From Inhibitors of Lap to Inhibitors of Pal

  • Łukasz Berlicki
  • Jolanta Grembecka
  • Edyta Dyguda-Kazimierowicz
  • PaweŁ Kafarski
  • W. Andrzej Sokalski
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 4)


Computer-aided techniques of rational design of enzyme inhibitors were reviewed. In silico lead generation and optimization protocols were outlined and several methods of inhibitor potency estimation by both empirical scoring functions as well as ab initio based calculations were described. Two representative examples of successful computer-aided analysis and design of novel, highly potent inhibitors of leucine aminopeptidase and glutamine synthetase were demonstrated. In addition fully nonempirical and systematic analysis of the physical nature of enzyme active site interactions has been performed for series of leucine aminopeptidase (LAP) and phenylalanine ammonia lyase (PAL) inhibitors. Results derived from ab initio calculations indicate that inhibitory activity is controlled by interactions with limited number of active site residues. Examination of entire hierarchy of theoretical models indicates that the inhibitory activity could be well represented by electrostatic interactions, leading to so called ‘‘electrostatic key-lock’’ principle


Drug design molecular modeling agrochemicals enzyme inhibitors ab initio intermolecular interactions leucine aminopeptidase glutamine synthetase phenylalanine ammonia lyase 


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  1. 1.
    L Pauling (1946) Molecular architecture and biological reactions, Chem Eng News 24, 1375–1377Google Scholar
  2. 2.
    L Pauling (1948) Chemical achievement and hope for the future, AmSci 36: 51–58Google Scholar
  3. 3.
    WL Jorgensen (2004) The many roles of computation in drugdiscovery, Science 303 (5665), 1813–1818PubMedADSCrossRefGoogle Scholar
  4. 4.
    Ł Berlicki, P Kafarski (2005) Computer-Aided Analysis andDesign of Phosphonic and Phosphinic Enzyme Inhibitors as PotentialDrugs and Agrochemicals, Curr Org Chem 9 (18): 1829–1850CrossRefGoogle Scholar
  5. 5.
    F Ooms (2000) Molecular Modeling and Computer Aided Drug DesignExamples of their Applications in Medicinal Chemistry, Curr MedChem 7: 141–158Google Scholar
  6. 6.
    I Muegge (2003) Selection criteria for drug-like compounds, MedRes Rev 23 (3): 302–321CrossRefGoogle Scholar
  7. 7.
    R Abagyan, M Totrov (2001) High-throughput docking for leadgeneration, Curr Opin Chem Biol 5 (4): 375–382PubMedCrossRefGoogle Scholar
  8. 8.
    KE Goodwill, MG Tennant, RC Stevens (2001) High-throughput x-raycrystallography for structure-based drug design, Drug DiscoveryToday 6 (2): 113–118CrossRefGoogle Scholar
  9. 9.
    KH Gardner, LE Kay (1998) The use of H-2, C-13, N-15multidimensional NMR to study the structure and dynamics ofproteins, Ann Rev Biophys Biomol Struct 27: 357–406CrossRefGoogle Scholar
  10. 10.
    V Kanelis, JD Forman-Kay, LE Kay (2001) Multidimensional NMRmethods for protein structure determination, IUBMB Life 52(6): 291–302PubMedGoogle Scholar
  11. 11.
    A Hillisch, LF Pineda, R Hilgenfeld (2004) Utility of homologymodels in the drug discovery process, Drug Discovery Today 9(15): 659–669PubMedCrossRefGoogle Scholar
  12. 12.
    RD Taylor, PJ Jewsbury, JW Essex (2002) A review of protein-smallmolecule docking methods, J Comput Aided Mol Des 16 (3): 151–166PubMedCrossRefGoogle Scholar
  13. 13.
    TJA Ewing, S Makino, AG Skillman, ID Kuntz (2001) DOCK 40: Searchstrategies for automated molecular docking of flexible moleculedatabases, J Comput Aided Mol Des 15 (5): 411–428PubMedCrossRefGoogle Scholar
  14. 14.
    G Jones, P Willett, RC Glen (1995) Molecular recognition ofreceptor sites using a genetic algorithm with a description ofdesolvation, J Mol Biol 245 (1): 43–53PubMedCrossRefGoogle Scholar
  15. 15.
