臺灣博碩士論文加值系統

近幾年的蛋白質研究中，雖然解三級結構的技術不斷的改進，但速度上仍遠不及解一級結構快，若能單由蛋白質序列得知其結構，對於往後的研究幫助極大，與預測蛋白質二級結構不同的是，我們在此只需用到部分區域的序列資訊，藉著組合這些整個蛋白質的各個區域資訊以確認不同種類的蛋白質；在前人的研究中，機器學習的方法對於直接以整個蛋白質所轉譯所得的序列資訊辨認不同的蛋白質有相當的準確度，在此，我們使用了不同的轉譯法─n-peptide distribution，並以support vector machine method (SVM) 針對Structural Classification of Protein (SCOP) 資料庫中代表分屬27類的385個蛋白質進行學習。
在我們實驗結果中，對於一獨立資料組的預測率可達 69.6%，經過10次的交叉測試確認(10-fold cross validation)預測率可達55.5%，結果顯示若以適合的轉譯法轉譯整個蛋白質的序列資訊將可改進SVM預測蛋白質種類的結果，並希望能找出對protein modeling更有用的方法或規則。

Fold assignment directly from sequences is valuable in the prediction of protein structures. Unlike secondary structure prediction, where a local coding scheme of sequence information will usually suffice, fold identification calls for global protein descriptors as well local descriptors for the whole protein sequences. Previous studies have shown that machine learning methods can yield reasonable prediction accuracy of fold assignment directly from sequences by a variety of global sequence coding schemes. In this thesis, using global protein descriptors based on -peptide distribution, we apply the support vector machine method (SVM) to the 27 most populated folds that contain 386 representative proteins in the Structural Classification of Protein (SCOP) database. Our approach achieved a prediction accuracy 69.6% on an independent set, and 55.5% in the ten-fold cross validation, both of which are an order of magnitude higher than the current methods. Our results show that SVM using suitable global sequence coding schemes can significantly improve prediction in fold recognition from sequences, and should offer a useful tool in structure modeling.

Introduction 1
Methods 3
Data sets and input coding schemes 3
Training and testing procedures 5
Results 7
Discussion 9
References 11
Tables 13
Figures 17
Appendix 22

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