Water molecules in protein–ligand interfaces. Evaluation of software tools and SAR comparison

  • Eva NittingerEmail author
  • Paul GibbonsEmail author
  • Charles EigenbrotEmail author
  • Doug R. Davies
  • Brigitte Maurer
  • Christine L. Yu
  • James R. Kiefer
  • Andreas Kuglstatter
  • Jeremy Murray
  • Daniel F. Ortwine
  • Yong Tang
  • Vickie Tsui


Targeting the interaction with or displacement of the ‘right’ water molecule can significantly increase inhibitor potency in structure-guided drug design. Multiple computational approaches exist to predict which waters should be targeted for displacement to achieve the largest gain in potency. However, the relative success of different methods remains underexplored. Here, we present a comparison of the ability of five water prediction programs (3D-RISM, SZMAP, WaterFLAP, WaterRank, and WaterMap) to predict crystallographic water locations, calculate their binding free energies, and to relate differences in these energies to observed changes in potency. The structural cohort included nine Bruton’s Tyrosine Kinase (BTK) structures, and nine bromodomain structures. Each program accurately predicted the locations of most crystallographic water molecules. However, the predicted binding free energies correlated poorly with the observed changes in inhibitor potency when solvent atoms were displaced by chemical changes in closely related compounds.

Graphical abstract


Water Water prediction Water placement Water scoring 3D-RISM SZMAP WaterFLAP WaterMap WaterRank BTK Bruton’s Tyrosine kinase BRD Bromodomain 





Bruton’s Tyrosine Kinase



We thank Matthias Rarey for his support during the project. We thank Terry Crawford, Shumei Wang, Lina Chan, Alex Cote, Chris Nasveschuk, and Matthew Berlin for their syntheses of BRD and TAF small molecule inhibitors. We also acknowledge Wendy Young, Gina Wang, Kevin Currie, and the other chemists at CGI Pharmaceuticals (now Gilead) for their support of the BTK program and syntheses of BTK small molecule inhibitors. Additionally, we thank Laura E. Zawadzke and Eneida Pardo of Constellation Pharmaceuticals for their help assaying the BRD and TAF inhibitors; and Julie DiPaolo for conducting the BTK Lanthascreen assays. Results shown in this report are derived from work performed at Argonne National Laboratory, Structural Biology Center (SBC) at the Advanced Photon Source. SBC-CAT is operated by UChicago Argonne, LLC, for the U.S. Department of Energy, Office of Biological and Environmental Research under contract DE-AC02-06CH11357. Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. The SSRL Structural Molecular Biology Program is supported by the DOE Office of Biological and Environmental Research, and by the National Institutes of Health, National Institute of General Medical Sciences (including P41GM103393). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of NIGMS or NIH. The Berkeley Center for Structural Biology is supported in part by the National Institutes of Health, National Institute of General Medical Sciences, and the Howard Hughes Medical Institute. The Advanced Light Source is a Department of Energy Office of Science User Facility under Contract No. DE-AC02-05CH11231.

Author contributions

EN wrote the manuscript, developed the strategy and conducted the evaluations. DD, CE, JK, JM, and YT determined the BRD and BTK crystal structures used in the analysis. DFO and PG have contributed to the manuscript and have supervised the project. VT also assisted in project supervision.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interest.

