【作者】 刘涛；

【导师】 王晓钢； 刘伟；

【作者基本信息】 大连理工大学 ， 生物医学工程， 2009， 博士

【摘要】 在T细胞介导的特异性免疫应答中,T细胞表面抗原受体(T-cell receptor,TCR)仅能识别抗原肽与主要组织相容性复合体(major histocompatibility complex,MHC)分子结合形成的复合物。此复合物的形成是依赖于抗原的加工提呈途径。内源性抗原(病毒、肿瘤抗原等)需要经过蛋白酶体(proteaome)的降解、与抗原提呈相关的转运蛋白(transporter associated with antigen processing,TAP)的转运、MHCⅠ类分子的结合后才能被细胞毒性T细胞(cytotoxic T lymphocyte,CTL)识别,相应的抗原肽称为CTL表位;外源性抗原(细菌产生的毒素)也需要经过溶酶体酶的降解和MHCⅡ类分子的结合后才能被辅助性T细胞(helper T cell,Th)识别,相应的抗原肽称为Th表位。一般来讲,抗原的加工提呈途径决定了T细胞对表位的选择性。为了进一步弄清抗原的加工提呈机制,提高T细胞表位预测的准确性和合理性,本文应用支持向量机方法对抗原加工提呈途径中三个重要的选择性阶段进行了理论预测研究。1.在内源性抗原的加工提呈途径中,真核细胞中的泛素-蛋白酶体系统对抗原蛋白发挥着重要的酶切降解功能。为了进一步理解蛋白酶体的酶切机制,本文对蛋白酶体的裂解位点特异性进行了研究。文中采用支持向量分类器(support vector classifier,SVC)方法建立了蛋白酶体的裂解位点预测模型,预测准确度达到83.1%。在相同检验集下,本模型的性能表现要优于其他预测模型。通过分析预测模型中不同位置上氨基酸对裂解位点形成的权重系数,本文获取了蛋白酶体裂解位点及其两侧区域氨基酸的裂解特异性。这些裂解特异性反映了蛋白酶体与抗原蛋白相互作用信息,同时表明蛋白酶体对抗原蛋白的酶切处理不是随机的,而是有一定模式和选择性的。研究结果为进一步揭示蛋白酶体裂解抗原蛋白的机理提供了重要的信息。2.在内源性抗原的加工提呈途径中,MHCⅠ类分子发挥着启动和调节免疫应答的重要作用。抗原肽只有结合MHCⅠ类分子后,才能被细胞毒性T细胞(CTL)识别,然而,对于一个给定的MHCⅠ类分子,只有一组特定的抗原肽才能与之结合。因此,准确判断哪些抗原肽能与指定MHCⅠ类分子结合,不仅有助于理解免疫机制,而且有助于开发高效的抗肿瘤疫苗。为了进一步了解MHCⅠ类分子与抗原肽结合的特异性,本文利用支持向量回归机(support vector regression,SVR)方法以及四种氨基酸编码方式建立了四个抗原肽与MHCⅠ类分子结合亲和力的预测模型。四个模型的性能比较显示,基于氨基酸物理化学性质建立的模型具有更好的预测能力。此外,本文通过分析抗原肽中不同位置氨基酸对结合MHCⅠ类分子形成的权重系数,获取了抗原肽与MHCⅠ类分子的结合特异性。3.在外源性抗原的加工提呈途径中,抗原肽与MHCⅡ类分子的结合是激活辅助性T细胞特异性免疫应答的先决条件。对于给定的一种MHCⅡ类分子,准确预测与之结合的抗原肽,不仅有助于人们进一步理解免疫的基本原理,还对表位疫苗的开发、自身免疫性疾病(如类风湿关节炎、胰岛素依赖性糖尿病等)的治疗等有着重要的意义。本文应用迭代自洽(iterative self-consistent,ISC)策略与支持向量回归机(SVR)的组合方法和四种氨基酸编码方式,对17种MHCⅡ分子(包括14种人类的HLA DR分子和3种鼠类的H2 IA分子)的配体数据进行了回归分析,分别建立了预测模型。与其他预测模型的比较结果显示,本文模型具有更优的性能表现。此外,本文以HLA DRB1*0101为例,通过分析抗原肽中不同位置氨基酸对结合MHCⅡ类分子形成的权重系数,获取了抗原肽与MHCⅡ类分子的结合特异性。研究结果为进一步揭示Th细胞表位的产生机制提供了重要的信息。更多还原

