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

Protein remote homology detection and structural alignment using deep learning

Exploiting sequence-structure-function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure-structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.

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.

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/.

Metagenomic Insights into the Taxonomic and Functional Features of Traditional Fermented Milk Products from Russia

Fermented milk products (FMPs) contain probiotics that are live bacteria considered to be beneficial to human health due to the production of various bioactive molecules. In this study, nine artisanal FMPs (kefir, ayran, khurunga, shubat, two cottage cheeses, bryndza, khuruud and suluguni-like cheese) from different regions of Russia were characterized using metagenomics. A metagenomic sequencing of ayran, khurunga, shubat, khuruud and suluguni-like cheese was performed for the first time. The taxonomic profiling of metagenomic reads revealed that Lactococcus species, such as Lc. lactis and Lc. cremoris prevailed in khuruud, bryndza, one sample of cottage cheese and khurunga. The latter one together with suluguni-like cheese microbiome was dominated by bacteria, affiliated to Lactobacillus helveticus (32-35%). In addition, a high proportion of sequences belonging to the genera Lactobacillus, Lactococcus and Streptococcus but not classified at the species level were found in the suluguni-like cheese. Lactobacillus delbrueckii, as well as Streptococcus thermophilus constituted the majority in another cottage cheese, kefir and ayran metagenomes. The microbiome of shubat, produced from camel's milk, was significantly distinctive, and Lentilactobacillus kefiri, Lactobacillus kefiranofaciens and Bifidobacterium mongoliense represented the dominant components (42, 7.4 and 5.6%, respectively). In total, 78 metagenome-assembled genomes with a completeness ≥ 50.2% and a contamination ≤ 8.5% were recovered: 61 genomes were assigned to the Enterococcaceae, Lactobacillaceae and Streptococcaceae families (the Lactobacillales order within Firmicutes), 4 to Bifidobacteriaceae (the Actinobacteriota phylum) and 2 to Acetobacteraceae (the Proteobacteria phylum). A metagenomic analysis revealed numerous genes, from 161 to 1301 in different products, encoding glycoside hydrolases and glycosyltransferases predicted to participate in lactose, alpha-glucans and peptidoglycan hydrolysis as well as exopolysaccharides synthesis. A large number of secondary metabolite biosynthetic gene clusters, such as lanthipeptides, unclassified bacteriocins, nonribosomal peptides and polyketide synthases were also detected. Finally, the genes involved in the synthesis of bioactive compounds like β-lactones, terpenes and furans, nontypical for fermented milk products, were also found. The metagenomes of kefir, ayran and shubat was shown to contain either no or a very low count of antibiotic resistance genes. Altogether, our results show that traditional indigenous fermented products are a promising source of novel probiotic bacteria with beneficial properties for medical and food industries.

Global shape of SvGT, a metal-dependent bacteriocin modifying S/O-HexNActransferase from actinobacteria: c -terminal dimerization modulates the function of this GT

Here, we describe hitherto unknown shape-function of S/O-HexNActransferase SvGT (ORF AQF52_3101) instrumental in glycosylation of bacteriocin SvC (ORF AQF52_3099) in Streptomyces venezuelae ATCC 15439. Data from gel filtration, mass spectrometry, analytical ultracentrifugation, and Small Angle X-ray Scattering (SAXS), experiments confirmed elongated dimeric shape in solution for SvGT protein. Enzyme assays confirmed the dependence of SvGT on the availability of Mg2+ ions to be functionally activated. SAXS data analysis provided that apo and Mg2+-activated protein adopt a shape characterized by a radius of gyration and maximum linear dimension of 5.2 and 17.0 nm, and 5.3 and 17.8 nm, respectively. Alphafold2 server was used to model the monomeric chain of this protein which was docked on self to obtain different poses of the dimeric entity. Experimental SAXS data was used to select and refine the structure of SvGT dimer. Results showed that Mg2+ ions induce reorientation of the GT domain of one chain leading to a dimer with C2 symmetry, and the C-terminal portion entangles with each other in all states. Mutation-rendered alteration in activity profiles confirmed the role of conserved residues around catalytic motif. Global structure analysis puts forth the need to understand the role of constitutionally diverse C-terminal portion in regulating substrate selectivity.Communicated by Ramaswamy H. Sarma.

