Skip to main content

Advertisement

SpringerLink
Log in

Screening of benzamidine-based thrombin inhibitors via a linear interaction energy in continuum electrostatics model

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

Abstract

A series of 27 benzamidine inhibitors covering a wide range of biological activity and chemical diversity was analysed to derive a Linear Interaction Energy in Continuum Electrostatics (LIECE) model for analysing the thrombin inhibitory activity. The main interactions occurring at the thrombin binding site and the preferred binding conformations of inhibitors were explicitly biased by including into the LIECE model 10 compounds extracted from X-ray solved thrombin-inhibitor complexes available from the Protein Data Bank (PDB). Supported by a robust statistics (r 2 = 0.698; q 2 = 0.662), the LIECE model was successful in predicting the inhibitory activity for about 76% of compounds (r 2ext  ≥ 0.600) from a larger external test set encompassing 88 known thrombin inhibitors and, more importantly, in retrieving, at high sensitivity and with better performance than docking and shape-based methods, active compounds from a thrombin combinatorial library of 10240 mimetic chemical products. The herein proposed LIECE model has the potential for successfully driving the design of novel thrombin inhibitors with benzamidine and/or benzamidine-like chemical structure.

Screening of Benzamidine-based Thrombin Inhibitors via a Linear Interaction Energy in Continuum Electrostatics Model

Orazio Nicolotti, Ilenia Giangreco, Teresa Fabiola Miscioscia, Marino Convertino, Francesco Leonetti, Leonardo Pisani and Angelo Carotti

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
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Jain AN, Koile K, Chapman D (1994) Compass: predicting biological activities from molecular surface properties: performance comparisons on a steroid benchmarks. J Med Chem 37:2315–2327

    Article  CAS  Google Scholar 

  2. Mestres J, Rohrer DGC, Maggiora G (1997) MIMIC: a molecular field matching program. Exploiting applicability of molecular surface approaches. J Comput Chem 18:934–954

    Article  CAS  Google Scholar 

  3. Clark RD, Strizhev A, Leonard JM, Blake JF, Matthew JB (2002) Consensus scoring for ligand/protein interactions. J Mol Graphics Model 20:281–295

    Article  CAS  Google Scholar 

  4. Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16:11–26

    Article  CAS  Google Scholar 

  5. Wei H, Tsai K, Lin T (2005) Modeling ligand-receptor interaction for some MHC class II HLA-DR4 peptide mimetic inhibitors using several molecular docking and 3D QSAR techniques. J Chem Inf Model 45:1343–1351

    Article  CAS  Google Scholar 

  6. Prathipati P, Saxena AK (2006) Evolutionary of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening. J Chem Inf Model 46:39–51

    Article  CAS  Google Scholar 

  7. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhatt N, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acid Res 28:235–242

    Article  CAS  Google Scholar 

  8. Nicolotti O, Miscioscia TF, Leonetti F, Muncipinto G, Carotti A (2007) Screening of matrix metalloproteinases available from the PDB: insights into biological functions, domain organization and zinc binding groups. J Chem Inf Model 47:2439–2448

    Article  CAS  Google Scholar 

  9. Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CLIII (2004) Assessing scoring functions for protein-ligand interactions. J Med Chem 47:3032–3047

    Article  CAS  Google Scholar 

  10. 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) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  Google Scholar 

  11. Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565

    Article  CAS  Google Scholar 

  12. Nicolotti O, Miscioscia TF, Carotti A, Leonetti F, Carotti A (2008) An integrated approach to ligand- and structure-based drug design: development and application to a series of serine protease inhibitors. J Chem Inf Model 48:1211–1226

    Article  CAS  Google Scholar 

  13. Nicolotti O, Giangreco I, Miscioscia TF, Carotti A (2009) Investigating enzyme selectivity and hit enrichment by automatically interfacing ligand- and structure-based molecular design. QSAR & Comb Sci 28:861–864

    Article  CAS  Google Scholar 

  14. Nicolotti O, Giangreco I, Miscioscia TF, Carotti A (2009) Improving quantitative structure—activity relationships through multiobjective optimization. J Chem Inf Model 49:2290–2302

    Article  CAS  Google Scholar 

  15. Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J Med Chem 49:5851–5855

    Article  CAS  Google Scholar 

  16. Beveridge DL, DiCapua FM (1989) Free energy via molecular simulation: applications to chemical and biomolecular systems. Annu Rev Biophys Chem 18:431–492

