Antimicrobial Peptides Sequences Research Articles

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.

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.

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.

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.

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.

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.

Assessing the Antimicrobial Properties of Honey Protein Components through In Silico Comparative Peptide Composition and Distribution Analysis.

The availability of reference proteomes for two honeybee species (Apis mellifera and Apis cerana cerana) opens the possibility of in silico studies of diverse properties of the selected protein fractions. The antimicrobial activity of honey is well established and related to its composition, including protein components. We have performed a comparative study on a selected fraction of the honey-related proteins, as well as other bee-secreted proteins, utilizing a publicly available database of established and verified peptides with antimicrobial properties. Using a high-performance sequence aligner (diamond), protein components with antimicrobial peptide sequences were identified and analyzed. The identified peptides were mapped on the available bee proteome sequences, as well as on model structures provided by the AlphaFold project. The results indicate a highly conserved localization of the identified sequences within a limited number of the protein components. Putative antimicrobial fragments also show high sequence-based similarity to the multiple peptides contained in the reference databases. For the 2 databases used, the lowest calculated percentage of similarity ranged from 30.1% to 32.9%, with a respective average of 88.5% and 79.3% for the Apis mellifera proteome. It was revealed that the antimicrobial peptides (AMPs) site is a single, well-defined domain with potentially conserved structural features. In the case of the examples studied in detail, the structural domain takes the form of the two β-sheets, stabilized by α-helices in one case, and a six-β-sheet-only domain localized in the C-terminal part of the sequence, respectively. Moreover, no significant differences were found in the composition of the antibacterial fraction of peptides that were identified in the proteomes of both species.

Peptide-Resorcinarene Conjugates Obtained via Click Chemistry: Synthesis and Antimicrobial Activity.

Antimicrobial resistance (AMR) is one of the top ten threats to public health, as reported by the World Health Organization (WHO). One of the causes of the growing AMR problem is the lack of new therapies and/or treatment agents; consequently, many infectious diseases could become uncontrollable. The need to discover new antimicrobial agents that are alternatives to the existing ones and that allow mitigating this problem has increased, due to the rapid and global expansion of AMR. Within this context, both antimicrobial peptides (AMPs) and cyclic macromolecules, such as resorcinarenes, have been proposed as alternatives to combat AMR. Resorcinarenes present multiple copies of antibacterial compounds in their structure. These conjugate molecules have exhibited antifungal and antibacterial properties and have also been used in anti-inflammatory, antineoplastic, and cardiovascular therapies, as well as being useful in drug and gene delivery systems. In this study, it was proposed to obtain conjugates that contain four copies of AMP sequences over a resorcinarene core. Specifically, obtaining (peptide)4-resorcinarene conjugates derived from LfcinB (20-25): RRWQWR and BF (32-34): RLLR was explored. First, the synthesis routes that allowed obtaining: (a) alkynyl-resorcinarenes and (b) peptides functionalized with the azide group were established. These precursors were used to generate (c) (peptide)4-resorcinarene conjugates by azide-alkyne cycloaddition CuAAC, a kind of click chemistry. Finally, the conjugates' biological activity was evaluated: antimicrobial activity against reference strains and clinical isolates of bacteria and fungi, and the cytotoxic activity over erythrocytes, fibroblast, MCF-7, and HeLa cell lines. Our results allowed establishing a new synthetic route, based on click chemistry, for obtaining macromolecules derived from resorcinarenes functionalized with peptides. Moreover, it was possible to identify promising antimicrobial chimeric molecules that may lead to advances in the development of new therapeutic agents.

Converting antimicrobial into targeting peptides reveals key features governing protein import into mitochondria and chloroplasts

We asked what peptide features govern targeting to the mitochondria versus the chloroplast, using antimicrobial peptides as a starting point. This approach was inspired by the endosymbiotic hypothesis that organelle-targeting peptides derive from antimicrobial amphipathic peptides delivered by the host cell, to which organelle progenitors became resistant. To explore the molecular changes required to convert antimicrobial into targeting peptides, we expressed a set of 13 antimicrobial peptides in Chlamydomonas reinhardtii. Peptides were systematically modified to test distinctive features of mitochondrion- and chloroplast-targeting peptides, and we assessed their targeting potential by following the intracellular localization and maturation of a Venus fluorescent reporter used as a cargo protein. Mitochondrial targeting can be achieved by some unmodified antimicrobial peptide sequences. Targeting to both organelles is improved by replacing lysines with arginines. Chloroplast targeting is enabled by the presence of flanking unstructured sequences, additional constraints consistent with chloroplast endosymbiosis having occurred in a cell that already contained mitochondria. If indeed targeting peptides evolved from antimicrobial peptides, then required modifications imply a temporal evolutionary scenario with an early exchange of cationic residues and a late acquisition of chloroplast-specific motifs.

