Advertisement

Application of the quantum mechanical IEF/PCM-MST hydrophobic descriptors to selectivity in ligand binding

  • Tiziana Ginex
  • Jordi Muñoz-Muriedas
  • Enric Herrero
  • Enric Gibert
  • Pietro Cozzini
  • F. Javier Luque
Original Paper
Part of the following topical collections:
  1. MIB 2015 (Modeling Interaction in Biomolecules VII)

Abstract

We have recently reported the development and validation of quantum mechanical (QM)-based hydrophobic descriptors derived from the parametrized IEF/PCM-MST continuum solvation model for 3D-QSAR studies within the framework of the Hydrophobic Pharmacophore (HyPhar) method. In this study we explore the applicability of these descriptors to the analysis of selectivity fields. To this end, we have examined a series of 88 compounds with inhibitory activities against thrombin, trypsin and factor Xa, and the HyPhar results have been compared with 3D-QSAR models reported in the literature. The quantitative models obtained by combining the electrostatic and non-electrostatic components of the octanol/water partition coefficient yield results that compare well with the predictive potential of standard CoMFA and CoMSIA techniques. The results also highlight the potential of HyPhar descriptors to discriminate the selectivity of the compounds against thrombin, trypsin, and factor Xa. Moreover, the graphical representation of the hydrophobic maps provides a direct linkage with the pattern of interactions found in crystallographic structures. Overall, the results support the usefulness of the QM/MST-based hydrophobic descriptors as a complementary approach for disclosing structure-activity relationships in drug design and for gaining insight into the molecular determinants of ligand selectivity.

Graphical Abstract

Quantum Mechanical continuum solvation calculations performed with the IEF/PCM-MST method are used to derived atomic hydrophobic descriptors, which are then used to discriminate the selectivity of ligands against thrombin, trypsin and factor Xa. The descriptors provide complementary view to standard 3D-QSAR analysis, leading to a more comprehensive understanding of ligand recognition.

Keywords

Target selectivity Hydrophobic molecular field Continuum solvation model 3D-QSAR 

Notes

Acknowledgments

We thank the financial support from Ministerio de Economía y Competitividad (SAF2014-57094-R) and the Generalitat de Catalunya (2014-SGR-1189). We are grateful to the Consorci de Serveis Universitaris de Catalunya (CSUC) for computational resources. FJL acknowledges the support from ICREA Academia.

Supplementary material

894_2016_2991_MOESM1_ESM.docx (4 mb)
ESM 1 (DOCX 4103 kb)

