Antimicrobial Peptides Sequences Research Articles

MSCMamba: Prediction of Antimicrobial Peptide Activity Values by Fusing Multiscale Convolution with Mamba Module.

Antimicrobial peptides (AMPs) have important developmental prospects as potential candidates for novel antibiotics. Although many studies have been devoted to the identification of AMPs and the qualitative prediction of their functional activities, few methods address the quantitative prediction of their activity values. In this paper, we propose a regression model called MSCMamba, which fuses multiscale convolutional neural network with Mamba module to accurately predict the activity values of AMPs. AMPs sequences are feature-extracted by multiple encoding methods and fed into a multiscale convolutional network and a Mamba module to capture local and long-range dependent features, respectively. The model fuses these two outputs and predicts the activity values of AMPs through a linear layer. Experimental results show that MSCMamba outperforms the current state-of-the-art methods in several performance metrics, especially with an increase in R2 from 0.422 to 0.467, representing a 10.66% improvement. Additionally, we did a series of ablation experiments to verify the validity of each part of the MSCMamba model and the performance enhancement of feature diversification.This study provides a new method for activity prediction of AMPs, which is expected to accelerate the development of novel antibiotics.

Synthesis, Characterization, and Antimicrobial Activity of Urea-Containing α/β Hybrid Peptides against Pseudomonas aeruginosa and Methicillin-Resistant Staphylococcus aureus.

The insertion of β-amino acids and replacement of the amide bond with a urea bond in antimicrobial peptide sequences are promising approaches to enhance the antibacterial activity and improve proteolytic stability. Herein, we describe the synthesis, characterization, and antibacterial activity of short αβ cationic hybrid peptides LAU-Orn-β3,3Ac6c-PEA, DY-01; LAU-Lys-β3,3Ac6c-PEA, DY-02; and LAU-Arg-β3,3Ac6c-PEA, DY-03 in which a C12 lipid chain is conjugated at the N terminus of peptide through urea bonds. Further, we evaluated all the peptides against both Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA) and their multidrug resistant (MDR) clinical isolates. All of the peptides exhibited significant bactericidal efficacy with minimal inhibitory concentration (MIC) values ranging from 2.5 to 6.25 μM (1.4 to 3.9 μg/mL) against P. aeruginosa and its MDR clinical isolates, whereas the MIC values ranging from 0.78 to 6.25 μM (0.45 to 3.9 μg/mL) against MRSA and MDR clinical isolates of S. aureus. To understand the potency and mechanism of action of DY-01 to DY-03, time-kill kinetics, biofilm inhibition and disruption, synergistic interactions with standard antibiotics, swarming motility, scanning electron microscopy (SEM) analyses, and ex vivo infection assay were performed. The SEM images revealed that all of the peptides exert antibacterial activity through a membrane disruption mechanism. Additionally, negligible cytotoxicity was observed against mammalian cell lines RAW 264.7 and J774A.1, with mild hemolysis at higher concentrations. The comprehensive antimicrobial assessments of DY-01 to DY-03 against P. aeruginosa and MRSA highlight their potential for clinical applications in combating resistant microbial infections.

Aggregation-Prone Antimicrobial Peptides Target Gram-negative Bacterial Nucleic Acids and Protein Synthesis

