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POPISK: T-cell reactivity prediction using support vector machines and string kernels

机译:POPISK:使用支持向量机和字符串内核的T细胞反应性预测

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摘要

BackgroundAccurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity.
机译:背景技术准确预测肽的免疫原性以及表征肽序列与肽的免疫原性之间的关系将对疫苗设计和理解免疫系统有很大帮助。与抗原加工和呈递途径的预测相反,对随后的T细胞反应性的预测要困难得多。先前鉴定T细胞受体(TCR)识别位置的研究是基于仅使用少数几种肽的小规模分析得出的,并得出了不同的识别位置,例如长度9的肽的4、6和8位。以更好地表征肽序列变异对T细胞反应性的影响,并设计肽T细胞反应性的预测因子(以及免疫原性)。鉴定和表征影响T细胞反应性的重要位置将提供对免疫原性潜在机制的见解。

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