Skip to main content

Advertisement

SpringerLink
Log in

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations

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

Abstract

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

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
Fig. 7

Similar content being viewed by others

References

  1. Kato S, Takikawa M, Nakashima K, Hirano A, Cleveland DW, Kusaka H, Shibata N, Kato M, Nakano I, Ohama E (2000) New consensus research on neuropathological aspects of familial amyotrophic lateral sclerosis with superoxide dismutase 1 (SOD1) gene mutations: inclusions containing SOD1 in neurons and astrocytes. Amyotroph Lateral Scler Other Motor Neuron Disord. 1:163–184

    CAS  PubMed  Google Scholar 

  2. Bruening W, Roy J, Giasson B, Figlewicz DA, Mushynski WE, Durham HD (1999) Up-regulation of protein chaperones preserves viability of cells expressing toxic Cu/Zn-superoxide dismutase mutants associated with amyotrophic lateral sclerosis. J. Neurochem. 72:693–699

    CAS  PubMed  Google Scholar 

  3. Johnston JA, Dalton MJ, Gurney ME, Kopito RR (2000) Formation of high molecular weight complexes of mutant Cu, Zn-superoxide dismutase in a mouse model for familial amyotrophic lateral sclerosis. Proc Natl Acad Sci USA 97:12571–12576

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Rosen DR, Siddique T, Patterson D, Figlewicz DA, Sapp P, Hentati A, Donaldson D, Goto J, O'Regan JP, Deng H-X, Rahmani Z, Krizus A, McKenna-Yasek D, Cayabyab A, Gaston SM, Berger R, Tanzi RE, Halperin JJ, Herzfeldt B, Van den Bergh R, Hung W-Y, Bird T, Deng G, Mulder DW, Smyth C, Laing NG, Soriano E, Pericak-Vance MA, Haines J, Rouleau GA, Gusella JS, Horvitz HR, Brown RH Jr (1993) Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis. Nature 362:59–62

    CAS  PubMed  Google Scholar 

  5. https://alsod.iop.kcl.ac.uk. Accessed 3 Apr 2019

  6. Lacomblez L, Bensimon G, Leigh PN, Guillet P, Meininger V (1996) Dose-ranging study of riluzole in amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis/Riluzole Study Group II. Lancet 347:1425–1431

    CAS  PubMed  Google Scholar 

  7. Okada M, Yamashita S, Ueyama H, Ishizaki M, Maeda Y, Ando Y (2018) Long-term effects of edaravone on survival of patients with amyotrophic lateral sclerosis. ENeurologicalSci. 11:11–14

    PubMed  PubMed Central  Google Scholar 

  8. Khare SD, Caplow M, Dokholyan NV (2004) The rate and equilibrium constants for a multistep reaction sequence for the aggregation of superoxide dismutase in amyotrophic lateral sclerosis. Proc Natl Acad Sci USA 101:15094–15099

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ray SS, Nowak RJ, Brown RH Jr, Lansbury PT Jr (2005) Small-molecule-mediated stabilization of familial amyotrophic lateral sclerosis-linked superoxide dismutase mutants against unfolding and aggregation. Proc Natl Acad Sci USA 102:3639–3644

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Nowak RJ, Cuny GD, Choi S, Lansbury PT, Ray SS (2010) Improving binding specificity of pharmacological chaperones that target mutant superoxide dismutase-1 linked to familial amyotrophic lateral sclerosis using computational methods. J Med Chem 53:2709–2718

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Lansbury PT, Choi A, Ray SS, Nowak RJ (2006) Compounds inhibiting the aggregation of superoxide dismutase-1. Patent WO2006089221

  12. Antonyuk S, Strange RW, Hasnain SS (2010) Structural discovery of small molecule binding sites in Cu–Zn human superoxide dismutase familial amyotrophic lateral sclerosis mutants provides insights for lead optimization. J Med Chem 53:1402–1406

    CAS  PubMed  Google Scholar 

  13. Grad LI, Guest WC, Yanai A, Pokrishevsky E, O'Neill MA, Gibbs E, Semenchenko V, Yousefi M, Wishart DS, Plotkin SS, Cashman NR (2011) Intermolecular transmission of superoxide dismutase 1 misfolding in living cells. Proc Natl Acad Sci USA 108:16398–16403