Aggregation of antimicrobial peptides (AMPs) enhances their efficacy by destabilising the bacterial cell wall, membrane, and cytosolic proteins. Developing aggregation-prone AMPs offers a promising strategy to combat antibiotic resistance, though predicting such AMPs and understanding bacterial responses remain challenging. Octopus bimaculoides, a cephalopod species, lacks known AMP gene families, yet its protein fragments were used to predict AMPs via artificial intelligence tools. Four peptides (Oct-P1, Oct-P2, Oct-P3, and Oct-P4) were identified based on their aggregation propensity. Among them, Oct-P2 reduced the viability of Escherichia coli and Staphylococcus aureus by up to 90%, confirmed by confocal laser scanning microscopy and scanning electron microscopy. It further aggregated plasmid DNA in vitro, and the presence of extracellular DNA reduced their antibacterial activity. With knockout mutants, it revealed that Oct-P2 was internalised into bacterial cells, possibly through membrane transport proteins, enhancing its antibacterial effect. Aggregation-induced emission assays and molecular dynamics simulations revealed that Oct-P2 aggregates with transcription promoter DNA, inhibiting transcription and translation in vitro. This dual-target mechanism not only highlights the potential of Oct-P2 as a lead template for new antimicrobial drug development, but also opens a new window for discovering AMPs from protein fragments against the upcoming challenge of bacterial infections. Statement of significanceA popular strategy for identifying antimicrobial peptides (AMPs) in specific genomes uses the conserved regions of AMP families, but this strategy has limitations in organisms lacking classical AMP gene families, such as Octopus. Fragments from non-antimicrobial proteins serve as a rich source for the identification of new AMPs. In this study, we used artificial intelligence tools to search for potential candidate AMP sequences from non-antimicrobial proteins in Octopus bimaculoides. The successful identification of aggregation-prone AMPs was shown to decrease bacterial viability, increase permeability, and reduce biomass. One candidate, Oct-P2, kills the gram-negative bacteria E. coli by aggregating with DNA and inhibiting transcription and translation, suggesting a new intracellular mechanism of AMP activity.

Identification and synthesis of a long-chain antimicrobial peptide from the venom of the Liocheles australasiae scorpion.

Scorpion venom contains linear peptides without disulfide bonds in addition to peptides with disulfide bonds. Many such linear peptides have an amphiphilic α-helical structure, often with antimicrobial activity and can be classified into three groups based on their molecular size. Among them, long-chain antimicrobial peptides consisting of more than 40 residues have not been thoroughly studied due to the difficulty of synthesizing them. We have previously reported a transcriptome analysis of the venom gland of Liocheles australasiae that revealed precursor sequences of long-chain antimicrobial peptides. In the study reported here, we identified the mature structure of one such long-chain antimicrobial peptide, LaCT1, which we synthesized using chemical ligation to confirm its structure and evaluate its biological activities. The result showed that LaCT1 exhibited significant antimicrobial activity. In addition, we identified its partial peptides consisting of an N- or C-terminal region, which may be generated by enzymatic cleavage in the venom. Among them, only the peptide containing the N-terminal half region was active. LaCT1 also not only showed insecticidal activity but also synergistically enhanced the effects of another insecticidal peptide identified in L. australasiae venom as well. These results provide insights into the role of antimicrobial peptides in scorpion venom.

Novel Antimicrobial Peptide Design Using Motif Match Score Representation.

Antimicrobial peptides (AMPs) have drawn the interest of the researchers since they offer an alternative to the traditional antibiotics in the fight against antibiotic resistance and they exhibit additional pharmaceutically significant properties. Recently, computational approaches attemp to reveal how antibacterial activity is determined from a machine learning perspective and they aim to search and find the biological cues or characteristics that control antimicrobial activity via incorporating motif match scores. This study is dedicated to the development of a machine learning framework aimed at devising novel antimicrobial peptide (AMP) sequences potentially effective against Gram-positive /Gram-negative bacteria. In order to design newly generated sequences classified as either AMP or non-AMP, various classification models were trained. These novel sequences underwent validation utilizingthe "DBAASP:strain-specific antibacterial prediction based on machine learning approaches and data on AMP sequences" tool. The findings presented herein represent a significant stride in this computational research, streamlining the process of AMP creation or modification within wet lab environments.

AbAMPdb: a database of Acinetobacter baumannii specific antimicrobial peptides.

