Home » Key Scientific Articles » Using over-represented tetrapeptides to predict protein submitochondria locations

Using over-represented tetrapeptides to predict protein submitochondria locations

Lin H, Chen W, Yuan LF, Li ZQ, Ding H.

Acta Biotheor. 2013;61(2):259-68.

Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. [email protected]

Abstract

 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 submitochondrialocations 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 ≤25% 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.

Go To PubMed

 

predict protein submitochondria locations - global medical discovery