    ML Verdonk, JC Cole, MJ Hartshorn, CW Murray, RD Taylor (2003) Improved Protein-Ligand Docking Using GOLD, Proteins 52(4): 609–623PubMedCrossRefGoogle Scholar
  16. 16.
    M Rarey, B Kramer, T Lengauer, G Klebe (1996) A Fast FlexibleDocking Method using an Incremental Construction Algorithm, J MolBiol 261(3): 470–489CrossRefGoogle Scholar
  17. 17.
    P Kirkpatrick (2004) Virtual screening: Gliding to success, NatureReviews Drug Discovery 3: 299–299CrossRefGoogle Scholar
  18. 18.
    T A Halgren, R B Murphy, R A Friesner, H S Beard, L L Frye, W TPollard, J L Banks (2004) Glide: A New Approach for Rapid,Accurate Docking and Scoring 2 Enrichment Factors in DatabaseScreening, J Med Chem 47(7): 1750–1759PubMedCrossRefGoogle Scholar
  19. 19.
    R A Friesner, J L Banks, R B Murphy, T A Halgren, J J Klicic, D TMainz, M P Repasky, E H Knoll, M Shelley, J K Perry, D E Shaw, PFrancis, P S Shenkin (2004) Glide: A New Approach for Rapid,Accurate Docking and Scoring 1 Method and Assessment of DockingAccuracy, J Med Chem 47(7): 1739–1749PubMedCrossRefGoogle Scholar
  20. 20.
    H-J Böhm (1993) A novel computational tool for automatedstructure-based drug design, J Mol Rec 6(3): 131–137ADSCrossRefGoogle Scholar
  21. 21.
    G M Morris, DS Goodsell, RS Halliday, R Huey, WE Hart, RK Belew,AJ Olson (1998) Automated Docking Using a Lamarckian GeneticAlgorithm and Empirical Binding Free Energy Function, JComputChem 19: 1639–1662Google Scholar
  22. 22.
    H Gohlke, G Klebe, Approaches to the Description and Prediction ofthe Binding Affinity of Small-Molecule Ligands to MacromolecularReceptors, Angew Chem Int Ed 41(15): 2644–2676Google Scholar
  23. 23.
    hm1994 HJ Böhm (1994) The development of a simple empirical scoringfunction to estimate the binding constant for a protein-ligandcomplex of known three-dimensional structure, J Comput Aided MolGoogle Scholar
  24. 24.
    MD Eldridge, CW Murray, TR Auton, GV Paolini, RP Mee (1997) Empirical scoring functions: I The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes, J Comput Aided Mol Des 11(5):425–445PubMedCrossRefGoogle Scholar
  25. 25.
    G Jones, P Willett, RC Glen, AR Leach, R Taylor (1997) Development and Validation of a Genetic Algorithm for Flexible Docking, J Mol Biol 267(3):727–748PubMedCrossRefGoogle Scholar
  26. 26.
    R Wang, L Lai, S Wang (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction, J Comput Aided Mol Des 16(1):11–26PubMedCrossRefGoogle Scholar
  27. 27.
    TJA Ewing, ID Kuntz (1997) Critical evaluation of search algorithms for automated molecular docking and database screening, J Comput Chem 18(9):1175–1189CrossRefGoogle Scholar
  28. 28.
    H Gohlke, M Hendlich, G Klebe (2000) Knowledge-based scoring function to predict protein-ligand interactions, J Mol Biol 295(2):337–356PubMedCrossRefGoogle Scholar
  29. 29.
    I Muegge, Y C Martin (1999) A General and Fast Scoring Function for Protein-Ligand Interactions: A Simplified Potential Approach, J Med Chem 42(5):791–804PubMedCrossRefGoogle Scholar
  30. 30.
    P S Charifson, J J Corkery, M A Murcko, W P Walters (1999) Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins, J Med Chem 42(25):5100–5109PubMedCrossRefGoogle Scholar
  31. 31.
    R Wang, Y Lu, S Wang (2003) Comparative Evaluation of 11 Scoring Functions for Molecular Docking, J Med Chem 46(12):2287–2303PubMedCrossRefGoogle Scholar
  32. 32.