References

  1. 1.
    Gohlke H, Klebe G (2002) Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew Chem Int Ed 41:2644–2676CrossRefGoogle Scholar
  2. 2.
    Lipinski C, Hopkins A (2004) Navigating chemical space for biology and medicine. Nature 432:855–861CrossRefGoogle Scholar
  3. 3.
    Walters WP, Murcko MA (2002) Prediction of ‘drug-likeness’. Adv Drug Deliv Rev 54:255–271CrossRefGoogle Scholar
  4. 4.
    Vistoli G, Pedretti A, Testa B (2008) Assessing drug-likeness—what are we missing? Drug Discov Today 13:285–294CrossRefGoogle Scholar
  5. 5.
    Urus O, Rayan A, Goldblum A, Oprea TI (2011) Understanding drug-likeness. WIREs Comput Mol Sci 1:760–781CrossRefGoogle Scholar
  6. 6.
    Rognan D (2007) Chemogenomic approaches to rational drug design. Br J Pharmacol 1–15Google Scholar
  7. 7.
    Kawasaki Y, Freire E (2011) Finding a better path to drug selectivity. Drug Discov Today 16:985–990CrossRefGoogle Scholar
  8. 8.
    Huggins DJ, Sherman W, Tidor B (2012) Rational approaches to improving selectivity in drug design. J Med Chem 55:1424–1444CrossRefGoogle Scholar
  9. 9.
    Knight ZA, Shokat KM (2005) Features of selective kinase inhibitors. Chem Biol 12:621–637CrossRefGoogle Scholar
  10. 10.
    Anastassiadis T, Deacon SW, Devarajan K, Ma H, Peterson JR (2011) Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat Biotechnol 29:1039–1045CrossRefGoogle Scholar
  11. 11.
    Davis MI, Hunt JP, Herrgard S, Ciceri P, Wodicka LM, Pallares G, Hocker M, Treiber DK, Zarrinkar PP (2011) Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29:1046–1051CrossRefGoogle Scholar
  12. 12.
    Youdim MB, Weinstock M (2004) Therapeutic applications of selective and non-selective inhibitors of monoamine oxidase A and B taht do not cause significant tyramine potentiation. Neurotoxicology 25:243–250CrossRefGoogle Scholar
  13. 13.
    Bertolini A, Ottani A, Sandrini M (2009) Selective COX-2 inhibitors and dual acting anti-inflammatory drugs: critical remarks. Curr Med Chem 9:1033–1043CrossRefGoogle Scholar
  14. 14.
    Card GL, England BP, Suzuki Y, Fong D, Powell B, Lee B, Luu C, Tabrizizad M, Gillete S, Ibrahim PN, Artis DR, Bollag G, Milburn MV, Kim S-H, Schlessinger J, Zhang KYJ (2004) Structural basis for the activity of drugs that inhibit phosphodiesterase. Structure 12:2233–2247CrossRefGoogle Scholar
  15. 15.
    Kastenholz MA, Pastor M, Cruciani G, Haaksma EEJ, Fox T (2000) GRID/CPCA: a new computational toll to design selective ligands. J Med Chem 43:3033–3044CrossRefGoogle Scholar
  16. 16.
    Ortiz AR, Gomez-Puertas P, Leo-Macias A, Lopez-Romero P, Lopez-Viñas E, Morreale A, Murcia M, Wang K (2006) Computational approaches to model ligand selectivity in drug design. Curr Top Med Chem 6:41–55CrossRefGoogle Scholar
  17. 17.
    Kold P, Phan K, Gao Z-G, Marko AC, Sali A, Jaconson KA (2012) Limits of ligand selectiviy from docking to models: in silico screening for A1 adenosine receptor antagonists. PLoS ONE 7, e49910CrossRefGoogle Scholar
  18. 18.
    Rodrigues T, Kudoh T, Roudnicky F, Lim YF, Lin Y-C, Koch CP, Seno M, Detmar M, Schneider G (2013) Steering target selectivity and potency by fragment-based de novo drug design. Angew Chem Int Ed 52:10006–10009CrossRefGoogle Scholar
  19. 19.
    Rath SL, Senapati S (2013) Molecular basis of differential selectivity of cyclobutyl-substituted imidazole inhibitors against CDKs: insights for rational drug design PLoS ONE 8:e73836Google Scholar
  20. 20.
    Tarcsay A, Keserú GM (2015) Is there a link between selectivity and binding thermodynamic profiles? Drug Discov Today 20:86–94CrossRefGoogle Scholar
  21. 21.
    Freyhult E, Gustafsson MG, Strömbergsson H (2015) A machine learning approach to explain drug selectivity to solublre and membrane protein targets. Mol Inf 34:44–52CrossRefGoogle Scholar
  22. 22.
    Cramer RD, Wendt B (2007) Pushing the boundaries of 3D-QSAR. J Comput Aided Mol Des 21:23–32CrossRefGoogle Scholar
  23. 23.
    Verma J, Khedar VM, Coutinho EC (2010) 3D-QSAR in drug design—a review. Curr Top Med Chem 10:95–115CrossRefGoogle Scholar
  24. 24.
    Artese A, Cross S, Costa G, Distinto S, Parrotta L, Alcaro S, Ortuso F, Cruciani G (2013) Molecular interaction fields in drug discovery: recent advances and future perspectives. WIREs Comput Mol Sci 3:594–613CrossRefGoogle Scholar
  25. 25.
    Cramer RD III, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefGoogle Scholar
  26. 26.
    Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146CrossRefGoogle Scholar
  27. 27.
