Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science, [2005]-

Plastids*/metabolism ; Plastids*/genetics ; Mitochondria*/metabolism ; Algorithms* ; Computational Biology*/methods; Reproducibility of Results ; Evolution, Molecular ; Arabidopsis/genetics ; Arabidopsis/metabolism ; Chlamydomonas reinhardtii/metabolism ; Chlamydomonas reinhardtii/genetics ; Zea mays/genetics ; Zea mays/metabolism ; Proteome/metabolism ; Protein Transport/physiology

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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Gould et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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0 (Proteome)

Date Created: 20241111 Date Completed: 20241121 Latest Revision: 20241123

20241123

PMC11581415

10.1371/journal.pcbi.1012575

39527633