Estimation of affinity of HLA-A*0201 restricted CTL epitope based on the SCORE function

Protein and Peptide Letters

Volumn 16, Issue 5, 2009, Pages 561-569

  Zhi Hua, Lin ^a   Huai Liang, Wang ^a   Bo, Zhu ^b   Yuan Qiang, Wang ^a   Yong, Lin ^a   Yu Zhang, Wu ^b  

^a CHONGQING UNIVERSITY OF TECHNOLOGY   (China)

^b THIRD MILITARY MEDICAL UNIVERSITY   (China)

Author keywords

Affinity; CTL epitope; HLA A 0201; QSAR; Score function

Indexed keywords

EPITOPE; HLA A ANTIGEN; HLA A*0201 ANTIGEN; HLA-A*0201 ANTIGEN; PEPTIDE; VACCINE;

ALGORITHM; ARTICLE; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; CYTOTOXIC T LYMPHOCYTE; DRUG DESIGN; HYDROGEN BOND; HYDROPHOBICITY; IMMUNOLOGY; PROTEIN BINDING; PROTEIN CONFORMATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REPRODUCIBILITY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DRUG DESIGN; EPITOPES, T-LYMPHOCYTE; HLA-A ANTIGENS; HYDROGEN BONDING; HYDROPHOBICITY; MODELS, MOLECULAR; PEPTIDES; PROTEIN BINDING; PROTEIN CONFORMATION; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REPRODUCIBILITY OF RESULTS; T-LYMPHOCYTES, CYTOTOXIC; VACCINES;

EID: 67649361714     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986609788167815     Document Type: Article

References (85)

