【摘要】：蛋白質翻譯后修飾在細胞生命過程中起到至關重要的作用,多種蛋白質翻譯后修飾相互影響、相互協(xié)調,共同維持、促進各種細胞活動的正常進行。然而,翻譯后修飾的鑒定在生物學上往往是繁復的實驗工作,效率較低。因此,開發(fā)有效的生物信息學預測工具來提高修飾位點鑒定工作的效率勢在必行。本文以蛋白質序列為基本研究對象,結合多種特征提取方法,通過計算的方法,對蛋白質翻譯后磷酸化修飾和磷酸甘油酯化修飾的修飾位點進行了預測研究。針對磷酸化修飾,本文從其修飾的功能出發(fā),從磷酸化修飾數(shù)據(jù)庫中抽取了多條與信號傳導功能相關的蛋白質序列,構建了數(shù)據(jù)集。在特征提取上,提出了一種新的提取方法,將氨基酸殘基理化性質的分組信息融入到以氨基酸殘基在滑窗中出現(xiàn)頻率為基礎的特征提取中。通過實驗發(fā)現(xiàn),在融合氨基酸殘基理化性質分組信息后,同種修飾位點在相同的預測模型下,預測結果有了很大的提升。在本文中,利用基于粒子群算法優(yōu)化的神經(jīng)網(wǎng)絡模型的預測準確率從58%左右提升到86%。本文在此基礎上還圍繞氨基酸殘基序列的大小對實驗結果的影響進行了初步實驗,結果發(fā)現(xiàn)當?shù)鞍踪|微序列包含23個氨基酸殘基時,預測結果達到最優(yōu)值。之后,本文將數(shù)據(jù)集按照十折交叉驗證的方法進行整理,利用神經(jīng)網(wǎng)絡、支持向量機和柔性神經(jīng)樹三種模型集成學習的方法,按照新的特征提取方法對數(shù)據(jù)集進行實驗。其中三種模型的組合策略按照少數(shù)服從多數(shù)原則進行投票。實驗結果顯示,三種預測模型進行集成學習后,預測準確率可以達到87.50%,較以前研究結果有了很大提升。針對磷酸甘油酯化修飾,本文利用柔性神經(jīng)樹模型對這種修飾展開預測修飾位點的研究工作,并將實驗結果與本領域最新研究進展進行了比較。其中,數(shù)據(jù)集通過十折交叉驗證的方式進行處理,并且蛋白質微序列的窗口值采用了以往研究人員的結論。實驗結果顯示,柔性神經(jīng)樹在等量的正負樣本下,具有較大的優(yōu)勢,其預測準確率能達到90%以上,遠高于先前研究人員發(fā)表的實驗結果。柔性神經(jīng)樹預測結果中馬修相關系數(shù)最高達到0.807,隨著負樣本比例的增大,雖然預測結果的準確率得到提高,但馬修相關系數(shù)逐漸降低。當數(shù)據(jù)集包含全部樣本時,預測結果的馬修相關系數(shù)為0.326,降低幅度較大,可見正負樣本數(shù)據(jù)不平衡對實驗的結果影響較大。綜上所述,本文在新的特征提取方法上,利用多種預測模型集成學習進行了蛋白質磷酸化修飾位點的預測工作,且集成后的模型表現(xiàn)良好。同時本文應用柔性神經(jīng)樹模型進行了磷酸甘油酯化修飾位點預測的研究,與最新的研究結果相比,該模型較大幅度的提升了預測性能。
[Abstract]:Posttranslational modification of proteins plays an important role in the process of cell life. Many kinds of post-translational modification of proteins interact with each other, coordinate with each other, maintain together, and promote the normal development of various cell activities. However, the identification of post-translational modification is often a complicated experiment in biology, and its efficiency is low. Therefore, it is imperative to develop effective bioinformatics prediction tools to improve the efficiency of the identification of modified sites. In this paper, the protein sequence is taken as the basic research object, combining with many methods of feature extraction, the modified sites of post-translational phosphorylation and glycerol phosphate modification of proteins are predicted by means of calculation. According to the function of phosphorylation modification, several protein sequences related to signal transduction function were extracted from the phosphorylation modification database, and the data set was constructed. In feature extraction, a new extraction method is proposed, in which the grouping information of the physical and chemical properties of amino acid residues is incorporated into the feature extraction based on the frequency of amino acid residues appearing in the sliding window. It was found by experiments that the homologous modified sites improved greatly under the same prediction model after the fusion of amino acid residues' physical and chemical properties. In this paper, the prediction accuracy of neural network model based on particle swarm optimization is improved from about 58% to 86%. On this basis, the influence of the size of amino acid residues on the experimental results is also studied. The results show that when the protein microsequences contain 23 amino acid residues, the predicted results reach the optimal value. After that, the data set is sorted out according to the method of ten fold cross validation, and the data set is tested according to the new feature extraction method using three integrated learning methods: neural network, support vector machine and flexible neural tree. The combination strategies of three models are voted according to the majority principle. The experimental results show that the prediction accuracy can reach 87.50 after the integration learning of the three prediction models, which is greatly improved compared with the previous research results. In this paper, a flexible neural tree model was used to predict the modification sites of glycerol phosphate, and the experimental results were compared with the latest research progress in this field. The data sets are processed by 10% cross-validation, and the window values of protein microsequences are based on previous researchers' conclusions. The experimental results show that the flexible neural tree has a great advantage in the same number of positive and negative samples, and its prediction accuracy can reach more than 90%, which is much higher than the experimental results published by previous researchers. The Mathieu correlation coefficient is the highest 0.807 in the prediction results of the flexible neural tree. With the increase of the negative sample ratio, the accuracy of the prediction results is improved, but the Mathieu correlation coefficient decreases gradually. When the data set contains all the samples, the Mathieu correlation coefficient of the predicted results is 0.326, which decreases greatly. It can be seen that the imbalance of the positive and negative sample data has a great influence on the experimental results. In conclusion, in the new feature extraction method, we use a variety of predictive model ensemble learning to predict protein phosphorylation modified sites, and the integrated model performs well. At the same time, the prediction of the modified sites of glycerol phosphate was studied by using the flexible neural tree model. Compared with the latest research results, the prediction performance of the model was greatly improved.
【學位授予單位】：濟南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：Q51;TP18