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Münch C, O'Brien J, Bertolotti A (2011) Prion-like propagation of mutant superoxide dismutase-1 misfolding in neuronal cells. Proc Natl Acad Sci USA 108:3548–3553

    PubMed  PubMed Central  Google Scholar 

  15. DuVal MG, Hinge VK, Snyder N, Kanyo R, Bratvold J, Pokrishevsky E, Cashman NR, Blinov N, Kovalenko A, Allison WT (2019) Tryptophan 32 mediates SOD1 toxicity in a in vivo motor neuron model of ALS and is a promising target for small molecule therapeutics. Neurobiol Dis 124:297–310

    CAS  PubMed  Google Scholar 

  16. Pokrishevsky E, McAlary L, Farrawell NE, Zhao B, Sher M, Yerbury JJ, Cashman NR (2018) Tryptophan 32-mediated SOD1 aggregation is attenuated by pyrimidine-like compounds in living cells. Sci Rep 8:15590

    PubMed  PubMed Central  Google Scholar 

  17. Wright GSA, Antonyuk SV, Kershaw NM, Strange RW, Hasnain SS (2013) Ligand binding and aggregation of pathogenic Sod1. Nat Commun 4:1758–1768

    PubMed  Google Scholar 

  18. Valkov E, Sharpe T, Marsh M, Greive S, Hyvönen M (2012) Targeting protein–protein interactions and fragment-based drug discovery. Top Curr Chem 317:145–179

    CAS  PubMed  Google Scholar 

  19. Levinson NM, Boxer SG (2014) A conserved water-mediated hydrogen bond network defines bosutinib's kinase selectivity. Nat Chem Biol 10:127–132

    CAS  PubMed  Google Scholar 

  20. Chodera JD, Mobley DL (2013) Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design. Annu Rev Biophys 42:121–142

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Clarke C, Woods RJ, Gluska J, Cooper A, Nutley MA, Boons GJ (2001) Involvement of water in carbohydrate−protein binding. J Am Chem Soc 123:12238–12247

    CAS  PubMed  Google Scholar 

  22. Ravishankar R, Suguna K, Surolia A, Vijayan M (1999) Structures of the complexes of peanut lectin with methyl-beta-galactose and N-acetyllactosamine and a comparative study of carbohydrate binding in Gal/GalNAc-specific legume lectins. Acta Crystallogr D Biol Crystallogr 55:1375–1382

    CAS  PubMed  Google Scholar 

  23. Elgavish S, Shaanan B (1997) Lectin–carbohydrate interactions: different folds, common recognition principles. Trends Biochem Sci 22:462–467

    CAS  PubMed  Google Scholar 

  24. Casset F, Hamelryck T, Loris R, Brisson J-R, Tellier C, Dao-Thi M-H, Wyns L, Poortmans F, Pérez S, Imberty A (1995) NMR, molecular modeling, and crystallographic studies of lentil lectin–sucrose interaction. J Biol Chem 270:25619–25628

    CAS  PubMed  Google Scholar 

  25. Kadirvelraj R, Foley BL, Dyekjaer JD, Woods RJ (2008) Involvement of water in carbohydrate–protein binding: concanavalin A revisited. J Am Chem Soc 130:16933–16942

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Huang W, Blinov N, Wishart DS, Kovalenko A (2015) Role of water in ligand binding to maltose-binding protein: insight from a new docking protocol based on the 3D-RISM-KH molecular theory of solvation. J Chem Inf Model 55:317–328

    CAS  PubMed  Google Scholar 

  27. Santos R, Hritz J, Oostenbrink C (2010) Role of water in molecular docking simulations of cytochrome P450 2D6. J Chem Inf Model 50:146–154

    CAS  PubMed  Google Scholar 

  28. Minke WE, Diller DJ, Hol WG, Verlinde CL (1999) The role of waters in docking strategies with incremental flexibility for carbohydrate derivatives: heat-labile enterotoxin, a multivalent test case. J Med Chem 42:1778–1788

    CAS  PubMed  Google Scholar 

  29. Ladbury JE (1996) Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem Biol 3:973–980

    CAS  PubMed  Google Scholar 

  30. Wang H, Ben-Naim A (1996) A possible involvement of solvent-induced interactions in drug design. J Med Chem 39:1531–1539

    CAS  PubMed  Google Scholar 

  31. Poornima CS, Dean PM (1995) Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein-ligand interactions. J Comput Aided Mol Des 9:500–512

