Wiener Medizinische Wochenschrift

, Volume 159, Issue 5–6, pp 112–125 | Cite as

Homology modelling: a review about the method on hand of the diabetic antigen GAD 65 structure prediction

  • Marco Wiltgen
  • Gernot P. Tilz


This introductory paper describes the basic principles and clinical applications of the protein 3D structure prediction by homology modelling. The paper mainly addresses physicians and medical chemists. Because many proteins are of immediate clinical importance, the determination of their structures is crucial for molecular medicine. In homology modelling, a protein sequence with unknown structure is aligned with sequences of known protein structures. By exploiting structural information from the known configurations, the new structure can be predicted. The necessary condition for successful homology modelling is a sufficient similarity between the protein sequences. Because in the near future for every protein family at least one member with a known structure will be available, the importance and applicability of homology modelling is steadily increasing. We demonstrate the principles of homology modelling on hand of the Glutamic Acid Decarboxylase (GAD 65) structure prediction, which is a typical autoantigen involved in Diabetes Mellitus Type 1.


Bioinformatics Homology modelling GAD 65 Epitopes Active site 

Homology Modelling: Eine Übersicht über die Methode am Beispiel der Strukturbestimmung vom Diabetes Antigen GAD 65


Dieser Übersichtsartikel behandelt die Grundlagen und klinische Anwendungen der Protein-Strukturvorhersage mit der "Homology Modelling"-Methode. Der Artikel richtet sich vornehmlich an Mediziner und medizinische Chemiker. Da viele Proteine unmittelbar von klinischer Bedeutung sind, ist die Strukturbestimmung entscheidend für das Verständnis der molekularen Grundlage von Stoffwechsel, Signalübertragung, Immunreaktionen und damit assoziierten Krankheiten. "Homology Modelling" beruht auf dem Prinzip, dass Proteine mit ähnlichen Sequenzen hohe Strukturübereinstimmungen aufweisen. Dabei werden Informationen, wie Atomabstände, Bindungslängen, Bindungswinkel etc., über bereits bekannten Strukturen verwendet, um die unbekannte Proteinstruktur anhand ihrer Sequenz vorauszusagen. Da in Zukunft für jede Proteinfamilie mindestens ein Mitglied mit bekannter Struktur verfügbar sein wird, ist zu erwarten, dass Bedeutung und Anwendbarkeit der "Homology Modelling"-Methode weiter zunehmen werden. In dieser Arbeit werden die Grundlagen der Methode anhand der Strukturvorhersage des Enzyms Glutamate Decarboxylase (GAD 65) erläutert. GAD 65 spielt als Antigen bei Diabetes Mellitus Typ 1 eine entscheidende Rolle.


