本文關(guān)鍵詞： 生物信息學(xué) 蛋白質(zhì)翻譯后修飾 甲基化 支持向量機(jī) 多標(biāo)簽分類(lèi)　出處：《大連理工大學(xué)》2016年博士論文　論文類(lèi)型：學(xué)位論文

【摘要】：蛋白質(zhì)翻譯后修飾是對(duì)翻譯后的蛋白質(zhì)進(jìn)行共價(jià)加工的過(guò)程,它在調(diào)控蛋白質(zhì)構(gòu)象變化、活性以及功能等方面發(fā)揮著重要作用。精確地識(shí)別翻譯后修飾位點(diǎn)是深入探究翻譯后修飾分子機(jī)制的關(guān)鍵。近十年來(lái),基于機(jī)器學(xué)習(xí)的蛋白質(zhì)翻譯后修飾位點(diǎn)預(yù)測(cè)研究取得了快速發(fā)展,已經(jīng)成為生物信息學(xué)領(lǐng)域的一個(gè)研究熱點(diǎn)。本文根據(jù)蛋白質(zhì)翻譯后修飾位點(diǎn)預(yù)測(cè)的研究現(xiàn)狀,從蛋白質(zhì)序列角度出發(fā),利用機(jī)器學(xué)習(xí)中的支持向量機(jī)(SVM)及其改進(jìn)算法,對(duì)目前翻譯后修飾位點(diǎn)預(yù)測(cè)中存在的幾個(gè)問(wèn)題進(jìn)行研究,主要工作概括如下：1.建立了一個(gè)蛋白質(zhì)賴(lài)氨酸甲基化位點(diǎn)及程度預(yù)測(cè)模型iLM-2L,以解決現(xiàn)有的賴(lài)氨酸甲基化位點(diǎn)預(yù)測(cè)方法的預(yù)測(cè)精度較低,且不具備甲基化程度預(yù)測(cè)功能的問(wèn)題。首先,針對(duì)現(xiàn)有賴(lài)氨酸甲基化位點(diǎn)預(yù)測(cè)方法準(zhǔn)確率較低的問(wèn)題,將有效的k-spaced氨基酸對(duì)組成編碼方法應(yīng)用于甲基化位點(diǎn)預(yù)測(cè)模型的構(gòu)建,提高了甲基化位點(diǎn)預(yù)測(cè)準(zhǔn)確率。其次,針對(duì)現(xiàn)有甲基化預(yù)測(cè)方法忽略甲基化程度預(yù)測(cè)的問(wèn)題,將甲基化程度預(yù)測(cè)建模為一個(gè)多標(biāo)簽學(xué)習(xí)問(wèn)題并利用多標(biāo)簽SVM算法對(duì)其進(jìn)行訓(xùn)練。仿真實(shí)驗(yàn)結(jié)果表明,iLM-2L的預(yù)測(cè)性能要優(yōu)于現(xiàn)有的5個(gè)甲基化位點(diǎn)預(yù)測(cè)方法：MeMo、MASA、BPB-PPMS、PMeS以及iMethyl-PseAAC。此外,iLM-2L還能夠有效地進(jìn)行甲基化程度預(yù)測(cè),彌補(bǔ)了現(xiàn)有預(yù)測(cè)方法不具備甲基化程度預(yù)測(cè)功能的不足。通過(guò)對(duì)最優(yōu)的k-spaced氨基酸對(duì)組成特征的分析,給出了賴(lài)氨酸甲基化修飾位點(diǎn)周?chē)臐撛谛蛄心Ｊ狡�。最�?基于iLM-2L模型,構(gòu)建了甲基化位點(diǎn)預(yù)測(cè)服務(wù)平臺(tái),為研究人員提供在線(xiàn)預(yù)測(cè)服務(wù)(http://123.206.31. 171/iLM 2L/)。2.建立了一個(gè)原核生物pupylation位點(diǎn)預(yù)測(cè)模型IMP-PUP。針對(duì)pupylation修飾位點(diǎn)數(shù)據(jù)較少而導(dǎo)致現(xiàn)有預(yù)測(cè)模型性能不佳的問(wèn)題,提出了一個(gè)半監(jiān)督自訓(xùn)練SVM算法作為IMP-PUP模型的核心分類(lèi)算法。所提出的自訓(xùn)練SVM算法可以充分挖掘PupDB數(shù)據(jù)庫(kù)中未帶有修飾位點(diǎn)標(biāo)注的pupylation蛋白所隱含的位點(diǎn)信息,擴(kuò)充了可用于模型訓(xùn)練的修飾位點(diǎn)數(shù)據(jù),進(jìn)而提高了預(yù)測(cè)性能。該算法在迭代訓(xùn)練過(guò)程中引入一個(gè)最小距離準(zhǔn)則設(shè)計(jì)置信度函數(shù)來(lái)抽取可信樣本,克服了原始半監(jiān)督自訓(xùn)練SVM算法在訓(xùn)練過(guò)程中容易過(guò)早出現(xiàn)誤分類(lèi)情況的不足。仿真驗(yàn)證結(jié)果表明,IMP-PUP模型的預(yù)測(cè)性能要優(yōu)于其它3個(gè)現(xiàn)有預(yù)測(cè)器：GPS-PUP、iPUP和pbPUP�；贗MP-PUP模型,構(gòu)建了相應(yīng)的在線(xiàn)預(yù)測(cè)平臺(tái)(http://123.206.31.171/IMP_PUP/).3.建立了一個(gè)賴(lài)氨酸phosphoglycerylation位點(diǎn)預(yù)測(cè)模型CKSAAP_PhoglySite。首先,針對(duì)phosphoglycerylation位點(diǎn)預(yù)測(cè)中正負(fù)訓(xùn)練樣本不平衡且含有噪聲的問(wèn)題,提出了一個(gè)模糊SVM算法。所提出的模糊SVM算法在設(shè)計(jì)樣本的模糊隸屬度函數(shù)時(shí),不僅考慮樣本到其類(lèi)中心距離,而且考慮樣本周?chē)木o密程度,大大提升了算法處理噪聲數(shù)據(jù)的能力,并通過(guò)賦予正、負(fù)類(lèi)樣本分別以較大、較小的懲罰因子,較好地克服了數(shù)據(jù)不平衡性對(duì)分類(lèi)器的影響。其次,為了尋找有效的編碼技術(shù)來(lái)提取phosphoglycerylation位點(diǎn)周?chē)男蛄刑卣?分析并比較了氨基酸組成、二進(jìn)制編碼、k-spaced氨基酸對(duì)組成、位置特異性得分矩陣和二級(jí)結(jié)構(gòu)這5種常用的特征對(duì)模型預(yù)測(cè)效果的影響。最后,利用所提出的模糊SVM算法結(jié)合k-spaced氨基酸對(duì)組成特征對(duì)CKSAAP_PhoglySite模型進(jìn)行構(gòu)建。Jackknife測(cè)試結(jié)果表明,CKSAAP_PhoglySite模型的預(yù)測(cè)準(zhǔn)確率比現(xiàn)有的預(yù)測(cè)工具Phogly-PseAAC提高了14.2%�；贑KSAAP_PhoglySite模型,構(gòu)建了相應(yīng)的在線(xiàn)預(yù)測(cè)服器(http://123.206.31.171/CKSAAP_PhoglySite/)。
[Abstract]:PTMs is a process of covalent processing of protein after translation, it changes in the regulation of protein conformation, plays an important role in the activity and function. Accurate identification of post-translational modification sites is a key molecular mechanism of modification after translation. In the past ten years, machine learning of post-translational modification based on site prediction has achieved rapid development, has become a hot research topic in the field of bioinformatics. In this paper, according to the current research status of site prediction of PTMs, starting from the angle of protein sequence, using support vector machine in machine learning (SVM) and its improved algorithm, research on the current problems of post-translational modification sites in the prediction, the main works are as follows: 1.. To establish a prediction model of iLM-2L amino acid protein lysine methylation sites and the degree to solve The existing prediction of lysine methylation sites with low prediction