    CAS  PubMed  Google Scholar 

  32. Poornima CS, Dean PM (1995) Hydration in drug design. 2. Influence of local site surface shape on water binding. J Comput Aided Mol Des 9:513–520

    CAS  PubMed  Google Scholar 

  33. Poornima CS, Dean PM (1995) Hydration in drug design. 3. Conserved water molecules at the ligand-binding sites of homologous proteins. J Comput Aided Mol Des 9:521–531

    CAS  PubMed  Google Scholar 

  34. Lam PYS, Jadhav PK, Eyermann CJ, Hodge CN, Ru Y, Bacheler LT, Meek JL, Otto MJ, Rayner MM, Wong YN, Chang C-H, Weber PC, Jackson DA, Sharpe TR, Erickson-Viitanen S (1994) Rational design of potent, bioavailable, nonpeptide cyclic ureas as HIV protease inhibitors. Science 263:380–384

    CAS  PubMed  Google Scholar 

  35. Miko V, Papageorgiou C, Borer X (1995) The role of water molecules in the structure-based design of (5-hydroxynorvaline)-2-cyclosporin: synthesis, biological activity, and crystallographic analysis with cyclophilin A. J Med Chem 38:3361–3367

    Google Scholar 

  36. García-Sosa AT (2013) Hydration properties of ligands and drugs in protein binding sites: tightly-bound, bridging water molecules and their effects and consequences on molecular design strategies. J Chem Inf Model 53:1388–1405

    PubMed  Google Scholar 

  37. Michel J, Tirado-Rives J, Jorgensen WL (2009) Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization. J Am Chem Soc 131:15403–15411

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Marrone TJ, Briggs JM, McCammon JA (1997) Structure-based drug design: computational advances. Annu Rev Pharmacol Toxicol 37:71–90

    CAS  PubMed  Google Scholar 

  39. Chen JM, Xu SL, Wawrzak Z, Basarab GS, Jordan DB (1998) Structure-based design of potent inhibitors of scytalone dehydratase: displacement of a water molecule from the active site. Biochemistry 37:17735–21774

    CAS  PubMed  Google Scholar 

  40. Dunitz JD (1994) The entropic cost of bound water in crystals and biomolecules. Science 264:670–670

    CAS  PubMed  Google Scholar 

  41. Dunitz JD (1995) Win some, lose some: enthalpy-entropy compensation in weak intermolecular interactions. Chem Biol 2:709–712

    CAS  PubMed  Google Scholar 

  42. Yang JM, Chen CC (2004) GEMDOCK: a generic evolutionary method for molecular docking. Proteins 55:288–304

    CAS  PubMed  Google Scholar 

  43. Pospisil P, Kuoni T, Scapozza L, Folkers G (2002) Methodology and problems of protein-ligand docking: case study of dihydroorotate dehydrogenase, thymidine kinase, and phosphodiesterase 4. J Recept Signal Transduct Res 22:141–154

    CAS  PubMed  Google Scholar 

  44. de Graaf C, Oostenbrink C, Keizers PH, van der Wijst T, Jongejan A, Vermeuloen NPE (2006) Catalytic site prediction and virtual screening of cytochrome P450 2D6 substrates by consideration of water and rescoring in automated docking. J Med Chem 49:2417–2430

    PubMed  Google Scholar 

  45. de Graaf C, Pospisil P, Pos W, Folkers G, Vermeulen NPE (2005) Binding mode prediction of cytochrome p450 and thymidine kinase protein–ligand complexes by consideration of water and rescoring in automated docking. J Med Chem 48:2308–2318

    PubMed  Google Scholar 

  46. Rarey M, Kramer B, Lengauer T (1999) The particle concept: placing discrete water molecules during protein–ligand docking predictions. Proteins 34:17–28

    CAS  PubMed  Google Scholar 

  47. Thilagavathi R, Mancera RL (2010) Ligand-protein cross-docking with water molecules. J Chem Inf Model 50:415–421

    CAS  PubMed  Google Scholar 

  48. Gunther J, Bergner A, Hendlich M, Klebe G (2003) Utilising structural knowledge in drug design strategies: applications using Relibase. J Mol Biol 326:621–636

    CAS  PubMed  Google Scholar 

  49. Hussain A, Melville JL, Hirst JD (2010) Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin. J Comput Aided Mol Des 24:1–15

