A database for prediction of unique peptide motifs as linear epitopes

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Please use this identifier to cite or link to this item: http://ir.cmu.edu.tw/ir/handle/310903500/6934

Title:	A database for prediction of unique peptide motifs as linear epitopes
Authors:	(Chang, M. D.-T.);張顥騰(Hao-Teng Chang);(Tzou, W.-S.);(Zhung, W.-J.);(Liu, C.-H.);(Pai, T.-W.)*
Contributors:	醫學院分子系統生物醫學研究所
Keywords:	unique peptide motif;linear epitope;antigenicity;specificity;protein family
Date:	2007
Issue Date:	2009-08-26 16:05:00 (UTC+8)
Abstract:	A linear epitope prediction database (LEPD) is designed for identifi- cation of unique peptide motifs (UPMs) as specific linear epitopes for all protein families defined by Pfam. The UPMs in LEPD are extracted from each protein family by employing reinforced merging techniques that merge the primary unique patterns into a consecutive peptide based on the neighboring relationships and various levels of parameter settings. These merged peptide motifs are examined using the physicochemical and structural propensity scales for antigenic characteristics and are verified by employing background model analysis for specificity. The filtered UPMs with high antigenicity and specificity are considered as linear epitopes that provide important information for designing antibodies and vaccines. The predicted epitopes of each protein family in the LEPD can be searched in a straightforward manner, and the corresponding chemical properties be displayed in graphical and tabular formats. To verify the specificity of the predicted epitopes, each identified UPM is analyzed by scanning over the complete genomes of a series of model organisms. For any query protein possessing a resolved 3D structure, the proposed database also provides interactive visualization of the protein structures for allocation and comparison of the predicted linear epitopes. The accuracy of the prediction algorithm is evaluated to be higher than 70% in terms of mapping a UPM as a linear epitope as compared to the known databases.
Relation:	LECTURE NOTES IN COMPUTER SCIENCE (4463 )430 ~440
Appears in Collections:	[Graduate Institute of Molecular System Biomedicine] Journal articles

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