Applying generalized hydrophobicity scale of amino acids to quantitative prediction of human leukocyte antigen-A*0201-restricted cytotoxic T lymphocyte epitope

Chinese Science Bulletin

Volumn 51, Issue 12, 2006, Pages 1439-1443

  Zhou, Peng ^a,b   Tian, Feifei ^a   Zhang, Mengjun ^c   Li, Zhiliang ^a,b  

^a CHONGQING UNIVERSITY   (China)

^b State Key Laboratory of Chemo/Biosensing and Chemometrics   (China)

^c Department of Medical Analysis   (China)

Author keywords

CTL epitope; Generalized hydrophobicity scale of amino acids; Genetic algorithm partial least square regression; HLA A*0201; Quantitative structure activity relationship

Indexed keywords

EID: 33748926289     PISSN: 10016538     EISSN: 18619541     Source Type: Journal    
DOI: 10.1007/s11434-006-1439-z     Document Type: Article

References (15)

1. Bodmer J. World distribution of HLA alleles and implications for disease. CibaFound Symp, 1996, 197:233-253
2. McMichael A J, Parham P, Brodsky F M, et al. Influenza virus-specific cytotoxic T lymphocytes recognize HLA-molecules.
3. Blocking by monoclonal anti-HLA antibodies. J Exp Mod, 1980, 152(2): 195-203
4. Schendel D J, Gansbachcr B, Oberneder R, et al. Tumor-specific lysis of human renal cell carcinomas by tumor-Infiltrating lymphocytes. I. HLA-A2-restricted recognition of autologous and allogeneic tumor lines. J Immunol, 1993, 151: 4209-4220
5. Falk K, Rötzschke O, Stefanovic S, et al. Allele specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature, 1991, 351:290-296
6. Ruppert J, Sidney J, Celis E, et al. Prominent role of secondary anchor residues in peptide binding to HLA-A*0201 molecules. Cell, 1993, 74:929-937
7. Odunsi K, Ganesan T. Motif analysis of HLA class II molecules that determine the HPV associated risk of cervical carcinogenesis. Int Mol Med, 2001, 8(4): 405-412
8. Brusic V, Rudy G, Honeyman G, et al. Prediction of MHC class II -binding peptides using an evolutional algorithm and artificial neural network. Bioinformatics, 1998, 14(2): 121-130
9. Honeyman M C, Brusic V, Stone N L, et al. Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol, 1998, 16(10): 966-969
10. Tanford C. The hydrophobic effect: formation of micelles and biological membranes. New York: Wiley, 1980
11. Tanford C. How protein chemists learned about the hydrophobic factor. Protein Soi, 1997, 6:1358-1366
12. Tanford C. The hydrophobic effect and the organization of living matter. Science, 1978, 200: 1012-1018
13. Doytchinova I A, Flower D R. 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, 2001, 44:3572-3581
14. Garboczi D N, Ghosh P, Utz U, et al. Structure of the complex between human T-cell receptor, viral peptide and HLA-A2. Nature, 1996, 384: 134-141
15. Tropsha A, Gramatica P, Gombar V K. The importance of being earnest: validation is the absolute essential for successful application and inerpretation of QSPR models. QSAR Comb Sci, 2003, 22: 69-77