CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides

Structural and functional analyses of nematode-derived antimicrobial peptides support the occurrence of direct mechanisms of worm-microbiota interactions

The complex relationships between gastrointestinal (GI) nematodes and the host gut microbiota have been implicated in key aspects of helminth disease and infection outcomes. Nevertheless, the direct and indirect mechanisms governing these interactions are, thus far, largely unknown. In this proof-of-concept study, we demonstrate that the excretory-secretory products (ESPs) and extracellular vesicles (EVs) of key GI nematodes contain peptides that, when recombinantly expressed, exert antimicrobial activity in vitro against Bacillus subtilis. In particular, using time-lapse microfluidics microscopy, we demonstrate that exposure of B. subtilis to a recombinant saposin-domain containing peptide from the 'brown stomach worm', Teladorsagia circumcincta, and a metridin-like ShK toxin from the 'barber's pole worm', Haemonchus contortus, results in cell lysis and significantly reduced growth rates. Data from this study support the hypothesis that GI nematodes may modulate the composition of the vertebrate gut microbiota directly via the secretion of antimicrobial peptides, and pave the way for future investigations aimed at deciphering the impact of such changes on the pathophysiology of GI helminth infection and disease.

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification

The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .

Omics-driven bioinformatics for plant lectins discovery and functional annotation - A comprehensive review

Lectins are known for their specific and reversible binding capacity to carbohydrates. These molecules have been particularly explored in plants due to their reported properties, highlighting antimicrobial, antiviral, anticancer, antiparasitic, insecticidal, and immunoregulatory actions. The increasing availability of lectin and lectin-like sequences in omics data banks provides an opportunity to identify important candidates, inferring their roles in essential signaling pathways and processes in plants. Bioinformatics enables a fast and low-cost scenario for elucidating sequences and predicting functions in the lectinology universe. Thus, this review addresses the state of the art of annotation, structural characterization, classification, and predicted applications of plant lectins. Their allergenic and toxic properties are also discussed, as well as tools for predicting such effects from the primary structure. This review uncovers a promising scenario for plant lectins and new study possibilities, particularly for studies in lectinology in the omics era.

Human microbiome derived synthetic antimicrobial peptides with activity against Gram-negative, Gram-positive, and antibiotic resistant bacteria

The prevalence of antibacterial resistance has become one of the major health threats of modern times, requiring the development of novel antibacterials. Antimicrobial peptides are a promising source of antibiotic candidates, mostly requiring further optimization to enhance druggability. In this study, a series of new antimicrobial peptides derived from lactomodulin, a human microbiome natural peptide, was designed, synthesized, and biologically evaluated. Within the most active region of the parent peptide, linear peptide LM6 with the sequence LSKISGGIGPLVIPV-NH2 and its cyclic derivatives LM13a and LM13b showed strong antibacterial activity against Gram-positive bacteria, including resistant strains, and Gram-negative bacteria. The peptides were found to have a rapid onset of bactericidal activity and transmission electron microscopy clearly shows the disintegration of the cell membrane, suggesting a membrane-targeting mode of action.

Identification of Multifunctional Putative Bioactive Peptides in the Insect Model Red Palm Weevil (Rhynchophorus ferrugineus)

Innate immunity, the body's initial defense against bacteria, fungi, and viruses, heavily depends on antimicrobial peptides (AMPs), which are small molecules produced by all living organisms. Insects, with their vast biodiversity, are one of the most abundant and innovative sources of AMPs. In this study, AMPs from the red palm weevil (RPW) Rhynchophorus ferrugineus (Coleoptera: Curculionidae), a known invasive pest of palm species, were examined. The AMPs were identified in the transcriptomes from different body parts of male and female adults, under different experimental conditions, including specimens collected from the field and those reared in the laboratory. The RPW transcriptomes were examined to predict antimicrobial activity, and all sequences putatively encoding AMPs were analyzed using several machine learning algorithms available in the CAMPR3 database. Additionally, anticancer, antiviral, and antifungal activity of the peptides were predicted using iACP, AVPpred, and Antifp server tools, respectively. Physicochemical parameters were assessed using the Antimicrobial Peptide Database Calculator and Predictor. From these analyses, 198 putatively active peptides were identified, which can be tested in future studies to validate the in silico predictions. Genome-wide analysis revealed that several AMPs have predominantly emerged through gene duplication. Noticeably, we detect a newly originated defensin allele from an ancestral defensin via the deletion of two amino acids following gene duplication in RPW, which may confer an enhanced resilience to microbial infection. Our study shed light on AMP gene families and shows that high duplication and deletion rates are essential to achieve a diversity of antimicrobial mechanisms; hence, we propose the RPW AMPs as a model for exploring gene duplication and functional variations against microbial infection.

Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides

In recent years, the prediction of antimicrobial peptides (AMPs) has gained prominence due to their high antibacterial activity and reduced susceptibility to drug resistance, making them potential antibiotic substitutes. To advance the field of AMP recognition, an increasing number of natural language processing methods are being applied. These methods exhibit diversity in terms of pretraining models, pretraining data sets, word vector embeddings, feature encoding methods, and downstream classification models. Here, we provide a comprehensive survey of current BERT-based methods for AMP prediction. An independent benchmark test data set is constructed to evaluate the predictive capabilities of the surveyed tools. Furthermore, we compared the predictive performance of these computational methods based on six different AMP public databases. LM_pred (BFD) outperformed all other surveyed tools due to abundant pretraining data set and the unique vector embedding approach. To avoid the impact of varying training data sets used by different methods on prediction performance, we performed the 5-fold cross-validation experiments using the same data set, involving retraining. Additionally, to explore the applicability and generalization ability of the models, we constructed a short peptide data set and an external data set to test the retrained models. Although these prediction methods based on BERT can achieve good prediction performance, there is still room for improvement in recognition accuracy. With the continuous enhancement of protein language model, we proposed an AMP prediction method based on the ESM-2 pretrained model called iAMP-bert. Experimental results demonstrate that iAMP-bert outperforms other approaches. iAMP-bert is freely accessible to the public at http://iamp.aielab.cc/.

