Using Fourier Spectrum Analysis and Pseudo Amino Acid Composition for Prediction of Membrane Protein Types |
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Authors: | Hui?Liu Jie?Yang Meng?Wang Li?Xue Email author" target="_blank">Kuo-Chen?ChouEmail author |
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Institution: | (1) Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030, China;(2) Microsoft Research Asia, 49 Zhichun Road, Beijing, 100080, China;(3) Gordon Life Science Institute, San Diego, 92130, CA, USA |
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Abstract: | Membrane proteins are generally classified into the following five types: (1) type I membrane protein, (2) type II membrane protein, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins, and (5) GPI-anchored membrane proteins. Given the sequence of an uncharacterized membrane protein, how can we identify which one of the above five types it belongs to? This is important because the biological function of a membrane protein is closely correlated with its type. Particularly, with the explosion of protein sequences entering into databanks, it is in high demand to develop an automated method to address this problem. To realize this, the key is to catch the statistical characteristics for each of the five types. However, it is not easy because they are buried in a pile of long and complicated sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. (2001). PROTEINS: Structure, Function, and Genetics 43: 246–255), the technique of Fourier spectrum analysis is introduced. By doing so, the sample of a protein is represented by a set of discrete components that can incorporate a considerable amount of the sequence order effects as well as its amino acid composition information. On the basis of such a statistical frame, the support vector machine (SVM) is introduced to perform predictions. High success rates were yielded by the self-consistency test, jackknife test, and independent dataset test, suggesting that the current approach holds a promising potential to become a high throughput tool for membrane protein type prediction as well as other related areas. |
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Keywords: | Discrete model Fourier spectrum analysis jackknife test pseudo amino acid composition support vector machines |
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