Bioinformatik Homology Modelling GAD 65 Epitope Aktiv-Zentrum 


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  1. Rorsman P, Berggren PO, Bokvist K, Ericson H, Mohler H, Ostenson, CG, Smith PA. Glucose-inhibition of glucagon secretion involves activation of GABAA-receptor choride channels. Nature, 341(6239): 233–236, 1989PubMedCrossRefGoogle Scholar
  2. Tong JC, Myers MA, Mackay IR, Zimmet PZ, Rowley MJ. The PEVKEK region of the pyridoxal phosphare binding domain of GAD 65 expresses a dominant B cell epitope for type 1 diabetes sera. Ann N Y Acad Sci, 958: 182–189, 2002PubMedCrossRefGoogle Scholar
  3. Tree TI, Morgenthaler NG, Duhindan N, Hicks KE, Madec AM, Scherbaum WA, Banga JP. Two amino acids in glutamic acid decarboxylase act in concert for maintenance of conformational determinants recognised by Type I diabetic autoantibodies. Diabetologia, 43(7): 881–889, 2000PubMedCrossRefGoogle Scholar
  4. Liberatore Rdel R Jr, Damiani D. Insulin pump therapy in type 1 diabetes mellitus. J Pediatr (Rio J), 82(4): 249–254, 2006CrossRefGoogle Scholar
  5. Veleminsky Sr M, Buresova G. Health related quality of life of children and adolescents with type 1 diabetes. Neuro Endocrinol Lett, 5: 29(6), 2008Google Scholar
  6. Codner E, Cassorla F. Puberty and ovarian function in girls with type 1 diabetes mellitus. Horm Res, 71(1): 12–21, 2008PubMedCrossRefGoogle Scholar
  7. Rashidi HH, Bühler LK. Bioinformatics basics, applications in biological sciences and medicine. CRC Press LLC, Boca Raton/London/New York/Washington DC, 2000Google Scholar
  8. Mount DW. Bioinformatics: sequence and genome analysis. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, 2001Google Scholar
  9. Lesk AM. Introduction to bioinformatics. Oxford University Press, Oxford, 2002Google Scholar
  10. Campbell AM, Heyer LJ. Discovering genomics, proteomics and bioinformatics. Benjamin Cummings, San Francisco, Boston, New York, 2002Google Scholar
  11. Gibas C, Jambeck P. Developing bioinformatics computer skills. O'Reilly, 2001Google Scholar
  12. Baxevanis AD. Searching the NCBI databases using Entrez. Curr Protoc Hum Genet, Chapter 6: Unit 6.10, 2006Google Scholar
  13. Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL, Schuler GD, Tatusova TA, Rapp BA. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res, 28(1): 10–14, 2000PubMedCrossRefGoogle Scholar
  14. Tatusova T. GenBank, RefSeq, TPA and UniProt: What's in a Name? Microbe Magazine 2007; LettersGoogle Scholar
  15. Bairoch A, Apweiler R. The SWISS-PROT protein sequence databank and its supplement TrEMBL in 1998. Nucl Acids Res, 26: 38–42, 1998PubMedCrossRefGoogle Scholar
  16. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The protein data bank. Nucleic Acids Res, 28: 235–242, 2000PubMedCrossRefGoogle Scholar
  17. McKusick VA. Mendelian inheritance in man. A catalogue of human genes and genetic disorders, 12th edn. Johns Hopkins University Press, Baltimore, 1998Google Scholar
  18. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Rapp BA, Wheeler DL. GenBank. Nucleic Acids Res, 28(1): 15–18, 2000PubMedCrossRefGoogle Scholar
  19. Smigielski EM, Sirotkin K, Ward M, Sherry ST. dbSNP: a database of single nucleotide polymorphisms. Nucleic Acids Res, 28(1): 352–355, 2000PubMedCrossRefGoogle Scholar
  20. Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res, 35: 26–31, 2007CrossRefGoogle Scholar
