QSAR study on MHC class i A alleles based on the novel parameters of amino acids

Protein and Peptide Letters

Volumn 18, Issue 9, 2011, Pages 956-963

  Wang, Juan ^a   Wang, Xiao Yu ^a   Shu, Mao ^a   Wang, Yuan Qiang ^a   Lin, Yong ^a   Wang, Li ^a,b   Cheng, Xiao Ming ^b   Lin, Zhi Hua ^a  

^a CHONGQING UNIVERSITY OF TECHNOLOGY   (China)

^b THIRD MILITARY MEDICAL UNIVERSITY   (China)

Author keywords

Amino acids descriptors; Major Histocompatibility Complex (MHC); MHC class I allele; Quantitative structure activity relationship (QSAR); Stepwise regression multiple linear regression (STR MLR)

Indexed keywords

AMINO ACID; EPITOPE; HLA A ANTIGEN; HLA A*02:01 ANTIGEN; HLA A2 ANTIGEN; HLA-A*02:01 ANTIGEN;

ALLELE; AMINO ACID SEQUENCE; ARTICLE; BIOLOGICAL MODEL; CHEMISTRY; IMMUNOLOGY; PROTEIN BINDING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICAL MODEL;

ALLELES; AMINO ACID SEQUENCE; AMINO ACIDS; EPITOPES; HLA-A ANTIGENS; HLA-A2 ANTIGEN; LINEAR MODELS; MODELS, BIOLOGICAL; PROTEIN BINDING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 79959416258     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986611796011437     Document Type: Article

References (80)