    Article  CAS  Google Scholar 

  17. Kollman P (1993) Free-energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93:2395–2417

    Article  CAS  Google Scholar 

  18. Tembe BL, McCammon JA (1984) In: Comput Chem (ed) Ligand-Receptor Interactions. Elsevier, Oxford

  19. Hansson T, Åqvist J (1995) Estimation of binding free energies for HIV proteinase inhibitors by molecular dynamics simulations. Protein Eng 8:1137–1144

    Article  CAS  Google Scholar 

  20. Åqvist J (1996) Calculation of absolute binding free energies for charged ligands and effects of long-range electrostatic interactions. J Comput Chem 17:1587–1597

    Article  Google Scholar 

  21. Lamb ML, Jorgensen WL (1997) Computational approaches to molecular recognition. Curr Opin Chem Biol 1:449–457

    Article  CAS  Google Scholar 

  22. Åqvist J, Medina C, Samuelsson JE (1994) New method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385–391

    Article  Google Scholar 

  23. Carlson HA, Jorgensen WL (1995) An extended linear response method for determining free energies of hydration. J Phys Chem 99:10667–10673

    Article  CAS  Google Scholar 

  24. McDonald NA, Carlson HA, Jorgensen WL (1997) Free energies of solvation in chloroform and water from a linear response approach. J Phys Org Chem 10:563–576

    Article  CAS  Google Scholar 

  25. Still WC, Tempczyk A, Hawley RC, Hendrickson T (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112:6127–6129

    Article  CAS  Google Scholar 

  26. Lee MC, Yang R, Duan Y (2005) Comparison between generalized-born and poisson-boltzmann methods in physics based scoring functions for protein structure prediction. J Mol Model 12:101–110

    Article  CAS  Google Scholar 

  27. Lee MR, Sun Y (2007) Improving docking accuracy through molecular mechanics generalized born optimization and scoring. J Chem Theory Comput 3:1106–1119

    Article  CAS  Google Scholar 

  28. Guimarães CRW, Cardozo M (2008) MM-GB/SA rescoring of docking poses in structure-based lead optimization. J Chem Inf Model 48:958–970

    Article  CAS  Google Scholar 

  29. Huang D, Caflisch A (2004) Efficient evaluation of binding free energy using continuum electrostatics solvation. J Med Chem 47:5791–5797

    Article  CAS  Google Scholar 

  30. Thompson DC, Humblet C, Joseph-McCarty D (2008) Investigation of MM-PBSA rescoring of docking poses. J Chem Inf Model 48:1081–1091

    Article  CAS  Google Scholar 

  31. Grazioso G, Cavalli A, De Amici M, Recanatini M, De Micheli C (2008) Alpha7 nicotinic acetylcholine receptor agonists: prediction of their binding affinity through a molecular mechanics poisson-boltzmann surface area approach. J Comput Chem 29:2593–2602

    Article  CAS  Google Scholar 

  32. Huang D, Caflisch A (2009) Library screening by fragment-based docking. J Mol Recognit. doi: 10.1002/jmr.981

  33. Zhou T, Huang D, Caflisch A (2008) Is quantum mechanics necessary for predicting binding free energy? J Med Chem 51:4280–4288

    Article  CAS  Google Scholar 

  34. Kolb P, Huang D, Caflisch A (2008) Discovery of kinase Inhibitors by high-throughput docking and scoring based on a transferable linear interaction energy model. J Med Chem 51:1179–1188

    Article  CAS  Google Scholar 

  35. Chen Z, Li Y, Mulichak AM, Lewis SD, Shafer JA (1995) Crystal structure of human α-thrombin complexed with hirugen and p-amidinophenylpyruvate at 1.6 Å resolution. Arch Biochem Biophys 322:198–203

    Article  CAS  Google Scholar 

  36. Katz BA, Elrod K, Luong C, Rice MJ, Mackman RL, Sprengeler PA, Spencer J, Hataye J, Janc J, Link J, Litvak J, Rai R, Rice K, Sideris S, Verner E, Young W (2001) A novel serine protease inhibition motif involving a multi-centered short hydrogen bonding network at the active site. J Mol Biol 307:1451–1486

    Article  CAS  Google Scholar 

  37. Dullweber F, Stubbs MT, Musil D, Sturzebecher J, Klebe G (2001) Factorising ligand affinity: a combined thermodynamic and crystallographic study of trypsin and thrombin inhibition. J Mol Biol 313:593–614

    Article  CAS  Google Scholar 

  38. Hauel NH, Nar H, Priepke H, Ries U, Stassen JM, Wienen W (2002) Structure-based design of novel potent non peptide thrombin inhibitors. J Med Chem 45:1757–1766

