In Silico Safety Assessment of Bacillus Isolated from Polish Bee Pollen and Bee Bread as Novel Probiotic Candidates.

Bacillus species isolated from Polish bee pollen (BP) and bee bread (BB) were characterized for in silico probiotic and safety attributes. A probiogenomics approach was used, and in-depth genomic analysis was performed using a wide array of bioinformatics tools to investigate the presence of virulence and antibiotic resistance properties, mobile genetic elements, and secondary metabolites. Functional annotation and Carbohydrate-Active enZYmes (CAZYme) profiling revealed the presence of genes and a repertoire of probiotics properties promoting enzymes. The isolates BB10.1, BP20.15 (isolated from bee bread), and PY2.3 (isolated from bee pollen) genome mining revealed the presence of several genes encoding acid, heat, cold, and other stress tolerance mechanisms, adhesion proteins required to survive and colonize harsh gastrointestinal environments, enzymes involved in the metabolism of dietary molecules, antioxidant activity, and genes associated with the synthesis of vitamins. In addition, genes responsible for the production of biogenic amines (BAs) and D-/L-lactate, hemolytic activity, and other toxic compounds were also analyzed. Pan-genome analyses were performed with 180 Bacillus subtilis and 204 Bacillus velezensis genomes to mine for any novel genes present in the genomes of our isolates. Moreover, all three isolates also consisted of gene clusters encoding secondary metabolites.

Antimicrobial Peptides: An Update on Classifications and Databases.

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.