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Prediction of Apoptosis Protein’s Subcellular Localization by Fusing Two Different Descriptors Based on Evolutionary Information

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Abstract

The apoptosis protein has a central role in the development and the homeostasis of an organism. Obtaining information about the subcellular localization of apoptosis protein is very helpful to understand the apoptosis mechanism and the function of this protein. Prediction of apoptosis protein’s subcellular localization is a challenging task, and currently the existing feature extraction methods mainly rely on the protein’s primary sequence. In this paper we develop a feature extraction model based on two different descriptors of evolutionary information, which contains the 192 frequencies of triplet codons (FTC) in the RNA sequence derived from the protein’s primary sequence and the 190 features from a detrended forward moving-average cross-correlation analysis (DFMCA) based on a position-specific scoring matrix (PSSM) generated by the PSI-BLAST program. Hence, this model is called FTC-DFMCA-PSSM. A 382-dimensional (382D) feature vector is constructed on the ZD98, ZW225 and CL317 datasets. Then a support vector machine is adopted as classifier, and the jackknife cross-validation test method is used for evaluating the accuracy. The overall prediction accuracies are further improved by an objective and rigorous jackknife test. Our model not only broadens the source of the feature information, but also provides a more accurate and reliable automated calculation method for the prediction of apoptosis protein’s subcellular localization.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 11601407), the Fundamental Research Funds for the Central Universities (Nos. JB160711 and JBG160703) and Doctoral Scientific Research Foundation of Xi’an Polytechnic University (No. BS1710).

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Authors and Affiliations

  1. School of Science, Xi’an Polytechnic University, Xi’an, 710048, People’s Republic of China

    Yunyun Liang

  2. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, People’s Republic of China

    Shengli Zhang

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  1. Yunyun Liang
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  2. Shengli Zhang
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Correspondence to Yunyun Liang or Shengli Zhang.

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Liang, Y., Zhang, S. Prediction of Apoptosis Protein’s Subcellular Localization by Fusing Two Different Descriptors Based on Evolutionary Information. Acta Biotheor 66, 61–78 (2018). https://doi.org/10.1007/s10441-018-9319-x

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