    Article  CAS  Google Scholar 

  39. Schaerer K, Morgenthaler M, Seiler P, Diederich F, Banner DW, Tschopp T, Obst-Sander U (2004) Enantiomerically pure thrombin inhibitors for exploring the molecular-recognition features of the oxyanion hole. Helv Chim Acta 87:2517–2539

    Article  CAS  Google Scholar 

  40. Fokkens J, Klebe G (2006) A simple protocol to estimate differences in protein binding affinity for enantiomers without prior resolution of racemates. Angew Chem Int Ed Engl 45:985–989

    Article  CAS  Google Scholar 

  41. Schweizer E, Hoffmann-Roeder A, Olsen JA, Seiler P, Obst-Sander U, Wagner B, Kansy M, Banner DW, Diederich F (2006) Multipolar interactions in the D pocket of thrombin: large differences between tricyclic imide and lactam inhibitors. Org Biomol Chem 4:2364–2369

    Article  CAS  Google Scholar 

  42. Brandstetter H, Turk D, Hoeffken HW, Grosse D, Stürzebecher J, Martin PD, Edwards BF, Bode W (1992) Refined 2.3 Å X-ray crystal structure of bovine thrombin complexes formed with the benzamidine and arginine-based thrombin inhibitors NAPAP, 4-TAPAP and MQPA. A starting point for improving antithrombotics. J Mol Biol 226:1085–1099

    Article  CAS  Google Scholar 

  43. Breu B, Silber K, Gohlke H (2007) Consensus adaptation of field for molecular comparison (AFMoC) models incorporate ligand and receptor conformational variability into tailor-made scoring function. J Chem Inf Model 47:2283–2400

    Article  CAS  Google Scholar 

  44. Böhm M, Stürzebecher 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 42:458–477

    Article  Google Scholar 

  45. Murcia M, Morreale A, Ortiz AR (2006) Comparative binding energy analysis considering multiple receptors: a step toward 3D-QSAR models for multiple targets. J Med Chem 49:6241–6253

    Article  CAS  Google Scholar 

  46. Illgen K, Enderle T, Broger C, Weber L (2000) Simulated molecular evolution in a full combinatorial library. Chem Biol 7:433–441

    Article  CAS  Google Scholar 

  47. Maestro, version 7.5.112 (2006) Schrödinger LLC, New York

  48. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS All-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236

    Article  CAS  Google Scholar 

  49. SYBYL, version 7.1 (2007) Tripos Inc, St. Louis, MO

  50. Mohamadi F, Richards NGJ, Guida WC, Liskamp R, Lipton M, Caufield C, Chang G, Hendrikson T, Still WC (1990) Macromodel: an integrated software system for modeling organic and bioorganic molecules using molecular mechanics. J Comput Chem 11:440–467

    Article  CAS  Google Scholar 

  51. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801

    Article  CAS  Google Scholar 

  52. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S et al (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217

    Article  CAS  Google Scholar 

  53. Warwicker J, Watson HC (1982) Calculation of the electric potential in the active site cleft due to R-helix dipoles. J Mol Biol 157:671–679

    Article  CAS  Google Scholar 

  54. Im W, Beglov D, Roux B (1998) Continuum solvation model: computation of electrostatic forces from numerical solutions to the Poisson-Boltzmann equation. Comput Phys Commun 111:59–75

    Article  CAS  Google Scholar 

  55. Widmer A, Novartis Pharma, Basel, Switzerland

  56. No K, Grant J, Scheraga H (1990) Determination of net atomic charges using a modified partial equalization of orbital electronegativity method. 1. Application to neutral molecules as models for polypeptides. J Phys Chem 94:4732–4739

    Article  CAS  Google Scholar 

  57. No K, Grant J, Jhon M, Scheraga H (1990) Determination of net atomic charges using a modified partial equalization of orbital electronegativity method. 2. Application to ionic and aromatic molecules as models for polypeptides. J Phys Chem 94:4740–4746

    Article  CAS  Google Scholar 

  58. Jones G, Willett P, Glen RC, Leach ARL, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  CAS  Google Scholar 

  59. Nissink JW, Murray C, Hartshorn M, Verdonk ML, Cole JC, Taylor R (2002) A new test set for validating predictions of protein-ligand interaction. Proteins 49:457–471

    Article  CAS  Google Scholar 

  60. ROCS, version 2.4.2 (2005) OpenEye Scientific Software Inc, Santa Fe, NM, USA, http://www.eyesopen.com

  61. Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11:1046–1053

    Article  CAS  Google Scholar 

  62. Gerstein M, Levitt M (1998) Comprehensive assessment of automatic structural alignment against a manual standard, the scop classification of proteins. Protein Sci 7:445–456