  21. Marchler-Bauer A, Panchenko AR, Shoemaker BA, Thiessen PA, Geer LY, Bryant SH. CDD: a database of conserved domain alignments with links to domain three-dimensional structure. Nucleic Acids Res, 30: 281–283, 2002PubMedCrossRefGoogle Scholar
  22. Hersh WR, Greenes RA. Information retrieval in medicine: state of the art. MD Comput, 7(5): 302–311, 1990PubMedGoogle Scholar
  23. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res, 31: 3784–3788, 2003PubMedCrossRefGoogle Scholar
  24. Sussman JL, Abola EE, Lin D, Jiang J, Manning NO, Prilusky J. The protein data bank: Bridging the gap between the sequence and 3D structure world. Genetica, 106(1–2): 149–158, 1999PubMedCrossRefGoogle Scholar
  25. Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J, Ritter O, Abola EE. Protein Data Bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallogr D Biol Crystallogr, 54: 1078–1084, 1998PubMedCrossRefGoogle Scholar
  26. Westbrook JD, Fitzgerald PM. The PDB format, mmCIF, and other data formats. Methods Biochem Anal, 44: 161–179, 2003PubMedGoogle Scholar
  27. Xu D, Xu Y, Uberbacher EC. Computational tools for protein modelling. Curr Protein Pept Sci, 1: 1–21, 2000PubMedCrossRefGoogle Scholar
  28. Johnson MS, Srinivasan N, Sowdhamini R, Blundell TL. Knowledge-based protein modelling. CRC Crit Rev Biochem Mol Biol, 29: 1–68, 1994CrossRefGoogle Scholar
  29. Schwede T, Diemand A, Guex N, Peitsch MC. Protein structure computing in the genomic era. Res Microbiol, 151: 107–112, 2000PubMedCrossRefGoogle Scholar
  30. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res, 231(13): 3381–3385, 2003CrossRefGoogle Scholar
  31. Sanchez R, Sali A. Comparative protein structure modeling. Introduction and practical examples with modeller. Methods Mol Biol, 143: 97–129, 2000PubMedGoogle Scholar
  32. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol, 215: 403–410, 1990PubMedGoogle Scholar
  33. Burkhard P, Domonici P, Borri-Voltattori C, Jansonius JN, Malashkevich VN. Structural insight into Parkinson's disease treatment from drug-inhibited DOPA decarboxylase. Nat Struct Biol, 8(11): 963–967, 2001PubMedCrossRefGoogle Scholar
  34. Malashkevich VN, Burkhard P, Dominici P, Moore PS, Borri-Voltattorni C, Jansonius JN. Preliminary X-ray analysis of a new crystal form of pig kidney DOPA decarboxylase. Acta Crystallogr, Sect D: 555–568, 1999Google Scholar
  35. Dalton JA, Jackson RM. An evaluation of automated homology modelling methods at low target template sequence similarity. Bioinformatics, 23(15): 1901–1908, 2007PubMedCrossRefGoogle Scholar
  36. Wallner B, Elofsson A. All are not equal: a benchmark of different homology modeling programs. Protein Sci, 14(5): 1315–1327, 2005PubMedCrossRefGoogle Scholar
  37. Eswar N, Eramian D, Webb B, Shen MY, Sali A. Protein structure modelling with MODELLER. Methods Mol Biol, 426: 145–159, 2008PubMedCrossRefGoogle Scholar
  38. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol, 234: 779–815, 1993PubMedCrossRefGoogle Scholar
  39. Sanchez R, Sali A. Advances in comparative protein-structure modelling. Curr Opin Struct Biol, 7: 206–214, 1997PubMedCrossRefGoogle Scholar
  40. Peitsch MC. Large scale protein modelling and model repository. Proc Int Conf Intell Syst Mol Biol, 5: 234–236, 1997PubMedGoogle Scholar
  41. Fiser A, Kinh G, Do R, Sali A. Modelling of loops in protein structures. Protein Sci, 9: 1753–1773, 2000PubMedCrossRefGoogle Scholar
  42. Guvench O, MacKerell AD Jr. Comparison of protein force fields for molecular dynamics simulations. Methods Mol Biol, 443: 63–88, 2008PubMedCrossRefGoogle Scholar
