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Structure modeling based computer aided T-cell epitope design

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Progress in computational modeling for structural biology has motivated the use of molecular mechanics calculations for synthetic peptide design as potential T-cell epitopes (peptides inducing immunogenicity). Short antigen peptides from virus/ bacteria/parasite are recognized by host specific human leukocyte antigen (HLA) molecules for T-cell sensitive cellular immune response. However, HLA molecules are highly polymorphic at the sequence level among ethnic population (American Indian, Australian aboriginal, Black, Caucasoid, Hispanic, Mixed, Oriental, Pacific Islander and Unknown ethnicity). The binding of peptides to host HLA molecules are both specific and sensitive. The use of computer aided molecular modeling principles has been shown for the design of T-cell specific epitopes as potential vaccine candidates. Application of computational techniques such as molecular dynamics simulation (MDS), self consistent ensemble optimization (SCEO), free energy (FE) estimation, computational combinatorial ligand design (CCLD), 3D quantitative structure activity relationship (3D-QSAR) and structure based virtual pockets (SBVP) in HLA-peptide binding prediction is discussed. The ability of modeling and design to predict peptide binding to a wide array of defined HLA alleles finds application in proteome wide scanning of bacteria/virus/parasite proteomes towards cocktail peptide vaccines.
Słowa kluczowe
Rocznik
Strony
5--13
Opis fizyczny
Bibliogr. 32 poz., rys.
Twórcy
autor
  • Biomedical Informatics, Pondicherry, 607402, India
autor
  • Biomedical Informatics, Pondicherry, 607402, India
autor
  • Biomedical Informatics, Pondicherry, 607402, India
  • AIMST University, Kedha 08100, Malaysia
Bibliografia
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  • 11. Ren E.C., Kangueane P., Kolatkar P., Lin M.T., 11. Tseng L.H., Hansen J.A. (2000), Molecular modeling of the minor histocompatibility antigen HA-1 peptides binding to HLA-A alleles. Tissue Antigens 55: 24.
  • 12. Kangueane P., Sakharkar M.K., Lim K.S., Hao H., Lin K., Chee R.E., Kolatkar P.R. (2000), Knowledge-based grouping of modeled HLA peptide complexes. Hum. Immunol . 61: 460.
  • 13.Schueler-Furman O., Elber R., Margalit H., (1998), Knowledge based structure prediction of MHC class I bound-peptides: a study of 23 complexes. Fold Des . 3: 549.
  • 14. Altuvia Y., Schueler O., Margalit H. (1995), Ranking potential binding peptides to MHC molecules by a computational threading approach. J. Mol. Biol 249: 244.
  • 15. Altuvia Y., Sette A., Sidney J., Southwood S., Margalit H. (1997), A structure-based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Hum. Immunol 58: 1.
  • 16. Schueler-Furman O., Altuvia Y., Sette A., Margalit H. (2000), Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci 9: 1838.
  • 17. Rognan D., Lauemoller S.L., Holm A., Buus S., 17. Tschinke V. (1999), Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J. Med. Chem . 42: 4650.
  • 18. Jones D.T., Thornton J.M. (1996), Potential energy functions for threading. Curr. Opin. Struct. Biol. 6: 210.
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  • 22. Betancourt M.R., Thirumalai D. (1999), Pair potentials for protein folding; choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci. 8: 361.
  • 23. Logean A., Sette A., Rognan D. (2001), Customized versus universal scoring functions: application to class I MHC-peptide binding free energy predictions. Bioorg. Med. Chem. Lett. 11: 675.
  • 24. Logean A., Rognan D. (2002), Recovery of known T-cell epitopes by computational scanning of a viral genome. J. Comput. Aided Mol. Des. 16: 229.
  • 25. Zeng J., Treutlein H.R., Rudy G.B. (2001), Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach. J. Comput. Aided Mol. Des. 15: 573.
  • 26. Doytchinova I.A., Flower D.R. (2001), Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. J. Med. Chem. 44: 3572.
  • 27. Doytchinova I.A., Flower D.R. (2002), A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif. J. Comput. Aided Mol. Des. 16: 535.
  • 28. Zhao B., Mathura V.S., Rajaseger G., Moochhala S., Sakharkar M.K., Kangueane P. (2003), A novel MHCp binding prediction model. Hum. Immunol. 64: 1123.
  • 29. Mohanapriya A., Lulu S., Kayathri R., Kangueane P. (2009), Class II HLA-peptide binding prediction using structural principles. Hum. Immunol. 70: 159.
  • 30. Den Haan J.M., Meadows L.M., Wang W., Pool J., Blokland E., Bishop T.L., Reinhardus C., Shabanowitz J., Offringa R., Hunt D.F., Engelhard V.H., Goulmy E. (1998), The minor histocompatibility antigen HA-1: a diallelic gene with a single amino acid polymorphism. Science 279: 1054.
  • 31. Kangueane P., Sakharkar M.K., Kolatkar P.R., Ren E.C. (2001), Towards the MHC-peptide combinatorics. Hum. Immunol. 62: 539.
  • 32. Venkatarajan M.S., Braun W. (2001), New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical-chemical properties. J. Mol. Model. 7: 445.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ab9e3500-164c-4d3e-b20e-a76f8b9fe511
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