Acinetobacter baumannii has emerged as a prominent nosocomial pathogen, exhibiting a progressive rise in resistance to therapeutic interventions. This rise in resistance calls for alternative strategies. Here, we propose an alternative yet specialized resource on antimicrobial peptides (AMPs) against A. baumannii. Database 'AbAMPdb' is the manually curated collection of 300 entries containing the 250 experimental AMP sequences and 50 corresponding synthetic or mutated AMP sequences. The mutated sequences were modified with reported amino acid substitutions intended for decreasing the toxicity and increasing the antimicrobial potency. AbAMPdb also provides 3D models of all 300 AMPs, comprising 250 natural and 50 synthetic or mutated AMPs. Moreover, the database offers docked complexes comprising 5000 AMPs and their corresponding A. baumannii target proteins. These complexes, accessible in Protein Data Bank format, enable the 2D visualization of the interacting amino acid residues. We are confident that this comprehensive resource furnishes vital information concerning AMPs, encompassing their docking interactions with virulence factors and antibiotic resistance proteins of A. baumannii. To enhance clinical relevance, the characterized AMPs could undergo further investigation both in vitro and in vivo. Database URL: https://abampdb.mgbio.tech/.

ProT-Diff: A Modularized and Efficient Strategy for De Novo Generation of Antimicrobial Peptide Sequences by Integrating Protein Language and Diffusion Models.

Antimicrobial peptides (AMPs) are a promising solution for treating antibiotic-resistant pathogens. However, efficient generation of diverse AMPs without prior knowledge of peptide structures or sequence alignments remains a challenge. Here, ProT-Diff is introduced, a modularized deep generative approach that combines a pretrained protein language model with a diffusion model for the de novo generation of AMPs sequences. ProT-Diff generates thousands of AMPs with diverse lengths and structures within a few hours. After silico physicochemical screening, 45 peptides are selected for experimental validation. Forty-four peptides showed antimicrobial activity against both gram-positive or gram-negative bacteria. Among broad-spectrum peptides, AMP_2 exhibited potent antimicrobial activity, low hemolysis, and minimal cytotoxicity. An in vivo assessment demonstrated its effectiveness against a drug-resistant E. coli strain in acute peritonitis. This study not only introduces a viable and user-friendly strategy for de novo generation of antimicrobial peptides, but also provides potential antimicrobial drug candidates with excellent activity. It is believed that this study will facilitate the development of other peptide-based drug candidates in the future, as well as proteins with tailored characteristics.

Immunometabolic interplay in Edwardsiella tarda-infected crucian carp (Carassius auratus) and in vitro identification of the antimicrobial activity of apolipoprotein D (ApoD) by utilization of multiomics analyses

Edwardsiella tarda is an intracellular pathogenic bacteria that can imperil the health of farmed fish. However, the interactive networks of immune regulation and metabolic response in E. tarda-infected fish are still unclear. In this investigation, we aimed to explore immunometabolic interplay in crucian carp after E. tarda infection by utilizing multiomics analyses. Crucian carp (Carassius auratus) receiving E. tarda infection showed increased levels of tissue damage and oxidative injury in liver. Multiomics analyses suggested that carbon and amino acid metabolism may be considered as crucial metabolic pathways in liver of crucian carp following E. tarda infection, while spaglumic acid, isocitric acid and tetrahydrocortisone were the crucial liver biomarkers. After that, a potential antimicrobial peptide (AMP) sequence called apolipoprotein D (ApoD) was identified from omics study. Then, tissue-specific analysis indicated that liver CaApoD showed the highest expression among isolated tissues. After Aeromonas hydrophila stimulated, CaApoD expressions increased sharply in immune-related tissues. Moreover, CaApoD fusion protein could mediate the in vitro binding to A. hydrophila and E. tarda, attenuate bacterial growth as well as diminish bacterial biofilm forming activity. These findings may have a comprehensive implication for understanding immunometabolic response in crucian carp upon infection.

MyxoPortal: a database of myxobacterial genomic features.