Genomic Analysis of Bacteriocin-Producing Staphylococci: High Prevalence of Lanthipeptides and the Micrococcin P1 Biosynthetic Gene Clusters

Bacteriocins are antimicrobial peptides produced by bacteria. This study aimed to in silico analyze the presence of bacteriocin gene clusters (BGCs) among the genomes of 22 commensal Staphylococcus isolates from different origins (environment/human/food/pet/wild animals) previously identified as bacteriocin producers. The resistome and plasmidome were studied in all isolates. Five types of BGC were detected in 18 genomes of the 22 bacteriocin-producing staphylococci included in this study: class I (Lanthipeptides), class II, circular bacteriocins, the non-ribosomal-peptide lugdunin and the thiopeptide micrococcin P1 (MP1). A high frequency of lanthipeptides was detected in this collection: BGC variants of BSA, bacCH91, and epilancin15X were identified in two Staphylococcus aureus and one Staphylococcus warneri isolates from food and wild animals. Moreover, two potentially new lanthipeptide-like BGCs with no identity to database entries were found in Staphylococcus epidermidis and Staphylococcus simulans from food and wild animal, respectively. Interestingly, four isolates (one S. aureus and one Staphylococcus hominis, environmental origin; two Staphylococcus sciuri, food) carried the MP1 BGC with differences to those previously described. On the other hand, seven of the 22 genomes (~32%) lacked known genes related with antibiotic or disinfectant-acquired resistance mechanisms. Moreover, the potential carriage of plasmids was evaluated, and several Rep-proteins were identified (~73% of strains). In conclusion, a wide variety of BGCs has been observed among the 22 genomes, and an interesting relationship between related Staphylococcus species and the type of bacteriocin has been revealed. Therefore, bacteriocin-producing Staphylococcus and especially coagulase-negative staphylococci (CoNS) can be considered good candidates as a source of novel bacteriocins.

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

BACKGROUND

Antibiotic resistance is a major public health concern around the globe. As a result, researchers always look for new compounds to develop new antibiotic drugs for combating antibiotic-resistant bacteria. Bacteriocin becomes a promising antimicrobial agent to fight against antibiotic resistance, due to cases of both broad and narrow killing spectra. Sequence matching methods are widely used to identify bacteriocins by comparing them with the known bacteriocin sequences; however, these methods often fail to detect new bacteriocin sequences due to their high diversity. The ability to use a machine learning approach can help find new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. The aim of this work is to develop a machine learning-based software tool called BaPreS (Bacteriocin Prediction Software) using an optimal set of features for detecting bacteriocin protein sequences with high accuracy. We extracted potential features from known bacteriocin and non-bacteriocin sequences by considering the physicochemical and structural properties of the protein sequences. Then we reduced the feature set using statistical justifications and recursive feature elimination technique. Finally, we built support vector machine (SVM) and random forest (RF) models using the selected features and utilized the best machine learning model to implement the software tool.

RESULTS

We applied BaPreS to an established dataset and evaluated its prediction performance. Acquired results show that the software tool can achieve a prediction accuracy of 95.54% for testing protein sequences. This tool allows users to add new bacteriocin or non-bacteriocin sequences in the training dataset to further enhance the predictive power of the tool. We compared the prediction performance of the BaPreS with a popular sequence matching-based tool and a deep learning-based method, and our software tool outperformed both.

CONCLUSIONS

BaPreS is a bacteriocin prediction tool that can be used to discover new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. This software tool can be used with Windows, Linux and macOS operating systems. The open-source software package and its user manual are available at https://github.com/suraiya14/BaPreS .

Metabolome-guided genome mining of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

Insect Antimicrobial Peptides: Advancements, Enhancements and New Challenges

Several insects are known as vectors of a wide range of animal and human pathogens causing various diseases. However, they are also a source of different substances, such as the Antimicrobial Peptides (AMPs), which can be employed in the development of natural bioactive compounds for medical, veterinary and agricultural applications. It is well known that AMP activity, in contrast to most classical antibiotics, does not lead to the development of natural bacterial resistance, or at least the frequency of resistance is considered to be low. Therefore, there is a strong interest in assessing the efficacy of the various peptides known to date, identifying new compounds and evaluating possible solutions in order to increase their production. Moreover, implementing AMP modulation in insect rearing could preserve insect health in large-scale production. This review describes the current knowledge on insect AMPs, presenting the validated ones for the different insect orders. A brief description of their mechanism of action is reported with focus on proposed applications. The possible effects of insect diet on AMP translation and synthesis have been discussed.