  43. Li Z, Yu H, Zhuang W, Mukamel S. Geometry and excitation energy fluctuations of NMA in aqueous solution with CHARMM, AMBER, OPLS, and GROMOS force fields: implications for protein ultraviolet spectra simulation. Chem Phys Lett, 452(1–3): 78–83, 2008PubMedGoogle Scholar
  44. Guex N, Peitsch MC. SWISS-MODEL and the Swiss-Pdb Viewer: an environment for comparative protein modelling. Electrophoresis, 18: 2714–2723, 1997PubMedCrossRefGoogle Scholar
  45. Peitsch MC. ProMod and Swiss-Model: Internet-based tools for automated comparative protein modelling. Biochem Soc Tran, 24: 274–279, 1996Google Scholar
  46. Arnold K, Bordoli L, Kopp J, Schwede T. The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics, 222: 195–201, 2006Google Scholar
  47. Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T. The SWISS-MODEL Repository and associated resources. Nucleic Acids Res, 37: 387–392, 2009CrossRefGoogle Scholar
  48. Kopp J, Schwede T. The SWISS-MODEL Repository: new features and functionalities. Nucleic Acids Res, 34: 315–318, 2006CrossRefGoogle Scholar
  49. Kopp J, Schwede T. The SWISS-MODEL Repository of annotated three-dimensional protein structure homology models. Nucleic Acids Res, 32: 230–234, 2004CrossRefGoogle Scholar
  50. Peitsch MC, Wilkins MR, Tonella L, Sanchez JC, Appel RD, Hochstrasser DF. Large-scale protein modelling and integration with the SWISS-PROT and SWISS-2DPAGE databases: the example of Escherichia coli. Electrophoresis, 18(3–4): 498–501, 1997PubMedCrossRefGoogle Scholar
  51. Peitsch MC. ProMod and Swiss-Model: Internet-based tools for automated comparative protein modelling. Biochem Soc Trans, 24(1): 274–279, 1996PubMedGoogle Scholar
  52. Kaplan W, Littlejohn TG. Swiss-PDB Viewer (Deep View). Brief Bioinform, 2(2): 195–197, 2001PubMedCrossRefGoogle Scholar
  53. Peitsch MC. Protein modelling by e-mail. Bio Technology, 13: 658–660, 1995Google Scholar
  54. Bao Y, Li L, Zhang G. The manganese superoxide dismutase gene in bay scallop Argopecten irradians: cloning, 3D modelling and mRNA expression. Fish Shellfish Immun, 25(4): 425–432, 2008CrossRefGoogle Scholar
  55. Sun S, Zhang Z, Li S, Hu J, Zhang F. Cloning, sequencing analysis and expression of a putative mannose-binding lectin gene from Polygonatum roseum. Xinjiang Sheng Wu Gong Cheng Xue Bao, 24(3): 387–394, 2008Google Scholar
  56. Liang YY, Zhang C, Chen SL, Feng SW. Preliminary study of the spatial structural and functional changes of dystrophin after exon-3 deletion. Nan Fang Yi Ke Da Xue Xue Bao, 28(6): 938–941, 2008PubMedGoogle Scholar
  57. Sauter P, Chehadeh W, Lobert PE, Lazrek M, Goffard A, Soumillon M, Caloone D, Vantyghem MC, Weill J, Fajardy I, Alm G, Lucas B, Hober D. A part of the VP4 capsid protein exhibited by coxsackievirus B4 E2 is the target of antibodies contained in plasma from patients with type 1 diabetes. J Med Virol, 80(5): 866–878, 2008PubMedCrossRefGoogle Scholar
  58. Ranieri DI, Corgliano DM, Franco EJ, Hofstetter H, Hofstetter O. Investigation of the stereoselectivity of an anti-amino acid antibody using molecular modeling and ligand docking. Chirality, 20(3–4): 559–570, 2008PubMedCrossRefGoogle Scholar
  59. Duan HY, Zhang BY, Hu Y, Song LH, Zhu H, Duan Q. Study on the ability of mammalian reovirus BYD1 to induce apoptosis and analysis of the structure of viral major membrane penetration protein involved in proapoptosis induction. Zhonghua Shi Yan He Lin Chuang Bing Du Xue Za Zhi, 21(3): 223–225, 2007PubMedGoogle Scholar