    M Jacobsson, P Liden, E Stjernschantz, H Bostrom, U Norinder (2003) Improving Structure-Based Virtual Screening by Multivariate Analysis of Scoring Data, J Med Chem 46(26):5781–5789PubMedCrossRefGoogle Scholar
  33. 33.
    AC Anderson (2003) The Process of Structure-Based Drug Design, Chem Biol 10(9):787–797PubMedCrossRefGoogle Scholar
  34. 34.
    RS Bohacek, C McMartin (1997) Modern computational chemistry and drug discovery: structure generating programs, Curr Opin Chem Biol 1(2):157–161PubMedCrossRefGoogle Scholar
  35. 35.
    Y Nishibata, A Itai (1993) Confirmation of usefulness of a structure construction program based on three-dimensional receptor structure for rational lead generation, J Med Chem 36(20):2921–2928PubMedCrossRefGoogle Scholar
  36. 36.
    RS Bohacek, C McMartin (1994) Multiple Highly Diverse Structures Complementary to Enzyme Binding Sites: Results of Extensive Application of a de Novo Design Method Incorporating Combinatorial Growth, J Am Chem Soc 116(13):5560–5571CrossRefGoogle Scholar
  37. 37.
    SH Rotstein, MA Murcko (1993) GenStar: a method for de novo drug design, J Comput Aided Mol Des 7(1):23–43PubMedCrossRefGoogle Scholar
  38. 38.
    DK Gehlhaar, KE Moerder, D Zichi, CJ Sherman, RC Ogden, ST Freer (1995) De novo design of enzyme inhibitors by Monte Carlo ligand generation, J Med Chem 38(3):466–472PubMedCrossRefGoogle Scholar
  39. 39.
    V Gillet, AP Johnson, P Mata, S Sike, P Williams (1993) SPROUT: a program for structure generation, J Comput Aided Mol Des 7(2):127–153PubMedCrossRefGoogle Scholar
  40. 40.
    DA Pearlman, MA Murcko (1993) CONCEPTS: New dynamic algorithm for de novo drug suggestion, J Comput Chem 14(10):1184–1193CrossRefGoogle Scholar
  41. 41.
    JB Moon, WJ Howe (1991) Computer design of bioactive molecules: A method for receptor-based de novo ligand design, Proteins: Struct, Funct, Genet 11(4):314–328CrossRefGoogle Scholar
  42. 42.
    V Tschinke, NC Cohen (1993) The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses, J Med Chem 36(24):3863–3870PubMedCrossRefGoogle Scholar
  43. 43.
    SH Rotstein, MA Murcko (1993) GroupBuild: a fragment-based method for de novo drug design, J Med Chem 36(12):1700–1710CrossRefGoogle Scholar
  44. 44.
    DC Roe, ID Kuntz (1993) BUILDER v2: improving the chemistry of a de novo design strategy, J Comput Aided Mol Des 9(3):269–282CrossRefGoogle Scholar
  45. 45.
    MB Eisen, DC Wiley, M Karplus, RE Hubbard (1994) HOOK: A program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site, Proteins: Struct, Funct, Genet 19(3):199–221CrossRefGoogle Scholar
  46. 46.
    DA Pearlman, MA Murcko (1996) CONCERTS: Dynamic Connection of Fragments as an Approach to de Novo Ligand Design, J Med Chem 39(8): 1651–1663PubMedCrossRefGoogle Scholar
  47. 47.
    R S DeWitte, E I Shakhnovich (1996) SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates 1 Methodology and Supporting Evidence, J Am Chem Soc 118(47): 11733–11744CrossRefGoogle Scholar
  48. 48.
    R S DeWitte, A V Ishchenko, E I Shakhnovich (1997) SMoG: de Novo Design Method Based on Simple, Fast, and Accurate Free Energy Estimates 2 Case Studies in Molecular Design, J Am Chem Soc 119(20): 4608–4617CrossRefGoogle Scholar
  49. 49.
    H-J Böhm (1992a) The computer program LUDI: a new method for the de novo design of enzyme inhibitors, J Comput Aided Mol Design 6(1): 61–78CrossRefGoogle Scholar
  50. 50.
    H-J Böhm (1992b) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads, J Comput Aided Mol Design 6(6): 593–606CrossRefGoogle Scholar
  51. 51.
