The unicellular green alga Chlamydomonas reinhardtii is a prime model for deciphering processes occurring in the intracellular compartments of the photosynthetic cell. Organelle-specific proteomic studies have started to delineate its various subproteomes, but sequence-based prediction software is necessary to assign proteins subcellular localizations at whole genome scale. Unfortunately, existing tools are oriented toward land plants and tend to mispredict the localization of nuclear-encoded algal proteins, predicting many chloroplast proteins as mitochondrion targeted. We thus developed a new tool called PredAlgo that predicts intracellular localization of those proteins to one of three intracellular compartments in green algae: the mitochondrion, the chloroplast, and the secretory pathway. At its core, a neural network, trained using carefully curated sets of C. reinhardtii proteins, divides the N-terminal sequence into overlapping 19-residue windows and scores the probability that they belong to a cleavable targeting sequence for one of the aforementioned organelles. A targeting prediction is then deduced for the protein, and a likely cleavage site is predicted based on the shape of the scoring function along the N-terminal sequence. When assessed on an independent benchmarking set of C. reinhardtii sequences, PredAlgo showed a highly improved discrimination capacity between chloroplast- and mitochondrion-localized proteins. Its predictions matched well the results of chloroplast proteomics studies. When tested on other green algae, it gave good results with Chlorophyceae and Trebouxiophyceae but tended to underpredict mitochondrial proteins in Prasinophyceae. Approximately 18% of the nuclear-encoded C. reinhardtii proteome was predicted to be targeted to the chloroplast and 15% to the mitochondrion.

Mitochondria play a key role in the life and death of eukaryotic cells, yet the full spectrum of mitochondrial functions is far from being fully understood, especially in photosynthetic organisms. To advance our understanding of mitochondrial functions in a photosynthetic cell, an extensive proteomic survey of Percoll-purified mitochondria from the metabolically versatile, hydrogen-producing green alga Chlamydomonas reinhardtii was performed. Different fractions of purified mitochondria from Chlamydomonas cells grown under aerobic conditions were analyzed by nano-liquid chromatography–electrospray ionization–mass spectrometry after protein separation on sodium dodecyl sulfate polyacrylamide gel electrophoresis or on blue-native polyacrylamide gel electrophoresis. Of the 496 nonredundant proteins identified, 149 are known or predicted to reside in other cellular compartments and were thus excluded from the molecular and evolutionary analyses of the Chlamydomonas proteome. The mitochondrial proteome of the photosynthetic alga reveals important lineage-specific differences with other mitochondrial proteomes, reflecting the high metabolic diversity of the organelle. Some mitochondrial metabolic pathways in Chlamydomonas appear to combine typical mitochondrial enzymes and bacterial-type ones, whereas others are unknown among mitochondriate eukaryotes. The comparison of the Chlamydomonas proteins to their identifiable homologs predicted from 354 sequenced genomes indicated that Arabidopsis is the most closely related nonalgal eukaryote. Furthermore, this phylogenomic analysis shows that free-living α-proteobacteria from the metabolically versatile orders Rhizobiales and Rhodobacterales better reflect the gene content of the ancestor of the chlorophyte mitochondria than parasitic α-proteobacteria with reduced and specialized genomes.