  60. Hooley E, McEwan PA, Emsley J. Molecular modelling of the prekallikrein structure provides insights into high-molecular-weight kininogen binding and zymogen activation. J Thromb Haemost, 5(12): 2461–2466, 2007PubMedCrossRefGoogle Scholar
  61. Heng CK, Othman RY. Bioinformatics in molecular immunology laboratories demonstrated: Modeling an anti-CMV scFv antibody. Bioinformation, 271(4): 118–120, 2006Google Scholar
  62. Sáenz H, Lareo L, Poutou RA, Sosa AC, Barrera LA. Computational prediction of the tertiary structure of the human iduronate 2-sulfate sulfatase. Biomedica, 27(1): 7–20, 2007PubMedGoogle Scholar
  63. Hindley J, Wünschmann S, Satinover SM, Woodfolk JA, Chew FT, Chapman MD, Pomés A. Bla g 6: a troponin C allergen from Blattella germanica with IgE binding calcium dependence. J Allergy Clin Immun, 117(6): 1389–1395, 2006PubMedCrossRefGoogle Scholar
  64. Yao K, Sun ZH, Shentu XC, Wang KJ, Tan J. Computer construction and analysis of protein models of the mutant gammaD-crystallin gene. Chin Med J, 118(9): 738–741, 2005PubMedGoogle Scholar
  65. Sukumaran S, Atkins WM, Shanker R. Engineering cytochrome P-450s: chimeric enzymes. Appl Biochem Biotechol, 102–103(1–6): 291–302, 2002CrossRefGoogle Scholar
  66. Furtado PB, Furmonaviciene R, McElveen J, Sewell HF, Shakib F. Prediction of the interacting surfaces in a trimolecular complex formed between the major dust mite allergen Der p 1, a mouse monoclonal anti-Der p 1 antibody, and its anti-idiotype. Mol Pathol, 53(6): 324–332, 2000PubMedCrossRefGoogle Scholar
  67. Amoresano A, Minchiotti L, Cosulich ME, Campagnoli M, Pucci P, Andolfo A, Gianazza E, Galliano M. Structural characterization of the oligosaccharide chains of human alpha1-microglobulin from urine and amniotic fluid. Eur J Biochem, 267(7): 2105–2112, 2000PubMedCrossRefGoogle Scholar
  68. Peitsch MC, Herzyk P, Wells TN, Hubbard RE. Automated modelling of the transmembrane region of G-protein coupled receptor by Swiss-model. Receptor Channel 4(3): 161–164, 1996Google Scholar
  69. Edman M, Berg S, Storm P, Wikström M, Vikström S, Ohman A, Wieslander A. Structural features of glycosyltransferases synthesizing major bilayer and nonbilayer-prone membrane lipids in Acholeplasma laidlawii and Streptococcus pneumoniae. J Biol Chem, 278(10): 8420–8428, 2003PubMedCrossRefGoogle Scholar
  70. Gershoni JM, Roitburd-Berman A, Siman-Tov DD, Tarnovitski Freund N, Weiss Y. Epitope mapping: the first step in developing epitope-based vaccines. BioDrugs, 21(3): 145–156, 2007PubMedCrossRefGoogle Scholar
  71. Schwartz Hl, Chandonia JM, Kash SF, Kanaani J, Tunnell E, Domingo A, Cohen FE, Banga JP, Madec AM, Richter W, Baekkeskov S. High-resolution autoreactive epitope mapping and structural modelling of the 65 kDa form of human glutamic acid decarboxylase. J Mol Biol, 287(5): 983–999, 1999PubMedCrossRefGoogle Scholar
  72. Myers MA, Fenalti G, Gray R, Scealy M, Tong JC, El-Kabbani O, Rowley MJ. A diabetes-related epitope of GAD 65: a major diabetes-related conformational epitope on GAD 65. Ann NY Acad Sci, 1005: 250–255, 2003PubMedCrossRefGoogle Scholar
  73. Carson M, Bugg C, Delucas L, Narayana S. Comparison of homology models with the experimental structure of a novel serine protease. Acta Crystallogr, D50: 889–899, 1994Google Scholar