1. Sangha, R.; Butts, C. L-BLP25: a peptide vaccine strategy in nonmall cell lung cancer. Clin. Cancer Res., 2007, 13(15 Pt 2), s46524654. Review.
2. Noguchi, M.; Mine, T.; Yamada, A.; Obata, Y.; Yoshida, K.Mizoguchi, J.; Harada, M.; Suekane, S.; Itoh, K.; Matsuoka, KCombination therapy of personalized peptide vaccination and low-ose estramustine phosphate for metastatic hormone refractoryprostate cancer patients: an analysis of prognostic factors in thetreatment. Oncol. Res., 2007, 16(7), 341-349.
3. Chou, K.C. Structural bioinformatics and its impact to biomedicalscience. Curr. Med. Chem., 2004, 11(16), 2105-2134.
4. Chou, K.C.; Wei, D.Q.; Zhong, W.Z. Binding mechanism of coronavirus main proteinase with ligands and its implication torug design against SARS. Biochem. Biophys. Res. Comm., 2003,308(1), 148-151.
5. Zhang, R.; Wei, D.Q.; Du, Q.S.; Chou, K.C. Molecular modelingstudies of peptide drug candidates against SARS. Med. Chem.,2006, 2(3), 309-314.
6. Gao, W.N.; Wei, D.Q.; Li, Y.; Gao, H.; Xu, W.R.; Li, A.X.; Chou,K.C. Agaritine and its derivatives are potential inhibitors againstHIV proteases. Med. Chem., 2007, 3(3), 221-226.
7. Zheng, H.; Wei, D.Q.; Zhang, R.; Wang, C.; Wei, H.; Chou, K.C.Screening for new agonists against Alzheimer's disease. Med Chem., 2007, 3(5), 488-493.
8. Li, Y.; Wei, D.Q.; Gao, W.N.; Gao, H.; Liu, B.N.; Huang, C.J.; Xu,W.R.; Liu, D.K.; Chen, H.F.; Chou, K.C. Computational approachto drug design for oxazolidinones as antibacterial agents. Med.Chem., 2007, 3(6), 576-582.
9. Wang, J.F.; Wei, D.Q.; Chen, C.; Li, Y.; Chou, K.C. Molecularmodeling of two CYP2C19 SNPs and its implications for personalized drug design. ProteinPept. Lett., 2008, 15(1), 27-32.
10. Sirois, S.; Wei, D.Q.; Du, Q.S.; Chou, K.C. Virtual screening forSARS-CoV protease based on KZ7088 pharmacophore points. J.Chem. Inf. Comput. Sci., 2004, 44(3), 1111-1122.
11. [II]Chou, K.C.; Wei, D.Q.; Du, Q.S.; Sirois, S.; Zhong, W.Z. Progressin computational approach to drug development against SARS.Curr. Med. Chem., 2006, 13(27), 3263-3270.
12. Gonzalez-Diaz, H.; Gonzalez-Diaz, Y.; Santana, L.; Ubeira, F.M.Uriarte, E. Proteomics, networks and connectivity indices. Pro-teomics, 2008, 8(4), 750-778
13. Du, Q.S.; Mezey, P.G.; Chou, K.C. Heuristic molecular lipophilicty potential (HMLP): a 2D-QSAR study to LADH of molecularfamily pyrazole and derivatives. J. Comput. Chem., 2005, 26(5),461-470.
14. Du, Q.S.; Huang, R.B.; Wei, Y.T.; Du, L.Q.; Chou, K.C. Multiplefield three dimensional quantitative structure-activity relationship(MF-3D-QSAR). J. Comput. Chem., 2008, 29(2), 211-219.
15. Du, Q.S.; Huang, R.B.; Chou, K.C. Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr. Protein Pept. Sci.,2008, 9(3), 248-260.
16. Du, Q.S.; Huang, R.B.; Wei, Y.T.; Pang, Z.W.; Du, L.Q.; Chou,K.C. Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design. J. Comput. Chem.,2008, DOI 10.1002/jcc.
17. Prado-Prado, F.J.; Gonzalez-Diaz, H.; de la Vega, O.M.; Ubeira,F.M.; Chou, K.C. Unified QSAR approach to antimicrobials. Part3: first multi-tasking QSAR model for input-coded prediction,structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem., 2008, 16(11), 58715880.
18. Chou, K.C. A vectorized sequence-coupling model for predictingHIV protease cleavage sites in proteins. J. Biol. Chem., 1993, 268(23), 16938-16948.
19. Chou, K.C. Prediction of human immunodeficiency virus protease cleavage sites in proteins. Analy. Biochem., 1996, 233(1), 1-14.
20. Du, Q.S.; Wang, S.Q.; Jiang, Z.Q.; Gao, W.N.; Li, Y.D.; Wei,D.Q.; Chou, K.C. Application of bioinformatics in search for cleavable peptides of SARS-CoV M(pro) and chemical modification of octapeptides. Med. Chem., 2005, 1(3), 209-213.
21. Shen, H.B.; Chou, K.C. HIVcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins. Analy. Biochem., 2008, 375(2), 388-390.
