本文關鍵詞：基于蛋白質磷酸化相關位點—修飾網(wǎng)絡的翻譯后修飾位點預測研究　出處：《中國科學技術大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 翻譯后修飾位點預測 磷酸化相關位點-修飾網(wǎng)絡 多核支持向量機 高斯互作譜相似性核

【摘要】：蛋白質翻譯后修飾(post-translational modification,PTM)是一種十分重要的生命活動調控方式,可以改變蛋白質的結構并完善蛋白質的功能。因此深入研究蛋白質翻譯后修飾的原理、種類、作用機制對于理解人類疾病的發(fā)病機制具有重要意義。近年來,隨著實驗技術的不斷發(fā)展,積累了大量的蛋白質翻譯后修飾位點數(shù)據(jù),極大地推動了蛋白質翻譯后修飾的研究進展。然而實驗方法往往耗時耗力且成本較高,因此有必要發(fā)展高效、精確的計算方法預測翻譯后修飾位點,為后續(xù)實驗工作提供有用的參考信息�，F(xiàn)有的計算方法大部分使用蛋白質氨基酸序列信息進行預測,忽視了蛋白質翻譯后修飾間的功能聯(lián)系信息。有研究表明in situ PTM指的是相同蛋白質同一個位點上發(fā)生多種翻譯后修飾類型,可以反映出翻譯后修飾間的功能聯(lián)系。因此由多位點-多修飾相互作用關系的insituPTM啟發(fā)本文從磷酸化相關位點-修飾網(wǎng)絡的角度充分考慮網(wǎng)絡拓撲結構信息,進而應用于翻譯后修飾位點預測中來。本文主要研究內容如下:1、利用多種翻譯后修飾數(shù)據(jù)庫中收集的絲/蘇/酪氨酸位點上的翻譯后修飾位點數(shù)據(jù),構建了磷酸化相關位點-修飾網(wǎng)絡。在該網(wǎng)絡的基礎上,提出了基于資源配置的網(wǎng)絡鏈路預測算法 SMNBI(site-modification network based inference)用于磷酸化位點預測。該算法主要利用網(wǎng)絡中已知的鏈路信息對未知的位點與修飾相互作用關系進行預測。將SMNBI算法與現(xiàn)有的網(wǎng)絡鏈路預測算法及磷酸化位點預測方法進行比較,結果表明磷酸化相關位點-修飾網(wǎng)絡在磷酸化位點預測中發(fā)揮重要作用,能夠大幅度提高預測精度。2、為解決磷酸化相關位點-修飾網(wǎng)絡中孤立節(jié)點的預測問題,本文進一步提出了 多核支持向量機算法 MK-SVM(multiple kernels support vector machine)用于全面預測絲/蘇/酪氨酸位點上的多種翻譯后修飾類型。首先采用高斯核函數(shù)和氨基酸置換矩陣BLOSUM62分別設計了高斯互作譜相似性核和蛋白質局部序列相似性核,然后通過線性加權組合核函數(shù)的方式輸入到SVM進行訓練和預測。MK-SVM算法不僅有效解決了磷酸化相關位點-修飾網(wǎng)絡中孤立節(jié)點的預測問題,而且可對絲/蘇/酪氨酸位點上的多種翻譯后修飾進行預測。與多種常用的翻譯后修飾預測方法的比較結果顯示,該算法對于磷酸化、O-GlcNAc、硝基化、硝化等翻譯后修飾類型均取得了良好的預測性能。
[Abstract]:Post-translational modification (PTM) is a very important way to regulate life activities. It can change the structure of protein and improve the function of protein. Therefore, it is very important to study the mechanism of post-translational modification of protein for understanding the pathogenesis of human disease in recent years. With the development of experimental technology, a large amount of post-translational modification site data have been accumulated, which has greatly promoted the research progress of protein post-translational modification. However, the experimental methods are often time-consuming and costly. Therefore, it is necessary to develop efficient and accurate calculation methods for prediction of post-translational modification sites. It provides useful reference information for further experiments. Most of the existing calculation methods use protein amino acid sequence information to predict. Some studies have shown that in situ PTM refers to multiple types of posttranslational modification at the same site of the same protein. It can reflect the functional relationship between posttranslational modifications. Therefore, insituPTM inspired this paper to consider the network extension from the point of view of phosphorylation related locus-modified network. Structure information. The main contents of this study are as follows: 1, using the post-translational modification site data collected from a variety of post-translational modification databases on the silk / Su / tyrosine locus. The phosphorylation related locus, modified network, was constructed on the basis of this network. A resource allocation based network link prediction algorithm, SMNBI(site-modification network based in Conference, is proposed. This algorithm mainly uses the known link information in the network to predict the interaction between the unknown sites and the modified sites. The SMNBI algorithm is combined with the existing network link prediction algorithms and phosphoric acid. The prediction methods of chemical sites were compared. The results show that phosphorylation related locus-modified network plays an important role in the prediction of phosphorylation sites and can greatly improve the prediction accuracy of .2. In order to solve the problem of prediction of isolated nodes in phosphorylation related loci-modified networks. In this paper, a new multi-kernel support vector machine algorithm, MK-SVM(multiple kernels support vector machine, is proposed. It is used to fully predict the types of post-translational modifications on silk / Sudan / tyrosine loci. First, we designed the Gao Si interaction spectrum similarity nuclei and proteins using Gao Si nuclear function and amino acid replacement matrix BLOSUM62, respectively. Local sequence similarity kernel. Then input into SVM by linear weighted combination kernel function to train and predict. MK-SVM algorithm not only effectively solves the prediction problem of isolated nodes in phosphorylated related locus-modified network. In addition, we can predict various post-translational modifications on silk / Su / tyrosine locus. Compared with other commonly used post-translational modification prediction methods, the proposed algorithm can be used for phosphorylated O-GlcNAc. After-translation modification such as nitration and nitration have achieved good predictive performance.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R3411

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