  74. Sutcliff M, Dobson C, Oswald R. Solution structure of neural bungarotoxin determined by two-dimensional NMR spectroscopy: Calculation of tertiary structure using systematic homologous model building, dynamical simulated annealing, and restrained molecular dynamics. Biochemistry, 31: 2962–2970, 1992CrossRefGoogle Scholar
  75. Ring CS, Sun E, McKerrow JH, Lee GK, Rosenthal PJ, Kuntz ID, Cohen FE. Structure-based inhibitor design by using protein models for the development of antiparasitic agents. Proc Natl Acad Sci USA, 90: 3583–3587, 1993PubMedCrossRefGoogle Scholar
  76. Lambert JM, Siezen RJ, de Vos WM, Kleerebezem M. Improved annotation of conjugated bile acid hydrolase superfamily members in Gram-positive bacteria. Microbiology, 154(8): 2492–2500, 2008PubMedCrossRefGoogle Scholar
  77. Rocher A, Marchand-Geneste N. Homology modelling of the Apis mellifera nicotinic acetylcholine receptor (nAChR) and docking of imidacloprid and fipronil insecticides and their metabolites. SAR QSAR Environ Res, 19(3–4): 245–261, 2008PubMedCrossRefGoogle Scholar
  78. Sgobba M, Degliesposti G, Ferrari AM, Rastelli G. Structural models and binding site prediction of the C-terminal domain of human Hsp90: a new target for anticancer drugs. Chem Biol Drug Des, 71(5): 420–433, 2008PubMedCrossRefGoogle Scholar
  79. Hou S, Li B, Wang L, Qian W, Zhang D, Hong X, Wang H, Guo Y. Humanization of an anti-CD34 monoclonal antibody by complementarity-determining region grafting based on computer-assisted molecular modelling. J Biochem, 144(1): 115–120, 2008PubMedCrossRefGoogle Scholar
  80. Capitani G, De Biase D, Gut H, Ahmed A, Grütter MG. Structural model of human GAD 65: prediction and interpretation of biochemical and immunogenic features. Proteins, 59: 7–14, 2005PubMedCrossRefGoogle Scholar
  81. O'Connor KH, Banga JP, Darmanin C, El-Kabbani O, Mackay IR, Rowley MJ. Characterisation of an autoreactive conformational epitope on GAD65 recognised by the human monoclonal antibody b78 using a combination of phage display, in vitro mutagenesis and molecular modelling. J Autoimmun, 26(3): 172–181, 2006PubMedCrossRefGoogle Scholar
  82. Caputo A, James MN, Powers JC, et al. Conversion of the substrate specifity of mouse proteinase granzyme B. Nature Struct Biol, 1: 364–367, 1994PubMedCrossRefGoogle Scholar
  83. Sali A, Matsumoto R, McNeil HP, Karplus M, Stevens RL. Three-dimensional models of four mouse mast cell chymases. Identification of proteoglycian-binding regions and protease-specific antigenic epitopes. J Biol Chem, 268: 9023–9034, 1993PubMedGoogle Scholar
  84. Pallarès I, Fernández D, Comellas-Bigler M, Fernández-Recio J, Ventura S, Avilés FX, Bode W, Vendrell J. Direct interaction between a human digestive protease and the mucoadhesive poly(acrylic acid). Acta Crystallogr D Biol Crystallogr, D64(Pt 7): 784–791, 2008PubMedCrossRefGoogle Scholar
  85. Hernandez CC, Zaika O, Tolstykh GP, Shapiro MS. Regulation of neural KCNQ channels: signalling pathways, structural motifs and functional implications. J Physiol, 586(7): 1811–1821, 2008PubMedCrossRefGoogle Scholar
  86. Hussein IT, Miguel RN, Tiley LS, Field HJ. Substrate specificity and molecular modelling of the feline herpesvirus-1 thymidine kinase. Arch Virol, 153(3): 495–505, 2008PubMedCrossRefGoogle Scholar

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© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Institute for Medical Informatics, Statistics and DocumentationMedical University of GrazGrazAustria
  2. 2.Medical University of GrazGrazAustria

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