Journal of Computer-Aided Molecular Design

, Volume 30, Issue 9, pp 791–804 | Cite as

Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

  • Sergei GrudininEmail author
  • Maria Kadukova
  • Andreas Eisenbarth
  • Simon Marillet
  • Frédéric Cazals


The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3–9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2–35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.


Protein-ligand docking Machine learning Scoring function Ridge regression Parameter estimation 



The authors thank Dr. Petr Popov from MIPT Moscow for the initial analysis of the HSP90 targets.

Supplementary material

10822_2016_9976_MOESM1_ESM.pdf (89 kb)
Supplementary material 1 (pdf 89 KB)


  1. 1.
    Smith RD, Dunbar JJB, Ung PM, Esposito EX, Yang CY, Wang S, Carlson HA (2011) J Chem Inf Model 51:2115CrossRefGoogle Scholar
  2. 2.
    Damm-Ganamet KL, Smith RD, Dunbar JB Jr, Stuckey JA, Carlson HA (2013) J Chem Inf Model 53(8):1853CrossRefGoogle Scholar
  3. 3.
    Grudinin S, Popov P, Neveu E, Cheremovskiy G (2015) J Chem Inf Model. doi: 10.1021/acs.jcim.5b00339 Google Scholar
  4. 4.
    Crawford TD, Ndubaku CO, Chen H, Boggs JW, Bravo BJ, Delatorre K, Giannetti AM, Gould SE, Harris SF, Magnuson SR, McNamara E, Murray LJ, Nonomiya J, Sambrone A, Schmidt S, Smyczek T, Stanley M, Vitorino P, Wang L, West K, Wu P, Ye W (2014) J Med Chem 57(8):3484. doi: 10.1021/jm500155b CrossRefGoogle Scholar
  5. 5.
    Homeyer N, Gohlke H (2013) J Comput Chem 34(11):965CrossRefGoogle Scholar
  6. 6.
    Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J et al (2015) J Am Chem Soc 137(7):2695CrossRefGoogle Scholar
  7. 7.
    Wang L, Berne B, Friesner RA (2012) PNAS 109(6):1937CrossRefGoogle Scholar
  8. 8.
    Lensink MF, Velankar S, Kryshtafovych A, Huang SY, Schneidman-Duhovny D, Sali A, Segura J, Fernandez-Fuentes N, Viswanath S, Elber R, Grudinin S, Popov P, Neveu E, Lee H, Baek M, Park S, Heo L, Rie G, Lee C Seok, Qin S, Zhou HX, Ritchie DW, Maigret B, Devignes MD, Ghoorah A, Torchala M, Chaleil RAG, Bates PA, Ben-Zeev E, Eisenstein M, Negi SS, Weng Z, Vreven T, Pierce BG, Borrman TM, Yu J, Ochsenbein F, Guerois R, Vangone A, Rodrigues JPGLM, van Zundert G, Nellen M, Xue L, Karaca E, Melquiond ASJ, Visscher K, Kastritis PL, Bonvin AMJJ, Xu X, Qiu L, Yan C, Li J, Ma Z, Cheng J, Zou X, Shen Y, Peterson LX, Kim HR, Roy A, Han X, Esquivel-Rodriguez J, Kihara D, Yu X, Bruce NJ, Fuller JC, Wade RC, Anishchenko I, Kundrotas PJ, Vakser IA, Imai K, Yamada K, Oda T, Nakamura T, Tomii K, Pallara C, Romero-Durana M, Jiménez-García B, Moal IH, JFérnandez-Recio IH, Joung JY, Kim JY, Joo K, Lee J, Kozakov D, Vajda S, Mottarella S, Hall DR, Beglov D, Mamonov A, Xia B, Bohnuud T, Del Carpio CA, Ichiishi E, Marze N, Kuroda D, Roy Burman SS, Gray JJ, Chermak E, Cavallo L, Oliva R, Tovchigrechko A, Wodak SJ (2016) Proteins. doi: 10.1002/prot.25007
  9. 9.
    Popov P, Grudinin S (2015) J Chem Inf Model 55(10):2242. doi: 10.1021/acs.jcim.5b00372 CrossRefGoogle Scholar
  10. 10.
    Marillet S, Boudinot P, Cazals F (2015) Proteins: Struct Funct Bioinform 1(84): 9 (2015). doi: 10.1002/prot.24946.
  11. 11.
    Kastritis P, Moal I, Hwang H, Weng Z, Bates P, Bonvin A, Janin J (2011) Protein Sci 20:482CrossRefGoogle Scholar
  12. 12.
    Huang SY, Zou X (2008) Proteins: Struct Funct Bioinform 72(2):557
  13. 13.
    Chuang GY, Kozakov D, Brenke R, Comeau SR, Vajda S (2008) Biophys J 95(9):4217CrossRefGoogle Scholar
  14. 14.