    Klebe G, Abraham U (1999) Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J Comput Aided Mol Des 13:1–10CrossRefGoogle Scholar
  28. 28.
    Böhm M, Klebe G (2002) Development of new hydrogen-bond descriptors and their application to comparative molecular field analyses. J Med Chem 45:1585–1597CrossRefGoogle Scholar
  29. 29.
    Böhm M, Sturzebecher J, Klebe G (1999) Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem 42:458–477CrossRefGoogle Scholar
  30. 30.
    Baskin II, Tikhonova IG, Palyulin VA, Zefirov NS (2003) Selectivity fields: comparative molecular field analysis (CoMFA) of the glycine NMDA and AMPA receptors. J Med Chem 46:4063–4069CrossRefGoogle Scholar
  31. 31.
    Walline CC, Nichols DE, Carroll FI, Barker EL (2008) Comparative molecular field analysis using selectivity fields reveals residues in the third transmembrane helix of the serotonin transporter associated with substrate and antagonist recognition. J Pharmacol Exp Ther 325:791–800CrossRefGoogle Scholar
  32. 32.
    Sharma RN, Thakar HM, Vasu KK, Chaturvedi SC, Pancholi SS (2009) Pair wise binding affinity: 3D QSAR studies on a set of triazolo [1,5-a] quinoxalines as antagonists of AMPA and KA receptors. J Enzyme Inhib Med Chem 24:1008–1014CrossRefGoogle Scholar
  33. 33.
    Ginex T, Muñoz-Muriedas J, Herrero E, Gibert E, Cozzini P, Luque FJ (2016) Development and validation of hydrophobic molecular fields from the quantum mechanical IEF/PCM-MST solvation models in 3D-QSAR. J Comput Chem in pressGoogle Scholar
  34. 34.
    Arkin MR, Wells JA (2004) Small-molecule inhibitors of protein-protein interactions: progressing towards the dream. Nat Rev Drug Discov 3:301–317CrossRefGoogle Scholar
  35. 35.
    Hajduk PJ, Huth JR, Fesik SW (2005) Druggability índices for protein targets derived from NMR-based screening data. J Med Chem 45:2615–2623Google Scholar
  36. 36.
    Nayal M, Honig B (2006) On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins 63:892–906CrossRefGoogle Scholar
  37. 37.
    Egner U, Hillig RC (2008) A structural biology view of target druggability. Expert Opin Drug Discovery 3:391–401CrossRefGoogle Scholar
  38. 38.
    Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, Salzberg AC, Huang ES (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol 25:71–75CrossRefGoogle Scholar
  39. 39.
    Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53:5858–5867CrossRefGoogle Scholar
  40. 40.
    Schmidtke P, Luque FJ, Murray JB, Barril X (2011) Shielded hydrogen bonds as structural determinants of binding kinetics: application in drug design. J Am Chem Soc 133:18903–18910CrossRefGoogle Scholar
  41. 41.
    Alvarez-Garcia D, Barril X (2014) J Med Chem 57:8530–8539CrossRefGoogle Scholar
  42. 42.
    Gaillard P, Carrupt P-A, Testa B, Boudon A (1994) Molecular lipophilicity potential, a tool in 3D QSAR: method and applications. J Comput Aided Mol Des 8:83–96CrossRefGoogle Scholar
  43. 43.
    Kellog GE, Semus SF, Abraham DJ (2000) HINT: a new method of empirical hydrophobic field calculation for CoMFA. J Comput Aided Mol Des 5:545–552CrossRefGoogle Scholar
  44. 44.
    Kellog GE, Abraham DJ (2000) Hydrophobicity: is LogPo/w more than the sum of its parts? Eur J Med Chem 35:651–661CrossRefGoogle Scholar
  45. 45.
    Mennucci B (2012) Polaizable continuum model. WIREs Comput Mol Sci 2:386–404CrossRefGoogle Scholar
  46. 46.
    Luque FJ, Curutchet C, Muñoz-Muriedas J, Bidon-Chanal A, Soteras I, Morreale A, Gelpí JL, Orozco M (2003) Continuum solvation models: dissecting the free energy of solvation. Phys Chem Chem Phys 5:3827–3836CrossRefGoogle Scholar
  47. 47.
    Cramer CJ, Truhlar DG (2008) A universal approach to solvation modeling. Acc Chem Res 41:760–768CrossRefGoogle Scholar
  48. 48.
    Klamt A, Mennucci B, Tomasi J, Barone V, Curutchet C, Orozco M, Luque FJ (2009) On the performance of continuum solvation methods. Acc Chem Res 42:489–492CrossRefGoogle Scholar
  49. 49.
    Sander T, Freyss J, von Korff M, Rufener C (2015) DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 55:460–473CrossRefGoogle Scholar
  50. 50.
    Kastenholz MA, Pastor M, Cruciani G, Haaksma EEJ, Fox T (2000) GRID/CPCA: a new computational tool to design selective ligands. J Med Chem 43:3033–3044CrossRefGoogle Scholar
  51. 51.
    Sheridan RP, Holloway MK, McGaughey G, Mosley RT, Sing SB (2002) A simple method for visualizing the differences between related receptor sites. J Mol Graph Model 2002:71–79CrossRefGoogle Scholar
  52. 52.
    Murcia M, Ortiz AR (2004) Virtual screening with flexible docking and COMBINE-based models. Application to a series of factor Xa inhibitors. J Med Chem 47:805–820CrossRefGoogle Scholar
