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Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation

Abstract

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.

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Acknowledgments

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).

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Correspondence to Xiaoqi Zheng.

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Yu, X., Zheng, X., Liu, T. et al. Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids 42, 1619–1625 (2012). https://doi.org/10.1007/s00726-011-0848-8

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Keywords

  • Apoptosis proteins
  • Subcellular location
  • Substitution matrix
  • Auto covariance transformation
  • Support vector machine