    CAS  PubMed  Google Scholar 

  50. Pastor M, Cruciani G, Watson KA (1997) A strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure–activity relationship analysis. J Med Chem 40:4089–4102

    CAS  PubMed  Google Scholar 

  51. Taha MO, Habash M, Al-Hadidi Z, Al-Bakri A, Younis K, Sisan S (2011) Docking-based comparative intermolecular contacts analysis as new 3-D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies. J Chem Inf Model 51:647–669

    CAS  PubMed  Google Scholar 

  52. Wallnoefer HG, Handschuh S, Liedl KR, Fox T (2010) Stabilizing of a globular protein by a highly complex water network: a molecular dynamics simulation study on factor Xa. J Phys Chem B 114:7405–7412

    CAS  PubMed  Google Scholar 

  53. 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

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Kyte J, Doolittle RFA (1982) simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132

    CAS  PubMed  Google Scholar 

  55. Patel H, Grüning BA, Günther S, Merfort I (2014) PyWATER: a PyMOL plug-in to find conserved water molecules in proteins by clustering. Bioinformatics 30:2978–2980

    CAS  PubMed  Google Scholar 

  56. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857

    CAS  PubMed  Google Scholar 

  57. García-Sosa AT, Mancera RL, Dean PM (2003) WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes. J Mol Model 9:172–182

    PubMed  Google Scholar 

  58. Sanschagrin PC, Kuhn LA (1998) Cluster analysis of consensus water sites in thrombin and trypsin shows conservation between serine proteases and contributions to ligand specificity. Protein Sci 7:2054–2064

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 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 110:10095–10112

    CAS  Google Scholar 

  60. Kovalenko A (2003) Three-dimensional RISM theory for molecular liquids and solid-liquid interfaces. In: Hirata F (ed) Molecular theory of solvation. Mezey PG (ed) Series: understanding chemical reactivity, vol 24. Kluwer, Dordrecht, pp 169–275

  61. Imai T, Oda K, Kovalenko A, Hirata F, Kidera A (2009) Ligand mapping on protein surfaces by the 3D-RISM theory: toward computational fragment-based drug design. J Am Chem Soc 131:12430–21244

    CAS  PubMed  Google Scholar 

  62. Stumpe MC, Blinov N, Wishart D, Kovalenko A, Pande VS (2011) Calculation of local water densities in biological systems: a comparison of molecular dynamics simulations and the 3D-RISM-KH molecular theory of solvation. J Phys Chem B 115:319–328

    CAS  PubMed  Google Scholar 

  63. Molecular Operating Environment (MOE) (2016) 2013.08. Chemical Computing Group Inc., Montreal

  64. Imai T, Hiraoka R, Kovalenko A, Hirata FL (2007) Locating missing water molecules in protein cavities by the three-dimensional reference interaction site model theory of molecular solvation. Proteins Struct Funct Bioinf 66:804–813

    CAS  Google Scholar 

  65. Case DA, Darden TA, Cheatham TE III, Simmerling CL, Wang J, Duke RE, Luo R, Crowley M, Walker RC, Zhang W, Merz KM, Wang B, Hayik S, Roitberg A, Seabra G, Kolossváry I, Wong KF, Paesani F, Vanicek J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Mathews DH, Seetin MG, Sagui C, Babin V, Kollman PA (2008) AMBER 10. University of California, San Francisco

    Google Scholar 

  66. Gerber PR, Müller K (1995) MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry. J Comput Aided Mol Des 9:251–268

    CAS  PubMed  Google Scholar 

  67. 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

    CAS  Google Scholar 

  68. Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC Model: II. Parameterization and Validation. J Comput Chem 23:1623–1641

    CAS  PubMed  Google Scholar 

  69. Nikitin A, Milchevskiy Y, Lyubartsev A (2015) AMBER-II: new combining rules and force field for perfluoroalkanes. J Phys Chem B 119:14563–14573

    CAS  PubMed  Google Scholar 

  70. Gill P, Murray W, Wright M (1981) Practical optimization. Academic Press, London

    Google Scholar 

  71. Kovalenko A, Ten-no S, Hirata F (1999) Solution of the three- dimensional RISM/HNC equations for SPC water by the modified method of direct inversion in the iterative subspace. J Comput Chem 20:928–993