Myxobacteria are predatory bacteria with antimicrobial activity, utilizing complex mechanisms to kill their prey and assimilate their macromolecules. Having large genomes encoding hundreds of secondary metabolites, hydrolytic enzymes and antimicrobial peptides, these organisms are widely studied for their antibiotic potential. MyxoPortal is a comprehensive genomic database hosting 262 genomes of myxobacterial strains. Datasets included provide genome annotations with gene locations, functions, amino acids and nucleotide sequences, allowing analysis of evolutionary and taxonomical relationships between strains and genes. Biosynthetic gene clusters are identified by AntiSMASH, and dbAMP-generated antimicrobial peptide sequences are included as a resource for novel antimicrobial discoveries, while curated datasets of CRISPR/Cas genes, regulatory protein sequences, and phage associated genes give useful insights into each strain's biological properties. MyxoPortal is an intuitive open-source database that brings together application-oriented genomic features that can be used in taxonomy, evolution, predation and antimicrobial research. MyxoPortal can be accessed at http://dicsoft1.physics.iisc.ac.in/MyxoPortal/. Database URL: http://dicsoft1.physics.iisc.ac.in/MyxoPortal/. Graphical Abstract.

Discovery of antimicrobial peptides in the global microbiome with machine learning

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both invitro and invivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Evaluation of antioxidant, antimicrobial, and bioactive properties and peptide sequence composition of Malatya apricot kernels.

This study used four different apricot (Prunus armeniaca) kernels cultivated in Malatya during two consecutive years. The varieties were Hacihaliloglu, Hasanbey, Kabaasi, and Zerdali. The physicochemical properties of the kernels were determined, and the bioactive content of the kernels was evaluated using kernel hydrolysates prepared using trypsin. With regard to the physicochemical properties of the kernels, the dry matter ratio and protein content were the highest in the Hacihaliloglu variety; the ash ratio was the highest in the Kabaasi variety, and the free oil ratio was the highest in the Hasanbey variety. The bioactive compound content changed according to kernel variety. Angiotensin-converting enzyme inhibitors activity was found to be the highest in the Hacihaliloglu and Hasanbey varieties, which had the lowest amygdalin content, and Zerdali had the highest amygdalin content. The antioxidant and antimicrobial effects of the kernels varied, with Hasanbey and Kabaasi generally having the highest content in both analyses. Moreover, a concentration of 20 mg mL-1 of the hydrolysate was determined to have a destructive effect for the microorganisms used in this study. The storage protein of the kernels, except Hacihaliloglu, was found to be Prunin 1, with the longest matching protein chain in the kernels being R.QQQGGQLMANGLEETFCSLRLK.E. The results suggest that the peptide sequences identified in the kernels could have antihypertensive, antioxidative, and Dipeptidyl peptidase IV (DPP-IV)inhibitory effects. Consequently, apricot kernels show potential for use in the production of functional food products. Of the kernels evaluated in this study, Hacihaliloglu and Hasanbey were deemed the most suitable varieties due to their higher bioactive content and lower amygdalin content. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

UniproLcad: Accurate Identification of Antimicrobial Peptide by Fusing Multiple Pre-Trained Protein Language Models

Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.

Diversity and Mechanisms of Action of Plant, Animal, and Human Antimicrobial Peptides.

Antimicrobial peptides (AMPs) are usually made up of fewer than 100 amino acid residues. They are found in many living organisms and are an important factor in those organisms' innate immune systems. AMPs can be extracted from various living sources, including bacteria, plants, animals, and even humans. They are usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi, and other pathogens. They can act against both Gram-negative and Gram-positive pathogens and have various modes of action against them. Some attack the pathogens' membranes, while others target their intracellular organelles, as well as their nucleic acids, proteins, and metabolic pathways. A crucial area of AMP use is related to their ability to help with emerging antibiotic resistance: some AMPs are active against resistant strains and are susceptible to peptide engineering. This review considers AMPs from three key sources-plants, animals, and humans-as well as their modes of action and some AMP sequences.

Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design.