Two novel antimicrobial peptides P33-57 and mP168-187 from zebrafish showing potent antibacterial activities

It is known that overuse or abuse of antibiotics has undoubtedly accelerated the global antibiotic resistance crisis, and long-time use of antibiotics may have adverse effects on the health of animal, human, and ecosystem. Therefore, it is necessary to find antibiotic alternatives that can be used in aquaculture and are non-toxic to the human. Here we clearly demonstrated that both the PH and FYVE domain of Plekhf2 in zebrafish have antibacterial properties and can interact with PGN in this study. Therefore, we screened four candidate peptides from the two domains. It was demonstrated that P152-172 and P168-187 had no obvious antibacterial activities, while P33-57 and mP168-187 had strong antibacterial activities, which may be used as antimicrobial peptides. Additionally, transmission electron microscopy experiment revealed that P33-57 and mP168-187 can destroy the cell wall of bacteria, thereby directly killing bacteria. Importantly, it was found that P33-57 and mP168-187 had no hemolysis to red blood cells and lacked cytotoxicity. In summary, P33-57 and mP168-187could be seen as antibacterial activity centers of PLEKHF2 and may be promising antimicrobial peptides to combat bacterial infections facing an antibiotic-resistance crisis.

AmpClass: an Antimicrobial Peptide Predictor Based on Supervised Machine Learning

In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources. In this work, we consolidate a database with 15945 AMPs and 12535 non-AMPs taken as the base to train a pool of supervised learning models to recognize peptides with antimicrobial activity. Results show that the proposed tool (AmpClass) outperforms classical state-of-the-art prediction models and achieves similar results compared with deep learning models.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Genome mining for ribosomally synthesized and post-translationally modified peptides (RiPPs) in Streptomyces bacteria

Ribosomally synthesized post-translationally modified peptides (RiPPs) are a novel category of bioactive natural products (NPs). Streptomyces bacteria are a potential source of many bioactive NPs. Limited opportunities are available to characterize all the bioactive NP gene clusters. In this study, 410 sequences of Streptomyces were analyzed for RiPPs through genome mining using the National Center for Biotechnology Information (NCBI), by combining BAGEL and anti-SMASH. A total of 4098 RiPPs were found; including both classified (lanthipeptide, RiPP-like, bacteriocin, LAPs, lassopeptide, thiopeptides) and nonclassified RiPPs. Soil was identified as a rich habitat for RiPPs. These data may offer alternative future remedies for various health issues.

In silico-driven identification and experimental confirmation of antifungal proteins (AFPs) against Candidaalbicans

Mycoses infect millions of people annually across the world. The most common mycosis agent, Candida albicans is responsible for a great deal of illness and death. C. albicans infection is becoming more widespread and the current antifungals polyenes, triazoles, and echinocandins are less efficient against it. Investigating antifungal peptides (AFPs) as therapeutic is gaining momentum. Therefore, we used MALDI-TOF/MS analysis to identify AFPs and protein-protein docking to analyze their interactions with the C. albicans target protein. Some microorganisms with strong antifungal action against C. albicans were selected for the isolation of AFPs. Using MALDI-TOF/MS, we identified 3 AFPs Chitin binding protein (ACW83017.1; Bacillus licheniformis), the bifunctional protein GlmU (BBQ13478.1; Stenotrophomonas maltophilia), and zinc metalloproteinase aureolysin (BBA25172.1; Staphylococcus aureus). These AFPs showed robust interactions with C. albicans target protein Sap5. We deciphered some important residues in identified APFs and highlighted interaction with Sap5 through hydrogen bonds, protein-protein interactions, and salt bridges using protein-protein docking and MD simulations. The three discovered AFPs-Sap5 complexes exhibit different levels of stability, as seen by the RMSD analysis and interaction patterns. Among protein-protein interactions, the remarkable stability of the BBQ25172.1-2QZX complex highlights the role of salt bridges and hydrogen bonds. Identified AFPs could be further studied for developing successful antifungal candidates and peptide-based new antifungal therapeutic strategies as fresh insights into addressing antifungal resistance also.

Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies

Owing to their limited accuracy and narrow applicability, current antimicrobial peptide (AMP) prediction models face obstacles in industrial application. To address these limitations, we developed and improved an AMP prediction model using Comparing and Optimizing Multiple DEep Learning (COMDEL) algorithms, coupled with high-throughput AMP screening method, finally reaching an accuracy of 94.8% in test and 88% in experiment verification, surpassing other state-of-the-art models. In conjunction with COMDEL, we employed the phage-assisted evolution method to screen Sortase in vivo and developed a cell-free AMP synthesis system in vitro, ultimately increasing AMPs yields to a range of 0.5-2.1 g/L within hours. Moreover, by multi-omics analysis using COMDEL, we identified Lactobacillus plantarum as the most promising candidate for AMP generation among 35 edible probiotics. Following this, we developed a microdroplet sorting approach and successfully screened three L. plantarum mutants, each showing a twofold increase in antimicrobial ability, underscoring their substantial industrial application values.

