利用RBF網路預測運輸蛋白中的電子傳遞鏈蛋白

Classification categories of electron transport proteins based on efficient radial basis function networks with position-specific scoring matrices and biochemical properties

王志宏(Chih-Hung Wang)

指導教授 ： 歐昱言

元智大學/資訊學院/資訊工程學系/碩士(2012年)

https://doi.org/10.6838/YZU.2012.00137

摘要

電子傳遞鏈是生物呼吸作用中非常重要的一環，透過五個複合體作用於粒線體上，前四個複合體透過一系列氧化還原反應逐步傳遞電子，並且利用傳遞電子所產生的化學能將氫離子打至膜外累積氫離子濃度梯度，濃度梯度達成後氫離子流入第五個複合體並產生化學能導致合成生物重要的能量ATP。
由於細胞膜分離實驗困難，就算分離之後要標註蛋白質功能也是浩大的工程，所以希望用機器學習的方式分析序列特徵，進而標註蛋白質代替繁雜的實驗
在這個實驗我們以PSSM基本屬性，並且從544種胺基酸物化屬性當中篩選出來當作額外屬性，並且使用RBF網路作為分類器，用這些方式分析電子傳遞鏈蛋白的序列特徵。

關鍵字

電子傳遞鏈 ； 運輸蛋白 ； 分類 ； 屬性選取

並列摘要

Electron transport chain is a important part of the biological respiration, it working in the inner member of mitochondrion through the chain of the five protein complexes, the first four complexes transfer electron from a donor complex to an accept complex, and then create hydrogen ion concentration gradient by using the chemicals energy produced by passing electrons to transfer hydrogen to the outer membrane, finally hydrogen ion pass through and lead fifth complex to the synthesis of biologically important energy ATP.Membrane protein separation experiment and anotation is very difficult, so we use machine learning technology to analysis and then classification the protein instead of a complicated experiment.
We analyzed the sequence of electron transport chain by used PSSM as the basic features and extract from the 544 amino acids biochemical property as additional features, and using the RBF network as a classifier.

並列關鍵字

electron transport chain ； transporter ； classification ； feature selection

參考文獻

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