title: Using over-represented tetrapeptides to predict protein submitochondria locations.
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 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 ≤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.
authors.affiliation: 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. hlin@uestc.edu.cn
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Lu-Feng
authors.initials: LF
authors.lastname: Yuan
authors.forename: Zi-Qiang
authors.initials: ZQ
authors.lastname: Li
authors.forename: Hui
authors.initials: H
authors.lastname: Ding
mesh_terms: D008962:Models, Theoretical; D009842:Oligopeptides / Q000378:metabolism*; D013367:Submitochondrial Particles / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D009842:Oligopeptides