Dual-Mechanism Peptide SR25 has Broad Antimicrobial Activity and Potential Application for Healing Bacteria-infected Diabetic Wounds

The rise of antibiotic resistance poses a significant public health crisis, particularly due to limited antimicrobial options for the treatment of infections with Gram-negative pathogens. Here, an antimicrobial peptide (AMP) SR25 is characterized, which effectively kills both Gram-negative and Gram-positive bacteria through a unique dual-targeting mechanism without detectable resistance. Meanwhile, an SR25-functionalized hydrogel is developed for the efficient treatment of infected diabetic wounds. SR25 is obtained through genome mining from an uncultured bovine enteric actinomycete named Nonomuraea Jilinensis sp. nov. Investigations reveal that SR25 has two independent cellular targets, disrupting bacterial membrane integrity and restraining the activity of succinate:quinone oxidoreductase (SQR). In a diabetic mice wound infection model, the SR25-incorporated hydrogel exhibits high efficacy against mixed infections of Escherichia coli (E. coli) and methicillin-resistant Staphylococcus aureus (MRSA), accelerating wound healing. Overall, these findings demonstrate the therapeutic potential of SR25 and highlight the value of mining drugs with multiple mechanisms from uncultured animal commensals for combating challenging bacterial pathogens.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

A Novel Workflow for In Silico Prediction of Bioactive Peptides: An Exploration of Solanum lycopersicum By-Products

Resource-intensive processes currently hamper the discovery of bioactive peptides (BAPs) from food by-products. To streamline this process, in silico approaches present a promising alternative. This study presents a novel computational workflow to predict peptide release, bioactivity, and bioavailability, significantly accelerating BAP discovery. The computational flowchart has been designed to identify and optimize critical enzymes involved in protein hydrolysis but also incorporates multi-enzyme screening. This feature is crucial for identifying the most effective enzyme combinations that yield the highest abundance of BAPs across different bioactive classes (anticancer, antidiabetic, antihypertensive, anti-inflammatory, and antimicrobial). Our process can be modulated to extract diverse BAP types efficiently from the same source. Here, we show the potentiality of our method for the identification of diverse types of BAPs from by-products generated from Solanum lycopersicum, the widely cultivated tomato plant, whose industrial processing generates a huge amount of waste, especially tomato peel. In particular, we optimized tomato by-products for bioactive peptide production by selecting cultivars like Line27859 and integrating large-scale gene expression. By integrating these advanced methods, we can maximize the value of by-products, contributing to a more circular and eco-friendly production process while advancing the development of valuable bioactive compounds.

Prokaryotic Expression, Purification, and Biological Properties of a Novel Bioactive Protein (PFAP-1) from Pinctada fucata

Pinctada fucata meat is the main by-product of the pearl harvesting industry. It is rich in nutrition, containing a lot of protein and peptides, and holds significant value for both medicine and food. In this study, a new active protein was discovered and expressed heterogeneously through bioinformatics analysis. It was then identified using Western blot, molecular weight, and mass spectrometry. The antibacterial activity, hemolysis activity, antioxidant activity, and Angiotensin-Converting Enzyme II (ACE2) inhibitory activity were investigated. An unknown functional protein was screened through the Uniprot protein database, and its primary structure did not resemble existing proteins. It was an α-helical cationic polypeptide we named PFAP-1. The codon-optimized full-length PFAP-1 gene was synthesized and inserted into the prokaryotic expression vector pET-30a. The induced expression conditions were determined with a final isopropyl-β-d-thiogalactoside (IPTG) concentration of 0.2 mM, an induction temperature of 15 °C, and an induction time of 16 h. The recombinant PFAP-1 protein, with low endotoxin and sterility, was successfully prepared. The recombinant PFAP-1 protein exhibited strong antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) in vitro, and the diameter of the inhibition zone was 15.99 ± 0.02 mm. Its minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were 37.5 μg/mL and 150 μg/mL, respectively, and its hemolytic activity was low (11.21%) at the bactericidal concentration. The recombinant PFAP-1 protein significantly inhibited the formation of MRSA biofilm and eradicated MRSA biofilm. It also demonstrated potent 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH) scavenging activity with a half-maximal inhibitory concentration (IC50) of 40.83 μg/mL. The IC50 of ACE2 inhibition was 5.66 μg/mL. Molecular docking results revealed that the optimal docking fraction of PFAP-1 protein and ACE2 protein was -267.78 kcal/mol, with a confidence level of 0.913. The stable binding complex was primarily formed through nine groups of hydrogen bonds, three groups of salt bridges, and numerous hydrophobic interactions. In conclusion, recombinant PFAP-1 can serve as a promising active protein in food, cosmetics, or medicine.

deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

Background

Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development.

Methods

In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps.

Results

In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors.

Conclusion

deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.

Peptide-functionalized, -assembled and -loaded nanoparticles in cancer therapy

The combination of peptides and nanoparticles in cancer therapy has shown synergistic results. Nanoparticle functionalization with peptides can increase their targeting ability towards tumor cells. In some cases, the peptides can develop self-assembled nanoparticles, in combination with drugs, for targeted cancer therapy. The peptides can be loaded into nanoparticles and can be delivered by other drugs for synergistic cancer removal. Multifunctional types of peptide-based nanoparticles, including pH- and redox-sensitive classes, have been introduced in cancer therapy. The tumor microenvironment remolds, and the acceleration of immunotherapy and vaccines can be provided by peptide nanoparticles. Moreover, the bioimaging and labeling of cancers can be mediated by peptide nanoparticles. Therefore, peptides can functionalize nanoparticles in targeted cancer therapy.

In silico optimization of analogs derived pro-adrenomedullin peptide to evaluate antimicrobial potential

Diverse computational approaches have been widely used to assist in designing antimicrobial peptides with enhanced activities. This tactic has also been used to address the need for new treatment alternatives to combat resistant bacterial infections. Herein, we have designed eight variants from a natural peptide, pro-adrenomedullin N-terminal 20 peptide (PAMP), using an in silico pattern insertion approach, the Joker algorithm. All the variants show an α-helical conformation, but with differences in the helix percentages according to circular dichroism (CD) results. We found that the C-terminal portion of PAMP may be relevant for its antimicrobial activities, as revealed by the molecular dynamics, CD, and antibacterial results. The analogs showed variable antibacterial potential, but most were not cytotoxic. Nevertheless, PAMP2 exhibited the most potent activities against human and animal-isolated bacteria, showing cytotoxicity only at a substantially higher concentration than its minimal inhibitory concentration (MIC). Our results suggest that the enhanced activity in the profile of PAMP2 may be related to their particular physicochemical properties, along with the adoption of an amphipathic α-helical arrangement with the conserved C-terminus portion. Finally, the peptides designed in this study can constitute scaffolds for the design of improved sequences.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Genomic and physiological characterization of Kitasatospora sp. nov., an actinobacterium with potential for biotechnological application isolated from Cerrado soil

An Actinobacteria - Kitasatospora sp. K002 - was isolated from the soil of Cerrado, a savanna-like Brazilian biome. Herein, we conducted a phylogenetic, phenotypic and physiological characterization, revealing its potential for biotechnological applications. Kitasatospora sp. K002 is an aerobic, non-motile, Gram-positive bacteria that forms grayish-white mycelium on solid cultures and submerged spores with vegetative mycelia on liquid cultures. The strain showed antibacterial activity against Bacillus subtilis, Pseudomonas aeruginosa and Escherichia coli. Genomic analysis indicated that Kitasatospora xanthocidica JCM 4862 is the closest strain to K002, with a dDDH of 32.8-37.8% and an ANI of 86.86% and the pangenome investigations identified a high number of rare genes. A total of 60 gene clusters of 22 different types were detected by AntiSMASH, and 22 gene clusters showed low similarity (< 10%) with known compounds, which suggests the potential production of novel bioactive compounds. In addition, phylogenetic analysis and morphophysiological characterization clearly distinguished Kitasatospora sp. K002 from other related species. Therefore, we propose that Kitasatospora sp. K002 should be recognized as a new species of the genus Kitasatospora - Kitasatospora brasiliensis sp. nov. (type strains = K002).

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and Multihead Attention

Antimicrobial peptides (AMPs) are small molecular polypeptides that can be widely used in the prevention and treatment of microbial infections. Although many computational models have been proposed to help identify AMPs, a high-performance and interpretable model is still lacking. In this study, new benchmark data sets are collected and processed, and a stacking deep architecture named AMPpred-MFA is carefully designed to discover and identify AMPs. Multiple features and a multihead attention mechanism are utilized on the basis of a bidirectional long short-term memory (LSTM) network and a convolutional neural network (CNN). The effectiveness of AMPpred-MFA is verified through five independent tests conducted in batches. Experimental results show that AMPpred-MFA achieves a state-of-the-art performance. The visualization interpretability analyses and ablation experiments offer a further understanding of the model behavior and performance, validating the importance of our feature representation and stacking architecture, especially the multihead attention mechanism. Therefore, AMPpred-MFA can be considered a reliable and efficient approach to understanding and predicting AMPs.

Antibacterial activity of a short de novo designed peptide against fish bacterial pathogens

In the face of increasing antimicrobial resistance in aquaculture, researchers are exploring novel substitutes to customary antibiotics. One potential solution is the use of antimicrobial peptides (AMPs). We aimed to design and evaluate a novel, short, and compositionally simple AMP with potent activity against various bacterial pathogens in aquaculture. The resulting peptide, KK12YW, has an amphipathic nature and net charge of + 7. Molecular docking experiments disclosed that KK12YW has a strong affinity for aerolysin, a virulence protein produced by the bacterial pathogen Aeromonas sobria. KK12YW was synthesized using Fmoc chemistry and tested against a range of bacterial pathogens, including A. sobria, A. salmonicida, A. hydrophila, Edwardsiella tarda, Vibrio parahaemolyticus, Pseudomonas aeruginosa, Escherichia coli, Staphylococcus epidermidis, and methicillin-resistant S. aureus. The AMP showed promising antibacterial activity, with MIC and MBC values ranging from 0.89 to 917.1 µgmL-1 and 3.67 to 1100.52 µgmL-1, respectively. In addition, KK12YW exhibited resistance to high temperatures and remained effective even in the presence of serum and salt, indicating its stability. The peptide also demonstrated minimal hemolysis toward fish RBCs, even at higher concentrations. Taken together, these findings indicate that KK12YW could be a highly promising and viable substitute for conventional antibiotics to combat microbial infections in aquaculture.

