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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.
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.  This paper was cited by:In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure–activity relationship approach F. Tian, L. Yang, F. Lv, Q. Yang, P. Zhou Amino Acids. Apr 2009, Vol. 36, No. 3: 535-554 CrossRef Gaussian process: an alternative approach for QSAM modeling of peptides Peng Zhou, Xiang Chen, Yuqian Wu, Zhicai Shang Amino Acids. Feb 2009 CrossRef Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): a novel rotation-translation invariant 3D structure descriptor and its applications to peptides Feifei Tian, Peng Zhou, Fenglin Lv, Rong Song, Zhiliang Li Journal of Peptide Science. Sep 2007, Vol. 13, No. 8: 549-566 CrossRef An altered peptide ligand for naïve cytotoxic T lymphocyte epitope of TRP-2(180–188) enhanced immunogenicity Yan Tang, Zhihua Lin, Bing Ni, Jing Wei, Junfeng Han, Huiming Wang, Yuzhang Wu Cancer Immunology, Immunotherapy. Jan 2007, Vol. 56, No. 3: 319-329 CrossRef
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