Computer-Based Prediction of Mitochondria-Targeting Peptides
Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where the mature protein is active and very difficult to characterize experimentally. Mitochondrial proteins encoded on the cytosolic ribosomes carry specific patterns in the precursor sequence from where it is possible to recognize a peptide targeting the protein to its final destination. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequences and benchmark our and other methods on the human mitochondrial proteins endowed with experimentally characterized targeting peptides. Furthermore, we illustrate our newly implemented web server and its usage on the whole human proteome in order to infer mitochondrial targeting peptides, their cleavage sites, and whether the targeting peptide regions contain or not arginine-rich recurrent motifs. By this, we add some other 2,800 human proteins to the 124 ones already experimentally annotated with a mitochondrial targeting peptide.
Key wordsTargeting peptide Prediction of subcellular localization Arginine motifs Cleavage site Machine learning
This work was supported by the following projects: PRIN 2010–2011 project 20108XYHJS (to P.L.M.) (Italian Ministry for University and Research: MIUR), COST BMBS Action TD1101 (European Union RTD Framework Program, to R.C.), PON projects PON01_02249 and PAN Lab PONa3_00166 (Italian Ministry for University and Research, to R.C. and P.L.M.), and FARB-UNIBO 2012 (to R.C.).