Antibiotic Resistance and Food Safety: Perspectives on New Technologies and Molecules for Microbial Control in the Food Industry

Antibiotic resistance (ABR) has direct and indirect repercussions on public health and threatens to decrease the therapeutic effect of antibiotic treatments and lead to more infection-related deaths. There are several mechanisms by which ABR can be transferred from one microorganism to another. The risk of transfer is often related to environmental factors. The food supply chain offers conditions where ABR gene transfer can occur by multiple pathways, which generates concerns regarding food safety. This work reviews mechanisms involved in ABR gene transfer, potential transmission routes in the food supply chain, the prevalence of antibiotic residues in food and ABR organisms in processing lines and final products, and implications for public health. Finally, the paper will elaborate on the application of antimicrobial peptides as new alternatives to antibiotics that might countermeasure ABR and is compatible with current food trends.

What Is the Microbiome? A Description of a Social Network

The gut microbiome has coevolved with its hosts over the years, forming a complex and symbiotic relationship. It is formed by what we do, what we eat, where we live, and with whom we live. The microbiome is known to influence our health by training our immune system and providing nutrients for the human body. However, when the microbiome becomes out of balance and dysbiosis occurs, the microorganisms within can cause or contribute to diseases. This major influencer on our health is studied intensively, but it is unfortunately often overlooked by the surgeon and in surgical practice. Because of that, there is not much literature about the microbiome and its influence on surgical patients or procedures. However, there is evidence that it plays a major role, showing that it needs to be a topic of interest for the surgeon. This review is written to show the surgeon the importance of the microbiome and why it should be taken into consideration when preparing or treating patients.

Advances in mining and expressing microbial biosynthetic gene clusters

Natural products (NPs) especially the secondary metabolites originated from microbes exhibit great importance in biomedical, industrial and agricultural applications. However, mining biosynthetic gene clusters (BGCs) to produce novel NPs has been hindered owing that a large population of environmental microbes are unculturable. In the past decade, strategies to explore BGCs directly from (meta)genomes have been established along with the fast development of high-throughput sequencing technologies and the powerful bioinformatics data-processing tools, which greatly expedited the exploitations of novel BGCs from unculturable microbes including the extremophilic microbes. In this review, we firstly summarized the popular bioinformatics tools and databases available to mine novel BGCs from (meta)genomes based on either pure cultures or pristine environmental samples. Noticeably, approaches rooted from machine learning and deep learning with focuses on the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs) were dramatically increased in recent years. Moreover, synthetic biology techniques to express the novel BGCs in culturable native microbes or heterologous hosts were introduced. This working pipeline including the discovery and biosynthesis of novel NPs will greatly advance the exploitations of the abundant but unexplored microbial BGCs.

BADASS: BActeriocin-Diversity ASsessment Software

BACKGROUND

Bacteriocins are defined as thermolabile peptides produced by bacteria with biological activity against taxonomically related species. These antimicrobial peptides have a wide application including disease treatment, food conservation, and probiotics. However, even with a large industrial and biotechnological application potential, these peptides are still poorly studied and explored. BADASS is software with a user-friendly graphical interface applied to the search and analysis of bacteriocin diversity in whole-metagenome shotgun sequencing data.

RESULTS

The search for bacteriocin sequences is performed with tools such as BLAST or DIAMOND using the BAGEL4 database as a reference. The putative bacteriocin sequences identified are used to determine the abundance and richness of the three classes of bacteriocins. Abundance is calculated by comparing the reads identified as bacteriocins to the reads identified as 16S rRNA gene using SILVA database as a reference. BADASS has a complete pipeline that starts with the quality assessment of the raw data. At the end of the analysis, BADASS generates several plots of richness and abundance automatically as well as tabular files containing information about the main bacteriocins detected. The user is able to change the main parameters of the analysis in the graphical interface. To demonstrate how the software works, we used four datasets from WMS studies using default parameters. Lantibiotics were the most abundant bacteriocins in the four datasets. This class of bacteriocin is commonly produced by Streptomyces sp.

CONCLUSIONS

With a user-friendly graphical interface and a complete pipeline, BADASS proved to be a powerful tool for prospecting bacteriocin sequences in Whole-Metagenome Shotgun Sequencing (WMS) data. This tool is publicly available at https://sourceforge.net/projects/badass/ .