1. Frahm, N.; Korber, B.T.; Adams, C.M.; Szinger, J.J.; Draenert, R.; Addo, M.M.; Feeney, M.E.; Yusim, K.; Sango, K.; Brown, N.V.; SenGupta, D.; Piechocka-Trocha, A.; Simonis, T.; Marincola, F.M.; Wurcel, A.G.; Stone, D.R.; Russell, C.J.; Adolf, P.; Cohen, D.; Roach, T.; StJohn, A.; Khatri, A.; Davis, K.; Mullins, J.; Goulder, P.J.R.; Walker, B.D.; and Brander, C. Consistent cytotoxic-Tlymphocyte targeting of immunodominant regious in human immunodeficiency virus across multiple ethnicities. J. Virol., 2004, 78, 2187-2200. (Pubitemid 38228912)
2. Zhao, B.; Kishore, R.S.; Chu, S.L.; Pandjassarame, K.; Meena, K.S. MHC-peptide binding prediction for epitope based vaccine design. IJIB, 2007, 1, 127-140.
3. Vire, C. and Janeway, CA. Jr. MHC and T cell development. Rev. Immunogenet, 1999, 1, 91-104.
4. Irini, A.; Doytchinova and Darren, R.; 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. (Pubitemid 33026664)
5. Saffari, B.; Mohabatkar, H.; Mohsenzadeh, S. T and B-cell Epitopes Prediction of Iranian Saffron (Crocus sativus) Profilin by Bioinformatics Tools. Protein Pept. Lett., 2008, 15, 280-285. (Pubitemid 351457377)
6. Gagnon, E.; Xu, C.; Yang, W.; Chu, H. H.; Call, M. E.; Chou, J. J.; Wucherpfennig, K. W. Response multilayered control of T cell receptor phosphorylation. Cell, 2010, 142, 669-671.
7. Xu, C.; Gagnon, E.; Call, M. E.; Schnell, J. R.; Schwieters, C. D.; Carman, C. V.; Chou, J. J.; Wucherpfennig, K. W. Regulation of T cell receptor activation by dynamic membrane binding of the CD3epsilon cytoplasmic tyrosine-based motif. Cell, 2008, 135, 702-713.
8. Falk, K.; Rötzschke, O.; Stefanovic, S.; Jung, G. and Rammensee, H.G. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature, 1991, 351, 290-296. (Pubitemid 21896578)
9. Madden, D.R.; Garboczi, D.N.; Wiley, D.C. The antigenic identity of peptide-MHC complexes: A comparison of the conformations of five viral peptides presented by HLA-A2. Cell, 1993, 75, 693-708. (Pubitemid 23346368)
10. 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, 929-937. (Pubitemid 23270612)
11. Madden, D.R. The three-dimensional structure of peptide-MHC complexes. Annu. Rev. Immunol., 1995, 13, 587-622.
12. Korber, B.; LaBute, M.; Yusim, K. Immunoinformatics comes of age. PloS Comput. Biol., 2006, 2, e71.
13. Hu, L.; Huang, T.; Shi, X.; Lu, W. C.; Cai, Y. D.; Chou, K. C. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One, 2011, 6, e14556.
14. Chou, K. C. Review: Structural bioinformatics and its impact to biomedical science. Curr.Med. Chem., 2004, 11, 2105-2134.
15. Huang, T.; Shi, X. H.; Wang, P.; He, Z.; Feng, K. Y.; Hu, L.; Kong, X.; Li, Y. X.; Cai, Y. D.; Chou, K. C. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS One, 2010, 5, e10972.
16. Chou, K. C.; Wei, D. Q.; Zhong, W. Z. Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. Biochem. Biophys. Res. Comm., 2003, 308, 148-151.
17. He, Z. S.; Zhang, J.; Shi, X. H.; Hu, L. L.; Kong, X. G.; Cai, Y. D.; Chou, K. C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One, 2010, 5, e9603.
18. Chou, K. C.; Shen, H. B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS One, 2010, 5, e9931.
19. Chou, K. C.; Shen, H. B. Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization. PLoS One, 2010, 5, e11335.
20. Chou, K. C.; Shen, H. B. ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem. Biophys. Res. Comm., 2008, 376, 321-325.
21. 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, 633-640.
22. Lin, Z.H.; Wu, Z.; Wei, Y.L.; Ni, B.; Zhu, B.; Wang, L. A rapid method for quantitative prediction of high affinity CTL epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A.0201. Lett. Peptide Sci. , 2003, 10, 15-23. (Pubitemid 38270418)
23. 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, 405-411. (Pubitemid 32601989)
24. Katritzky, A.R.; Lobanov, V.S.; Karelson, M. CODESSA: Training Manual; University of Florida: Gainesville, 1995.
25. Lin, Z. H.; Wang, H. L.; Zhu, B.; Wang, Y. Q.; Lin, Y.; Wu, Y. Z. Estimation of Affinity of HLA-A*0201 Restricted CTL Epitope Based on the SCORE Function. Protein Pept. Lett., 2009, 16, 561-569.
26. 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, 423-428. (Pubitemid 47347952)
27. 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 Engineering, Design & Selection, 2007, 20, 417-423.