Heterologous Production of Antimicrobial Peptides: Notes to Consider

Heavy and irresponsible use of antibiotics in the last century has put selection pressure on the microbes to evolve even faster and develop more resilient strains. In the confrontation with such sometimes called "superbugs", the search for new sources of biochemical antibiotics seems to have reached the limit. In the last two decades, bioactive antimicrobial peptides (AMPs), which are polypeptide chains with less than 100 amino acids, have attracted the attention of many in the control of microbial pathogens, more than the other types of antibiotics. AMPs are groups of components involved in the immune response of many living organisms, and have come to light as new frontiers in fighting with microbes. AMPs are generally produced in minute amounts within organisms; therefore, to address the market, they have to be either produced on a large scale through recombinant DNA technology or to be synthesized via chemical methods. Here, heterologous expression of AMPs within bacterial, fungal, yeast, plants, and insect cells, and points that need to be considered towards their industrialization will be reviewed.

ESKtides: a comprehensive database and mining method for ESKAPE phage-derived antimicrobial peptides

'Superbugs' have received increasing attention from researchers, such as ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.), which directly led to about 1 270 000 death cases in 2019. Recently, phage peptidoglycan hydrolases (PGHs)-derived antimicrobial peptides were proposed as new antibacterial agents against multidrug-resistant bacteria. However, there is still a lack of methods for mining antimicrobial peptides based on phages or phage PGHs. Here, by using a collection of 6809 genomes of ESKAPE isolates and corresponding phages in public databases, based on a unified annotation process of all the genomes, PGHs were systematically identified, from which peptides were mined. As a result, a total of 12 067 248 peptides with high antibacterial activities were respectively determined. A user-friendly tool was developed to predict the phage PGHs-derived antimicrobial peptides from customized genomes, which also allows the calculation of peptide phylogeny, physicochemical properties, and secondary structure. Finally, a user-friendly and intuitive database, ESKtides (http://www.phageonehealth.cn:9000/ESKtides), was designed for data browsing, searching and downloading, which provides a rich peptide library based on ESKAPE prophages and phages. Database URL:  10.1093/database/baae022.

Peptidome Profiling of Bubalus bubalis Urine and Assessment of Its Antimicrobial Activity against Mastitis-Causing Pathogens

Urinary proteins have been studied quite exhaustively in the past, however, the small sized peptides have remained neglected for a long time in dairy cattle. These peptides are the products of systemic protein turnover, which are excreted out of the body and hence can serve as an important biomarker for various pathophysiologies. These peptides in other species of bovine have been reported to possess several bioactive properties. To investigate the urinary peptides in buffalo and simultaneously their bioactivities, we generated a peptidome profile from the urine of Murrah Buffaloes (n = 10). Urine samples were processed using <10 kDa MWCO filter and filtrate obtained was used for peptide extraction using Solid Phase Extraction (SPE). The nLC-MS/MS of the aqueous phase from ten animals resulted in the identification of 8165 peptides originating from 6041 parent proteins. We further analyzed these peptide sequences to identify bioactive peptides and classify them into anti-cancerous, anti-hypertensive, anti-microbial, and anti-inflammatory groups with a special emphasis on antimicrobial properties. With this in mind, we simultaneously conducted experiments to evaluate the antimicrobial properties of urinary aqueous extract on three pathogenic bacterial strains: S. aureus, E. coli, and S. agalactiae. The urinary peptides observed in the study are the result of the activity of possibly 76 proteases. The GO of these proteases showed the significant enrichment of the antibacterial peptide production. The total urinary peptide showed antimicrobial activity against the aforementioned pathogenic bacterial strains with no significant inhibitory effects against a buffalo mammary epithelial cell line. Just like our previous study in cows, the present study suggests the prime role of the antimicrobial peptides in the maintenance of the sterility of the urinary tract in buffalo by virtue of their amino acid composition.

sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

A novel stacking-based predictor for accurate prediction of antimicrobial peptides

Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.

Antimicrobial peptides designed by computational analysis of proteomes

Antimicrobial peptides (AMPs) are promising cationic and amphipathic molecules to fight antibiotic resistance. To search for novel AMPs, we applied a computational strategy to identify peptide sequences within the organisms' proteome, including in-house developed software and artificial intelligence tools. After analyzing 150.450 proteins from eight proteomes of bacteria, plants, a protist, and a nematode, nine peptides were selected and modified to increase their antimicrobial potential. The 18 resulting peptides were validated by bioassays with four pathogenic bacterial species, one yeast species, and two cancer cell-lines. Fourteen of the 18 tested peptides were antimicrobial, with minimum inhibitory concentrations (MICs) values under 10 µM against at least three bacterial species; seven were active against Candida albicans with MICs values under 10 µM; six had a therapeutic index above 20; two peptides were active against A549 cells, and eight were active against MCF-7 cells under 30 µM. This study's most active antimicrobial peptides damage the bacterial cell membrane, including grooves, dents, membrane wrinkling, cell destruction, and leakage of cytoplasmic material. The results confirm that the proposed approach, which uses bioinformatic tools and rational modifications, is highly efficient and allows the discovery, with high accuracy, of potent AMPs encrypted in proteins.

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

Introduction

Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.

Methods

N-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.

Results

The models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.

Discussion

Hydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.

