MitoFates: improved prediction of mitochondrial targeting sequences and their cleavage sites.
Bottom Line: Here we describe MitoFates, an improved prediction method for cleavable N-terminal mitochondrial targeting signals (presequences) and their cleavage sites.Interestingly, these include candidate regulators of parkin translocation to damaged mitochondria, and also many genes with known disease mutations, suggesting that careful investigation of MitoFates predictions may be helpful in elucidating the role of mitochondria in health and disease.MitoFates is open source with a convenient web server publicly available.
Affiliation: From the ‡Department of Computational Biology, Graduate School of Frontier Sciences, The University Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan;Show MeSH
Mentions: MitoFates improves the state-of-the-art in presequence prediction, but unfortunately still fails to predict a sizable number of presequences. Visual inspection of these false negatives reveals that they usually have fewer positively charged residues or poor score for MPP cleavage, suggesting they may belong to a different class of presequences than the true positives. To investigate this, we clustered 243 yeast presequences as described in Methods. The results suggests yeast presequences can be grouped into at least three clusters (supplemental Table S3), as visualized by primary component analysis (PCA) in Fig. 4A. The largest cluster (cluster I, blue in Fig. 4A) consists of 144 presequences that are strongly enriched for arginine and contain almost no negatively charged residues, exhibit typically low conservation (i.e. average value for presequences), a relatively well defined length distribution centered at an average of 25 residues, high PA score, and significantly higher MPP cleavage scores than other presequences. These properties are consistent with known features of presequences. However, the two remaining clusters differ in some ways from the classical view of presequences.
Affiliation: From the ‡Department of Computational Biology, Graduate School of Frontier Sciences, The University Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan;