TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs.

Abstract

Targeting peptides are N-terminal sorting signals in proteins that promote their translocation to mitochondria through the interaction with different protein machineries. We recently developed TPpred, a machine learning-based method scoring among the best ones available to predict the presence of a targeting peptide into a protein sequence and its cleavage site. Here we introduce TPpred2 that improves TPpred performances in the task of identifying the cleavage site of the targeting peptides. TPpred2 is now available as a web interface and as a stand-alone version for users who can freely download and adopt it for processing large volumes of sequences. Availability and implementaion: TPpred2 is available both as web server and stand-alone version at http://tppred2.biocomp.unibo.it. gigi@biocomp.unibo.it Supplementary data are available at Bioinformatics online.