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dc.contributorYen-Wei Chuen_US
dc.contributor.authorChian-Ying Chenen_US
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dc.description.abstractPost-Translational Modifications (PTMs) of proteins have the significant associations with regulating cell physiological function, gene regulation, protein function, generation of major diseases. However, most of the current studies only explore for the single protein-translational modification, two or more post-translational modifications of proteins between the role relevant information on their interaction still very few. As a result, in this study of breast cancer for example. According to the methods of bioinformatics, after collecting a large number of breast cancer associated proteins and verified post-translational modifications data, motif and domain related information. In this study, we obtain the post-translational sites between two or more interaction of post-translational modifications after the statistical tests and functional analysis of species conservation, and the analysis of sequence, motif and domain, and the results of the protein-protein, domain-domain interaction network related tools DOMINE, 3did and PTMcode. In addition to investigate the correlation between the interactions of post-translational modifications, this study will supply the biological laboratories to do extended discussion with further regulatory pathway of biological and physiological functions related diseases.en_US
dc.description.abstract蛋白質轉譯後修飾作用與調節細胞生理功能、基因調控、蛋白質功能與重大疾病生成等息息相關,但目前的研究大部分只針對單一的蛋白質轉譯後修飾作用做探討,兩個或兩個以上蛋白質轉譯後修飾作用間,其交互影響的相關資料卻寥寥可數。 因此,本研究以乳癌為例,藉由生物資訊學的方法,蒐集大量與乳癌相關且已驗證的蛋白質轉譯後修飾資料,以及相關的motif和domain資訊,經過物種保留性、序列、motif及domain的分析,再利用DOMINE、3did與PTMcode蛋白質交互作用網絡相關工具得到蛋白質轉譯後修飾作用之間的調控網絡。 本研究最後藉由統計檢定以及功能性分析,找出更多可能擁有至少兩個或兩個以上蛋白質後修飾作用交互影響的domain與蛋白質後修飾作用位點,除了能探討蛋白質轉譯後修飾作用間其交互影響的關聯性,並能提供生物實驗室對此研究,進一步對生物生理功能調控路徑與相關疾病做延伸探討。zh_TW
dc.description.tableofcontents論文摘要 i Abstract ii Content iii List of Tables v List of Figures vi 1、 Introduction 1 1.1 Background 1 1.2 Purpose and Contribution 2 1.3 Architecture 2 2、 Related Works 4 2.1 Breast Cancer 4 2.2 Post-Translational Modifications,PTMs 6 2.2.1 Phosphorylation 7 2.2.2 Glycosylation 7 2.2.3 Acetylation 8 2.2.4 Methylation 9 2.3 PTMs Predicted Tools 10 2.3.1 NetPhos 10 2.3.2 NetNGlyc 12 2.3.3 NetOGlyc 13 2.3.4 NetAcet 14 2.3.5 PMes 15 2.3.6 YinOYang 16 2.3.7 Motif-x 18 2.3.8 InterPro 19 2.3.9 PROTTER 20 2.3.10 PTMcode 21 3、 Materials and Methods 23 3.1 Data Collection 23 3.2 Flow chart 24 3.3 Sequence-PTMs sites Prediction and PROTTER 26 3.4 Motif Prediction 27 3.5 Domain Prediction 27 3.6 PTMcode Model 28 3.7 3did Domain Function 30 3.8 Reduction of False Positive Predictions 31 4、 Results and Discussions 32 4.1 Statistics of PTMs sites with Human Breast Cancer Protein 32 4.2 Statistics of Domain PTMs sites with Human Breast Cancer Protein 34 4.2.1 Statistics of Domain PTMs sites with Human Breast Cancer Protein 34 4.2.2 Statistics of Molecular Biological Function PTMs sites on Domain with Human Breast Cancer Protein 36 4.2.3 Verification with Previous Research- PTMcode、dbPTM 41 4.3 Investigation of Protein Function with PTMs sites 57 4.3.1 Aggregation of O-Glycosylation sites 57 4.3.2 Aggregation of Phosphorylation sites 58 4.4 Investigation of Acetylation sites with Human Breast Cancer Protein 60 5、 Conclusions and Future work 61 5.1 Conclusions 61 5.2 Future Work 63 Supplementary data 64 Reference 81zh_TW
dc.subjectPost-Translational Modificationen_US
dc.subjectInteraction (Crosstalk)en_US
dc.subjectBreast Canceren_US
dc.titleUsing Bioinformatics Methods to Identify PTMs Crosstalk: An Example of Breast Canceren_US
dc.typeThesis and Dissertationen_US
item.openairetypeThesis and Dissertation-
item.fulltextwith fulltext-
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