Computational Methods and Tools in Antimicrobial Peptide Research

Harnessing advanced computational approaches to design novel antimicrobial peptides against intracellular bacterial infections

Intracellular bacterial infections pose a significant challenge to current therapeutic strategies due to the limited penetration of antibiotics through host cell membranes. This study presents a novel computational framework for efficiently screening candidate peptides against these infections. The proposed strategy comprehensively evaluates the essential properties for the clinical application of candidate peptides, including antimicrobial activity, permeation efficiency, and biocompatibility, while also taking into account the speed and reliability of the screening process. A combination of multiple AI-based activity prediction models allows for a thorough assessment of sequences in the cell-penetrating peptides (CPPs) database and quickly identifies candidate peptides with target properties. On this basis, the CPP microscopic dynamics research system was constructed. Exploration of the mechanism of action at the atomic level provides strong support for the discovery of promising candidate peptides. Promising candidates are subsequently validated through in vitro and in vivo experiments. Finally, Crot-1 was rapidly identified from the CPPsite 2.0 database. Crot-1 effectively eradicated intracellular MRSA, demonstrating significantly greater efficacy than vancomycin. Moreover, it exhibited no apparent cytotoxicity to host cells, highlighting its potential for clinical application. This work offers a promising new avenue for developing novel antimicrobial materials to combat intracellular bacterial infections.

Novel hepcidin genes in gilthead seabream: Implications for immune response and iron metabolism

Antimicrobial peptides (AMPs) are highly conserved small molecules present in various organisms, including fish. In gilthead seabream (Sparus aurata), one hamp1 and 15 hamp2 genes have been identified. This study aimed to characterize two novel hamp2 genes, hamp2.0 and hamp2.15, located on chromosome 17 of the gilthead seabream genome. Evolutionary analysis revealed that orthologs of both genes first appeared in the Clupeocephala clade 229 million years ago. In silico analysis predicted that the mature peptides, Hamp2α and Hamp2Ω, possess antimicrobial properties. Both peptides exhibited bactericidal activity against Vibrio harveyi, with Hamp2α showing concentration-dependent inhibition and Hamp2Ω demonstrating time-dependent inhibition. Neither peptide displayed cytotoxicity against SAF-1 cells; instead, they promoted cell proliferation. Basal expression of both genes was observed in all tissues analyzed, with the highest levels in liver and gonad. In head kidney leucocytes (HKLs), expression of both genes increased upon stimulation with lypopolysaccharide, poly I:C, nodavirus, or V. anguillarum. In vivo, hamp2.0 expression significantly increased in various tissues of V. harveyi-infected fish, while hamp2.15 expression increased in liver, spleen, head kidney, skin, and brain. In nodavirus-infected fish, hamp2.15 expression decreased in head kidney and brain. Finally, both genes showed significantly increased expression in head kidney and liver 72 h post-iron dextran injection. These findings suggest that the two novel hamp2 genes in gilthead seabream play a role in the immune response to bacterial and viral infections and may be involved in iron metabolism regulation.

Advances in antimicrobial peptides: From mechanistic insights to chemical modifications

This review provides a comprehensive analysis of antimicrobial peptides (AMPs), exploring their diverse sources, secondary structures, and unique characteristics. The review explores into the mechanisms underlying the antibacterial, immunomodulatory effects, antiviral, antiparasitic and antitumour of AMPs. Furthermore, it discusses the three principal synthesis pathways for AMPs and assesses their current clinical applications and preclinical research status. The paper also addresses the limitations of AMPs, including issues related to stability, resistance, and toxicity, while offering insights into strategies for their enhancement. Recent advancements in AMP research, such as chemical modifications (including amino acid sequence optimisation, terminal and side-chain modifications, PEGylation, conjugation with small molecules, conjugation with photosensitisers, metal ligands, polymerisation, cyclisation and specifically targeted antimicrobial peptides) are highlighted. The goal is to provide a foundation for the future design and optimisation of AMPs.

