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Journal of Computational Biology
Toward the Quantitative Prediction of T-Cell Epitopes: QSAR Studies on Peptides Having Affinity with the Class I MHC Molecular HLA-A*0201

To cite this article:
Lin Zhihua, Wu Yuzhang, Zhu Bo, Ni Bing, Wang Li. Journal of Computational Biology. 2004, 11(4): 683-694. doi:10.1089/cmb.2004.11.683.

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Lin Zhihua
Institute of Immunology, PLA, The Third Military Medical University, Chongqing 400038, China.
College of Bioengineering, Chongqing Institute of Technology, Chongqing 400050, China.
Wu Yuzhang
Institute of Immunology, PLA, The Third Military Medical University, Chongqing 400038, China.
Zhu Bo
Institute of Immunology, PLA, The Third Military Medical University, Chongqing 400038, China.
Ni Bing
Institute of Immunology, PLA, The Third Military Medical University, Chongqing 400038, China.
Wang Li
Institute of Immunology, PLA, The Third Military Medical University, Chongqing 400038, China.

It would be useful for vaccine development to develop a method of rapidly identifying peptide epitopes. In this paper, the empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models yielded a q 2 = 0.5724 and a high r 2 pred = 0.6955. The standard regression coefficients indicated that the hydrophobic interactions played an important role in peptide-MHC molecule binding and predicted the specific amino acid residue essential at a certain position of the peptide. The approach tested in the current paper is highly complementary to many of the methods described in references and possesses good predictability. It is a rapid and convenient method to detect high affinity peptide epitopes.

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