【參考文獻】

相關期刊論文 前10條

1 李偉哲;王洪巖;杜海寧;;非組蛋白甲基化修飾的研究進展[J];生物化學與生物物理進展;2015年11期

2 呂斌娜;梁文星;;蛋白質乙�；揎椦芯窟M展[J];生物技術通報;2015年04期

3 邱望仁;鄒國英;查娟娟;霍立田;;蛋白質翻譯后修飾研究概述[J];景德鎮(zhèn)學院學報;2014年06期

4 王偉;何華勤;;基于LibSVM的CKSAAP蛋白特征提取預測水稻蛋白質磷酸化位點[J];湖北科技學院學報;2014年07期

5 阮班軍;代鵬;王偉;孫建斌;張文濤;顏真;楊靜華;;蛋白質翻譯后修飾研究進展[J];中國細胞生物學學報;2014年07期

6 黃淑云;;決策樹算法預測人類病毒的蛋白質磷酸化位點[J];萍鄉(xiāng)高等專科學校學報;2013年03期

7 梁前進;王鵬程;白燕榮;;蛋白質磷酸化修飾研究進展[J];科技導報;2012年31期

8 黃秀;陳月輝;曹毅;;基于柔性神經(jīng)樹的蛋白質結構預測[J];計算機工程;2011年01期

9 姜錚;王芳;何湘;劉大偉;陳宣男;趙紅慶;黃留玉;袁靜;;蛋白質磷酸化修飾的研究進展[J];生物技術通訊;2009年02期

10 阮曉鋼,孫海軍;編碼方式對蛋白質二級結構預測精度的影響[J];北京工業(yè)大學學報;2005年03期

相關博士學位論文 前2條

1 李晶;綜合蛋白質翻譯后修飾數(shù)據(jù)庫的構建和分析[D];華中科技大學;2014年

2 涂娟娟;PSO優(yōu)化神經(jīng)網(wǎng)絡算法的研究及其應用[D];江蘇大學;2013年

相關碩士學位論文 前4條

1 陳祥;蛋白質翻譯后修飾位點識別研究和應用[D];南昌大學;2014年

2 索生寶;蛋白質翻譯后修飾位點預測及其功能分析[D];南昌大學;2013年

3 蔣君寶;基于序列多信息融合的蛋白質亞細胞定位預測方法研究[D];湖南大學;2011年

4 劉欽鋒;蛋白質序列編碼與功能預測[D];湖南大學;2011年

，

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