Adaptation of plateau frog peptide: From antimicrobial to angiogenic and proliferative functions

INTRODUCTION

Amphibian skin peptides, particularly defensins, play important roles in environmental adaptation, but often exhibit functional redundancy. SC17-2, a novel peptide from the high altitude frog Nanorana parkeri, exhibits unique angiogenesis and cell migration promoting activities, allowing adaptation to the extreme environment of the Tibetan Plateau with high UV radiation and low microbial diversity.

OBJECTIVES

This study aimed to investigate the adaptive role of SC17-2 in high-UV environments, its functional differences from typical defensins, and its potential biomedical applications in wound healing and angiogenesis.

METHODS

Bioinformatics analyses, including sequence alignment and ancestral reconstruction, identified positively selected amino acid sites in SC17-2. Molecular docking examined its interaction with the epidermal growth factor receptor (EGFR). In vitro and in vivo experiments, using mouse and zebrafish models, assessed its wound healing and angiogenic properties.

RESULTS

SC17-2 exhibited no antimicrobial activity, but it demonstrated antioxidant activity and potent wound healing and angiogenic properties. Molecular docking indicated that SC17-2 interacts with EGFR, potentially activating downstream signalling pathways. In vivo experiments showed that SC17-2 significantly accelerated wound healing by promoting collagen regeneration and angiogenesis, in some aspects outperforming VEGF.

CONCLUSION

SC17-2 represents a unique functional divergence in amphibian peptides, driven by ecological adaptation rather than microbial pressure. Its ability to promote angiogenesis and cell migration highlights its potential as a novel therapeutic agent for regenerative medicine, shaped by the extreme conditions of the Tibetan Plateau.

Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides

Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad-spectrum activity and minimum resistance development against the rapidly evolving antibiotic-resistant pathogens. Herein, to accelerate the discovery process of new AMPs, a predictive and generative algorithm is build, which constructs new peptide sequences, scores their antimicrobial activity using a machine learning (ML) model, identifies amino acid motifs, and assembles high-ranking motifs into new peptide sequences. The eXtreme Gradient Boosting model achieves an accuracy of ≈87% in distinguishing between AMPs and non-AMPs. The generated peptide sequences are experimentally validated against the bacterial pathogens, and an accuracy of ≈60% is achieved. To refine the algorithm, the physicochemical features are analyzed, particularly charge and hydrophobicity of experimentally validated peptides. The peptides with specific range of charge and hydrophobicity are then removed, which lead to a substantial increase in an experimental accuracy, from ≈60% to ≈80%. Furthermore, generated peptides are active against different fungal strains with minimal off-target toxicity. In summary, in silico predictive and generative models for functional motif and AMP discovery are powerful tools for engineering highly effective AMPs to combat multidrug resistant pathogens.

Membrane Selectivity Mechanisms of the Antimicrobial Peptide Snakin-Z Against Prokaryotic and Eukaryotic Membrane Models

Snakin-Z, a novel cationic antimicrobial peptide (AMP) derived from Zizyphus jujuba fruits, exhibits broad-spectrum antimicrobial activity against bacteria and fungi. Importantly, it displays minimal hemolytic activity toward human red blood cells (RBCs). Elucidating the molecular basis of membrane selectivity of Snakin-Z is essential for its development as a novel antimicrobial agent. In this study, all-atom molecular dynamics (MD) simulations were employed to provide detailed molecular insights into the interactions between Snakin-Z and bacterial, fungal, and RBC membrane models. The simulations revealed a helical-coil conformation for Snakin-Z, with its amphipathic structure, polarity, and residues such as Arg, Lys, Ser, and Tyr playing crucial roles in mediating selective interactions with the microbial membrane models. Specifically, Arg28, Lys29, and Arg3 were identified as playing a crucial role in mediating membrane binding and stability. Snakin-Z was observed to be deeply embedded in the Candida albicans and Bacillus subtilis membrane models, followed by Escherichia coli and RBC membrane models. A considerable thinning and strong disordering of Candida albicans, Bacillus subtilis and Escherichia coli membranes acyl chains were observed. The presence of cholesterol in the RBC membrane contributes to its resistance to Snakin-Z-mediated disruption. This study presents the first comprehensive investigation of the selective mechanism underlying the antimicrobial activity of Snakin-Z against bacterial membrane models. Our findings provide insights into the antimicrobial properties of Snakin-Z at the molecular level, highlighting its significant potential for use in the food and biotechnology industries as a promising alternative to conventional antibiotics and preservatives.