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification

The abuse of traditional antibiotics has led to an increase in the resistance of bacteria and viruses. Similar to the function of antibacterial peptides, bacteriocins are more common as a kind of peptides produced by bacteria that have bactericidal or bacterial effects. More importantly, the marine environment is one of the most abundant resources for extracting marine microbial bacteriocins (MMBs). Identifying bacteriocins from marine microorganisms is a common goal for the development of new drugs. Effective use of MMBs will greatly alleviate the current antibiotic abuse problem. In this work, deep learning is used to identify meaningful MMBs. We propose a random multi-scale convolutional neural network method. In the scale setting, we set a random model to update the scale value randomly. The scale selection method can reduce the contingency caused by artificial setting under certain conditions, thereby making the method more extensive. The results show that the classification performance of the proposed method is better than the state-of-the-art classification methods. In addition, some potential MMBs are predicted, and some different sequence analyses are performed on these candidates. It is worth mentioning that after sequence analysis, the HNH endonucleases of different marine bacteria are considered as potential bacteriocins.

Lactic Acid Bacteria and Bacteriocins: Novel Biotechnological Approach for Biopreservation of Meat and Meat Products

Meat and meat products are perishable in nature, and easily susceptible to microbial contamination and chemical deterioration. This not only results in an increased risk to health of consumers, but also causes economic loss to the meat industry. Some microorganisms of the lactic acid bacteria (LAB) group and their ribosomal-synthesized antimicrobial peptides-especially bacteriocins-can be used as a natural preservative, and an alternative to chemical preservatives in meat industry. Purified or partially purified bacteriocins can be used as a food additive or incorporated in active packaging, while bacteriocin-producing cells could be added as starter or protective cultures for fermented meats. Large-scale applications of bacteriocins are limited, however, mainly due to the narrow antimicrobial spectrum and varying stability in different food matrixes. To overcome these limitations, bioengineering and biotechnological techniques are being employed to combine two or more classes of bacteriocins and develop novel bacteriocins with high efficacy. These approaches, in combination with hurdle concepts (active packaging), provide adequate safety by reducing the pathogenicity of spoilage microorganisms, improving sensory characteristics (e.g., desirable flavor, texture, aroma) and enhancing the shelf life of meat-based products. In this review, the biosynthesis of different classes of LAB bacteriocins, their mechanism of action and their role in the preservation of meats and meat products are reviewed.

Antimicrobial Potential of the Genera Geobacillus and Parageobacillus, as Well as Endolysins Biosynthesized by Their Bacteriophages

In the recent decades, antibiotic resistance has emerged and spread rapidly among clinically relevant pathogens. The natural ability of bacteria to transmit resistance determinants through horizontal gene transfer poses constant challenges to drug development. Natural molecules produced by soil microorganisms continue to be a key source of new antimicrobial agents. In this context, bacteria from the Geobacillus and Parageobacillus genera deserve special attention. Although there is commercial and industrial interest in these microorganisms, the full range of antibacterial compounds biosynthesized by the Geobacillus and Parageobacillus species remains largely unexplored. The aim of this review is to present the strong antimicrobial potential of these bacteria and endolysins produced by their bacteriophages.

The Central Role of Interbacterial Antagonism in Bacterial Life

The study of bacteria interacting with their environment has historically centered on strategies for obtaining nutrients and resisting abiotic stresses. We argue this focus has deemphasized a third facet of bacterial life that is equally central to their existence: namely, the threat to survival posed by antagonizing bacteria. The diversity and ubiquity of interbacterial antagonism pathways is becoming increasingly apparent, and the insidious manner by which interbacterial toxins disarm their targets emphasizes the highly evolved nature of these processes. Studies examining the role of antagonism in natural communities reveal it can serve many functions, from facilitating colonization of naïve habitats to maintaining bacterial community stability. The pervasiveness of antagonistic pathways is necessarily matched by an equally extensive array of defense strategies. These overlap with well characterized, central stress response pathways, highlighting the contribution of bacterial interactions to shaping cell physiology. In this review, we build the case for the ubiquity and importance of interbacterial antagonism.