Antimicrobial peptides are peptides that are effective against bacteria and viruses, and the discovery of new antimicrobial peptides is of great importance to human life and health. Although the design of antimicrobial peptides using machine learning methods has achieved good results in recent years, it remains a challenge to learn and design novel antimicrobial peptides with multiple properties of interest from peptide data with certain property labels. To this end, we propose Multi-CGAN, a deep generative model-based architecture that can learn from single-attribute peptide data and generate antimicrobial peptide sequences with multiple attributes that we need, which may have a potentially wide range of uses in drug discovery. In particular, we verified that our Multi-CGAN generated peptides with the desired properties have good performance in terms of generation rate. Moreover, a comprehensive statistical analysis demonstrated that our generated peptides are diverse and have a low probability of being homologous to the training data. Interestingly, we found that the performance of many popular deep learning methods on the antimicrobial peptide prediction task can be improved by using Multi-CGAN to expand the data on the training set of the original task, indicating the high quality of our generated peptides and the robust ability of our method. In addition, we also investigated whether it is possible to directionally generate peptide sequences with specified properties by controlling the input noise sampling for our model.

Antimicrobial activity, membrane interaction and structural features of short arginine-rich antimicrobial peptides.

Antimicrobial activity of many AMPs can be improved by lysine-to-arginine substitution due to a more favourable interaction of arginine guanidinium moiety with bacterial membranes. In a previous work, the structural and functional characterization of an amphipathic antimicrobial peptide named RiLK1, including lysine and arginine as the positively charged amino acids in its sequence, was reported. Specifically, RiLK1 retained its β-sheet structure under a wide range of environmental conditions (temperature, pH, and ionic strength), and exhibited bactericidal activity against Gram-positive and Gram-negative bacteria and fungal pathogens with no evidence of toxicity on mammalian cells. To further elucidate the influence of a lysine-to-arginine replacement on RiLK1 conformational properties, antimicrobial activity and peptide-liposome interaction, a new RiLK1-derivative, named RiLK3, in which the lysine is replaced with an arginine residue, was projected and characterised in comparison with its parental compound. The results evidenced that lysine-to-arginine mutation not only did not assure an improvement in the antimicrobial potency of RiLK1 in terms of bactericidal, virucidal and fungicidal activities, but rather it was completely abolished against the hepatitis A virus. Therefore, RiLK1 exhibited a wide range of antimicrobial activity like other cationic peptides, although the exact mechanisms of action are not completely understood. Moreover, tryptophan fluorescence measurements confirmed that RiLK3 bound to negatively charged lipid vesicles with an affinity lower than that of RiLK1, although no substantial differences from the structural and self-assembled point of view were evidenced. Therefore, our findings imply that antimicrobial efficacy and selectivity are affected by several complex and interrelated factors related to substitution of lysine with arginine, such as their relative proportion and position. In this context, this study could provide a better rationalisation for the optimization of antimicrobial peptide sequences, paving the way for the development of novel AMPs with broad applications.

BamA-targeted antimicrobial peptide design for enhanced efficacy and reduced toxicity.

The emergence of drug-resistant superbugs has necessitated a pressing need for innovative antibiotics. Antimicrobial peptides (AMPs) have demonstrated broad-spectrum antibacterial activity, reduced susceptibility to resistance, and immunomodulatory effects, rendering them promising for combating drug-resistant microorganisms. This study employed computational simulation methods to screen and design AMPs specifically targeting ESKAPE pathogens. Particularly, AMPs were rationally designed to target the BamA and obtain novel antimicrobial peptide sequences. The designed AMPs were assessed for their antibacterial activities, mechanisms, and stability. Molecular docking and dynamics simulations demonstrated the interaction of both designed AMPs, 11pep and D-11pep, with the β1, β9, β15, and β16 chains of BamA, resulting in misfolding of outer membrane proteins and antibacterial effects. Subsequent antibacterial investigations confirmed the broad-spectrum activity of both 11pep and D-11pep, with D-11pep demonstrating higher potency against resistant Gram-negative bacteria. D-11pep exhibited MICs of 16, 8, and 32μg/mL against carbapenem-resistant Escherichia coli, carbapenem-resistant Pseudomonas aeruginosa, and multi-drug-resistant Acinetobacter baumannii, respectively, with a concomitant lower resistance induction. Mechanism of action studies confirmed that peptides could disrupt the bacterial outer membrane, aligning with the findings of molecular dynamics simulations. Additionally, D-11pep demonstrated superior stability and reduced toxicity in comparison to 11pep. The findings of this study underscore the efficacy of rational AMP design that targets BamA, along with the utilization of D-amino acid replacements as a strategy for developing AMPs against drug-resistant bacteria.