Enhancing Antimicrobial Peptides from Frog Skin: A Rational Approach

Antimicrobial resistance is a global health threat, which has been worsened by the slow development of new antibiotics. The rational design of natural-derived antimicrobial peptides (AMPs) offers a promising alternative for enhancing the efficacy of AMPs and accelerating drug discovery. This paper describes the rational design of improved peptide derivatives starting from hylin-Pul3, a peptide previously isolated from the frog Boana pulchella, by optimizing its hydrophobicity, cationicity, and amphipathicity. In silico screening identified six promising candidates: dHP3-31, dHP3-50, dHP3-50.137, dHP3-50.190, dHP3-84, and dHP3-84.39. These derivatives exhibited enhanced activity against Gram-negative bacteria, emphasizing the role of cationicity and the strategic arginine incorporation. Hemolytic assays revealed the derivatives' improved selectivity, particularly for the derivatives with "imperfect amphipathicity". In fibroblast assays, dHP3-84 was well-tolerated, while dHP3-84.39 promoted cell proliferation. Antioxidant assays (ABTS assays) highlighted the Trp-containing derivatives' (dHP3-50.137, dHP3-31) significant activity. The lipid membrane interaction studies showed that hylin-Pul3 disrupts membranes directly, while dHP3-84.39, dHP3-50, and dHP3-50.137 promote vesicle aggregation. Conversely, dHP3-84 did not induce membrane disruption or aggregation, suggesting an intracellular mode of action. Machine learning models were effective in predicting bioactivity, as these predicted AMPs showed enhanced selectivity and potency. Among them, dHP3-84 demonstrated broad-spectrum potential. These findings highlight the value of rational design, in silico screening, and structure-activity studies in optimizing AMPs for therapeutic applications.

Deep Learning for Antimicrobial Peptides: Computational Models and Databases

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (especially deep learning) has provided new opportunities for antimicrobial peptide prediction. Various computational models have been proposed to predict antimicrobial peptide. In this review, we focus on deep learning models for antimicrobial peptide prediction. We first collected and summarized available data resources for antimicrobial peptides. Subsequently, we summarized existing deep learning models for antimicrobial peptides and discussed their limitations and challenges. This study aims to help computational biologists design better deep learning models for antimicrobial peptide prediction.

Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides

Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty and diversity, and they are rarely applied to the generation of antifungal peptides. Here, we develop an alternative pipeline grounded in a diffusion model and molecular dynamics for the de novo design of AMPs. The peptides generated by our pipeline have lower similarity and identity than those of other reported methodologies. Among the 40 peptides synthesized for an experimental validation, 25 exhibit either antibacterial or antifungal activity. AMP-29 shows selective antifungal activity against Candida glabrata and in vivo antifungal efficacy in a murine skin infection model. AMP-24 exhibits potent in vitro activity against Gram-negative bacteria and in vivo efficacy against both skin and lung Acinetobacter baumannii infection models. The proposed approach offers a pipeline for designing diverse AMPs to counteract the threat of antibiotic resistance.