    CAS  Google Scholar 

  72. Edelsbrunner H (1992) Weighted alpha shapes. Technical Paper of the Department of Computer Science of the University of Illinois at Urbana-Champaign, Urbana, IL

  73. Corbeil CR, Williams CI, Labute P (2012) Variability in docking success rates due to dataset preparation. J Comput Aided Mol Des 26:775–786

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Stewart JP (2007) Optimization of parameters for semiempirical methods. V. Modification of NDDO approximations and application to 70 elements. J Mol Model 13:1173–1213

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Grimme S, Antony J, Ehrlich S, Krieg HA (2010) consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J Chem Phys 132:154104

    PubMed  Google Scholar 

  76. Stewart JJP (2008) MOPAC2016. Stewart Computational Chemistry, Colorado Springs. https://openmopac.net

  77. Grimme S, Ehrlich S, Goerigk L (2011) Effect of the damping function in dispersion corrected density functional theory. J Comput Chem 32:1456–1465

    CAS  PubMed  Google Scholar 

  78. Dunning THJr. (1989) Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J Chem Phys 90:1007–1023

    Google Scholar 

  79. Bader R (2005) Atoms in molecules: a quantum theory. Oxford University Press, Oxford

    Google Scholar 

  80. Lu T (2012) Multiwfn: a multifunctional wavefunction analyzer. J Comput Chem 33:580–592

    PubMed  Google Scholar 

  81. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian16, revision B.01, Gaussian Inc: Wallingford, CT

  82. Case DA, Babin V, Berryman JT, Betz RM, Cai Q, Cerutti DS, Cheatham TE III, Darden TA, Duke RE, Gohlke H, Goetz AW, Gusarov S, Homeyer N, Janowski P, Kaus J, Kolossváry I, Kovalenko A, Lee TS, LeGrand S, Luchko T, Luo R, Madej B, Merz KM, Paesani F, Roe DR, Roitberg A, Sagui C, Salomon-Ferrer R, Seabra G, Simmerling CL, Smith W, Swails J, Walker RC, Wang J, Wolf RM, Wu X, Kollman PA (2014) AMBER 14. University of California, San Francisco

    Google Scholar 

  83. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    CAS  PubMed  Google Scholar 

  84. Olsson MHM, Sondergaar CR, Rostkowsk M, Jense JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pK(a) predictions. J Chem Theory Comput 7:525–537

    CAS  PubMed  Google Scholar 

  85. 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

    CAS  Google Scholar 

  86. Quiocho FA, Spurlino JC, Rodseth LE (1997) Extensive features of tight oligosaccharide binding revealed in high-resolution structures of the maltodextrin transport/chemosensory receptor. Structure 5:997–1015

    CAS  PubMed  Google Scholar 

  87. Edelsbrunner H, Facello M, Fu R, Liang J (1995) Measuring proteins and voids in proteins. In: Proceedings of the 28th Hawaii international conference on systems science, pp 256–264.

  88. Soga S, Shirai H, Kobori M, Hirayama N (2007) Use of amino acid composition to predict ligand-binding sites. J Chem Inf Model 47:400–406

    CAS  PubMed  Google Scholar 

  89. Graham SE, Smith RD, Carlson HA (2018) Predicting displaceable water sites using mixed-solvent molecular dynamics. J Chem Inf Model 58:305–314

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Setny P (2015) Prediction of water binding to protein hydration sites with a discrete, semiexplicit solvent model. J Chem Theory Comput 11:5961–5972

    CAS  PubMed  Google Scholar 

  91. Martinez CR, Iverson BL (2012) Rethinking the term “pi-stacking”. Chem Sci 3:2191–2201

    CAS  Google Scholar 

  92. Janiaka CA (2000) Critical account on π–π stacking in metal complexes with aromatic nitrogen-containing ligands. J Chem Soc Dalton Trans 21:3885–3896

    Google Scholar 

  93. Alvareza SA (2013) cartography of the van der Waals territories. Dalton Trans 42:8617–8636

    Google Scholar 

  94. Karplus PA, Faerman C (1994) Ordered water in macromolecular structure. Curr Opin Struct Biol 4:770–776

    CAS  Google Scholar 

  95. Levitt M, Park BH (1993) Water: now you see it, now you don't. Structure 1:223–226

    CAS  PubMed  Google Scholar 

  96. Carugo O, Bordo D (1999) How many water molecules can be detected by protein crystallography? Acta Crystallogr D55:479–483