【Abstract】 In T-cell mediated specific immune response, the T-cell receptor (TCR) only recognizes the peptide binding to major histocompatibility complex (MHC) molecular. The formation of the peptide-MHC complex depends on the antigen processing and presentation pathway. Endogenous antigens (e.g. viruses, tumor antigens) need to be degradated by proteaome, transported by transporter associated with antigen processing (TAP) and bound by MHC classⅠmolecule before recognized by cytotoxic T lymphocytes (CTL), correspondingly, peptides generated from this pathway are called CTL eptitopes; Exogenous antigens (e.g. toxins produced by bacterias) also need to be degradated by lysosomal enzyme and bound by MHC classⅡmolecule before recognized by helper T cell (Th), and peptides generated from this pathway are called Th eptitope. Gernelly speaking, the antigen processing and presentation pathway determines the selection of T cell to eptitope. In order to further study the biological mechanism of the processing and presentation of antigen, and improve accuracy and rationality of T cells epitope prediction, support vector machine (SVM) was used to theoretically study following three important selective stages in the antigen processing and presentation pathway.1. The ubiquitin-proteasome system of the eukaryote plays an importance role in the endogenous antigen processing and presentation pathway. In order to further study the specificity of the proteasome cleavage sites, the support vector classifier (SVC) was used to build the predictive model of proteasomal cleavage sites and the predictive accuracy of the model is 83.1%. Compared to other models with the same test set, the performance of this model is more satisfying, The specificities of the cleavage sites and their adjacent positions come from analysis based on the weight coefficient of the amino acids to cleavage sites in the predictive model, showing the information about interaction of the proteasome with an antigen protein, which demonstrates that the proteasome cleaves the target protein selectively, but not randomly. This study is helpful to further reveal intrinsic mechanism how proteasome cleave antigen protein.2. In the endogenous antigen processing and presentation pathway, MHC classⅠmolecules play a critical role in initiating and regulating immune responses. Peptide must be bound to an MHC classⅠmolecule before recognized by the cytotoxic T lymphocytes (CTL), but only certain peptides can bind to any given MHC class I molecule. Determining which peptides bind to a specific MHC classⅠmolecule is not only helpful to understand the mechanism of immunity, but also to develop effective anti-tumor epitope vaccines. In order to further study the specificity of MHC classⅠmolecule binding antigen peptide, the support vector regression (SVR) and four amino acid encoding schemes were used to build four models of predicting binding affinities between peptides and MHC classⅠmolecules. Comparison among performances of the four models indicated that the model based on physicochemical properties of amino acids is more satisfying. Furthermore, the specificities of MHC classⅠmolecule binding antigen peptide were obtained through analysis based on the contribution of the amino acids to peptide-MHC classⅠmolecule binding affinities in the predictive model.3. In the exogenous antigen processing and presentation pathway, peptide binding MHC classⅡmolecule is an important prerequisite for activating helper-T-cell mediated immune response. Accurate prediction of peptide that bind a specific MHC classⅡmolecule is not only helpful for understanding the immune mechanism but also is useful for developing of epitope vaccine and immunotherapy of autoimmune disease, e.g. rheumatoid arthritis (RA) and Insulin-dependent diabetes mellitus (IDDM). In this paper, a method combine an iterative self-consistent (ISC) strategy with support vector regression (SVR) and four schemes of amino acid encoding was used to build models to predict binding affinities between peptides and MHC classⅡmolecules. The predictive performance of the method is validated on data sets of 17 MHC classⅡalleles covering 14 human HLA DR alleles and 3 mouse H2 IA alleles. Compared to other models with the same data set, the predictive performance of our model is more satisfying. Furthermore, the specificities of MHC classⅡmolecule binding peptide were obtained through analysis based on the contribution of the amino acids to peptide-MHC classⅡmolecule binding affinities in the predictive model. This study is helpful to further reveal mechanism of generation of Th epitope.更多还原