accuracy, and do not have the function of the degree of methylation prediction. Firstly, aiming at the lysine methylation prediction method of the problem of low accuracy, the effective composition of k-spaced amino acid encoding method should be used to establish the model of methylation site prediction and improve the prediction accuracy of methylation sites. Secondly, in view of the existing prediction methods ignore methylation methylation prediction problem, the methylation level prediction model for a multi label learning problem and using the multi label algorithm to train the SVM. Simulation results show that the prediction performance of iLM-2L to 5 methylation site is better than the existing prediction methods: MeMo, MASA, BPB-PPMS, PMeS and iMethyl-PseAAC. in addition, iLM-2L also can effectively predict the degree of methylation, make up the existing prediction methods not out The lack of preparation of predictive function of methylation level by k-spaced. The optimal analysis of amino acid composition characteristics, given lysine methylation potential sequence pattern modification sites around the bias. Finally, based on the iLM-2L model, constructed the methylation prediction service platform to provide online service for researchers (http://123.206.31. 171/iLM 2L/) the.2. establishes a prediction model for IMP-PUP. of prokaryotic pupylation sites pupylation modification sites less data problem caused by poor performance of the existing prediction models, puts forward 1.5 supervised self training SVM classification algorithm as the core algorithm of IMP-PUP model. The proposed algorithm can fully exploit the self training SVM site information modification sites labeled pupylation protein implied not with the PupDB database, the expansion can be used to modify the site data for training the model, and provided High prediction performance. The algorithm introduces a minimum distance criterion design confidence function to extract credible sample in the iterative training process, overcome the original semi supervised self training in the training process of SVM algorithm is prone to premature lack of misclassification. Simulation results show that the performance of the IMP-PUP model is better than the other 3 the current Predictor: GPS-PUP, iPUP and pbPUP. based on the IMP-PUP model, establishes the forecasting platform corresponding online (http://123.206.31.171/IMP_PUP/).3. established a lysine phosphoglycerylation locus CKSAAP_PhoglySite. prediction model for prediction of phosphoglycerylation site first, positive and negative training samples are not balanced and noise problems, put forward a fuzzy SVM algorithm of fuzzy SVM algorithm. The fuzzy membership function of the sample design, considering not only the sample to its class center distance From, and consider closely surrounding the samples, greatly enhance the ability of the algorithm to deal with noise data, and by giving positive and negative samples respectively with larger, smaller penalty factor, overcomes the influence of data imbalance on the classifier. Secondly, in order to seek the effective encoding technology to extract the sequence characteristics of phosphoglycerylation sites the analysis and comparison of amino acid composition, amino acid composition of binary encoding, k-spaced, the prediction effect of position specific scoring matrix and two level structure of the 5 kinds of characteristics influence the model. Finally, using the fuzzy SVM algorithm is proposed based on amino acid composition characteristics of k-spaced CKSAAP_PhoglySite model to construct the.Jackknife test results show that the CKSAAP_PhoglySite model prediction accuracy rate than the existing improved Phogly-PseAAC prediction tool based on 14.2%. CKSAAP_PhoglySit In the e model, the corresponding online predictive server (http://123.206.31.171/CKSAAP_PhoglySite/) is built.

【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：Q51;TP18

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