Supplementary material

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  1. 1.
    Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130(9):2817–2831. Google Scholar
  2. 2.
    Lazaridis T (1998) Inhomogeneous fluid approach to solvation thermodynamics. 1. Theory. J Phys Chem B 102(18):3531–3541. Google Scholar
  3. 3.
    lazaridis t (1998) inhomogeneous fluid approach to solvation thermodynamics. 2. Applications to simple fluids. J Phys Chem B 102(18):3542–3550. Google Scholar
  4. 4.
    Graham SE, Smith RD, Carlson HA (2018) Predicting displaceable water sites using mixed-solvent molecular dynamics. J Chem Inform Model. Google Scholar
  5. 5.
    Adams DJ (1975) Grand canonical ensemble Monte Carlo for a Lennard-Jones fluid. Mol Phys 29(1):307–311. Google Scholar
  6. 6.
    Michel J, Tirado-Rives J, Jorgensen WL (2009) Prediction of the water content in protein binding sites. J Phys Chem B 113(40):13337–13346. Google Scholar
  7. 7.
    Barillari C, Taylor J, Viner R, Essex JW (2007) Classification of water molecules in protein binding sites. J Am Chem Soc 129(9):2577–2587. Google Scholar
  8. 8.
    Baroni M, Cruciani G, Sciabola S, Perruccio F, Mason JS (2007) A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application. J Chem Inf Model 47(2):279–294. Google Scholar
  9. 9.
    Bayden AS, Moustakas DT, Joseph-McCarthy D, Lamb ML (2015) Evaluating free energies of binding and conservation of crystallographic waters using SZMAP. J Chem Inf Model 55(8):1552–1565. Google Scholar
  10. 10.
    Kovalenko A, Hirata F (1999) Self-consistent description of a metal–water interface by the Kohn–Sham density functional theory and the three-dimensional reference interaction site model. J Chem Phys 110(20):10095–10112. Google Scholar
  11. 11.
    Kovalenko A, Hirata F (1998) Three-dimensional density profiles of water in contact with a solute of arbitrary shape: a RISM approach. Chem Phys Lett 290(1–3):237–244. Google Scholar
  12. 12.
    Nguyen CN, Young TK, Gilson MK (2012) Grid inhomogeneous solvation theory: Hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. J Chem Phys 137(14):044101. Google Scholar
  13. 13.
    Kellogg GE, Chen DL (2004) The importance of being exhaustive. Optimization of bridging structural water molecules and water networks in models of biological systems. Chem Biodivers 1(1):98–105. Google Scholar
  14. 14.
    Amadasi A, Surface JA, Spyrakis F, Cozzini P, Mozzarelli A, Kellogg GE (2008) Robust classification of “relevant” water molecules in putative protein binding sites. J Med Chem 51(4):1063–1067. Google Scholar
  15. 15.
    Pitt WR, Goodfellow JM (1991) Modelling of solvent positions around polar groups in proteins. Protein Eng Des Sel 4(5):531–537. Google Scholar
  16. 16.
    Pitt WR, Murray-Rust J, Goodfellow JM (1993) AQUARIUS2: Knowledge-based modeling of solvent sites around proteins. J Comput Chem 14(9):1007–1018. Google Scholar
  17. 17.
    Verdonk ML, Cole JC, Taylor R (1999) SuperStar: a knowledge-based approach for identifying interaction sites in proteins. J Mol Biol 289(4):1093–1108. Google Scholar
  18. 18.
    Verdonk ML, Cole JC, Watson P, Gillet V, Willett P (2001) Superstar: improved knowledge-based interaction fields for protein binding sites11. J Mol Biol 307(3):841–859. Google Scholar
  19. 19.
    Raymer ML, Sanschagrin PC, Punch WF, Venkataraman S, Goodman ED, Kuhn, L a (1997) Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm. J Mol Biol 265(4):445–464. Google Scholar
  20. 20.
    Beuming T, Farid R, Sherman W (2009) High-energy water sites determine peptide binding affinity and specificity of PDZ domains. Protein Sci 18(8):1609–1619. Google Scholar
  21. 21.
    Chrencik JE, Patny A, Leung IK, Korniski B, Emmons TL, Hall T, Weinberg RA, Gormley JA, Williams JM, Day JE, Hirsch JL, Benson TE (2010) Structural and thermodynamic characterization of the TYK2 and JAK3 Kinase domains in complex with CP-690550 and CMP-6. J Mol Biol 400(3):413–433. Google Scholar
  22. 22.
    Laha JK, Zhang X, Qiao L, Liu M, Chatterjee S, Robinson S, Kosik KS, Cuny GD (2011) Structure-activity relationship study of 2,4-diaminothiazoles as Cdk5/p25 kinase inhibitors. Bioorg Med Chem Lett 21(7):2098–2101. Google Scholar
  23. 23.
    Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner RA (2012) Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. J Comput Aided Mol Des 26(6):787–799. Google Scholar
  24. 24.
    Knegtel RMA, Robinson DD (2011) A role for hydration in interleukin-2 inducible T cell kinase (Itk) selectivity. Mol Inform 30(11–12):950–959. Google Scholar
  25. 25.
    Nguyen CN, Cruz A, Gilson MK, Kurtzman T (2014) Thermodynamics of water in an enzyme active site: grid-based hydration analysis of coagulation factor Xa. J Chem Theory Comput 10(7):2769–2780Google Scholar
  26. 26.
    Bodnarchuk MS, Viner R, Michel J, Essex JW (2014) Strategies to calculate water binding free energies in protein–ligand complexes. J Chem Inform Model 54:1623–1633Google Scholar
  27. 27.