28. Vilar, S.; Gonzalez-Diaz, H.; Santana, L.; Uriarte, E. A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J. Theor. Biol., 2009, 261, 449-458.
29. Prado-Prado, F. J.; Martinez de la Vega, O.; Uriarte, E.; Ubeira, F. M.; Chou, K. C.; Gonzalez-Diaz, H. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg. Med. Chem., 2009, 17, 569-575.
30. Prado-Prado, F. J.; Gonzalez-Diaz, H.; de la Vega, O. M.; Ubeira, F. M.; Chou, K. C. Unified QSAR approach to antimicrobials. Part 3: First multi-tasking QSAR model for Input-Coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorganic & Medicinal Chemistry, 2008, 16, 5871-5880. (Pubitemid 351766720)
31. Dea-Ayuela, M. A.; Perez-Castillo, Y.; Meneses-Marcel, A.; Ubeira, F. M.; Bolas-Fernandez, F.; Chou, K. C.; Gonzalez-Diaz, H. HP-Lattice QSAR for dynein proteins: Experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg. Med. Chem., 2008, 16, 7770-7776.
32. Wei, H.; Wang, C. H.; Du, Q. S.; Meng, J.; Chou, K. C. Investigation into adamantane-based M2 inhibitors with FB-QSAR. Medicinal Chemistry, 2009, 5, 305-317.
33. 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 approach to drug design for oxazolidinones as antibacterial agents. Medicinal Chemistry, 2007, 3, 576-582. (Pubitemid 350303073)
34. Du, Q. S.; Mezey, P. G.; Chou, K. C. Heuristic Molecular Lipophilicity Potential (HMLP): A 2D-QSAR Study to LADH of Molecular Family Pyrazole and Derivatives. J. Comput. Chem., 2005, 26, 461-470. (Pubitemid 40449458)
35. Du, Q. S.; Huang, R. B.; Wei, Y. T.; Du, L. Q.; Chou, K. C. Multiple Field Three Dimensional Quantitative Structure-Activity Relationship (MF-3D-QSAR). J. Comput. Chem., 2008, 29, 211-219.
36. Du, Q. S.; Huang, R. B.; Chou, K. C. Review: 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, 248-259.
37. 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., 2009, 30, 295-304.
38. Sneath, P.H. Relations between chemical structure and biological activity in peptides. J. Theor. Biol., 1966, 12, 157-195.
39. Kidera, A.; Konishi, Y.; Oka, M. A statistical analysis of the physical properties of the 20 naturally occuring amino acids. J. Protein Chem., 1985, 4, 23-55. (Pubitemid 15245426)
40. Hellberg, S.; Eriksson, L.; Jonsson, J. Minimum analogue pep tidesets (MAPS) for quantitative structure-activity relationships. Int. J. Pept. Protein Res., 1991, 37, 414-424.
41. Sandberg, M.; Eriksson, L.; Jonsson, J.; Sjostrom, M.; Wold, S. New chemical descriptors relevant for the design of biologically active peptides, a multivariate characterization of 87 amino acids. J. Med. Chem., 1998, 41, 2481-2491. (Pubitemid 28321898)
42. Zaliani, A.; Gancia, E. MS-WHIM scores for amino acids: a new 3D-descrip tion for pep tide QSAR and QSPR studies. J.Chem. Inf. Comput. Sci., 1999, 39, 525-533. (Pubitemid 129539208)
43. Lin, Z.H.; Long, H.X.; Zhu, B.; Wang, Y.Q.; Wu, Y.Z. New descriptors of amino acids and their application to peptide QSAR study. Peptides, 2008, 28, 1798-1805.
44. Shu, M.; Jiang, Y.; Yang, L.; Li, Z. Application of 'HESH' descriptors for the structure-activity relationships of antimicrobial peptides. Protein Pept. Lett., 2009, 16, 143-149.
45. Shu, M.; Mei, H.; Yang, S.B.; Liao, L.M.; Li, Z.L. Structural parameter characterization and bioactivity simulation based on peptide sequence. Qsar Comb. Sci., 2009, 28, 27-35.
46. Yang, L.; Shu, M.; Jiang, Y.J.; Mei, H.; Li, Z. ST-scale as a new set of amino acid descriptors and its application in QSAM of peptides and analogues. Amino Acids, 2010, 38, 805-816.
47. Wang, X.Y.; Wang, J.; Lin, Y.; Wang, Y.Q.; Cheng, X.M.; Lin, Z.H. QSAR Study on Angiotensin-Converting Enzyme Inhibitor Oligopeptides based on a novel set of sequence information descriptors. J. Mol. Model, 2010, in press.
48. Chou, K. C. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J. Theor. Biol., 2011, 273, 236-247.
49. Peters, B.; Bui, H.H.; Frankild, S.; Nielsen, M.; Lundegaard, C. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PloS Comput. Biol., 2006, 2, 574-584.
50. Chou, K. C.; Zhang, C. T. Review: Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30, 275-349.
51. Chou, K. C.; Shen, H. B. Review: Recent progresses in protein subcellular location prediction. Anal. Biochem., 2007, 370, 1-16.
52. Chou, K. C.; Shen, H. B. Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Natural Science, 2010, 2, 1090-1103 (openly accessible at http://www.scirp.org/journal/NS/).