    HJ Böhm (1996) Towards the automatic design of synthetically accessible protein ligands: peptides, amides and peptidomimetics, J Comput Aided Mol Design 10(4): 265–272CrossRefGoogle Scholar
  52. 52.
    S F Boys, F Bernardi (1970) Calculation of small molecular interactions by differences of separate total energies – some procedures with reduced errors, Mol Phys 19(4): 553ADSCrossRefGoogle Scholar
  53. 53.
    WA Sokalski, S Roszak, K Pecul (1988) An efficient procedure for decomposition of the SCF interaction energy into components with reduced basis set dependence: Chem Phys Lett 153(2,3): 153–159ADSCrossRefGoogle Scholar
  54. 54.
    WA Sokalski, P Kedzierski, J Grembecka (2001) Ab initio study of the physical nature of interactions between enzyme active site fragments in vacuo, Phys Chem Chem Phys 3(5): 657–663CrossRefGoogle Scholar
  55. 55.
    B Szefczyk, A J Mulholland, K E Ranaghan, W A Sokalski (2004) Differential transition-state stabilization in enzyme catalysis: quantum chemical analysis of interactions in the chorismate mutase reaction and prediction of the optimal catalytic field, J Am Chem Soc 126 (49): 16148–16159PubMedCrossRefGoogle Scholar
  56. 56.
    J Grembecka, P Kedzierski, WA Sokalski (1999) Non-empirical analysis of the nature of the inhibitor-active-site interactions in leucine aminopeptidase, Chem Phys Lett 313(1): 385–392CrossRefGoogle Scholar
  57. 57.
    J Grembecka, WA Sokalski, P Kafarski (2001) Quantum chemical analysis of the interactions of transition state analogs with leucine aminopeptidase, Int J Quantum Chem 84(2), 302–310CrossRefGoogle Scholar
  58. 58.
    E Dyguda, J Grembecka, WA Sokalski, J Leszczynski (2005) Origins of the activity of PAL and LAP enzyme inhibitors: Toward ab initio binding affinity prediction, J Am Chem Soc 127(6): 1658–1659PubMedCrossRefGoogle Scholar
  59. 59.
    A Taylor (1993) Aminopeptidases: towards a mechanism of action, Trends Biochem Sci 18: 167–171PubMedGoogle Scholar
  60. 60.
    EL Smith, RL Hill, (1960) in: The enzymes, Academic Press, New York, pp 37–62Google Scholar
  61. 61.
    N Strater, WN Lipscomb (1995) Two-metal ion mechanism of bovine lens leucine aminopeptidase: active site solvent structure and binding mode of L-leucinal, a gem-diolate transition state analogue, by X-ray crystallography, Biochemistry 34: 14792–14800PubMedCrossRefGoogle Scholar
  62. 62.
    H Kim, WN Lipscomb (1994) Structure and mechanism of bovine lens leucine aminopeptidase, Adv Enzymol Relat Areas Mol Biol 68: 153–213PubMedCrossRefGoogle Scholar
  63. 63.
    N Strater, L Sun, ER Kantrowitz, WN Lipscomb (1999) A bicarbonate ion as a general base in the mechanism of peptide hydrolysis by dizinc leucine aminopeptidase, Proc Natl Acad Sci USA 96: 11151–11155PubMedADSCrossRefGoogle Scholar
  64. 64.
    A Taylor (1993) Aminopeptidases: structure and function, FASEB J 7: 290–298PubMedGoogle Scholar
  65. 65.
    H Umezawa (1980) Screening of small molecular microbial products modulating immune responses and bestatin, Recent Results Cancer Res 75: 115–125PubMedGoogle Scholar
  66. 66.
    SK Gupta, M Aziz, AA Khan (1989) Serum leucine aminopeptidase estimation: a sensitive prognostic indicator of invasiveness in breast carcinoma, Indian J Pathol Microbiol 32: 301–305PubMedGoogle Scholar
  67. 67.
    A Taylor, M Daims, J Lee, T Surgenor (1982) Identification and quantification of leucine aminopeptidase in aged normal and cataractous human lenses and ability of bovine lens LAP to cleave bovine crystallins, Curr Eye Res 2: 47–56PubMedGoogle Scholar
  68. 68.