22. Chou, K. C.; Shen, H. B. Cell-PLoc: A package of web-servers for predicting subcellular localization of proteins in various organisms. Nat. Protocols, 2008, 3(2), 153-162.
23. Chou, K.C.; Shen, H.B. Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers. J. ProteomeRes., 2006, 5(8), 1888-1897.
24. Chou, K.C.; Shen, H.B. Euk-mPLoc: a fusion classifier for large- scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J. Proteome Res., 2007, 6(5), 1728-1734.
25. Shen, H.B.; Chou, K.C. Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochem. Biophys. Res. Com-mun., 2007, 355(4), 1006-1011.
26. Chou, K.C.; Shen, H.B. Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. BBRC, 2006, 347(1), 150-157.
27. Chou, K.C.; Shen, H.B. MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Comm., 2007, 360(2), 339-345.
28. Shen, H.B.; Chou, K.C. EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. Biochem. Biophys. Res. Comm., 2007, 364(1), 53-59.
29. Chou, K.C.; Shen, H.B.Signal-CF: a subsite-coupled and window- fusing approach for predicting signal peptides. Biochem. Biophys Res. Comm., 2007, 357(3), 633-640.
30. Shen, H.B.; Chou, K.C. Signal-3L: A 3-layer approach for predicting signal peptides. Biochem. Biophys. Res. Comm., 2007, 363(2),297-303.
31. Drijfhout, J.W.; Brandt, R.M.; D'Amaro, J.; Kast, W.M.; Melief, C.J. Detailed motifs for peptide binding to HLA-A*0201 derived from large random sets of peptides using a cellular binding assay. Hum. Immunol., 1995, 43(1), 1-12.
32. D'Amaro, J.; Houbiers, J.G.; Drijfhout, J.W.; Brandt, R.M.; Schip- per, R.; Bavinck, J.N.; Melief, C.J.; Kast, W.M.; Doytchinova, I.A.; Paine, K. A computer program for predicting possible cytotoxic T lymphocyte epitopes based on HLA class I peptide-binding motifs. Hum. Immunol., 1995, 43(1), 13-18.
33. Meister, G.E.; Roberts, C.G.P.; Berzofsky, J.A.; De Groot, A.S.Two novel T cell epitope prediction algorithms based on MHC- binding motifs; comparison of predicted and published epitopes from Mycobacterium tuberculosis and HIV protein sequences. Vaccince, 1995, 13, (6)581-591.
34. Mallios, R.R. Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminate analysis meta-algorithm. Bioinformatics, 1999, 15(6),432-439.
35. Zhang, C.; Anderson, A.; DeLisi, C. Structural principles that govern the peptide-binding motifs of class I MHC molecules. J. Mol.Biol., 1998, 281(5), 929-947.
36. Davenport, M.P.; Ho Shon, I.A.; Hill, A.V. An empirical method for the prediction of T-cell epitopes. Immunogenetics , 1995, 42(5),392-397.
37. Bertoni, R.; Sidney, J.; Flower, P.; Chesnut, R.W.; Chisari, F.V.;Sette, A. Human histocompatibility leukocyte antigen-binding su- permotifs predict broadly cross-reactive cytotoxic T lymphocyte responses in patients with acute hepatitis. J. Clin. Invest., 1997, 100(3), 503-513.
38. Rammensee, H.G.; Friede, T.; Stevanoviic, S. MHC ligands and peptide motifs: first listing. Immunogenetics, 1995, 41(4), 178-228.
39. Rammensee, H.G.; Bachmann, J.; Emmerich, N.P.; Bachor, O.A.;Stevanovic, S. SYFPEITHI: Database for MHC ligands and pep- tide motifs. Immunogenetics, 1999, 50(3-4), 213-219.
40. Honeyman, M.C.; Brusic, V.; Stone, N.L.; Harrison, L.C. Neutral network-based prediction of candidate T-cell epitopes. Nat. Biotech., 1998, 16(10), 966-969.
41. Brusic, V.; Bucci, K.; Schonbach, C.; Petrovsky, N.; Zeleznikow, J.; Kazura, J.W. Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. J. Mol. Graph Model., 2001, 19(5), 405-411, 467.
42. Mamitsuka, H. Predicting peptides that bind to MHC molecules using supervised learning of hidden markov models. Protein, 1998,33(4), 460-474.
43. Ruppert, J.; Sidney, J.; Celis, E.; Kubo, R.T.; Grey, H.M.; Sette, A.Prominent role of secondary anchor residues in peptide binding to HLA-A*0201 molecules. Cell, 1993, 74(5), 929-937.
