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Comparative Analysis of Prokaryotic Extracellular Vesicle Proteins and Their Targeting Signals.

ABSTRACT: Prokaryotic extracellular vesicles (EVs) are vesicles that bud from the cell membrane and are secreted by bacteria and archaea. EV cargo in Gram-negative bacteria includes mostly periplasmic and outer membrane proteins. EVs are clinically important as their cargo can include toxins associated with bacterial virulence and toxicity; additionally, they have been proposed as efficient vaccine agents and as the ancestors of the eukaryotic endomembrane system. However, the mechanistic details behind EV cargo selection and release are still poorly understood. In this study, we have performed bioinformatics analysis of published data on EV proteomes from 38 species of bacteria and 4 archaea. Focusing on clusters of orthologous genes (COGs) and using the EggNOG mapper function, we have identified cargo proteins that are commonly found in EVs across species. We discuss the putative role of these prominent proteins in EV biogenesis and function. We also analyzed the published EV proteomes for conserved signal sequences and discuss the potential role of these signal sequences for EV cargo selection.

SUBMITTER: Stathatos I

PROVIDER: S-EPMC10458587 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Comparative Analysis of Prokaryotic Extracellular Vesicle Proteins and Their Targeting Signals.

Stathatos Ilias I Koumandou Vassiliki Lila VL

Microorganisms 20230731 8

Prokaryotic extracellular vesicles (EVs) are vesicles that bud from the cell membrane and are secreted by bacteria and archaea. EV cargo in Gram-negative bacteria includes mostly periplasmic and outer membrane proteins. EVs are clinically important as their cargo can include toxins associated with bacterial virulence and toxicity; additionally, they have been proposed as efficient vaccine agents and as the ancestors of the eukaryotic endomembrane system. However, the mechanistic details behind E ...[more]

PMID: 37630535

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Project description:MotivationAccurate prediction of genes encoding small proteins (on the order of 50 amino acids or less) remains an elusive open problem in bioinformatics. Some of the best methods for gene prediction use either sequence composition analysis or sequence similarity to a known protein coding sequence. These methods often fail for small proteins, however, either due to a lack of experimentally verified small protein coding genes or due to the limited statistical significance of statistics on small sequences. Our approach is based upon the hypothesis that true small proteins will be under selective pressure for encoding the particular amino acid sequence, for ease of translation by the ribosome and for structural stability. This stability can be achieved either independently or as part of a larger protein complex. Given this assumption, it follows that small proteins should display conserved local protein structure properties much like larger proteins. Our method incorporates neural-net predictions for three local structure alphabets within a comparative genomic approach using a genomic alignment of 22 closely related bacteria genomes to generate predictions for whether or not a given open reading frame (ORF) encodes for a small protein.ResultsWe have applied this method to the complete genome for Escherichia coli strain K12 and looked at how well our method performed on a set of 60 experimentally verified small proteins from this organism. Out of a total of 11 407 possible ORFs, we found that 6 of the top 10 and 27 of the top 100 predictions belonged to the set of 60 experimentally verified small proteins. We found 35 of all the true small proteins within the top 200 predictions. We compared our method to Glimmer, using a default Glimmer protocol and a modified small ORF Glimmer protocol with a lower minimum size cutoff. The default Glimmer protocol identified 16 of the true small proteins (all in the top 200 predictions), but failed to predict on 34 due to size cutoffs. The small ORF Glimmer protocol made predictions for all the experimentally verified small proteins but only contained 9 of the 60 true small proteins within the top 200 predictions.Contactjsamayoa@jhu.edu

Dataset Information

Comparative Analysis of Prokaryotic Extracellular Vesicle Proteins and Their Targeting Signals.

Publications

Comparative Analysis of Prokaryotic Extracellular Vesicle Proteins and Their Targeting Signals.

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