Fanfrlik J, Bronowska AK, Rezac J, Prenosil O, Konvalinka J, Hobza P (2010) J Phys Chem B 114(39):12666CrossRefGoogle Scholar
  21. 21.
    Ucisik MN, Dashti DS, Faver JC, Merz KM (2011) J Chem Phys 135:085101CrossRefGoogle Scholar
  22. 22.
    Baum B, Muley L, Smolinski M, Heine A, Hangauer D, Klebe G (2010) J Mol Biol 397(4):1042CrossRefGoogle Scholar
  23. 23.
    MacroModel. Version 9.9. New York, NY: Schrödinger, LLC; 2011Google Scholar
  24. 24.
    Prime. Version 3.0. New York, NY: Schrödinger, LLC; 2011Google Scholar
  25. 25.
    Jacobson MP, Friesner RA, Xiang ZX, Honig B (2002) J Mol Biol 320(3):597CrossRefGoogle Scholar
  26. 26.
    Jacobson MP, Pincus DL, Rapp CS, Day TJF, Honig B, Shaw DE, Friesner RA (2004) Proteins Struct Func Bioinf 55(2):351CrossRefGoogle Scholar
  27. 27.
    Klamt A, Schuurmann G (1993) J Chem Soc-Perkin Trans 2(5):799CrossRefGoogle Scholar
  28. 28.
    Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) J Am Chem Soc 120(37):9401CrossRefGoogle Scholar
  29. 29.
    Massova I, Kollman PA (1999) J Am Chem Soc 121(36):8133CrossRefGoogle Scholar
  30. 30.
    Kollman PA, Massova I, Reyes C, Kuhn B, Huo SH, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE (2000) Account Chem Res 33(12):889CrossRefGoogle Scholar
  31. 31.
    Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Proteins Struct Func Bioinfo 65(3):712CrossRefGoogle Scholar
  32. 32.
    Wang RX, Fang XL, Lu YP, Wang SM (2004) J Med Chem 47(12):2977CrossRefGoogle Scholar
  33. 33.
    LigPrep. Version 2.5. New York, NY: Schrödinger, LLC; 2011Google Scholar
  34. 34.
    Katz BA, Elrod K, Verner E, Mackman RL, Luong C, Shrader WD, Sendzik M, Spencer JR, Sprengeler PA, Kolesnikov A, Tai VWF, Hui HC, Breitenbucher G, Allen D, Janc JW (2003) J Mol Biol 329(1):93CrossRefGoogle Scholar
  35. 35.
    Cui J, Marankan F, Fu WT, Crich D, Mesecar A, Johnson ME (2002) Bioorg Med Chem 10(1):41CrossRefGoogle Scholar
  36. 36.
    Whitlow M, Arnaiz DO, Buckman BO, Davey DD, Griedel B, Guilford WJ, Koovakkat SK, Liang A, Mohan R, Phillips GB, Seto M, Shaw KJ, Xu W, Zhao ZC, Light DR, Morrissey MM (1999) Acta Crystallogr Sect D-Biol Crystallogr 55:1395CrossRefGoogle Scholar
  37. 37.
    Toyota E, Ng KKS, Sekizaki H, Itoh K, Tanizawa K, James MNG (2001) J Mol Biol 305(3):471CrossRefGoogle Scholar
  38. 38.
    Fokkens J, Klebe G (2006) Angewandte Chem Int Ed 45(6):985CrossRefGoogle Scholar
  39. 39.
    Presnell SR, Patil GS, Mura C, Jude KM, Conley JM, Bertrand JA, Kam CM, Powers JC, Williams LD (1998) Biochemistry 37(48):17068CrossRefGoogle Scholar
  40. 40.
    Katz BA, Mackman R, Luong C, Radika K, Martelli A, Sprengeler PA, Wang J, Chan HD, Wong L (2000) Chem Biol 7(4):299CrossRefGoogle Scholar
  41. 41.
    Leiros HKS, Brandsdal BO, Andersen OA, Os V, Leiros I, Helland R, Otlewski J, Willassen NP, Smalas AO (2004) Protein Sci 13(4):1056CrossRefGoogle Scholar
  42. 42.
    Dullweber F, Stubbs MT, Musil D, Sturzebecher J, Klebe G (2001) J Mol Biol 313(3):593CrossRefGoogle Scholar
  43. 43.
    Maignan S, Guilloteau JP, Pouzieux S, Choi-Sledeski YM, Becker MR, Klein SI, Ewing WR, Pauls HW, Spada AP, Mikol V (2000) J Med Chem 43(17):3226CrossRefGoogle Scholar
  44. 44.
    Di Fenza A, Heine A, Koert U, Klebe G (2007) ChemMedChem 2(3):297CrossRefGoogle Scholar
  45. 45.
    Nar H, Bauer M, Schmid A, Stassen JM, Wienen W, Priepke HWM, Kauffmann IK, Ries UJ, Hauel NH (2001) Structure 9(1):29Google Scholar
  46. 46.
    Yusuf D, Davis AM, Kleywegt GJ, Schmitt S (2008) J Chem Inf Model 48(7):1411CrossRefGoogle Scholar
  47. 47.
    Baber JC, Thompson DC, Cross JB, Humblet C (2009) J Chem Inf Model 49(8):1889CrossRefGoogle Scholar
  48. 48.
    Weininger D (1988) J Chem Inf Comput Sci 28(1):31CrossRefGoogle Scholar
  49. 49.
    Weininger D, Weininger A, Weininger JL (1989) J Chem Inf Comput Sci 29(2):97CrossRefGoogle Scholar
  50. 50.
    Sadowski J, Gasteiger J, Klebe G (1994) J Chem Inf Comput Sci 34(4):1000CrossRefGoogle Scholar
  51. 51.
    Brandt T, Holzmann N, Muley L, Khayat M, Wegscheid-Gerlach C, Baum B, Heine A, Hangauer D, Klebe G (2011) J Mol Biol 405(5):1170CrossRefGoogle Scholar
  52. 52.
    Creighton TE (1984) Proteins: structure and molecular properties. Freeman and Company, New York, NYGoogle Scholar
  53. 53.
    Hubbard RE (2006) Structure-based drug discovery: an overview. Royal Society of Chemistry, CambridgeCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Mark L. Benson
    • 1
  • John C. Faver
    • 1
  • Melek N. Ucisik
    • 1
  • Danial S. Dashti
    • 1
  • Zheng Zheng
    • 1
  • Kenneth M. MerzJr.
    • 1
    Email author
  1. 1.The Quantum Theory ProjectThe University of FloridaGainesvilleUSA

Personalised recommendations