44. Franco, A.; Tilly, D.A.; Gramaglia, I.; Croft, M.; Cipolla, L.; Mel- dal, M.; Grey, H.M. Epitope affinity for MHC class I determines helper requirement for CTL priming. Nat. Immunol., 2000, 7(2), 145-149.
45. Lin, Z.H.; Wu, Y.Z.; Zhu, B.; Ni, B.; Wang, L. Toward the Quantitative Prediction of T-Cell Epitopes: QSAR Studies on Peptides Having Affiniity with the ClassI MHC Molecular HLA-A*0201. J. Comput. Biol., 2004, 77(4), 683-694.
46. Doytchinova, I.A.; Guan, P.; Flower, D.R. Quantitative structure- activity relationships and the prediction of MHC supermotifs. Methods, 2004, 34(4), 444-453.
47. Holm, L.; Frech, K.; Dzhambazov, B.; Holmdahl, R.; Kihlberg, J.; Linusson, A. Quantitative structure-activity relationship of peptides binding to the class II major histocompatibility complex molecule Aq associated with autoimmune arthritis. J. Med. Chem., 2007, 50(9), 2049-2059.
48. Chen, J.; Liu, H.; Yang, J.; Chou, K.C. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids, 2007, 33(3), 423-428.
49. Du, Q.S.; Wei, Y.T.; Pang, Z.W.; Chou, K.C.; Huang, R.B. Predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201: an application of amino acid-based peptide prediction. Protein Eng. Design Select., 2007, 20(9), 417-423.
50. Wang, R.X.; Liu, L.; Lai, L.H.; Tang, Y.Q. SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein- Ligand Complex. J. Mol. Model., 1998, 4(4), 379-394.
51. Rongcun, Y.; Salazar, O. F.; Charo, J. Identification of new HER2/neu-derived peptide epitopes that can elicit specific CTL against autologous and allogeneic carcinomas and melanomas. J. Immunol., 1999, 163(2), 1037-1044.
52. Celis, E. Overlapping human leukocyte antigen class I/II binding peptide vaccine for the treatment of patients with stage IV melanoma: evidence of systemic immune dysfunction. Cancer, 2007, 110(1), 203-214.
53. Sette, A.; Vitiello, A.; Reherman, B.; Fowler, P.; Nayersina, R.;Kast, W.M.; Melief, C.J.; Oseroff, C.; Yuan, L.; Ruppert, J.; Sidney, J.; Guercio, M.F.; Southwood, S.; Kubo, R.T.; Chesnut, R.W.;Grey, H.M.; Chisari, F.V. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J. Immunol., 1994, 153(12), 5586-5592.
54. Kawakami, Y.; Eliyahu, S.; Jennings, C.; Sakaguchi, K.; Kang, X.; Southwood, S.; Robbins, P.F.; Sette, A.; Appella, E.; Rosenberg, S.A. Recognition of multiple epitopes in the human melanoma antigen gp100 by tumor-infiltrating T lymphocytes associated with in vivo tumor regression. J. Immunol., 1995, 154(8), 3961-3968.
55. 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(22), 3572-3581.
56. Chou, K. C.; Zhang, C. T. Review: Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30(4), 275-349.
57. Chou, K. C. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins, 2001, 43(3), 246-255.
58. Chou, K. C.; Shen, H. B. Review: Recent progresses in protein subcellular location prediction. Analy. Biochem., 2007, 370(3), 116.
59. Niu, B.; Cai, Y. D.; Lu, W. C.; Zheng, G. Y.; Chou, K. C. Predicting protein structural class with AdaBoost learner. Protein Pept. Lett., 2006, 13(5), 489-492.
60. Ding, Y. S.; Zhang, T. L.; Chou, K. C. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept. Lett., 2007, 14(4), 811-815.
61. Jin, Y.; Niu, B.; Feng, K. Y.; Lu, W. C.; Cai, Y. D.; Li, G. Z. Predicting subcellular localization with AdaBoost learner. ProteinPept. Lett., 2008, 15(3), 286-289.
62. Wu, G.; Yan, S. Prediction of Mutations in H3N2 Hemagglutinins of Influenza A Virus from North America Based on DifferentDatasets. Protein Pept. Lett., 2008, 15(2), 144-152.
63. Jiang, X.; Wei, R.; Zhang, T. L.; Gu, Q. Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy.Protein Pept. Lett., 2008, 15(4), 392-396.