    Mason JS, Bortolato A, Congreve M, Marshall FH (2012) New insights from structural biology into the druggability of G protein-coupled receptors. Trends Pharmacol Sci 33(5):249–260. Google Scholar
  28. 28.
    Bortolato A, Tehan BG, Bodnarchuk MS, Essex JW, Mason JS (2013) Water network perturbation in ligand binding: adenosine A2A antagonists as a case study. J Chem Inf Model 53(7):1700–1713. Google Scholar
  29. 29.
    Spyrakis F, Ahmed MH, Bayden AS, Cozzini P, Mozzarelli A, Kellogg GE (2017) The roles of water in the protein matrix: a largely untapped resource for drug discovery. J Med Chem 60(16):6781–6827. Google Scholar
  30. 30.
    Graves AP, Wall ID, Edge CM, Woolven JM, Cui G, Le Gall A, Hong X, Raha K, Manas ES (2017) A perspective on water site prediction methods for structure based drug design. Curr Top Med Chem 17(23):2599–2616. Google Scholar
  31. 31.
    Bucher D, Stouten P, Triballeau N (2018) Shedding light on important waters for drug design: simulations versus grid-based methods. J Chem Inform Model. Google Scholar
  32. 32.
    Crawford TD, Tsui V, Flynn EM, Wang S, Taylor AM, Côté A, Audia JE, Beresini MH, Burdick DJ, Cummings R, Dakin LA, Cochran AG (2016) Diving into the water: inducible binding conformations for BRD4, TAF1(2), BRD9, and CECR2 bromodomains. J Med Chem 59(11):5391–5402. Google Scholar
  33. 33.
    Albrecht BK, Gehling VS, Hewitt MC, Vaswani RG, Côté A, Leblanc Y, Nasveschuk CG, Bellon S, Bergeron L, Campbell R, Cantone N, Audia JE (2016) Identification of a benzoisoxazoloazepine inhibitor (CPI-0610) of the bromodomain and extra-terminal (BET) family as a candidate for human clinical trials. J Med Chem 59(4):1330–1339. Google Scholar
  34. 34.
    Johnson AR, Kohli PB, Katewa A, Gogol E, Belmont LD, Choy R, Penuel E, Burton L, Eigenbrot C, Yu C, Ortwine DF, Young WB (2016) Battling Btk mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations. ACS Chem Biol 11(10):2897–2907. Google Scholar
  35. 35.
    Di Paolo JA, Huang T, Balazs M, Barbosa J, Barck KH, Bravo BJ, Carano RA, Darrow J, Davies DR, DeForge LE, Diehl L, Currie KS (2011) Specific Btk inhibition suppresses B cell- and myeloid cell-mediated arthritis. Nat Chem Biol 7(1):41–50. Google Scholar
  36. 36.
    Young WB, Barbosa J, Blomgren P, Bremer MC, Crawford JJ, Dambach D, Eigenbrot C, Gallion S, Johnson AR, Kropf JE, Lee SH, Currie KS (2016) Discovery of highly potent and selective Bruton’s tyrosine kinase inhibitors: pyridazinone analogs with improved metabolic stability. Bioorg Med Chem Lett 26(2):575–579. Google Scholar
  37. 37.
    Young WB, Barbosa J, Blomgren P, Bremer MC, Crawford JJ, Dambach D, Gallion S, Hymowitz SG, Kropf JE, Lee SH, Liu L, Currie KS (2015) Potent and selective Bruton’s tyrosine kinase inhibitors: discovery of GDC-0834. Bioorg Med Chem Lett 25(6):1333–1337. Google Scholar
  38. 38.
    Wang X, Barbosa J, Blomgren P, Bremer MC, Chen J, Crawford JJ, Deng W, Dong L, Eigenbrot C, Gallion S, Hau J, Young WB (2017) Discovery of potent and selective tricyclic inhibitors of Bruton’s Tyrosine Kinase with improved druglike properties. ACS Med Chem Lett 8(6):608–613. Google Scholar
  39. 39.
    Crawford JJ, Johnson AR, Misner DL, Belmont LD, Castanedo G, Choy R, Coraggio M, Dong L, Eigenbrot C, Erickson R, Ghilardi N, Young WB (2018) Discovery of GDC-0853: a potent, selective, and noncovalent Bruton’s tyrosine kinase inhibitor in early clinical development. J Med Chem 61(6):2227–2245. Google Scholar
  40. 40.
    Nittinger E, Schneider N, Lange G, Rarey M (2015) Evidence of water molecules–a statistical evaluation of water molecules based on electron density. J Chem Inform Model 55(4):771–783. Google Scholar
  41. 41.
    Meyder A, Nittinger E, Lange G, Klein R, Rarey M (2017) Estimating electron density support for individual atoms and molecular fragments in X-ray structures. J Chem Inf Model 57(10):2437–2447. Google Scholar
  42. 42.
    Bietz S, Inhester T, Lauck F, Sommer K, von Behren MM, Fährrolfes R, Flachsenberg F, Meyder A, Nittinger E, Otto T, Hilbig M, Rarey M (2017) From cheminformatics to structure-based design: web services and desktop applications based on the NAOMI library. J Biotechnol 261:207–214. Google Scholar
  43. 43.
    Bietz S, Rarey M (2015) ASCONA: rapid detection and alignment of protein binding site conformations. J Chem Inf Model 55(8):1747–1756. Google Scholar
  44. 44.
    Bietz S, Rarey M (2016) SIENA: efficient compilation of selective protein binding site ensembles. J Chem Inf Model 56(1):248–259. Google Scholar
  45. 45.
    WaterRank—Desert Scientific Software. Accessed 17 Sep 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universität Hamburg, ZBH - Center for BioinformaticsHamburgGermany
  2. 2.GenentechSouth San FranciscoUSA
  3. 3.Beryllium DiscoveryBainbridge IslandUSA
  4. 4.F. Hoffman-La Roche LtdBaselSwitzerland
  5. 5.Constellation PharmaceuticalsCambridgeUSA
  6. 6.Gilead Sciences Inc.Foster CityUSA
  7. 7.Relay TherapeuticsCambridgeUSA

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