High Potential for Secondary Metabolite Production of Paracoccus marcusii CP157, Isolated From the Crustacean Cancer pagurus

Secondary metabolites are key components in microbial ecology by mediating interactions between bacteria and their environment, neighboring species or host organisms. Bioactivities can be beneficial for both interaction partners or provide a competitive advantage only for the producer. Colonizers of confined habitats such as biofilms are known as prolific producers of a great number of bioactive secondary metabolites and are a potential source for novel compounds. We investigated the strain Paracoccus marcusii CP157, which originates from the biofilm on the carapace of a shell disease-affected Cancer pagurus specimen, for its potential to produce bioactive secondary metabolites. Its closed genome contains 22 extrachromosomal elements and several gene clusters potentially involved in biosynthesis of bioactive polyketides, bacteriocins, and non-ribosomal peptides. Culture extracts of CP157 showed antagonistic activities against bacteria from different phyla, but also against microalgae and crustacean larvae. Different HPLC-fractions of CP157 culture extracts had antibacterial properties, indicating that several bioactive compounds are produced by CP157. The bioactive extract contains several small, antibacterial compounds that partially withstand elevated temperatures, extreme pH values and exposure to proteolytic enzymes, providing high stability toward environmental conditions in the natural habitat of CP157. Further, screening of 17 Paracoccus spp. revealed that antimicrobial activity, hemolysis and production of N-acyl homoserine lactones are common features within the genus. Taking into account the large habitat diversity and phylogenetic distance of the tested strains, we hypothesize that bioactive secondary metabolites play a central role in the ecology of Paracoccus spp. in their natural environments.

Antimicrobial and Amyloidogenic Activity of Peptides. Can Antimicrobial Peptides Be Used against SARS-CoV-2?

At present, much attention is paid to the use of antimicrobial peptides (AMPs) of natural and artificial origin to combat pathogens. AMPs have several points that determine their biological activity. We analyzed the structural properties of AMPs, as well as described their mechanism of action and impact on pathogenic bacteria and viruses. Recently published data on the development of new AMP drugs based on a combination of molecular design and genetic engineering approaches are presented. In this article, we have focused on information on the amyloidogenic properties of AMP. This review examines AMP development strategies from the perspective of the current high prevalence of antibiotic-resistant bacteria, and the potential prospects and challenges of using AMPs against infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Discovery of Novel Type II Bacteriocins Using a New High-Dimensional Bioinformatic Algorithm

Antimicrobial compounds first arose in prokaryotes by necessity for competitive self-defense. In this light, prokaryotes invented the first host defense peptides. Among the most well-characterized of these peptides are class II bacteriocins, ribosomally-synthesized polypeptides produced chiefly by Gram-positive bacteria. In the current study, a tensor search protocol-the BACIIα algorithm-was created to identify and classify bacteriocin sequences with high fidelity. The BACIIα algorithm integrates a consensus signature sequence, physicochemical and genomic pattern elements within a high-dimensional query tool to select for bacteriocin-like peptides. It accurately retrieved and distinguished virtually all families of known class II bacteriocins, with an 86% specificity. Further, the algorithm retrieved a large set of unforeseen, putative bacteriocin peptide sequences. A recently-developed machine-learning classifier predicted the vast majority of retrieved sequences to induce negative Gaussian curvature in target membranes, a hallmark of antimicrobial activity. Prototypic bacteriocin candidate sequences were synthesized and demonstrated potent antimicrobial efficacy in vitro against a broad spectrum of human pathogens. Therefore, the BACIIα algorithm expands the scope of prokaryotic host defense bacteriocins and enables an innovative bioinformatics discovery strategy. Understanding how prokaryotes have protected themselves against microbial threats over eons of time holds promise to discover novel anti-infective strategies to meet the challenge of modern antibiotic resistance.

Genome mining strategies for ribosomally synthesised and post-translationally modified peptides

Genome mining is a computational method for the automatic detection and annotation of biosynthetic gene clusters (BGCs) from genomic data. This approach has been increasingly utilised in natural product (NP) discovery due to the large amount of sequencing data that is now available. Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a class of structurally complex NP with diverse bioactivities. RiPPs have recently been shown to occupy a much larger expanse of genomic and chemical space than previously appreciated, indicating that annotation of RiPP BGCs in genomes may have been overlooked in the past. This review provides an overview of the genome mining tools that have been specifically developed to aid in the discovery of RiPP BGCs, which have been built from an increasing knowledgebase of RiPP structures and biosynthesis. Given these recent advances, the application of targeted genome mining has great potential to accelerate the discovery of important molecules such as antimicrobial and anticancer agents whilst increasing our understanding about how these compounds are biosynthesised in nature.

MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.

