本文關(guān)鍵詞： 人乳頭瘤病毒 HPV分型 結(jié)構(gòu)相似性比較 HPV突變 結(jié)構(gòu)域　出處：《浙江理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：宮頸癌是全世界女性最�；嫉哪[瘤類婦科疾病之一,其發(fā)病率僅次于乳腺癌。大量的基礎(chǔ)與臨床研究發(fā)現(xiàn),高危險型HPV持續(xù)感染是誘發(fā)宮頸癌的關(guān)鍵因素之一。近年來,中國報道了大量的HPV高危型的突變數(shù)據(jù),與野生型病毒相比,感染宮頸的HPV高危型存在多種突變模式。目前,大部分宮頸癌HPV的研究只關(guān)注通過HPV序列本身,忽略了臨床病變信息。本文以為宮頸癌HPV分型中蛋白質(zhì)序列、結(jié)構(gòu)為對象,圍繞蛋白質(zhì)結(jié)構(gòu)比較、分型預(yù)測模型,以及HPV突變與序列、結(jié)構(gòu)之間的關(guān)系開展研究。具體工作如下:1、綜述了人乳頭瘤病毒與宮頸癌的基礎(chǔ)知識,包括人乳頭瘤病毒分型、結(jié)構(gòu)、功能、人乳頭瘤病毒導(dǎo)致的相關(guān)疾病、人乳頭瘤病毒與宮頸癌的關(guān)系,為本文接下來的研究工作提供了理論基礎(chǔ)和依據(jù)。2、提出了一種基于馬爾科夫隨機(jī)場的蛋白質(zhì)結(jié)構(gòu)相似性分析方法。在距離矩陣分布和不同節(jié)點(diǎn)的鄰域系統(tǒng)的基礎(chǔ)上,建立了改進(jìn)的接觸圖矩陣,并通過計算馬爾科夫隨機(jī)場中的條件概率度量蛋白質(zhì)結(jié)構(gòu)之間的差異。結(jié)果表明,本文提出的蛋白質(zhì)結(jié)構(gòu)比較方法可以有效地度量不同多肽或者蛋白質(zhì)結(jié)構(gòu)之間的差異。此外,本文還發(fā)現(xiàn)alpha-C、O、和N端包含重要的結(jié)構(gòu)信息,而側(cè)鏈的原子集團(tuán)會影響到模型的效率;通過分析馬爾科夫隨機(jī)場鄰域系統(tǒng)的階數(shù),發(fā)現(xiàn)效率最高的馬爾科夫隨機(jī)場往往采用2個節(jié)點(diǎn)的鄰域系統(tǒng)。3、構(gòu)建了一個基于氨基酸特性的宮頸癌HPV分類預(yù)測模型。本文采用氨基酸的物化性質(zhì)對20種常見氨基酸進(jìn)行約化,6種特征信息提取方法提取蛋白質(zhì)序列信息,利用支持向量機(jī)實現(xiàn)對宮頸癌HPV分型預(yù)測。實驗表明,本文提出的預(yù)測模型可以準(zhǔn)確地識別高危型HPV和低危型HPV,比現(xiàn)有的方法更有效。此外,本文還發(fā)現(xiàn)若利用E5、E6、E7、L1和L2蛋白質(zhì)對HPV分型,最好選擇氨基酸的beta類物化性質(zhì)進(jìn)行約化;若利用E1、E2、E4、E5和E7蛋白質(zhì),則PRseAAC蛋白質(zhì)特征表現(xiàn)最優(yōu);而對于E6、L1和L2蛋白質(zhì),RTCD這類蛋白質(zhì)特征的表現(xiàn)優(yōu)于其余特征。4、研究了HPV突變與序列、結(jié)構(gòu)之間的關(guān)系。本文通過文獻(xiàn)檢索,整理了大量的國內(nèi)宮頸癌HPV的突變數(shù)據(jù),并研究了突變位點(diǎn)與序列保守區(qū)域、結(jié)構(gòu)保守區(qū)域的關(guān)系。結(jié)果表明,E6、E7和L1蛋白質(zhì)中突變個數(shù)分別是134、86和166,遠(yuǎn)遠(yuǎn)大于其余蛋白質(zhì)的突變數(shù)量;E2 N端的3159突變可以改變蛋白的免疫功能;HPV低危型E6有11個突變,其中9個突變落在p53蛋白結(jié)合區(qū)域或者是抗原決定簇區(qū)域;HPV高危型的E6有91個突變,有49個突變種類落在p53蛋白結(jié)合區(qū)域或者是抗原決定簇區(qū)域;突變落在E7功能域內(nèi)的比例最高,大約93%以上的突變都落在了功能域區(qū)。
[Abstract]:Cervical cancer is one of the most common tumorous gynecological diseases in women in the world, and its incidence is second only to breast cancer. A large number of basic and clinical studies have found that persistent infection of high-risk HPV is one of the key factors to induce cervical cancer. China has reported a great deal of mutation data of HPV high-risk type. Compared with wild-type virus, there are many mutation patterns in HPV high-risk type infected with cervix. At present, most studies on cervical cancer HPV are focused only on the HPV sequence itself. The clinical pathological information was ignored. The protein sequence and structure in HPV typing of cervical cancer were considered as the object. The protein structure was compared, the prediction model of typing, and the mutation and sequence of HPV were discussed. The relationship between human papillomavirus and cervical cancer is summarized as follows: 1. The basic knowledge of human papillomavirus and cervical cancer is summarized, including human papillomavirus typing, structure, function, and related diseases caused by human papillomavirus. The relationship between human papillomavirus and cervical cancer, This paper provides the theoretical basis and basis for the next research work in this paper. 2. A method of protein structure similarity analysis based on Markov random field is proposed. Based on the distance matrix distribution and the neighborhood system of different nodes, An improved contact graph matrix is established, and the difference between protein structures is measured by calculating the conditional probability in Markov random field. The protein structure comparison method proposed in this paper can effectively measure the differences between different peptides or protein structures. In addition, we also find that alpha-CO and N-terminal contain important structural information. The cluster of atoms in the side chain will affect the efficiency of the model, and by analyzing the order of Markov random field neighborhood system, It is found that the most efficient Markov random field usually uses the neighborhood system of two nodes. 3. A classification and prediction model of cervical cancer based on amino acid characteristics is constructed. In this paper, the physicochemical properties of amino acids are used to predict 20 kinds of common ammonia. The protein sequence information was extracted by six kinds of feature information extraction methods. The prediction of cervical cancer HPV classification using support vector machine is realized. The experimental results show that the proposed prediction model can accurately identify high-risk HPV and low-risk HPV, which is more effective than the existing methods. It is also found that if we use E5, E6, E7, L1 and L2 proteins to type HPV, it is better to select the physicochemical properties of amino acids for beta reduction, and if we use E1, E2, E4, E5 and E7 proteins, the characteristics of PRseAAC proteins will be the best. The relationship between HPV mutation, sequence and structure was studied. A large number of HPV mutation data of cervical cancer in China were collected by literature retrieval. The mutation sites and conserved regions were studied. The results showed that the number of mutations in E6 E7 and L1 proteins was 134n86 and 166respectively, which was much larger than that in the other proteins. 3159 mutations at the N terminal of E2 could change the immune function of HPVE6. There were 11 mutations in E6. Among them, 9 mutations were found in p53 protein binding region or antigen determinant region. There were 91 mutations in E6, and 49 mutations in p53 protein binding region or antigen determinant cluster. The proportion of mutations in the E7 functional domain is the highest, and about 93% of the mutations fall into the functional domain.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R737.33

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