53. Zhou, G. P. An intriguing controversy over protein structural class prediction. J. Protein Chem., 1998, 17, 729-738.
54. Zhou, G. P.; Assa-Munt, N. Some insights into protein structural class prediction. Proteins: Struct., Funct., Genet., 2001, 44, 57-59. (Pubitemid 32510584)
55. Zhou, G. P.; Doctor, K. Subcellular location prediction of apoptosis proteins. Proteins: Struct., Funct., Genet., 2003, 50, 44-48. (Pubitemid 36090607)
56. Chen, C.; Chen, L.; Zou, X.; Cai, P. Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein Pept. Lett., 2009, 16, 27-31.
57. Ding, H.; Luo, L.; Lin, H. Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. Protein Pept. Lett., 2009, 16, 351-355.
58. 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 Recognition Letters, 2008, 29, 1887-1892.
59. Gu, Q.; Ding, Y. S.; Zhang, T. L. Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chou's Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns. Protein Pept. Lett., 2010, 17, 559-567.
60. 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, 392-396. (Pubitemid 351679342)
61. 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, 612-616.
62. Lin, H. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J. Theor. Biol., 2008, 252, 350-356.
63. Lin, H.; Ding, H.; 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, 739-744.
64. Xiao, X.; Wang, P.; Chou, K. C. GPCR-2L: Predicting G proteincoupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Mol. BioSyst, 2011, 7, 911-919.
65. Xiao, X.; Wang, P.; Chou, K. C. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J. Comput. Chem., 2009, 30, 1414-1423.
66. Mohabatkar, H. Prediction of Cyclin Proteins Using Chou's Pseudo Amino Acid Composition. Protein Pept. Lett., 2010, 17, 1207-1214.
67. Qiu, J. D.; Huang, J. H.; Liang, R. P.; Lu, X. Q. Prediction of Gprotein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform. Anal. Biochem., 2009, 390, 68-73.
68. Sahu, S. S.; Panda, G. A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction. Comput. Biol. Chem., 2010, 34, 320-327.
69. Zeng, Y. H.; Guo, Y. Z.; Xiao, R. Q.; Yang, L.; Yu, L. Z.; Li, M. L. Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J. Theor. Biol., 2009, 259, 366-372.
70. Esmaeili, M.; Mohabatkar, H.; Mohsenzadeh, S. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. J. Theor. Biol., 2010, 263, 203-209.
71. Georgiou, D. N.; Karakasidis, T. E.; Nieto, J. J.; Torres, A. Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. J. Theor. Biol., 2009, 257, 17-26.
72. Zhang, G. Y.; Fang, B. S. Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo amino acid composition. J. Theor. Biol., 2008, 253, 310-315.
73. Zhang, Q.J.; Gavioli, R.; Klein, G.; Masucci, M.G. An HLA-A11-specific motif in nonamer peptides derived from viral and cellular proteins. Proc. Natl Acad. Sci. USA, 1993, 90, 2217-2221. (Pubitemid 23078597)
74. DiBrino, M.; Parker, K.C.; Shiloach, J.; Knierman, M.; Jukszo, J.; Turner, R.V.; Biddkson, W.E. and Coligan, J.E. Endogenous peptides bound to HLA-A3 possess a specific combination of anchor residues that permit identification of potential antigenic peptides. Proc. Natl Acad. Sci. USA, 1993, 90, 1508-1512. (Pubitemid 23058202)
75. Rammensee, H.G.; Friede, T. and Stevanovic, S. MHC ligands and peptide motifs: first listing. Immunogenetics, 1995, 41, 178-228.
76. Sidney, J.; Grey, H.M.; Southwood, S.; Celis, E.; Wentworth, P.A.; del Guercol; M.F.; Kubo, R.T.; Chestnut, R.W. and Sette, A. Definition of an HLA-A3-like supermotif demonstrates the overlapping peptide-binding repertoires of common HLA molecules. Hum. Immunol., 1996, 45, 79-93. (Pubitemid 26120016)
77. Armstrong, K.M.; Insaidoo, F.K.; Baker, B.M. Thermodynamics of T-cell receptor- peptide/MHC interactions: progress and opportunities. J. Mol. Recognit., 2008, 21, 275-287. (Pubitemid 352019057)
78. Tanford, C. How protein chemists learned about the hydrophobic factor. Protein Sci., 1997, 6, 1358-1366. (Pubitemid 27260172)
79. Sarika, N.; Mohd, Akram.; Iquebal, M.A.; Naimuddin, M. Prediction of MHC binding peptides and epitopes from coat protein of mungbean yellow mosaic India Virus-Ub05. J. Proteomics Bioinform., 2010, 3, 173-178.
80. Chou, K. C.; Shen, H. B. Review: recent advances in developing web-servers for predicting protein attributes. Natural Science, 2009, 2, 63-92 (openly accessible at http://www.scirp.org/journal/NS/).