Antimicrobial properties of tomato juice and peptides against typhoidal Salmonella

Tomatoes are readily available and affordable vegetables that offer a range of health benefits due to their bioactive molecules, such as antioxidants and antimicrobials. In contrast to the widely recognized antioxidant properties of tomatoes, their antimicrobial properties remain largely unexplored. Here, we present our findings on the antimicrobial properties of tomato juice and peptides, namely, tomato-derived antimicrobial peptides (tdAMPs), in relation to their effectiveness against typhoidal Salmonella. Our research has revealed that tomato juice demonstrates significant antimicrobial properties against Salmonella Typhi, a pathogen that specifically affects humans and is responsible for causing typhoid fever. By employing computational analysis of the tomato genome sequence, conducting molecular dynamics simulation, and performing functional analyses, we have successfully identified two tdAMPs, namely, tdAMP-1 and tdAMP-2. These tdAMPs have demonstrated potent antimicrobial properties by effectively disrupting bacterial membranes. The efficacy of tdAMP-2 is shown to be more effective than tdAMP-1. The efficacy of tdAMP-1 and tdAMP-2 has been demonstrated against drug-resistant S. Typhi, as well as hyper-capsular S. Typhi variants that possess hypervirulent characteristics, which are presently circulating in countries with endemicity. Tomato juice, along with the two tdAMPs, has demonstrated effectiveness against uropathogenic Escherichia coli as well. This underscores their potential as viable agents in combating certain Gram-negative pathogens. This study provides valuable insights into the development of effective and sustainable public health strategies that utilize tomato and its derivatives as lifestyle interventions.IMPORTANCEIn this study, we investigate the antimicrobial properties of tomato juice, the most widely consumed affordable vegetables, as well as tomato-derived antimicrobial peptides, in relation to their effectiveness against foodborne pathogens with an emphasis on Salmonella Typhi, a deadly human-specific pathogen.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

In-vitro antimicrobial activity of AF-DP protein and in-silico approach of cell membrane disruption

Microbial resistance against common antibiotics has become one of the most serious threats to human health. The increasing statistics on this problem show the necessity of finding a way to deal with it. In recent years, antimicrobial peptides with unique properties and the capability of targeting a wide range of pathogens, have been considered as a potential for replacing common antibiotics. A small chitin-binding protein with anticandidal activity was isolated from Moringa oleifera seeds by Neto and colleagues in 2017, which very much resembled antimicrobial peptides. In this study, the antimicrobial protein 'AF-DP' was identified and characterized. AF-DP was heterologously expressed, purified, and characterized, and its 3D structure was predicted. Six molecular dynamic simulations were performed to investigate how the protein interacts with Gram-negative inner and outer, Gram-positive, fungal, cancerous, and normal mammalian membranes. Also, its antimicrobial and anticancer activity was assessed in vitro via minimum inhibition concentration (MIC) and MTT assays, respectively. This protein with 111 amino acids and a total net charge (of 10.5) has been predicted to be mainly composed of alpha helix and random coils. Its MIC affecting the growth of Escherichia coli, Staphylococcus aureus, and Candida albicans was 30 µg/ml, 100 µg/ml, and 100 µg/ml, respectively; AF-DP showed anticancer activity against MCF-7 breast cancer cell line. Scanning electron microscopic analysis confirmed the creation of pores and scratches on the surface of the bacterial membrane. The results of this research show that AF-DP can be a candidate for the production of new drugs as an AMP with antimicrobial activity.Communicated by Ramaswamy H. Sarma.

EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features

Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers. The average of four classifiers is the final prediction result. Compared with other state-of-the-art algorithms on six datasets, EnAMP outperforms most existing models with similar computational costs, even when compared with high computational cost algorithms based on Bidirectional Encoder Representation from Transformers (BERT), the performance of our model is comparable. EnAMP source code and the data are available at https://github.com/ruisue/EnAMP.

Occurrence and evolutionary conservation analysis of α-helical cationic amphiphilic segments in the human proteome

The existence of encrypted fragments with antimicrobial activity in human proteins has been thoroughly demonstrated in the literature. Recently, algorithms for the large-scale identification of these segments in whole proteomes were developed, and the pervasiveness of this phenomenon was stated. These algorithms typically mine encrypted cationic and amphiphilic segments of proteins, which, when synthesized as individual polypeptide sequences, exert antimicrobial activity by membrane disruption. In the present report, the human reference proteome was submitted to the software kamal for the uncovering of protein segments that correspond to putative intragenic antimicrobial peptides (IAPs). The assessment of the identity of these segments, frequency, functional classes of parent proteins, structural relevance, and evolutionary conservation of amino acid residues within their corresponding proteins was conducted in silico. Additionally, the antimicrobial and anticancer activity of six selected synthetic peptides was evaluated. Our results indicate that cationic and amphiphilic segments can be found in 2% of all human proteins, but are more common in transmembrane and peripheral membrane proteins. These segments are surface-exposed basic patches whose amino acid residues present similar conservation scores to other residues with similar solvent accessibility. Moreover, the antimicrobial and anticancer activity of the synthetic putative IAP sequences was irrespective to whether these are associated to membranes in the cellular setting. Our study discusses these findings in light of the current understanding of encrypted peptide sequences, offering some insights into the relevance of these segments to the organism in the context of their harboring proteins or as separate polypeptide sequences.

Recent Advances on Antimicrobial Peptides from Milk: Molecular Properties, Mechanisms, and Applications

Traditional antibiotics are facing a tremendous challenge due to increased antimicrobial resistance; hence, there is an urgent need to find novel antibiotic alternatives. Milk protein-derived antimicrobial peptides (AMPs) are currently attracting substantial attention considering that they showcase an extensive spectrum of antimicrobial activities, with slower development of antimicrobial resistance and safety of raw materials. This review summarizes the molecular properties, and activity mechanisms and highlights the applications and limitations of AMPs derived from milk proteins comprehensively. Also the analytical technologies, especially bioinformatics methodologies, applied in the process of screening, identification, and mechanism illustration of AMPs were underlined. This review will give some ideas for further research and broadening of the applications of milk protein-derived AMPs in the food field.