Production of Bioactive Peptides from Lactic Acid Bacteria: A Sustainable Approach for Healthier Foods

Traditional fermented foods where lactic acid bacteria (LAB) are present have been associated with beneficial effects on human health, and some of those benefits are related to protein-derived products. Peptides produced by LAB have attracted the interest of food industries because of their diverse applications. These peptides include ribosomally produced (bacteriocins) and protein hydrolysates by-products (bioactive peptides), which can participate as natural preservatives and nutraceuticals, respectively. It is essential to understand the biochemical pathways and the effect of growth conditions for the production of bioactive peptides and bacteriocins by LAB, in order to suggest strategies for optimization. LAB is an important food-grade expression system that can be used in the simultaneous production of peptide-based products for the food, animal, cosmetic, and pharmaceutical industries. This review describes the multifunctional proteinaceous compounds generated by LAB metabolism and discusses a strategy to use a single-step production process, using an alternative protein-based media. This strategy will provide economic advantages in fermentation processes and will also provide an environmental alternative to industrial waste valorization. New technologies that can be used to improve production and bioactivity of LAB-derived peptides are also analyzed.

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks

MOTIVATION

Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broadening the available choices of antimicrobials. However, the discovery of new bacteriocins by genomic mining is hampered by their sequences' low complexity and high variance, which frustrates sequence similarity-based searches.

RESULTS

Here we use word embeddings of protein sequences to represent bacteriocins, and apply a word embedding method that accounts for amino acid order in protein sequences, to predict novel bacteriocins from protein sequences without using sequence similarity. Our method predicts, with a high probability, six yet unknown putative bacteriocins in Lactobacillus. Generalized, the representation of sequences with word embeddings preserving sequence order information can be applied to peptide and protein classification problems for which sequence similarity cannot be used.

AVAILABILITY AND IMPLEMENTATION

Data and source code for this project are freely available at: https://github.com/nafizh/NeuBI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mobilization of Microbiota Commensals and Their Bacteriocins for Therapeutics

With the specter of resurgence of pathogens due to the propagation of antibiotic-resistance genes, innovative antimicrobial strategies are needed. In this review, we summarize the beneficial aspects of bacteriocins, a set of miscellaneous peptide-based bacterium killers, compared with classical antibiotics, and emphasize their use in cocktails to curb the emergence of new resistance. We highlight that their prey spectrum, their molecular malleability, and their multiple modes of production might lead to specific and personalized treatments to prevent systemic disorders. Complementarily, we discuss how we might exploit prevailing bacterial commensals, such as Streptococcus salivarius, and deliberately mobilize their bacteriocin arsenal 'on site' to cure multiresistant infections or finely reshape the endogenous microbiota for prophylaxis purposes.

Bacteriocins: Classification, synthesis, mechanism of action and resistance development in food spoilage causing bacteria

Huge demand of safe and natural preservatives has opened new area for intensive research on bacteriocins to unravel the novel range of antimicrobial compounds that could efficiently fight off the food-borne pathogens. Since food safety has become an increasingly important international concern, the application of bacteriocins from lactic acid bacteria that target food spoilage/pathogenic bacteria without major adverse effects has received great attention. Different modes of actions of these bacteriocins have been suggested and identified, like pore-forming, inhibition of cell-wall/nucleic acid/protein synthesis. However, development of resistance in the food spoilage and pathogenic bacteria against these bacteriocins is a rising concern. Emergence and spread of mutant strains resistant to bacteriocins is hampering food safety. It has spurred an interest to understand the bacteriocin resistance phenomenon displayed by the food pathogens, which will be helpful in mitigating the resistance problem. Therefore, present review is focused on the different resistance mechanisms adopted by food pathogens to overcome bacteriocin.

Microbial production of bacteriocins: Latest research development and applications

