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  本文選題：抗原相關(guān)運轉(zhuǎn)蛋白　切入點：綁定結(jié)合力　出處：《華中農(nóng)業(yè)大學》2009年碩士論文　論文類型：學位論文

【摘要】：主要組織相溶性復(fù)合體MHCⅠ類抗原(Major Histocompatibility Complex Class I Antigens)的加工和遞呈對于免疫監(jiān)視非常重要,細胞毒素T淋巴細胞抗原表位的產(chǎn)生是一個復(fù)雜的過程,包括大量的細胞內(nèi)進程。內(nèi)源性抗原首先在細胞質(zhì)內(nèi)經(jīng)酶切,形成大小不等的多肽片段,由抗原肽載體TAP (Transporter Associated with Antigen Processing)轉(zhuǎn)運至內(nèi)質(zhì)網(wǎng),再與MHC I類分子綁定,經(jīng)細胞外排系統(tǒng)表達于細胞表面,便于CD8陽性T細胞識別,形成三聯(lián)體,以產(chǎn)生免疫應(yīng)答。其中內(nèi)源性抗原加工和遞呈相關(guān)的運轉(zhuǎn)蛋白——抗原肽載體TAP是一種跨膜蛋白,負責將抗原肽片段運輸?shù)絻?nèi)質(zhì)網(wǎng),在整個抗原加工遞呈過程中扮演了重要的角色。因此TAP對抗原多肽的結(jié)合偏愛對T細胞抗原表位的選擇具有重大影響。本文提出新的模型來預(yù)測人類抗原9肽和抗原肽載體TAP的綁定結(jié)合力的數(shù)量值。并對影響結(jié)合力的氨基酸位點及物理化學屬性進行了分析,解釋了其生物學含義。 本文的主要創(chuàng)新和結(jié)論： (1)在與結(jié)合力相關(guān)的眾多物理化學屬性中,選擇了20種氨基酸的15種物理化學屬性作為建模依據(jù)。通過機器學習方法,得出了對于人類TAP與抗原9肽綁定結(jié)合力較為重要的理化屬性和位點。 (2)對于抗原9肽,使用了15特征初始編碼方案。又在此基礎(chǔ)上,通過機器學習方法,選擇出排在前15位的影響重大的維數(shù),并結(jié)合統(tǒng)計學的主成分分析方法對相對次要的維數(shù)進行了綜合提煉,以部分主成分代替原來的維數(shù)參與建模,并進一步構(gòu)建了三種不同的新的編碼方案。(3)將數(shù)據(jù)集劃分為訓(xùn)練集,驗證集和測試集。對于每一種編碼方案,分別使用了支持向量回歸機和人工神經(jīng)網(wǎng)絡(luò)作為預(yù)測引擎進行了旁置法測試的試驗。訓(xùn)練模型,優(yōu)化參數(shù),獨立測試。并對三種編碼方案所得的試驗結(jié)果進行了比較說明。支持向量機測試,皮爾遜相關(guān)系數(shù)達到r=0.9029;交叉驗證相關(guān)系數(shù)q2=0.8068;人工神經(jīng)網(wǎng)絡(luò)達到r=0.8547;q2=0.6985。 (4)用五折交叉驗證的方法對整個數(shù)據(jù)集進行了交叉訓(xùn)練和測試。得到最優(yōu)參數(shù),并對試驗結(jié)果進行了分析。全部數(shù)據(jù)測試結(jié)果,支持向量機為r=0.8225;q2=0.6697。人工神經(jīng)網(wǎng)絡(luò)為r=0.9417,q2=0.8852。從而證明了該預(yù)測技術(shù)具有可靠性和可行性。 (5)根據(jù)模型測試的結(jié)果,分析了其相應(yīng)的生物學含義。提出了進一步研究的方向。
[Abstract]:The processing and presentation of Major Histocompatibility Complex Class I Antigenss, a major histocompatibility complex, is very important for immune surveillance, and the production of antigen epitopes of cytotoxin T lymphocytes is a complex process. Endogenous antigens were first digested in the cytoplasm to form polypeptide fragments of varying sizes, which were transported to the endoplasmic reticulum by TAP transporter Associated with Antigen processing, and then bound to MHC class I molecules. Expressed on the surface of the cell surface, CD8 positive T cells can be recognized and triplet formed to produce immune response. Among them, endogenous antigen processing and presenting related transporter protein-antigen peptide vector TAP is a transmembrane protein. Responsible for transporting antigenic peptide fragments to the endoplasmic reticulum, It plays an important role in the whole antigen processing and presenting process. Therefore, the binding preference of TAP to antigen peptides has a great influence on the selection of T cell epitopes. In this paper, a new model is proposed to predict human antigen 9 peptide and human antigen 9 peptide. The binding binding capacity of antigenic peptide vector TAP was evaluated. The amino acid sites and physicochemical properties affecting binding ability were analyzed. The biological implications are explained. The main innovations and conclusions of this paper are as follows:. Among the many physicochemical properties related to binding ability, 15 kinds of physicochemical properties of 20 amino acids are selected as the basis for modeling. The physical and chemical properties and sites of binding ability of human TAP to antigen 9 peptide were obtained. (2) for antigen 9 peptide, the initial coding scheme of 15 features was used. On this basis, the first 15 influential dimensions were selected by machine learning. Combined with the principal component analysis (PCA) method of statistics, the relative secondary dimension is abstracted synthetically, and some principal components are used instead of the original dimension to participate in modeling. Furthermore, three different new coding schemes are constructed. The data set is divided into training set, verification set and test set. For each coding scheme, the data set is divided into a training set, a verification set and a test set. Support vector regression machine (SVM) and artificial neural network (Ann) were used as prediction engine to test the side test. Independent test. The experimental results of three coding schemes are compared. The Pearson correlation coefficient is 0.9029, the cross-validation correlation coefficient is 0.8068, and the artificial neural network is 0.8547q20.6985 by support vector machine test. 4) the data set is cross-trained and tested by the method of 50% cross-validation. The optimal parameters are obtained and the test results are analyzed. The support vector machine is 0.6697 and the artificial neural network is 0.9417q20.8852.This method is proved to be reliable and feasible. (5) based on the results of the model test, the biological implications of the model are analyzed, and the direction of further research is proposed.
【學位授予單位】：華中農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2009
【分類號】：R392.1

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本文編號：1623762