BackgroundBacteriocins are peptide-derived molecules produced by bacteria, whose recently-discovered functions include virulence factors and signaling 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 context genes in this study. Although bacteriocins themselves are structurally diverse, context genes have been shown to be largely conserved across unrelated species.MethodsUsing 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 blocks and predict novel ones. BOA generates profile Hidden Markov Models from the clusters of bacteriocin context genes, and uses them to identify novel bacteriocin gene blocks and operons.Results and conclusionsWe provide a novel dataset of predicted bacteriocins and context genes. We also discover that several phyla have a strong preference for bacteriocin genes, suggesting distinct functions for this group of molecules.Software Availabilityhttps://github.com/idoerg/BOA

f Eukaryotic parasites of the genus Plasmodium cause malaria by invading and developing within host erythrocytes. Here, we demonstrate that PfShelph2, a gene product of Plasmodium falciparum that belongs to the Shewanella-like phosphatase (Shelph) subfamily, selectively hydrolyzes phosphotyrosine, as shown for other previously studied Shelph family members. In the extracellular merozoite stage, PfShelph2 localizes to vesicles that appear to be distinct from those of rhoptry, dense granule, or microneme organelles. During invasion, PfShelph2 is released from these vesicles and exported to the host erythrocyte. In vitro, PfShelph2 shows tyrosine phosphatase activity against the host erythrocyte protein Band 3, which is the most abundant tyrosine-phosphorylated species of the erythrocyte. During P. falciparum invasion, Band 3 undergoes dynamic and rapid clearance from the invasion junction within 1 to 2 s of parasite attachment to the erythrocyte. Release of Pfshelph2 occurs after clearance of Band 3 from the parasite-host cell interface and when the parasite is nearly or completely enclosed in the nascent vacuole. We propose a model in which the phosphatase modifies Band 3 in time to restore its interaction with the cytoskeleton and thus reestablishes the erythrocyte cytoskeletal network at the end of the invasion process.

No abstract

Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, represent an untapped source of promising antibiotic alternatives. However, bacteriocins display diverse mechanisms of action, a narrow spectrum of activity, and inherent challenges in natural product isolation making in vitro verification of putative bacteriocins difficult. A subset of bacteriocins exert their antimicrobial effects through favorable biophysical interactions with the bacterial membrane mediated by the charge, hydrophobicity, and conformation of the peptide. We have developed a pipeline for bacteriocin‐derived compound design and testing that combines sequence‐free prediction of bacteriocins using machine learning and a simple biophysical trait filter to generate 20 amino acid peptides that can be synthesized and evaluated for activity. We generated 28,895 total 20‐mer candidate peptides and scored them for charge, α‐helicity, and hydrophobic moment. Of those, we selected 16 sequences for synthesis and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against Escherichia coli and Pseudomonas aeruginosa. Our combined method incorporates machine learning and biophysical‐based minimal region determination to create an original approach to swiftly discover bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.