Bacteriocins are low molecular weight peptides secreted by the predator bacterial cells to kill sensitive cells present in the same ecosystem competing for food and other nutrients. Exceptionally few bacteriocins along with their native antibacterial property also exhibit additional anti-viral and anti-fungal properties. Bacteriocins are generally produced by Gm+, Gm- and archaea bacteria. Bacteriocins from Gm + bacteria especially from lactic acid bacteria (LAB) have been thoroughly investigated considering their great biosafety and broad industrial applications. LAB expressing bacteriocins were isolated from fermented milk and milk products, rumen of animals and soil using deferred antagonism assay. Nisin is the only bacteriocin that has got FDA approval for application as a food preservative, which is produced by Lactococcus lactis subsp. Lactis. Its crystal structure explains that its antimicrobial properties are due to the binding of NH₂ terminal to lipid II molecule inhibiting the peptidoglycan synthesis and carboxy terminal forming pores in bacterial cell membrane leading to cell lysis. The hinge region connecting NH₂ and carboxy terminus has been mutated to generate mutant variants with higher antimicrobial activity. In a 50 ton fermentation of the mutant strain 3807 derived from L. lactis subsp. lactis ATCC 11454, 9,960 IU/mL of nisin was produced. Currently, high purity of nisin (>99%) is very expensive and hardly commercially available. Development of more advanced tools for cost-effective separation and purification of nisin would be commercially attractive. Chemical synthesis and heterologous expression of bacteriocins ended in low yields of pure proteins. At present, bacteriocins are almost solely applied in food industries, but they have a great potential to be used in other fields such as feeds, organic fertilizers, environmental protection and personal care products. The future of bacteriocins is largely dependent on getting FDA approval for use of other bacteriocins in addition to nisin to promote the research and applications.

Bacterial antagonism in host-associated microbial communities

Antagonistic interactions are abundant in microbial communities and contribute not only to the composition and relative proportions of their members but also to the longer-term stability of a community. This Review will largely focus on bacterial antagonism mediated by ribosomally synthesized peptides and proteins produced by members of host-associated microbial communities. We discuss recent findings on their diversity, functions, and ecological impacts. These systems play key roles in ecosystem defense, pathogen invasion, spatial segregation, and diversity but also confer indirect gains to the aggressor from products released by killed cells. Investigations into antagonistic bacterial interactions are important for our understanding of how the microbiota establish within hosts, influence health and disease, and offer insights into potential translational applications.

High Throughput Identification of Antimicrobial Peptides from Fish Gastrointestinal Microbiota

Antimicrobial peptides (AMPs) are a group of small peptides, which are secreted by almost all creatures in nature. They have been explored in therapeutic and agricultural aspects as they are toxic to many bacteria. A considerable amount of work has been conducted in analyzing 16S and metagenomics of the gastrointestinal (GI) microbiome of grass carp (Ctenopharyngodon idellus). However, these datasets are still untapped resources. In this present study, a homologous search was performed to predict AMPs from our newly generated metagenome of grass carp. We identified five AMPs with high similarities to previously reported bacterial toxins, such as lantibiotic and class II bacteriocins. In addition, we observed that the top abundant genus in the GI microbiota of the grass carp was generally consistent with the putative AMP-producing strains, which are mainly from Lactobacillales. Furthermore, we constructed the phylogenetic relationship of these putative AMP-producing bacteria existing in the GI of grass carp and some popular commercial probiotics (commonly used for microecologics), demonstrating that they are closely related. Thus, these strains have the potential to be developed into novel microecologics. In a word, we provide a high-throughput way to discover AMPs from fish GI microbiota, which can be developed as alternative pathogen antagonists (toxins) for microecologics or probiotic supplements.

A Profile Hidden Markov Model to investigate the distribution and frequency of LanB-encoding lantibiotic modification genes in the human oral and gut microbiome

Background

The human microbiota plays a key role in health and disease, and bacteriocins, which are small, bacterially produced, antimicrobial peptides, are likely to have an important function in the stability and dynamics of this community. Here we examined the density and distribution of the subclass I lantibiotic modification protein, LanB, in human oral and stool microbiome datasets using a specially constructed profile Hidden Markov Model (HMM).

Methods

The model was validated by correctly identifying known lanB genes in the genomes of known bacteriocin producers more effectively than other methods, while being sensitive enough to differentiate between different subclasses of lantibiotic modification proteins. This approach was compared with two existing methods to screen both genomic and metagenomic datasets obtained from the Human Microbiome Project (HMP).

Results

Of the methods evaluated, the new profile HMM identified the greatest number of putative LanB proteins in the stool and oral metagenome data while BlastP identified the fewest. In addition, the model identified more LanB proteins than a pre-existing Pfam lanthionine dehydratase model. Searching the gastrointestinal tract subset of the HMP reference genome database with the new HMM identified seven putative subclass I lantibiotic producers, including two members of the Coprobacillus genus.

Conclusions

These findings establish custom profile HMMs as a potentially powerful tool in the search for novel bioactive producers with the power to benefit human health, and reinforce the repertoire of apparent bacteriocin-encoding gene clusters that may have been overlooked by culture-dependent mining efforts to date.

