Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation
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Apoptosis proteins are very important for understanding the mechanism of programmed cell death. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on amino acid substitution matrix and auto covariance transformation, we introduce a new sequence-based model, which not only quantitatively describes the differences between amino acids, but also partially incorporates the sequence-order information. This method is applied to predict the apoptosis proteins’ subcellular location of two widely used datasets by the support vector machine classifier. The results obtained by jackknife test are quite promising, indicating that the proposed method might serve as a potential and efficient prediction model for apoptosis protein subcellular location prediction.
KeywordsApoptosis proteins Subcellular location Substitution matrix Auto covariance transformation Support vector machine
This work was partially supported by the National Natural Science Foundation of China (No. 10731040), Shanghai Leading Academic Discipline Project (No. S30405) and Innovation Program of Shanghai Municipal Education Commission (No. 09zz134).
- Assfalg J, Gong J, Kriegel HP, Pryakhin A, Wei T, Zimek A (2010) Investigating a correlation between subcellular localization and fold of proteins. J UCS 16(5):604–621Google Scholar
- Chang C, Lin CJ (2009) Libsvm: a library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm
- Chen YL, Li QZ (2004) Prediction of the subcellular location apoptosis proteins using the algorithm of measure of diversity. Acta Sci Nat Univ Nei Mong 25:413–417Google Scholar
- Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: structure, function, and genetics (Erratum: ibid., 2001, vol. 44, 60) 43:246–255Google Scholar
- Chou KC, Shen HB (2010a) A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS ONE 5:e9931Google Scholar
- Dayhoff MO, Schwartz RM, Orcutt BC (1978) A model of evolutionary change in proteins, vol 5. National Biomedical Research Foundation, Washington, pp 345–352Google Scholar
- Leslid C, Eskin E, Noble WS (2002) The spectrum kernel: a string kernel for SVM protein classification. In: Pacific symposium on biocomputing (PSB), pp 564–575Google Scholar
- Zhou GP, Doctor K (2003) Subcellular location prediction of apoptosis proteins. Proteins 50:40–48Google Scholar