  53. 53.
    Murcia M, Morreale A, Ortiz AR (2006) Comparative binding energy analysis considering multiple receptors: a step toward 3D-QSAR models for multiple targets. 49: 6241–6253Google Scholar
  54. 54.
    Luque FJ, Barril X, Orozco M (1999) Fractional description of free energies of solvation. J Comput Aided Mol Des 13:139–152CrossRefGoogle Scholar
  55. 55.
    Luque FJ, Bofill JM, Orozco M (1995) Novel strategies to incorporate the solvent polarization in self-consistent reaction field and free-energy perturbation simulations. J Chem Phys 103:10183–10191CrossRefGoogle Scholar
  56. 56.
    Curutchet C, Orozco M, Luque FJ (2001) Solvation in octanol: parametrization of the continuum MST model. J Comput Chem 22:1180–1193CrossRefGoogle Scholar
  57. 57.
    Soteras I, Curutchet C, Bidon-Chanal A, Orozco M, Luque FJ (2005) Extension of the MST model to the IEF formalism: HF and B3LYP parametrizations. J Mol Struct (THEOCHEM) 727:29–40CrossRefGoogle Scholar
  58. 58.
    Tomasi J, Mennucci B, Cammi R (2005) Quantum mechanical continuum solvation models. Chem Rev 105:2999–3094CrossRefGoogle Scholar
  59. 59.
    PharmQSAR (2015) Pharmacelera; BarcelonaGoogle Scholar
  60. 60.
    Sutherland JJ, O’Brien LA, Weaver DF (2004) A comparison of methods for modeling quantitative structure-activity relationships. 47:5541–5554Google Scholar
  61. 61.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr, Peralta JE JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09, revision D.01. Gaussian Inc., WallingfordGoogle Scholar
  62. 62.
    Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130CrossRefGoogle Scholar
  63. 63.
    Clark M, Cramer RD III, van Opdenbosch (1989) Validation of the general purpose Tripos 5.2. J Comput Chem 10:982–1012CrossRefGoogle Scholar
  64. 64.
    Aguinis H, Gottfredson RK, Joo H (2013) Best-practice recommendations for defining, identifying, and handling outliers. Organ Res Methods 16:270–301CrossRefGoogle Scholar
  65. 65.
    Katakura S, Nagahara T, Hara T, Iwamoto M (1993) A novel factor Xa inhibitor: structure-activity relationships and selectivity between factor Xa and thrombin. Biochem Biophys Res Commun 197:965–972CrossRefGoogle Scholar
  66. 66.
    Guilford WJ, Shaw KJ, Dallas JL, Koovakkat S, Lee W, Liang A, Light DR, McCarrick MA, Whitlow M, Ye B, Morrisey MM (1999) Synthesis, characterization, and structure-activity relationships of amidine-substituted (bis)benzylidene-cycloketone olefin isomers as potent and selective factor Xa inhibitors. J Med Chem 42:5415–5425CrossRefGoogle Scholar
  67. 67.
    Du Q, Liu P-J, Mezey PG (2005) Theoretical derivation of heuristic molecular lipophilic potential. A quantum chemical description for molecular solvation. J Chem Inf Model 45:347–353CrossRefGoogle Scholar
  68. 68.
    Thormann M, Klamt A, Wichmann K (2012) COSMOsim3D: 3D-similarity and alignment based on COSMO polarization charge densities. J Chem Inf Model 52:2149–2156CrossRefGoogle Scholar
  69. 69.
    Klamt A, Thormann M, Wichmann K, Tosco P (2012) COSMOsar3D: molecular field analysis based on local COSMO σ-profiles. J Chem Inf Model 52:2157–2164CrossRefGoogle Scholar
  70. 70.
    Muñoz J, Barril X, Hernández B, Orozco M, Luque FJ (2002) Hydrophobic similarity between molecules: a MST-based hydrophobic similarity index. J Comput Chem 23:554–563CrossRefGoogle Scholar
  71. 71.
    Muñoz-Muriedas J, Perspicace S, Bech N, Guccione S, Orozco M, Luque FJ (2005) Hydrophobic molecular similarity from MST fractional contributions to the octanol/water partition coefficient. J Comput Aided Mol Des 19:401–419CrossRefGoogle Scholar
  72. 72.
    Muñoz-Muriedas J, Barril X, López JM, Orozco M, Luque FJ (2007) A hydrophobic similarity analysis of solvation effects on nucleic acid bases. J Mol Model 13:357–365CrossRefGoogle Scholar
  73. 73.
    Forti F, Barril X, Luque FJ, Orozco M (2008) Extension of the MST continuum solvent model to the RM1 semiempirical Hamiltonian. J Comput Chem 29:578–587CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Tiziana Ginex
    • 1
  • Jordi Muñoz-Muriedas
    • 2
  • Enric Herrero
    • 3
  • Enric Gibert
    • 3
  • Pietro Cozzini
    • 1
  • F. Javier Luque
    • 4
  1. 1.Department of Food ScienceUniversity of ParmaParmaItaly
  2. 2.GlaxoSmithKline, Medicines Research CentreStevenageUK
  3. 3.PharmaceleraBarcelonaSpain
  4. 4.Department of Chemical Physics and Institut de Biomedicina (IBUB), Faculty of PharmacyUniversity of BarcelonaSanta Coloma de GramenetSpain

Personalised recommendations