BaPreS: a software tool for predicting bacteriocins using an optimal set of features

Classification and Multi-Functional Use of Bacteriocins in Health, Biotechnology, and Food Industry

Bacteriocins is the name given to products of the secondary metabolism of many bacterial genera that must display antimicrobial activity. Although there are several bacteriocins described today, it has not been possible to reach a consensus on the method of classification for these biomolecules. In addition, many of them are not yet authorized for therapeutic use against multi-drug-resistant microorganisms due to possible toxic effects. However, recent research has achieved considerable progress in the understanding, classification, and elucidation of their mechanisms of action against microorganisms, which are of medical and biotechnological interest. Therefore, in more current times, protocols are already being conducted for their optimal use, in the hopes of solving multiple health and food conservation problems. This review aims to synthetize the information available nowadays regarding bacteriocins, and their classification, while also providing an insight into the future possibilities of their usage for both the pharmaceutical, food, and biotechnological industry.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

In Silico Screening of Bacteriocin Gene Clusters within a Set of Marine Bacillota Genomes

Due to their potential application as an alternative to antibiotics, bacteriocins, which are ribosomally synthesized antimicrobial peptides produced by bacteria, have received much attention in recent years. To identify bacteriocins within marine bacteria, most of the studies employed a culture-based method, which is more time-consuming than the in silico approach. For that, the aim of this study was to identify potential bacteriocin gene clusters and their potential producers in 51 marine Bacillota (formerly Firmicutes) genomes, using BAGEL4, a bacteriocin genome mining tool. As a result, we found out that a majority of selected Bacillota (60.78%) are potential bacteriocin producers, and we identified 77 bacteriocin gene clusters, most of which belong to class I bacteriocins known as RiPPs (ribosomally synthesized and post-translationally modified peptides). The identified putative bacteriocin gene clusters are an attractive target for further in vitro research, such as the production of bacteriocins using a heterologous expression system.

Revisiting the Multifaceted Roles of Bacteriocins : The Multifaceted Roles of Bacteriocins

Bacteriocins are gene-encoded antimicrobial peptides produced by bacteria. These peptides are heterogeneous in terms of structure, antimicrobial activities, biosynthetic clusters, and regulatory mechanisms. Bacteriocins are widespread in nature and may contribute to microbial diversity due to their capacity to target specific bacteria. Primarily studied as food preservatives and therapeutic agents, their function in natural settings is however less known. This review emphasizes the ecological significance of bacteriocins as multifunctional peptides by exploring bacteriocin distribution, mobility, and their impact on bacterial population dynamics and biofilms.

BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier

The use of bacteriocins has emerged as a propitious strategy in the development of new drugs to combat antibiotic resistance, given their ability to kill bacteria with both broad and narrow natural spectra. Hence, a compelling requirement arises for a precise and efficient computational model that can accurately predict novel bacteriocins. Machine learning's ability to learn patterns and features from bacteriocin sequences that are difficult to capture using sequence matching-based methods makes it a potentially superior choice for accurate prediction. A web application for predicting bacteriocin was created in this study, utilizing a machine learning approach. The feature sets employed in the application were chosen using alternating decision tree (ADTree), genetic algorithm (GA), and linear support vector classifier (linear SVC)-based feature evaluation methods. Initially, potential features were extracted from the physicochemical, structural, and sequence-profile attributes of both bacteriocin and non-bacteriocin protein sequences. We assessed the candidate features first using the Pearson correlation coefficient, followed by separate evaluations with ADTree, GA, and linear SVC to eliminate unnecessary features. Finally, we constructed random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), k-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB) models using reduced feature sets. We obtained the overall top performing model using SVM with ADTree-reduced features, achieving an accuracy of 99.11% and an AUC value of 0.9984 on the testing dataset. We also assessed the predictive capabilities of our best-performing models for each reduced feature set relative to our previously developed software solution, a sequence alignment-based tool, and a deep-learning approach. A web application, titled BPAGS (Bacteriocin Prediction based on ADTree, GA, and linear SVC), was developed to incorporate the predictive models built using ADTree, GA, and linear SVC-based feature sets. Currently, the web-based tool provides classification results with associated probability values and has options to add new samples in the training data to improve the predictive efficacy. BPAGS is freely accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/.