標題: | 使用分數卡方法發現組蛋白賴氨酸甲基轉移酶的功能性胜肽 Discovering functional peptides of histone lysine methyltransferases using a scoring card method |
作者: | 蕭瓊治 何信瑩 Hsiao, Chiung-Chih Ho, Shinn-Ying 生物資訊及系統生物研究所 |
關鍵字: | 表觀基因體學;甲基轉移酶;機器學習;Epigenetics;Methyltransferases;Motif;Machine Learning |
公開日期: | 2016 |
摘要: | 表觀遺傳學為近年來熱門的研究主題,最主要是探討在不改變DNA序列內容的情況下,各種會影響基因表現的修飾。主要分為兩種研究方向: DNA甲基化(Methylation)和組蛋白修飾(Histone Modification)。組蛋白賴氨酸甲基轉移酶(Lysine Methyltransferases)是藉由本身的SET domain專門對組蛋白上的賴氨酸做甲基化。許多文獻指出,在SET domain上擁有各種重要的motif,使SET domain能與一些小分子共同攜帶甲基,與辨認組蛋白上的賴氨酸完成甲基的轉移。因此,若能利用生物資訊的方法找出更多組蛋白賴氨酸甲基轉移酶上的motif或功能性胜肽,相信能更了解組蛋白甲基化的機制。
搜尋目前的文獻,關於組蛋白甲基轉移酶上的功能性胜肽研究,均是透過序列的排比(Alignment)找出保留性的區域,再利用各種生物實驗來確認。此論文提出了一個新方法SCMHKMT,利用本實驗室開發的計分卡方法(Scoring Card Method),透過組蛋白賴氨酸甲基轉移酶的序列搜集,計算相對於非組蛋白賴氨酸甲基轉移酶的雙肽(dipeptide)之性質分數,建立分數卡。再透過程式的攥寫,利用分數卡上的資訊篩選出可能的motif或功能性胜肽,再利用已知的motif NHS為基準,再篩選共發掘出十七個三胜肽,是很有可能為功能性胜肽,其中GEE、PNN、WPN、SCS是已有文獻所記載的功能性胜肽。利用其它預測motif工具也找到而DEE和KGE,證明這兩個三胜肽極有可能是尚未被發現的功能性胜肽。 Epigenetics is a popular research topic in recent years, which explores gene expression without modifying DNA sequences. There are two research fields: DNA methylation and histone modifications. The SET domain of the histone lysine methyltransferases (HKMT) modifies lysines of the histone. A lot of studies have verified the important role of motifs in the SET domain. If we can develop an efficient algorithm to discover novel motifs or functional peptides, we can further understand the mechanism of histone lysine methylation. The classical methods that identify functional peptides utilized the sequence alignment methods first and further verified those peptides using biological experiments. In this thesis, we propose a new method, SCMHKMT, based on a scoring card method (SCM) developed in our laboratory to discover novel functional peptides. We collected datasets and calculated the difference between HKMT and non-HKMT sequences in terms of the dipeptide score of SCM. Consequently, we used the SCMHKMT method to screen potential motifs of tripeptides which have high appearance probability and high propensity score. As a result, there were 17 functional tripeptides discovered in which GEE, PNN, WPN, and SCS have been reported in published papers. Using other motif prediction tools, DEE and KGE have also been identified, suggesting that these two peptides are highly potential functional peptides. |
URI: | http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070057210 http://hdl.handle.net/11536/139760 |
Appears in Collections: | Thesis |