Localizing proteins in the cell from their phylogenetic profiles

Abstract

We introduce a computational method for identifying subcellular locations of proteins from the phylogenetic distribution of the homologs of organellar proteins. This method is based on the observation that proteins localized to a given organelle by experiments tend to share a characteristic phylogenetic distribution of their homologs-a phylogenetic profile. Therefore any other protein can be localized by its phylogenetic profile. Application of this method to mitochondrial proteins reveals that nucleus-encoded proteins previously known to be destined for mitochondria fall into three groups: prokaryote-derived, eukaryote-derived, and organism-specific (i.e., found only in the organism under study). Prokaryote-derived mitochondrial proteins can be identified effectively by their phylogenetic profiles. In the yeast Saccharomyces cerevisiae, 361 nucleus-encoded mitochondrial proteins can be identified at 50% accuracy with 58% coverage. From these values and the proportion of conserved mitochondrial genes, it can be inferred that Ϸ630 genes, or 10% of the nuclear genome, is devoted to mitochondrial function. In the worm Caenorhabditis elegans, we estimate that there are Ϸ660 nucleus-encoded mitochondrial genes, or 4% of its genome, with Ϸ400 of these genes contributed from the prokaryotic mitochondrial ancestor. The large fraction of organism-specific and eukaryote-derived genes suggests that mitochondria perform specialized roles absent from prokaryotic mitochondrial ancestors. We observe measurably distinct phylogenetic profiles among proteins from different subcellular compartments, allowing the general use of prokaryotic genomes in learning features of eukaryotic proteins.

Predicting Subcellular Localization via Protein Motif Co-Occurrence

“…Homology-based predictors. Such methods include phylogenetic profiling of proteins (Marcotte et al 2000), which can be applied to the prediction of proteins in organelles of endosymbiotic origins, and a protein domain projection method, which uses a measure of the co-occurrence of SMART motifs to predict localization (Mott et al 2002). Support vector machines and a new hybridization approach have also been used to classify proteins into subcellular compartments (Chou andCai 2002, 2003).…”

An Automated Combination of Kernels for Predicting Protein Subcellular Localization

“…For the bacterial datasets, the BLAST kernels obtain more weight and perform better individually than the phylogenetic kernels. Phylogenetic profiles have only been shown to work well with organellar proteins [23], and the evidence in Figures 3(c) and 3(d) shows that they can help (slightly) for eukaryotes.…”

Parallels in Genome Evolution in Mitochondria and Bacterial Symbionts

“…Yet, estimates of the number of the nucleus-encoded mitochondrial proteins that originate from the endosymbiont remain speculative. In yeast, some 420 -630 nuclear genes (close to 10% of the genome) are considered to code for mitochondrial proteins (35,36). Only a minority of these proteins can be traced to an a-proteobacterial origin based on phylogenetic analysis, and only 50% relate to prokaryotic (eubacterial or archaeal, but not eukaryotic/nuclear) proteins in similarity searches.…”