ArticlePLoS computational biology2024
Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- CoMR: an integrative scoring pipeline for comprehensive mitochondrial proteome reconstruction across eukaryotes.Briefings in bioinformatics · 2026Article
- Genome-Wide Identification of theInternational journal of molecular sciences · 2026Article
- Rapid plastid isolation reveals the chloroplast proteome and structures of the chlororibosome large subunit and RuBisCO inbioRxiv : the preprint server for biology · 2026Article
- Structure determination and dual targeting of a plant TACO1 identifies its ancient role as an organelle translation regulator.bioRxiv : the preprint server for biology · 2026Article
- Structural mass spectrometry techniques for characterisation of plant and algal proteins.The Plant journal : for cell and molecular biology · 2026Review
- Lineage-specific expansion and functional diversification of mTERF proteins sculpt organellar regulation in plants.BMC plant biology · 2026Article
- Chlamy_ChloroPred: a deep learning-based, highly accurate binary classifier for chloroplast protein prediction in the model microalga,Frontiers in microbiology · 2026Article
- Evolutionary Refinement of Mitochondrial and Plastid Targeting Sequences Coincides with the Late Diversification of Land Plants.Molecular biology and evolution · 2025Article
- Structural dynamics of mitochondrial ATP synthase inbioRxiv : the preprint server for biology · 2025Article
- Assembly and analysis of the first complete mitochondrial genome sequencing of main Tea-oil Camellia cultivars Camellia drupifera (Theaceae): revealed a multi-branch mitochondrial conformation for Camellia.BMC plant biology · 2025Article
- A cryptic plastid and a novel mitochondrial plasmid inOpen biology · 2024Article
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4 authors.
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Abstract
Mitochondria and plastids import thousands of proteins. Their experimental localisation remains a frequent task, but can be resource-intensive and sometimes impossible. Hence, hundreds of studies make use of algorithms that predict a localisation based on a protein's sequence. Their reliability across evolutionary diverse species is unknown. Here, we evaluate the performance of common algorithms (TargetP, Localizer and WoLFPSORT) for four photosynthetic eukaryotes (Arabidopsis thaliana, Zea mays, Physcomitrium patens, and Chlamydomonas reinhardtii) for which experimental plastid and mitochondrial proteome data is available, and 171 eukaryotes using orthology inferences. The match between predictions and experimental data ranges from 75% to as low as 2%. Results worsen as the evolutionary distance between training and query species increases, especially for plant mitochondria for which performance borders on random sampling. Specificity, sensitivity and precision analyses highlight cross-organelle errors and uncover the evolutionary divergence of organelles as the main driver of current performance issues. The results encourage to train the next generation of neural networks on an evolutionary more diverse set of organelle proteins for optimizing performance and reliability.
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