    A Taylor, ,MJ Brown, ,MA Daims, ,J Cohen (1983) Localization of leucine aminopeptidase in normal hog lens by immunofluorescence and activity assays, Invest Ophthalmol Vis Sci 24:1172–1180PubMedGoogle Scholar
  69. 69.
    A Taylor, ,T Surgenor, ,DK Thomson, ,RJ Graham, ,H Oettgen (1984) Comparison of leucine aminopeptidase from human lens, beef lens and kidney, and hog lens and kidney, Exp Eye Res 38:217–229PubMedCrossRefGoogle Scholar
  70. 70.
    CS Scott, ,M Davey, ,A Hamilton, ,DR Norfolk (1986) Serum enzyme concentrations in untreated acute myeloid leukaemia, Blut 52:297–303PubMedCrossRefGoogle Scholar
  71. 71.
    J Beninga, ,KL Rock, ,AL Goldberg (1998) Interferon-gamma can stimulate post-proteasomal trimming of the N terminus of an antigenic peptide by inducing leucine aminopeptidase, J Biol Chem 273:18734–18742PubMedCrossRefGoogle Scholar
  72. 72.
    G Pulido-Cejudo, ,B Conway, ,P Proulx, ,R Brown, ,CA Izaguirre (1997) Bestatin-mediated inhibition of leucine aminopeptidase may hinder HIV infection, Antiviral Res 36:167–177PubMedCrossRefGoogle Scholar
  73. 73.
    J Grembecka, ,WA Sokalski, ,P Kafarski (2000) Computer-aided design and activity prediction of leucine aminopeptidase inhibitors, J Comput Aid Mol Des 14(6) 531–544CrossRefGoogle Scholar
  74. 74.
    J Grembecka, ,A Mucha, ,T Cierpicki, ,P Kafarski (2003) The most potent organophosphorus inhibitors of leucine aminopeptidase Structure-based design, chemistry, and activity, J Med Chem 46(13):2641–2655PubMedCrossRefGoogle Scholar
  75. 75.
    H-J Böhm (1998) Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs, J Comput Aided Mol Design 12(4):309–323CrossRefGoogle Scholar
  76. 76.
    M Drag, ,J Grembecka, ,M Pawelczak, ,P Kafarski (2005) alpha-aminoalkylphosphonates as a tool in experimental optimisation of P1 side chain shape of potential inhibitors in S1 pocket of leucine - and neutral aminopeptidases, Eur J Med Chem 40(8):764–771PubMedCrossRefGoogle Scholar
  77. 77.
    D Eisenberg, ,HS Gill, ,GM Pfluegl, ,SH Rotstein (2000) Structure-function relationships of glutamine synthetases, Biochim Biophys Acta 1477(1):122–145PubMedGoogle Scholar
  78. 78.
    ER Stadtman (2001) The Story of Glutamine Synthetase Regulation, J Biol Chem 276(48):44357–44364PubMedCrossRefGoogle Scholar
  79. 79.
    GM Kishore, ,DM Shah (1988) Amino acid biosynthesis inhibitors as herbicides, Ann Rev Biochem 57:627–663PubMedCrossRefGoogle Scholar
  80. 80.
    E Bayer, ,K H Gugel, ,K Hägele, ,H Hagenmaier, ,S Jessipow, ,W A Köonig, ,H Zähner (1972) Metabolic products of microorganisms 98 Phosphinothricin and phosphinothricyl-alanyl-analine, Helv Chim Acta 55:224–239PubMedCrossRefGoogle Scholar
  81. 81.
    K Tachibana (1987) Herbicidal characteristics of bialphos, in Pesticide Science and Biotechnology, R Greenhalgh T.R. Roberts (Eds) pp 145–148Google Scholar
  82. 82.
    A Wild, ,H Sauer, ,W Rühle (1987) The effect of phosphinothricin (glufosinate) on photosynthesis I: Inhibition of photosynthesis and accumulation of ammonia, Z Naturforsch 42:263–269MathSciNetGoogle Scholar
  83. 83.
    K Tachibana, ,T Watanabe, ,Y Sekizawa, ,T Takematsu (1986) Accumulation of ammonia in plants treated with bialphos, J Pest Sci 11:33–37Google Scholar
  84. 84.