    Article  CAS  Google Scholar 

  63. Wold S, Trygg J, Berglund A, Antti H (2001) Some recent developments in PLS modeling. Chemom Intell Lab Syst 58:131–150

    Article  CAS  Google Scholar 

  64. Jones-Hertzog DK, Jorgensen WL (1997) Binding affinities for sulfonamide inhibitors with human thrombin using monte carlo simulations with a linear response method. J Med Chem 40:1539–1549

    Article  CAS  Google Scholar 

  65. Ljungberg KB, Marelius J, Musil D, Svensson P, Norden B, Åqvist J (2002) Computational modeling of inhibitor binding to human thrombin. Eur J Pharm Sci 12:441–446

    Article  Google Scholar 

  66. Zhou R, Friesner RA, Ghosh A, Rizzo RC, Jorgensen WL, Levy RM (2001) New linear interaction method for binding affinity calculations using a continuum solvent model. J Phys Chem 105:10388–10397

    CAS  Google Scholar 

  67. Nicolotti O, Carotti A (2006) QSAR and QSPR studies of a highly structured physico-chemical domain. J Chem Inf Mod 46:264–276

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  69. Cramer RD III, Patterson DE, Bounce JD (1988) Comparative Molecular Field Analysis (CoMFA) 1. Effect of shape on binding of Steroids to carrier proteins. J Am Chem Soc 110:595–5967

    Article  Google Scholar 

  70. Van Drie JH (2003) Pharmacophore discovery–lessons learned. Curr Pharm Des 9:1649–1664

    Article  Google Scholar 

  71. Akifumi O, Keiichi T, Tadakazu T, Noriyuki Y, Shuichi H (2006) Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes. J Chem Inf Model 46:380–391

    Article  CAS  Google Scholar 

  72. Nicholls A (2008) What do we know and when do we know it? J Comput Aided Mol Des 22:239–255

    Article  CAS  Google Scholar 

  73. Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular frameworks. J Med Chem 39:2887–2893

    Article  CAS  Google Scholar 

  74. Bemis GW, Murcko MA (1999) Properties of known drugs. 2. Side chains guy. J Med Chem 42:5095–5099

    Article  CAS  Google Scholar 

  75. Comprehensive Medicinal Chemistry (CMC-3D) Release 94.1. MDL Information Systems Inc, San Leandro, CA

  76. Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, Olah I, Banda M, Simon Z, Mracec M, Oprea TI (2004) In: Chemoinformatics in Drug Discovery. Oprea TI (ed), WOMBAT: World of Molecular Bioactivity. Wiley-VCH, New York

  77. Olah M, Oprea TI (2006) Comprehensive Medicinal Chemistry II vol 3. Taylor JB, Triggle DJ (eds). Bioactivity Databases. Elsevier, Oxford

  78. Olah M, Rad R, Ostopovici L, Bora A, Hadaruga N, Hadaruga D, Moldovan R, Fulias A, Mracec M, Oprea TI (2007) Chemical biology: from small molecules to systems biology and drug design. In: Schreiber SL, Kapoor TM, Wess G (eds), WOMBAT and WOMBAT-PK: Bioactivity Databases for Lead and Drug Discovery. Wiley, New York

  79. Imming P, Sinning C, Meyer A (2006) Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov 5:821–834

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Credits are due to Prof. Amedeo Caflisch for fruitful and critical discussions. The authors thank the talented and dedicated graduate students Dr. Nicola Labianca, Dr. Giuseppe Mangiatordi, Dr. Lydia Siragusa and Dr. Paola Tedeschi. A grateful acknowledgement goes to the University of Bari and MIUR (Rome, Italy; PNR project RBNE01F5WT).

Author information

Authors and Affiliations

  1. Dipartimento Farmaco-Chimico, University of Bari, Via Orabona 4, 70125, Bari, Italy

    Orazio Nicolotti, Ilenia Giangreco, Teresa Fabiola Miscioscia, Marino Convertino, Francesco Leonetti, Leonardo Pisani & Angelo Carotti

Authors
  1. Orazio Nicolotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilenia Giangreco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Teresa Fabiola Miscioscia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marino Convertino
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francesco Leonetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leonardo Pisani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Angelo Carotti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Orazio Nicolotti.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 704 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nicolotti, O., Giangreco, I., Miscioscia, T.F. et al. Screening of benzamidine-based thrombin inhibitors via a linear interaction energy in continuum electrostatics model. J Comput Aided Mol Des 24, 117–129 (2010). https://doi.org/10.1007/s10822-010-9320-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-010-9320-1

Keywords

Search

Navigation