    Maiorov VN, Grippen GM (1992) J Mol Biol 227(3):876CrossRefGoogle Scholar
  15. 15.
    Qiu J, Elber R (2005) Proteins: Struct Funct Bioinform 61(1):44CrossRefGoogle Scholar
  16. 16.
    Rajgaria R, McAllister S, Floudas C (2006) Proteins: Struct Funct Bioinform 65(3):726CrossRefGoogle Scholar
  17. 17.
    Tobi D, Bahar I (2006) Proteins: Struct Funct Bioinform 62(4):970CrossRefGoogle Scholar
  18. 18.
    Ravikant D, Elber R (2010) Proteins: Struct Funct Bioinform 78(2):400CrossRefGoogle Scholar
  19. 19.
    Chae MH, Krull F, Lorenzen S, Knapp EW (2010) Proteins: Struct Funct Bioinform 4(78):1026CrossRefGoogle Scholar
  20. 20.
    Neudert G, Klebe G (2011) Bioinformatics 27(7):1021CrossRefGoogle Scholar
  21. 21.
    Conte L Lo, Chothia C, Janin J (1999) JMB 285(5):2177CrossRefGoogle Scholar
  22. 22.
    Janin J, Bahadur RP, Chakrabarti P (2008) Q Rev Biophysics 41(2):133CrossRefGoogle Scholar
  23. 23.
    Cazals F, Proust F, Bahadur R, Janin J (2006) Protein Sci 15(9):2082. doi: 10.1110/ps.062245906 CrossRefGoogle Scholar
  24. 24.
    Loriot S, Cazals F (2010) Bioinformatics 26(7):964. doi: 10.1093/bioinformatics/btq052.
  25. 25.
    Gerstein M, Richards F (2001) Protein geometry: volumes, areas, and distances. In: Rossmann MG, Arnold E (eds) The international tables for crystallography, vol F, Chap 22. Springer, Berlin, p 531–539Google Scholar
  26. 26.
    Cazals F, Kanhere H, Loriot S (2011) ACM Trans Math Softw 38(1):1. doi: 10.1145/2049662.2049665.
  27. 27.
    Meng G, Arkus N, Brenner M, Manoharan V (2010) Science 327(5965):560CrossRefGoogle Scholar
  28. 28.
    Dunitz J (1995) Chem Biol 2(11):709CrossRefGoogle Scholar
  29. 29.
    Kastritis P, Rodrigues J, Folkers G, Boelens R, Bonvin A (2014) JMB 426:2632CrossRefGoogle Scholar
  30. 30.
    Eisenberg D, Wesson M, Yamashita M (1989) Chem Scr A 29:217Google Scholar
  31. 31.
    Bouvier B, Grunberg R, Nilgès M, Cazals F (2009) Proteins: Struct Funct Bioinform 76(3):677. doi: 10.1002/prot.22381.
  32. 32.
    Wang R, Fang X, Lu Y, Yang CY, Wang S (2005) J Med Chem 48(12):4111. doi: 10.1021/jm048957q.
  33. 33.
    Wang R, Fang X, Lu Y, Wang S (2004) J Med Chem 47(12):2977. doi: 10.1021/jm030580l.
  34. 34.
    Barber D (2012) Bayesian reasoning and machine learning. Cambridge University Press, Cambridge.
  35. 35.
    Sonnenburg S, Rätsch G, Henschel S, Widmer C, Behr J, Zien A, Bona Fd, Binder A, Gehl C, Franc V (2010) J Mach Learn Res 11:1799Google Scholar
  36. 36.
    Kadukova M, Grudinin S (2016) J Chem Inf Model 56(8):1410. doi: 10.1021/acs.jcim.5b00512 CrossRefGoogle Scholar
  37. 37.
    Trott O, Olson AJ (2010) J Comput Chem 31(2):455Google Scholar
  38. 38.
    Seeliger D, de Groot BL (2010) J Comput Aided Mol Des 24(5):417. doi: 10.1007/s10822-010-9352-6 CrossRefGoogle Scholar
  39. 39.
    O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) J Cheminform 3:33CrossRefGoogle Scholar
  40. 40.
    Heifets A, Lilien R (2010) J Mol Graph Model 29(1):93. doi: 10.1016/j.jmgm.2010.05.005 CrossRefGoogle Scholar
  41. 41.
    The PyMOL Molecular Graphics System, Version 1.7 Schrödinger, LLCGoogle Scholar
  42. 42.
    Györfi L, Krzyzak A (2002) A distribution-free theory of nonparametric regression. Springer, BerlinGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Grenoble Alpes, LJKGrenobleFrance
  2. 2.CNRS, LJKGrenobleFrance
  3. 3.InriaGrenobleFrance
  4. 4.Université Côte d’Azur and InriaSophia AntipolisFrance
  5. 5.Virology and Molecular Immunology, INRAJouy-en-JosasFrance

Personalised recommendations