A large scale prediction of bacteriocin gene blocks suggests a wide functional spectrum for bacteriocins

TL;DR

This study introduces BOA, a software tool that predicts bacteriocin gene clusters across bacteria, revealing their diversity and potential functions, and identifying novel bacteriocins beyond known sequences.

Contribution

The paper presents BOA, a novel computational method for identifying and analyzing bacteriocin gene clusters, expanding understanding of their diversity and biosynthesis pathways.

Findings

Bacteriocin gene clusters are widespread across multiple bacterial phyla.

Several phyla show a strong preference for bacteriocin genes, indicating diverse functional roles.

BOA successfully predicts novel bacteriocin candidates and associated gene clusters.

Abstract

Bacteriocins are peptide-derived molecules produced by bacteria, whose recently-discovered functions include virulence factors and signalling molecules as well as their better known roles as antibiotics. To date, close to five hundred bacteriocins have been identified and classified. Recent discoveries have shown that bacteriocins are highly diverse and widely distributed among bacterial species. Given the heterogeneity of bacteriocin compounds, many tools struggle with identifying novel bacteriocins due to their vast sequence and structural diversity. Many bacteriocins undergo post-translational processing or modifications necessary for the biosynthesis of the final mature form. Enzymatic modification of bacteriocins as well as their export is achieved by proteins whose genes are often located in a discrete gene cluster proximal to the bacteriocin precursor gene, referred to as \textit{context genes} in this study. Although bacteriocins themselves are structurally diverse, context genes have been shown to be largely conserved across unrelated species. Using this knowledge, we set out to identify new candidates for context genes which may clarify how bacteriocins are synthesized, and identify new candidates for bacteriocins that bear no sequence similarity to known toxins. To achieve these goals, we have developed a software tool, Bacteriocin Operon and gene block Associator (BOA) that can identify homologous bacteriocin associated gene clusters and predict novel ones. We discover that several phyla have a strong preference for bactericon genes, suggesting distinct functions for this group of molecules. Availability: https://github.com/idoerg/BOA