    PJ Lea, ,KW Joy, ,JL Ramos, ,MG Guerrero (1984) The action of 2-amino-4-(methylphosphinyl)-butanoic acid (phosphinothricin) and its 2-oxo-derivative on the metabolism of cyanobacteria and higher plants, Phytochemistry 23:1–6CrossRefGoogle Scholar
  85. 85.
    A Wild, ,R Manderscheid (1984) The effect of phosphinothricin in the assimilation of ammonia in plants, Z Naturforsch 39:500–504Google Scholar
  86. 86.
    H Sauer, ,A Wild, ,W Rühle (1987) The effect of phosphinothricin (glufosinate) on photosynthesis II: The causes of inhibition of photosynthesis, Z Naturforsch 42:270–278Google Scholar
  87. 87.
    G Harth, ,MA Horwitz (1999) An inhibitor of exported Mycobacterium tuberculosis glutamine synthetase selectively blocks the growth of pathogenic mycobacteria in axenic culture and in human monocytes: extracellular proteins as potential novel drug targets, J Exp Med 189(9):1425–1436PubMedCrossRefGoogle Scholar
  88. 88.
    G Harth, ,MA Horwitz (2003) Inhibition of Mycobacterium tuberculosis glutamine synthetase as a novel antibiotic strategy against tuberculosis: demonstration of efficacy in vivo, Infect Immun 71(1):456–464PubMedCrossRefGoogle Scholar
  89. 89.
    RJ Almassy, CA Janson, R Hamlin, NH Xuong, D Eisenberg (1986) Novel subunit-subunit interactions in the structure of glutamine synthetase, Nature 323(6086):304–309PubMedADSCrossRefGoogle Scholar
  90. 90.
    MM Yamashita, RJ Almassy, CA Janson, D Cascio, D Eisenberg (1989) Refined atomic model of glutamine synthetase at 35 A resolution, J Biol Chem 264:17681–17690PubMedGoogle Scholar
  91. 91.
    S H Liaw, D Eisenberg (1994) Structural model for the reaction mechanism of glutamine synthetase, based on five crystal structures of enzyme-substrate complexes, Biochemistry 33(3):675–681PubMedCrossRefGoogle Scholar
  92. 92.
    RA Ronzio, A Meister (1968) Phosphorylation of Methionine Sulfoximine by Glutamine Synthetase, Proc Natl Acad Sci USA 59(1):164–170PubMedADSCrossRefGoogle Scholar
  93. 93.
    J A Colanduoni, J J Villafranca (1986) Inhibition of E coli glutamine synthetase by phosphino -thricin, Bioorg Chem 14:163–169CrossRefGoogle Scholar
  94. 94.
    H S Gill, D Eisenberg (2001) The Crystal Structure of Phosphinothricin in the Active Site of Glutamine Synthetase Illuminates the Mechanism of Enzymatic Inhibition, Biochemistry 40(7):1903–1912PubMedCrossRefGoogle Scholar
  95. 95.
    L Berlicki, P Kafarski (2006), Computer-aided analysis of the interactions of glutamine synthetase with its inhibitors, Bioorg Med Chem 14(13):4578–4585PubMedCrossRefGoogle Scholar
  96. 96.
    L Berlicki, A Obojska, G Forlani, P Kafarski (2005) Design, Synthesis, and Activity of Analogues of Phosphinothricin as Inhibitors of Glutamine Synthetase, J Med Chem 48(20):6340–6349PubMedCrossRefGoogle Scholar
  97. 97.
    K RHanson, E A Havir (1981) Phenylalanine ammonia-lyase In The Biochemistry of Plants, Vol 7: Secondary Plant Metabolites Conn, E E edt; Academic Press, New York; pp 577–625Google Scholar
  98. 98.
    H Griesbach,H Lignins (1981) In The Biochemistry of Plants, Vol 7: Secondary Plant Metabolites Conn, E E edt; Academic Press, New York; pp 457–478Google Scholar
  99. 99.
    C N Sarkissian, Z Shao, F Blain, R Peevers, H S Su, R Heft, T M S Chang, T S Scriver (1999) A different approach to treatment of phenylketonuria: Phenylalanine degradation with recombinant phenylalanine ammonia lyase, Proc Natl Acad Sci USA 96(5):2339–2344PubMedADSCrossRefGoogle Scholar
  100. 100.