    CAS  Google Scholar 

  97. Zhang X-J, Matthews BW (1994) Conservation of solvent-binding sites in 10 crystal forms of T4 lysozyme. Protein Sci 3:1031–1039

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Loris R, Langhorst U, De Vos S, Decanniere K, Bouckaert J, Maes D, Transue TR, Steyaert J (1999) Conserved water molecules in a large family of microbial ribonucleases. Proteins 36:117–134

    CAS  PubMed  Google Scholar 

  99. Mattos C (2002) Protein-water interactions in a dynamic world. Trends Biochem Sci 27:203–208

    CAS  PubMed  Google Scholar 

  100. Feig M, Pettitt BM (1998) Crystallographic water sites from a theoretical perspective. Structure 6:1351–1354

    CAS  PubMed  Google Scholar 

  101. Gauto DF, Petruk AA, Modenutti CP, Blanco JI, Di Lella S, Marti MA (2012) Solvent structure improves docking prediction in lectin–carbohydrate complexes. Glycobiology 23:241–258

    PubMed  Google Scholar 

  102. Ross GA, Morris GM, Biggin PC (2012) Rapid and accurate prediction and scoring of water molecules in protein binding sites. PLoS ONE 7:e32036

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Roberts BC, Mancera RL (2008) Ligand–protein docking with water molecules. J Chem Inf Model 48:397–408

    CAS  PubMed  Google Scholar 

  104. Kumar A, Zhang KYJ (2013) Investigation on the effect of key water molecules on docking performance in CSARdock exercise. J Chem Inf Model 53:1880–1892

    CAS  PubMed  Google Scholar 

  105. Finley JB, Atigadda VR, Duarte F, Zhao JJ, Brouillette WJ, Air GM, Luo M (1999) Novel aromatic inhibitors of influenza virus neuraminidase make selective interactions with conserved residues and water molecules in the active site. J Mol Biol 293:1107–1109

    CAS  PubMed  Google Scholar 

  106. Cherbavaz DB, Lee ME, Stroud RM, Koschl DE (2000) Active site water molecules revealed in the 2.1 Å resolution structure of a site-directed mutant of isocitrate dehydrogenase. J Mol Biol 295:377–385

    CAS  PubMed  Google Scholar 

  107. Quiocho FA, Wilson DK, Vyas NK (1989) Substrate specificity and affinity of a protein modulated by bound water molecules. Nature 340:404–407

    CAS  PubMed  Google Scholar 

  108. Zhang B, Tan VBC, Lim KM, Tay TE (2007) Significance of water molecules in the inhibition of cyclin-dependent kinase 2 and 5 complexes. J Chem Inf Model 47:1877–1885

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by funding from the Alberta Innovates, Alberta Prion Research Institute (Research Team II Program ABIBS APRIRTP 201300023 and Explorations V Program ABIBS APRIEP 201600034). VKH, NB, DR and AK acknowledge our industrial collaborator, Chemical Computing Group (CCG), for generous access to the Molecular Operating Environment (MOE) drug discovery software platform. Computational resources were provided by WestGrid (www.westgrid.ca) and Compute Canada - Calcul Canada (www.computecanada.ca). We thank Dr. Carol Ladner-Keay for help in editing the manuscript. The manuscript was written through contributions of all the authors. All the authors gave approval to the final version of the manuscript. VKH performed and interpreted 3D-RISM calculations, docking and MD simulations. DR performed and interpreted quantum chemical calculations.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Alberta, Edmonton, AB, T6G 1H9, Canada

    Vijaya Kumar Hinge, Nikolay Blinov, Dipankar Roy & Andriy Kovalenko

  2. Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada

    David S. Wishart

  3. Nanotechnology Research Centre, Edmonton, AB, T6G 2M9, Canada

    Andriy Kovalenko

Authors
  1. Vijaya Kumar Hinge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikolay Blinov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dipankar Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David S. Wishart
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andriy Kovalenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andriy Kovalenko.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1853 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hinge, V.K., Blinov, N., Roy, D. et al. The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations. J Comput Aided Mol Des 33, 913–926 (2019). https://doi.org/10.1007/s10822-019-00239-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-019-00239-3

Keywords

Search

Navigation