Prediction of adaptive T-cell immune response

• Main Authors: Tung, Chun-Wei, 童俊維
• Other Authors: Ho, Shinn-Ying
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/00937674842917994514

博士 === 國立交通大學 === 生物資訊及系統生物研究所 === 98 === The development of computer-aided vaccine design systems is a goal of immunoinformatics that can largely accelerate the design of vaccines. Accurate prediction of adaptive T-cell immune response is the critical step to develop computer-aided vaccine design systems. The core of this study is to develop high-performance optimization algorithms for solving large-scale parameter optimization problems of bioinformatics to mine informative physicochemical properties from known experimental data for predicting immunogenic pathway. The development of these algorithms involves three major phases: (a) collection of physicochemical properties for encoding peptide sequences; (b) formulation of optimization problems using domain knowledge and computing techniques and, and (c) development of efficient optimization algorithms for solving optimization problems. The developed informative feature mining algorithms can be used to mine informative physicochemical properties for predicting peptide immunogenicity. There are two major T cells including cytotoxic and helper T cells. For the prediction of adaptive T-cell immune response, previous studies mainly focused on modeling antigen processing and presentation pathways of MHC class I and II. However, the prediction of T-cell response is much harder and less addressed because of the complex nature of T-cell response. Moreover, because over-ubiquitylated protein correlated with its half life, ubiquitylation plays an important role in providing antigen sources. Accurate prediction of ubiquitylation sites is helpful to identify immunogenic peptides. This study proposed the first prediction systems POPI and UbiPred for predicting T-cell response and ubiquitylation sites, respectively. The poor performance of a well recognized affinity-based method shows that binding affinity only is not sufficient for predicting T-cell response. The informative physicochemical properties for cytotoxic and helper T cells are identified and analyzed. Subsequently, an improved prediction system POPISK is proposed to predict cytotoxic T-cell response. The POPISK prediction system incorporating MHC allele information is used to identify important positions for T-cell recognition, and can predict immunogenicity changes made by single residue modifications. This study yields insights into the mechanism of immune response and can accelerate the development of vaccines.