期刊
ACTA BIOTHEORETICA
卷 61, 期 2, 页码 259-268出版社
SPRINGER
DOI: 10.1007/s10441-013-9181-9
关键词
Submitochondria location; Tetrapeptide; Binomial distribution; Support vector machine
资金
- National Nature Scientific Foundation of China [61202256, 61100092]
- Project of Education Department in Sichuan [12ZA112]
- Fundamental Research Funds for the Central Universities [ZYGX2012J113]
- Scientific Research Startup Foundation of UESTC
The mitochondrion is a key organelle of eukaryotic cell that provides the energy for cellular activities. Correctly identifying submitochondria locations of proteins can provide plentiful information for understanding their functions. However, using web-experimental methods to recognize submitochondria locations of proteins are time-consuming and costly. Thus, it is highly desired to develop a bioinformatics method to predict the submitochondria locations of mitochondrion proteins. In this work, a novel method based on support vector machine was developed to predict the submitochondria locations of mitochondrion proteins by using over-represented tetrapeptides selected by using binomial distribution. A reliable and rigorous benchmark dataset including 495 mitochondrion proteins with sequence identity a parts per thousand currency sign25 % was constructed for testing and evaluating the proposed model. Jackknife cross-validated results showed that the 91.1 % of the 495 mitochondrion proteins can be correctly predicted. Subsequently, our model was estimated by three existing benchmark datasets. The overall accuracies are 94.0, 94.7 and 93.4 %, respectively, suggesting that the proposed model is potentially useful in the realm of mitochondrion proteome research. Based on this model, we built a predictor called TetraMito which is freely available at http://lin.uestc.edu.cn/server/TetraMito.
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