    B Schuster, J Retey (1994) Serine-202 is the putative precursor of the active site dehydroalanine of phenylalanine ammonia lyase FEBS Lett 349(2):252–254PubMedCrossRefGoogle Scholar
  101. 101.
    B Langer, D Rother, J Retey (1997) Identification of essential amino acids in phenylalanine ammonia-lyase by site-directed mutagenesis Biochemistry 36(36):10867–10871PubMedCrossRefGoogle Scholar
  102. 102.
    M Baedeker, G E Schulz (2002) Structures of two histidine ammonia-lyase modifications and implications for the catalytic mechanism Eur J Biochem 269(6):1790–1797Google Scholar
  103. 103.
    T F Schwede, J Retey, G E Schulz (1999) Crystal structure of histidine ammonia-lyase revealing a novel polypeptide modification as the catalytic electrophile Biochemistry 38(17):5355–5361PubMedCrossRefGoogle Scholar
  104. 104.
    D Rother, L Poppe, G Morlock, S Viergutz, J Retey (2002) An active site homology model of phenylalanine ammonia-lyase from Petroselinum crispum, Eur J Biochem 269(12):3065–3075PubMedCrossRefGoogle Scholar
  105. 105.
    A Skolaut, J Retey (2001) 1,4-Dihydrophenylalanine – its synthesis and behavior in the phenylalanine ammonia-lyase reaction Archiv Biochem Biophys 393(2):187–191CrossRefGoogle Scholar
  106. 106.
    J Zon, N Amrhein, R Gancarz (2002) Inhibitors of phenylalanine ammonia-lyase: 1-aminobenzylphosphonic acid substituted in the benzene ring Phytochemistry 59(1):9–21PubMedCrossRefGoogle Scholar
  107. 107.
    C Appert, J Zoň, N Amrhein (2003) Kinetic analysis of the inhibition of phenylalanine ammonia-lyase by 2-aminoindan-2-phosphonic acid and other phenylalanine analogues Phytochemistry 62(3):415–422PubMedCrossRefGoogle Scholar
  108. 108.
    J Zoň, P Miziak, N Amrhein, R Gancarz (2005) Inhibitors of Phenylanine Ammonia-Lyase (PAL): Synthesis and Biological Evaluation of 5-Substituted 2-Aminoindane-2-phosphonic Acids Chemistry and Biodiversity 2(9):1187–1194CrossRefGoogle Scholar
  109. 109.
    H Ritter, G E Schulz (2004) Structural Basis for the Entrance into the Phenylpropanoid Metabolism Catalyzed by Phenylalanine Ammonia-Lyase The Plant Cell 16(12):3426-3436PubMedCrossRefGoogle Scholar
  110. 110.
    J C Calabrese, D B Jordan, A Boodhoo, S Sariaslani, T Vannelli (2004) Crystal Structure of Phenylalanine Ammonia-Lyase: Multiple Helix Dipoles Implicated in Catalysis. Biochemistry 43(36):11403–11416PubMedCrossRefGoogle Scholar
  111. 111.
    B Langer, M Langer, J Retey (2001) Methylidene-imidazolone (MIO) from histidine and phenylalanine ammonia-lyase, Adv Protein Chem 58:175–214PubMedCrossRefGoogle Scholar
  112. 112.
    L Maier, P J Diel (1994) Synthesis, physical and biological properties of the phosphorus analogs of phenylalanine and related compounds, Phosphorus Sulfur 90(1–4):259–279Google Scholar
  113. 113.
    L E Chirlian, M M Francl (1987) Atomic charges derived from electrostatic potentials – a detailed study, J Comp Chem 8(6):894–905CrossRefGoogle Scholar
  114. 114.
    G Naray-Szabo (1984) Quantum chemical calculation of the enzyme ligand interaction energy for trypsin inhibition by benzamidines, J Am Chem Soc 106(16):4584–4589CrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Łukasz Berlicki
    • 1
  • Jolanta Grembecka
    • 1
  • Edyta Dyguda-Kazimierowicz
    • 1
  • PaweŁ Kafarski
    • 1
  • W. Andrzej Sokalski
    • 1
  1. 1.Department of ChemistryWrocŃaw University of TechnologyWrocŃawPoland

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