The neglected giants: Uncovering the prevalence and functional groups of huge proteins in proteomes

An often-overlooked aspect of biology is formed by the outliers of the protein length distribution, specifically those proteins with more than 5000 amino acids, which we refer to as huge proteins (HPs). By examining UniprotKB, we discovered more than 41 000 HPs throughout the tree of life, with the majority found in eukaryotes. Notably, the phyla with the highest propensity for HPs are Apicomplexa and Fornicata. Moreover, we observed that certain bacteria, such as Elusimicrobiota or Planctomycetota, have a higher tendency for encoding HPs, even more than the average eukaryote. To investigate if these macro-polypeptides represent "real" proteins, we explored several indirect metrics. Additionally, orthology analyses reveals thousands of clusters of homologous sequences of HPs, revealing functional groups related to key cellular processes such as cytoskeleton organization and functioning as chaperones or as E3-ubiquitin ligases in eukaryotes. In the case of bacteria, the major clusters have functions related to non-ribosomomal peptide synthesis/polyketide synthesis, followed by pathogen-host attachment or recognition surface proteins. Further exploration of the annotations for each HPs supported the previously identified functional groups. These findings underscore the need for further investigation of the cellular and ecological roles of these HPs and their potential impact on biology and biotechnology.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-09-16
DOI
https://doi.org/10.1371/journal.pcbi.1012459
Citations
1
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.23

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article

The neglected giants: Uncovering the prevalence and functional groups of huge proteins in proteomes

Damien P. Devos, Anibal S Amaral
1 citations
PLoS Computational Biology
Machine Learning in Bioinformatics
0.23
article

The neglected giants: Uncovering the prevalence and functional groups of huge proteins in proteomes

Damien P. Devos, Anibal S Amaral
article en
1 citations

Abstract

An often-overlooked aspect of biology is formed by the outliers of the protein length distribution, specifically those proteins with more than 5000 amino acids, which we refer to as huge proteins (HPs). By examining UniprotKB, we discovered more than 41 000 HPs throughout the tree of life, with the majority found in eukaryotes. Notably, the phyla with the highest propensity for HPs are Apicomplexa and Fornicata. Moreover, we observed that certain bacteria, such as Elusimicrobiota or Planctomycetota, have a higher tendency for encoding HPs, even more than the average eukaryote. To investigate if these macro-polypeptides represent "real" proteins, we explored several indirect metrics. Additionally, orthology analyses reveals thousands of clusters of homologous sequences of HPs, revealing functional groups related to key cellular processes such as cytoskeleton organization and functioning as chaperones or as E3-ubiquitin ligases in eukaryotes. In the case of bacteria, the major clusters have functions related to non-ribosomomal peptide synthesis/polyketide synthesis, followed by pathogen-host attachment or recognition surface proteins. Further exploration of the annotations for each HPs supported the previously identified functional groups. These findings underscore the need for further investigation of the cellular and ecological roles of these HPs and their potential impact on biology and biotechnology.

PLoS Computational BiologyVol. 20(9)
Centre National de la Recherche Scientifique (FR), Inserm (FR), Centre Hospitalier Universitaire de Lille (FR), Center for Infection and Immunity of Lille (FR), Centro Andaluz de Biología del Desarrollo (ES)
Gordon and Betty Moore Foundation
Life in Land
Openalex Percentile: Top 42%
Machine Learning in Bioinformatics
0.23
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The neglected giants: Uncovering the prevalence and functional groups of huge proteins in proteomes — Damien P. Devos, Anibal S Amaral · PLoS Computational Biology (2024) | TGRS Research Map | TGRS