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.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.

Antimicrobial activity of yak beta-defensin 116 against Staphylococcus aureus and its role in gut homeostasis

Staphylococcus aureus (S. aureus) is one of the most common food-borne poisoning microbial agent. However, the antimicrobial activity of β-defensin 116 in yak and its application in S. aureus-induced diarrheal disease have not been reported. In this study, 303 bp cDNA sequence of yak DEFB116 gene was obtained. In addition, the prokaryotic expression vector of DEFB116 protein with a molecular weight of 16 kDa was successfully constructed and expressed. The yak DEFB116 gene can encode 19 amino acids, the percentage of hydrophobic amino acids is 36 % and the total positive charge is 6, which has potential antibacterial potential. Sufficient DEFB116 protein concentration and time can destroy the integrity of the bacterial cell membrane, resulting in leakage of intracellular solutes and thus killing S. aureus. The intestinal histopathological features and the number of inflammatory cells were improved in the diarrhea mouse model under the action of DEFB116 protein. The decrease of goblet cells was reversed, the expression of mucoprotein was increased. DEFB116 protein increased the abundance of Lactobacillus johnsonii, Lactobacillus reuteri and Desulfovibrio, and inhibited the reproduction of pathogenic bacteria. These findings provide new insights into the potential future applications of yak β-defencins in the food industry and medical fields.

Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence

The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.

BsR1, a broad-spectrum antibacterial peptide with potential for plant protection

IMPORTANCE

This study addresses the critical need for new antibacterial drugs in the face of bacterial multidrug resistance resulting from antibiotic overuse. It highlights the significance of antimicrobial peptides as essential components of innate immunity in animals and plants, which have been proven effective against multidrug-resistant bacteria and are difficult to develop resistance against. This study successfully synthesizes a broad-spectrum antibacterial peptide, BsR1, with strong inhibitory activities against various Gram-positive and Gram-negative bacteria. BsR1 demonstrates favorable stability and a mode of action that damages bacterial cell membranes, leading to cell death. It also exhibits biological safety and shows potential in enhancing disease resistance in rice. This research offers a novel approach and potential medication for antibacterial drug development, presenting a valuable tool in combating pathogenic microorganisms, particularly in plants.

Improved Antimicrobial Activity of Bovine Lactoferrin Peptide (LFcinB) Based on Rational Design

Bovine lactoferrin peptide (LFcinB), as an antimicrobial peptide, is expected to be an alternative of antibiotics owing to its broad-spectrum antimicrobial activity and specific mechanism. However, the weak antimicrobial activity, high hemolysis, and poor stability of LFcinB limited its applications in the field of biomedicine, food and agriculture. In order to improve the antimicrobial activity of LFcinB, five mutants were designed rationally, of which mutant LF4 (M10W/P16R/A24L) showed highest antimicrobial activity. The bioinformatics analysis indicated that the improved antimicrobial activity of LF4 was related to its increased cations, higher amphiphilicity and the extension of the β-sheet in the structure. These studies will highlight the important role of bioinformatic tools in designing ideal biopeptides and lay a foundation for further development of antimicrobial peptides.

An attacin antimicrobial peptide, Hill_BB_C10074, from Hermetia illucens with anti-Pseudomonas aeruginosa activity

BACKGROUND

There is a global need to develop new therapies to treat infectious diseases and tackle the rise in antimicrobial resistance. To date, the larvae of the Black Solider Fly, Hermetia illucens, have the largest repertoire of antimicrobial peptides derived from insects. Antimicrobial peptides are of particular interest in the exploration of alternative antimicrobials due to their potent action and reduced propensity to induce resistance compared with more traditional antibiotics.

RESULTS

The predicted attacin from H. illucens, Hill_BB_C10074, was first identified in the transcriptome of H. illucens populations that had been fed a plant-oil based diet. In this study, recombinant Hill_BB_C10074 (500 µg/mL), was found to possess potent antimicrobial activity against the serious Gram-negative pathogen, Pseudomonas aeruginosa. Sequence and structural homology modelling predicted that Hill_BB_C10074 formed a homotrimeric complex that may form pores in the Gram-negative bacterial outer membrane. In vitro experiments defined the antimicrobial action of Hill_BB_C10074 against P. aeruginosa and transmission electron microscopy and electrochemical impedance spectroscopy confirmed the outer membrane disruptive power of Hill_BB_C10074 which was greater than the clinically relevant antibiotic, polymyxin B.

CONCLUSIONS

Combining predictive tools with in vitro approaches, we have characterised Hill_BB_C10074 as an important insect antimicrobial peptide and promising candidate for the future development of clinical antimicrobials.

Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota

Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.