Using bacterial genomes and essential genes for the development of new antibiotics

The shrinking antibiotic development pipeline together with the global increase in antibiotic resistant infections requires that new molecules with antimicrobial activity are developed. Traditional empirical screening approaches of natural and non-natural compounds have identified the majority of antibiotics that are currently available, however this approach has produced relatively few new antibiotics over the last few decades. The vast amount of bacterial genome sequence information that has become available since the sequencing of the first bacterial genome more than 20years ago holds potential for contributing to the discovery of novel antimicrobial compounds. Comparative genomic approaches can identify genes that are highly conserved within and between bacterial species, and thus may represent genes that participate in key bacterial processes. Whole genome mutagenesis studies can also identify genes necessary for bacterial growth and survival under different environmental conditions, making them attractive targets for the development of novel inhibitory compounds. In addition, transcriptomic and proteomic approaches can be used to characterize RNA and protein levels on a cellular scale, providing information on bacterial physiology that can be applied to antibiotic target identification. Finally, bacterial genomes can be mined to identify biosynthetic pathways that produce many intrinsic antimicrobial compounds and peptides. In this review, we provide an overview of past and current efforts aimed at using bacterial genomic data in the discovery and development of novel antibiotics.

Deciphering bioactive peptides and their action mechanisms through proteomics

INTRODUCTION

Bioactive peptides such as antimicrobial peptides (AMPs), ribosomally synthesized and post translationally modified peptides (RiPPs) and the non-ribosomal peptides (NRPs) have emerged with promising applications in medicine, agriculture and industry. However, their development has been limited by several difficulties making it necessary to search for novel discovery methods. In this context, proteomics has been considered a reliable tool. Areas covered: This review highlights recent developments in proteomic tools that facilitate the discovery of AMPs, RiPPs and NRPs as well as the elucidation of action mechanisms of AMPs and resistance mechanisms of pathogens to them. Expert commentary: Proteomic approaches have emerged as useful tools for the study of bioactive peptides, especially mass spectrometry-based peptidomics profiling, a promising strategy for AMP discovery. Furthermore, the rapidly expanding fields of genome mining and genome sequencing techniques, as well as mass spectrometry, have revolutionized the discovery of novel RiPPs and NRPs from complex biological samples.

Advances in the Microbiome: Applications to Clostridium difficile Infection

Clostridium difficile is a major cause of morbidity and mortality worldwide, causing over 400,000 infections and approximately 29,000 deaths in the United States alone each year. C. difficile is the most common cause of nosocomial diarrhoea in the developed world, and, in recent years, the emergence of hyper-virulent (mainly ribotypes 027 and 078, sometimes characterised by increased toxin production), epidemic strains and an increase in the number of community-acquired infections has caused further concern. Antibiotic therapy with metronidazole, vancomycin or fidaxomicin is the primary treatment for C. difficile infection (CDI). However, CDI is unique, in that, antibiotic use is also a major risk factor for acquiring CDI or recurrent CDI due to disruption of the normal gut microbiota. Therefore, there is an urgent need for alternative, non-antibiotic therapeutics to treat or prevent CDI. Here, we review a number of such potential treatments which have emerged from advances in the field of microbiome research.

Multigene disruption in undomesticated Bacillus subtilis ATCC 6051a using the CRISPR/Cas9 system

Bacillus subtilis ATCC 6051a is an undomesticated strain used in the industrial production of enzymes. Because it is poorly transformable, genetic manipulation in this strain requires a highly efficient genome editing method. In this study, a Streptococcus pyogenes CRISPR/Cas9 system consisting of an all-in-one knockout plasmid containing a target-specific guide RNA, cas9, and a homologous repair template was established for highly efficient gene disruption in B. subtilis ATCC 6051a. With an efficiency of 33% to 53%, this system was used to disrupt the srfC, spoIIAC, nprE, aprE and amyE genes of B. subtilis ATCC 6051a, which hamper its use in industrial fermentation. Compared with B. subtilis ATCC 6051a, the final mutant, BS5 (ΔsrfC, ΔspoIIAC, ΔnprE, ΔaprE, ΔamyE), produces much less foam during fermentation, displays greater resistant to spore formation, and secretes 2.5-fold more β-cyclodextrin glycosyltransferase into the fermentation medium. Thus, the CRISPR/Cas9 system proved to be a powerful tool for targeted genome editing in an industrially relevant, poorly transformable strain.