Testing Antimicrobial Properties of Selected Short Amyloids

Amyloids and antimicrobial peptides (AMPs) have many similarities, e.g., both kill microorganisms by destroying their membranes, form aggregates, and modulate the innate immune system. Given these similarities and the fact that the antimicrobial properties of short amyloids have not yet been investigated, we chose a group of potentially antimicrobial short amyloids to verify their impact on bacterial and eukaryotic cells. We used AmpGram, a best-performing AMP classification model, and selected ten amyloids with the highest AMP probability for our experimental research. Our results indicate that four tested amyloids: VQIVCK, VCIVYK, KCWCFT, and GGYLLG, formed aggregates under the conditions routinely used to evaluate peptide antimicrobial properties, but none of the tested amyloids exhibited antimicrobial or cytotoxic properties. Accordingly, they should be included in the negative datasets to train the next-generation AMP prediction models, based on experimentally confirmed AMP and non-AMP sequences. In the article, we also emphasize the importance of reporting non-AMPs, given that only a handful of such sequences have been officially confirmed.

Big Data Bot with a Special Reference to Bioinformatics

There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of data from web-based (point-and-click) resources or graphical user interfaces that cannot be accessed using command-line tools. The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides (AMP) sequences (46240 hits) from various MARVIN software panels, which can be later utilized to develop novel AMPs. Furthermore, for machine learning research, the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank. As a result, data collection from the web will become faster and less expensive, with no need for manual data extraction. The software is critical as a first step to preparing large datasets for subsequent stages of analysis, such as those using machine and deep-learning applications.

In Silico and In Vitro Analyses Reveal Promising Antimicrobial Peptides from Myxobacteria

Antimicrobial resistance (AMR) is a global concern, and as soon as new antibiotics are introduced, resistance to those agents emerges. Therefore, there is an increased appetite for alternative antimicrobial agents to traditional antibiotics. Here, we used in silico methods to investigate potential antimicrobial peptides (AMPs) from predatory myxobacteria. Six hundred seventy-two potential AMP sequences were extracted from eight complete myxobacterial genomes. Most putative AMPs were predicted to be active against Klebsiella pneumoniae with least activity being predicted against Staphylococcus aureus. One hundred seventeen AMPs (defined here as ‘potent putative AMPs’) were predicted to have very good activity against more than two bacterial pathogens, and these were characterized further in silico. All potent putative AMPs were predicted to have anti-inflammatory and antifungal properties, but none was predicted to be active against viruses. Twenty six (22%) of them were predicted to be hemolytic to human erythrocytes, five were predicted to have anticancer properties, and 56 (47%) were predicted to be biofilm active. In vitro assays using four synthesized AMPs showed high MIC values (e.g. So_ce_56_913 250 µg/ml and Coral_AMP411 125 µg/ml against E. coli). However, antibiofilm assays showed a substantial reduction in numbers (e.g. Coral_AMP411 and Myxo_mac104 showed a 69% and 73% reduction, respectively, at the lowest concentration against E. coli) compared to traditional antibiotics. Fourteen putative AMPs had high sequence similarity to proteins which were functionally associated with proteins of known function. The myxobacterial genomes also possessed a variety of biosynthetic gene clusters (BGCs) that can encode antimicrobial secondary metabolites, but their numbers did not correlate with those of the AMPs. We suggest that AMPs from myxobacteria are a promising source of novel antimicrobial agents with a plethora of biological properties.

The γ-Core Motif Peptides of Plant AMPs as Novel Antimicrobials for Medicine and Agriculture.

The γ-core motif is a structural element shared by most host antimicrobial peptides (AMPs), which is supposed to contribute to their antimicrobial properties. In this review, we summarized the available data on the γ-core peptides of plant AMPs. We describe γ-core peptides that have been shown to exhibit inhibitory activity against plant and human bacterial and fungal pathogens that make them attractive scaffolds for the development of novel anti-infective agents. Their advantages include origin from natural AMP sequences, broad-spectrum and potent inhibitory activity, and cost-effective production. In addition, some γ-core peptides combine antimicrobial and immunomodulatory functions, thus broadening the spectrum of practical applications. Some act synergistically with antimycotics and fungicides, so combinations of peptides with conventionally used antifungal agents can be suggested as an effective strategy to reduce the doses of potentially harmful chemicals. The presented information will pave the way for the design of novel antimicrobials on the basis of γ-core motif peptides, which can find application in medicine and the protection of crops from diseases.