Membrane-active and DNA binding related double-action antimycobacterial mechanism of antimicrobial peptide W3R6 and its synthetic analogs

The emergence of multidrug- or extremely drug-resistant M. tuberculosis strains has made very few drugs available for current tuberculosis treatment. Antimicrobial peptides can be employed as a promising alternative strategy for TB treatment. Here, we designed and synthesized a series of peptide sequences based on the structure-activity relationships of natural sequences of antimicrobial peptides. The peptide W3R6 and its analogs were screened and found to have potent antimycobacterial activity against M. smegmatis, and no hemolytic activity against human erythrocytes. The evidence from the mechanism of action study indicated that W3R6 and its analogs can interact with the mycobacterial membrane in a lytic manner and form pores on the outer membrane of M. smegmatis. Significant colocalization of D-W3R6 with mycobacterial DNA was observed by confocal laser scanning microscopy and DNA retardation assays, which suggested that the antimycobacterial mechanism of action of the peptide was associated with the unprotected genomic DNA of M. smegmatis. In general, W3R6 and its analogs act on not only the mycobacterial membrane but also the genomic DNA in the cytoplasm, which makes it difficult for mycobacteria to generate resistance due to the peptides having two targets. In addition, the peptides can effectively eliminate M. smegmatis cells from infected macrophages. Our findings indicated that the antimicrobial peptide W3R6 could be a novel lead compound to overcome the threat from drug-resistant M. tuberculosis strains in the development of potent AMPs for TB therapeutic applications.

Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures.

Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.

Characterization and Engineering Studies of a New Endolysin from the Propionibacterium acnes Bacteriophage PAC1 for the Development of a Broad-Spectrum Artilysin with Altered Specificity.

The emergence of multidrug-resistant (MDR) bacteria has risen rapidly, leading to a great threat to global public health. A promising solution to this problem is the exploitation of phage endolysins. In the present study, a putative N-acetylmuramoyl-L-alanine type-2 amidase (NALAA-2, EC 3.5.1.28) from Propionibacterium bacteriophage PAC1 was characterized. The enzyme (PaAmi1) was cloned into a T7 expression vector and expressed in E. coli BL21 cells. Kinetics analysis using turbidity reduction assays allowed the determination of the optimal conditions for lytic activity against a range of Gram-positive and negative human pathogens. The peptidoglycan degradation activity of PaAmi1 was confirmed using isolated peptidoglycan from P. acnes. The antibacterial activity of PaAmi1 was investigated using live P. acnes cells growing on agar plates. Two engineered variants of PaAmi1 were designed by fusion to its N-terminus two short antimicrobial peptides (AMPs). One AMP was selected by searching the genomes of Propionibacterium bacteriophages using bioinformatics tools, whereas the other AMP sequence was selected from the antimicrobial peptide databases. Both engineered variants exhibited improved lytic activity towards P. acnes and the enterococci species Enterococcus faecalis and Enterococcus faecium. The results of the present study suggest that PaAmi1 is a new antimicrobial agent and provide proof of concept that bacteriophage genomes are a rich source of AMP sequences that can be further exploited for designing novel or improved endolysins.