64. Li, F. M.; Li, Q. Z. Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein Pept. Lett., 2008, 15(6), 612-616.
65. Niu, B.; Jin, Y. H.; Feng, K. Y.; Liu, L.; Lu, W. C.; Cai, Y. D.; Li,G. Z. Predicting membrane protein types with bagging learner.Protein Pept. Lett., 2008, 15(6), 590-594.
66. Lin, H.; Ding, H.; Feng-Biao Guo, F. B.; Zhang, A. Y.; Huang, J.Predicting Subcellular Localization of Mycobacterial Proteins by using Chou's Pseudo Amino Acid Composition. Protein Pept. Lett.,2008, 15(7), 739-744.
67. Xiao, X.; Shao, S.; Ding, Y.; Huang, Z.; Chen, X.; Chou, K. C. AnApplication of Gene Comparative Image for Predicting the Effect on Replication Ratio by HBV Virus Gene Missense Mutation. J.Theor. Biol., 2005, 235(4), 555-565.
68. Cai, Y. D.; Zhou, G. P.; Chou, K. C. Predicting enzyme familyclasses by hybridizing gene product composition and pseudo-amino acid composition. J. Theor. Biol., 2005, 234(1), 145-149.
69. Cai, Y. D.; Feng, K. Y.; Lu, W. C.; Chou, K. C. Using LogitBoost classifier to predict protein structural classes. J. Theor. Biol., 2006, 238(1), 172-176.
70. Cai, Y. D.; Chou, K. C. Predicting membrane protein type by functional domain composition and pseudo amino acid composition. J. Theor. Biol., 2006, 238(2), 395-400.
71. Shen, H. B.; Yang, J.; Chou, K. C. Fuzzy KNN for predicting membrane protein types from pseudo amino acid composition. J. Theor. Biol., 2006, 240(1), 9-13.
72. Wang, S. Q.; Yang, J.; Chou, K. C. Using stacked generalization to predict membrane protein types based on pseudo amino acid composition. J. Theor. Biol., 2006, 242(4), 941-946.
73. Chen, Y. L.; Li, Q. Z. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo amino acid composition. J. Theor. Biol., 2007, 248(2), 377-381.
74. Zhou, X. B.; Chen, C.; Li, Z. C.; Zou, X. Y. Using Chou's am phiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J. Theor. Biol.2007,248(3), 546-551.
75. Chen, Y. L.; Li, Q. Z. Prediction of the subcellular location of apoptosis proteins. J. Theor. Biol., 2007, 245(4), 775-783.
76. Zhang, T. L.; Ding, Y. S.; Chou, K. C. Prediction protein structural classes with pseudo amino acid composition: approximate entropy and hydrophobicity pattern. J. Theor. Biol., 2008, 250(1), 186-193.
77. Lin, H. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J. Theor. Biol., 2008, 252(2), 350-356.
78. Zhang, G. Y.; Fang, B. S. Predicting the cofactors of oxidoreduc- tases based on amino acid composition distribution and Chou's am- phiphilic pseudo amino acid composition. J. Theor. Biol. , 2008, 253(2), 310-315.
79. Chen, C.; Chen, L. X.; Zou, X. Y.; Cai, P. X. Predicting proteinstructural class based on multi-features fusion. J. Theor. Biol. 2008, 253(2), 388-392.
80. Ding, Y. S.; Zhang, T. L. Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier. Pattern Recogn. Lett., 2008, 29(13), 1887-1892.
81. Wang, T.; Yang, J.; Shen, H. B.; Chou, K. C. Predicting membrane protein types by the LLDA algorithm. Protein Pept. Lett. , 2008, 15(9), 915-921.
82. Lin, Z.H.; Wu, Y.Z.; Wan, Y.; Ni, B. Structural Characteristic andMHC Restriction of Polypeptide Sequence. Chem. J. Chinese Univ., 2003, 24(5), 826-831.
83. Lin, Z.H.; Wang H.L.; Tang, Y.; Wu, Y.Z. Computer AidedScreening of Tyrosinase Related Protein-2 (180-188) Mimotope. Acta Chim. Sin., 2007, 65(8), 760-764.
84. Zhu, B.; Chen, Z.T.; Cheng, X.M.; Lin, Z.H.; Guo, J.X.; Jia, Z.C.; Zou, L.Y.; Wang, Z.X.; Hu, Y.D.; Wang, D.L.; Wu, Y.Z. Identification of HLA-A*0201-restricted cytotoxic T lymphocyte epitope from TRAG-3 antigen. Clin. Cancer Res., 2003, 9(5), 1850-1857.
85. Tang, Y.; Lin, Z.H.; Ni, B.; Wei, J.; Han, J.H.; Wang, H.M.; Wu,Y.Z. An altered peptide ligand for naive cytotoxic T lymphocyte epitope of TRP-2(180-188) enhanced immunogenicity. Cancer Immunol. Immunother., 2007, 56(3), 319-329.