Whole genome sequencing provides evidence for Bacillus velezensis SH-1471 as a beneficial rhizosphere bacterium in plants

Bacillus is widely used in agriculture due to its diverse biological activities. We isolated a Bacillus velezensis SH-1471 from the rhizosphere soil of healthy tobacco, which has broad-spectrum antagonistic activity against a variety of plant pathogenic fungi such as Fusarium oxysporum, and can be colonized in the rhizosphere of a variety of plants. This study will further explore its mechanism by combining biological and molecular biology methods. SH-1471 contains a ring chromosome of 4,181,346 bp with a mean G + C content of 46.18%. We identified 14 homologous genes related to biosynthesis of resistant secondary metabolite, and three clusters encoded potential new antibacterial substances. It also contains a large number of genes from colonizing bacteria and genes related to plant bacterial interactions. It also contains genes related to environmental stress, as well as genes related to drug resistance. We also found that there are many metabolites in the strain that can inhibit the growth of pathogens. In addition, our indoor pot test found that SH-1471 has a good control effect on tomato wilt, and could significantly improve plant height, stem circumference, root length, root weight, and fresh weight and dry weight of the aboveground part of tomato seedlings. Therefore, SH-1471 is a potential biological control strain with important application value. The results of this study will help to further study the mechanism of SH-1471 in biological control of plant diseases and promote its application.

In Silico-Based Design of a Hybrid Peptide with Antimicrobial Activity against Multidrug-Resistant Pseudomonas aeruginosa Using a Spider Toxin Peptide

The escalating prevalence of antibiotic-resistant bacteria poses an immediate and grave threat to public health. Antimicrobial peptides (AMPs) have gained significant attention as a promising alternative to conventional antibiotics. Animal venom comprises a diverse array of bioactive compounds, which can be a rich source for identifying new functional peptides. In this study, we identified a toxin peptide, Lycotoxin-Pa1a (Lytx-Pa1a), from the transcriptome of the Pardosa astrigera spider venom gland. To enhance its functional properties, we employed an in silico approach to design a novel hybrid peptide, KFH-Pa1a, by predicting antibacterial and cytotoxic functionalities and incorporating the amino-terminal Cu(II)- and Ni(II) (ATCUN)-binding motif. KFH-Pa1a demonstrated markedly superior antimicrobial efficacy against pathogens, including multidrug-resistant (MDR) Pseudomonas aeruginosa, compared to Lytx-Pa1a. Notably, KFH-Pa1a exerted several distinct mechanisms, including the disruption of the bacterial cytoplasmic membrane, the generation of intracellular ROS, and the cleavage and inhibition of bacterial DNA. Additionally, the hybrid peptide showed synergistic activity when combined with conventional antibiotics. Our research not only identified a novel toxin peptide from spider venom but demonstrated in silico-based design of hybrid AMP with strong antimicrobial activity that can contribute to combating MDR pathogens, broadening the utilization of biological resources by incorporating computational approaches.

Design methods for antimicrobial peptides with improved performance

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult. In this regard, developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority. Antimicrobial peptides (AMPs), a ubiquitous class of naturally occurring compounds with broad-spectrum antipathogenic activity, hold significant promise as an effective solution to the current antimicrobial resistance (AMR) crisis. Several AMPs have been identified and evaluated for their therapeutic application, with many already in the drug development pipeline. Their distinct properties, such as high target specificity, potency, and ability to bypass microbial resistance mechanisms, make AMPs a promising alternative to traditional antibiotics. Nonetheless, several challenges, such as high toxicity, lability to proteolytic degradation, low stability, poor pharmacokinetics, and high production costs, continue to hamper their clinical applicability. Therefore, recent research has focused on optimizing the properties of AMPs to improve their performance. By understanding the physicochemical properties of AMPs that correspond to their activity, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies used to optimize the performance of AMPs, including rational design and de novo synthesis. We also discuss the growing role of predictive computational tools, utilizing artificial intelligence and machine learning, in the design and synthesis of highly efficacious lead drug candidates.

Aquiluscidin, a Cathelicidin from Crotalus aquilus, and the Vcn-23 Derivative Peptide, Have Anti-Microbial Activity against Gram-Negative and Gram-Positive Bacteria

Anti-microbial peptides play a vital role in the defense mechanisms of various organisms performing functions that range from the elimination of microorganisms, through diverse mechanisms, to the modulation of the immune response, providing protection to the host. Among these peptides, cathelicidins, a well-studied family of anti-microbial peptides, are found in various animal species, including reptiles. Due to the rise in anti-microbial resistance, these compounds have been suggested as potential candidates for developing new drugs. In this study, we identified and characterized a cathelicidin-like peptide called Aquiluscidin (Aq-CATH) from transcripts obtained from the skin and oral mucosa of the Querétaro's dark rattlesnake, Crotalus aquilus. The cDNA was cloned, sequenced, and yielded a 566-base-pair sequence. Using bioinformatics, we predicted that the peptide precursor contains a signal peptide, a 101-amino-acid conserved cathelin domain, an anionic region, and a 34-amino-acid mature peptide in the C-terminal region. Aq-CATH and a derived 23-amino-acid peptide (Vcn-23) were synthesized, and their anti-microbial activity was evaluated against various species of bacteria in in vitro assays. The minimal inhibitory concentrations against bacteria ranged from 2 to 8 μg/mL for both peptides. Furthermore, at concentrations of up to 50 μM, they exhibited no significant hemolytic activity (<2.3% and <1.2% for Aquiluscidin and Vcn-23, respectively) against rat erythrocytes and displayed no significant cytotoxic activity at low concentrations (>65% cell viability at 25 µM). Finally, this study represents the first identification of an antimicrobial peptide in Crotalus aquilus, which belongs to the cathelicidin family and exhibits the characteristic features of these peptides. Both Aq-CATH and its derived molecule, Vcn-23, displayed remarkable inhibitory activity against all tested bacteria, highlighting their potential as promising candidates for further antimicrobial research.