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Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

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Abstract

Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.

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Acknowledgments

The authors would like to thank the reviewers for their helpful comments on our manuscript. This work was supported by a grant from the National Natural Science Foundation of China (61063016, 31160188), The Research Fund for the Doctoral Program of Higher Education of China (no.20101501110004), The Project for ‘211’ Innovative Talents of Inner Mongolia University (no. 2-1.2.1_035), and the Inner Mongolia University Fund for Young Scholars (no. 208152).

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  1. Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China

    Guo-Liang Fan & Qian-Zhong Li

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Fan, GL., Li, QZ. Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition. Amino Acids 43, 545–555 (2012). https://doi.org/10.1007/s00726-011-1143-4

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