Molecular insights into the differential membrane targeting of maximin 1 in prokaryotic and eukaryotic cells

Antimicrobial resistance is a pressing global health concern, underscoring the need for alternative treatments. Antimicrobial peptides (AMPs) have shown promise in this regard, with maximin 1 being a cationic, amphipathic AMP possessing antibacterial, antifungal, and antiviral activities with low hemolytic activity. In this study, we used molecular dynamics simulation to investigate the molecular basis for membrane selectivity of Maximin 1. By studying interactions between maximin 1 and different models of prokaryotic (anionic) and eukaryotic (zwitterionic) membranes, we found that Maximin 1 interacts more strongly with the prokaryotic membrane due to electrostatic attraction, while it weakly interacts with the zwitterionic eukaryotic membrane. Our simulations also revealed that Gly-1, Lys-5, Lys-11, Lys-15, and Lys-19 were identified to play a crucial role in the adsorption of maximin unto the prokaryotic membrane surface. The alpha-helical nature of the peptide, in addition to its amphipathic nature, was necessary for the adsorption of the peptide onto the surface of the prokaryotic membrane. Interestingly, the later transition of the alpha helix into a random coil was crucial in penetrating the prokaryotic membrane while hindering interactions with the eukaryotic membrane. Residues in the middle region of the peptide (residues 9-16) were also responsible for permeating the prokaryotic membrane over the eukaryotic membrane. These findings shed light on the peptide's selective targeting of bacterial membranes over human cell membranes and could inform the design of more effective AMPs.Communicated by Ramaswamy H. Sarma.

Advances in Artificially Designed Antibacterial Active Antimicrobial Peptides

Antibacterial resistance has emerged as a significant global concern, necessitating the urgent development of new antibacterial drugs. Antimicrobial peptides (AMPs) are naturally occurring peptides found in various organisms. Coupled with a wide range of antibacterial activity, AMPs are less likely to develop drug resistance and can act as potential agents for treating bacterial infections. However, their characteristics, such as low activity, instability, and toxicity, hinder their clinical application. Consequently, researchers are inclined towards artificial design and optimization based on natural AMPs. This review discusses the research advancements in the field of artificial designing and optimization of various AMPs. Moreover, it highlights various strategies for designing such peptides, aiming to provide valuable insights for developing novel AMPs.

Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics

Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as corals, sea anemones, and jellyfish, are a promising valuable resource of these bioactive peptides due to their robust innate immune systems yet are still poorly explored. Hence, we employed an in silico proteolysis strategy to search for novel AMPs from omics data of 111 Cnidaria species. Millions of peptides were retrieved and screened using shallow- and deep-learning models, prioritizing AMPs with a reduced toxicity and with a structural distinctiveness from characterized AMPs. After complex network analysis, a final dataset of 3130 Cnidaria singular non-haemolytic and non-toxic AMPs were identified. Such unique AMPs were mined for their putative antibacterial activity, revealing 20 favourable candidates for in vitro testing against important ESKAPEE pathogens, offering potential new avenues for antibiotic development.

Characterization of Novel Plantaricin-Derived Antiviral Peptides Against Flaviviruses

Flaviviruses, including West Nile virus, Zika virus, and Dengue virus, pose global health challenges due to their distribution, pathogenicity, and lack of effective treatments or vaccines. This study investigated the antiviral activity of novel truncated peptides derived from the two-peptide plantaricins PLNC8 αβ, PlnEF, PlnJK, and PlnA. The antiviral potential was predicted using machine learning tools, followed by in vitro evaluation against the Kunjin virus using plaque reduction assays in Vero cells. Molecular docking assessed peptide interactions with KUNV and ZIKV. Full-length and truncated peptides from PlnA, PlnE, PlnF, PlnJ, and PlnK demonstrated limited antiviral efficacy against KUNV in vitro, despite in silico predictions suggesting antiviral potential for PlnA, PlnE, and PlnJ. Large discrepancies were observed between the predicted and experimentally determined activities. However, complementary two-peptide plantaricins PlnEF and PlnJK exhibited significant synergistic effects. Furthermore, the truncated peptides PLNC8 α1-15 and PLNC8 β1-20 reduced KUNV viral load by over 90%, outperforming their full-length counterparts. Molecular docking revealed interactions of PLNC8 α and PLNC8 β, and their truncated variants, with KUNV and ZIKV, suggesting a mechanism involving viral envelope disruption. These findings highlight the potential of plantaricin-derived peptides as promising antiviral candidates against flaviviruses, warranting further investigation into their mechanisms and applications.

Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools

BACKGROUND

Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are highly active, non-hemolytic, and highly soluble. Machine learning tools allow the straightforward in silico identification of non-hemolytic antimicrobial peptides.

METHODS

Here, we utilized a number of these tools to rank the best peptides from two libraries comprised of: 1) a total of 8192 peptides with sequence bhxxbhbGAL, where b is the basic amino acid R or K, h is a hydrophobic amino acid, i.e. G, A, L, F, I, V, Y, or W and x is Q, S, A, or V; and 2) a total of 512 peptides with sequence RWhxbhRGWL, where b and h are as for the first library and x is Q, S, A, or G. The top 100 sequences from each library, as well as 10 peptides predicted to be active but hemolytic (for a total of 220 peptides), were SPOT synthesized and their IC50 values were determined against S. aureus USA 300 (MRSA).

RESULTS

Of these, 6 AMPs with low IC50's were characterized further in terms of: MICs against MRSA, E. faecalis, K. pneumoniae, E.coli and P. aeruginosa; RBC lysis; secondary structure in mammalian and bacterial model membranes; and activity against cancer cell lines HepG2, CHO, and PC-3.

CONCLUSION

Overall, the approach yielded a large family of active antimicrobial peptides with high solubility and low red blood cell toxicity. It also provides a framework for future designs and improved machine learning tools.

Screening and identification of antimicrobial peptides from the gut microbiome of cockroach Blattella germanica

BACKGROUND

The overuse of antibiotics has led to lethal multi-antibiotic-resistant microorganisms around the globe, with restricted availability of novel antibiotics. Compared to conventional antibiotics, evolutionarily originated antimicrobial peptides (AMPs) are promising alternatives to address these issues. The gut microbiome of Blattella germanica represents a previously untapped resource of naturally evolving AMPs for developing antimicrobial agents.

RESULTS

Using the in-house designed tool "AMPidentifier," AMP candidates were mined from the gut microbiome of B. germanica, and their activities were validated both in vitro and in vivo. Among filtered candidates, AMP1, derived from the symbiotic microorganism Blattabacterium cuenoti, demonstrated broad-spectrum antibacterial activity, low cytotoxicity towards mammalian cells, and a lack of hemolytic effects. Mechanistic studies revealed that AMP1 rapidly permeates the bacterial cell and accumulates intracellularly, resulting in a gradual and mild depolarization of the cell membrane during the initial incubation period, suggesting minimal direct impact on membrane integrity. Furthermore, observations from fluorescence microscopy and scanning electron microscopy indicated abnormalities in bacterial binary fission and compromised cell structure. These findings led to the hypothesis that AMP1 may inhibit bacterial cell wall synthesis. Furthermore, AMP1 showed potent antibacterial and wound healing effects in mice, with comparable performances of vancomycin.

CONCLUSIONS

This study exemplifies an interdisciplinary approach to screening safe and effective AMPs from natural biological tissues, and our identified AMP 1 holds promising potential for clinical application.

Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery

Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are even better at eliminating infections. The fundamental transformation in a variety of scientific disciplines, which led to the emergence of machine learning techniques, has presented significant opportunities for the development of antimicrobial peptides. Machine learning and deep learning are used to predict antimicrobial peptide efficacy in the study. The main purpose is to overcome traditional experimental method constraints. Gram-negative bacterium Escherichia coli is the model organism in this study. The investigation assesses 1,360 peptide sequences that exhibit anti- E. coli activity. These peptides' minimal inhibitory concentrations have been observed to be correlated with a set of 34 physicochemical characteristics. Two distinct methodologies are implemented. The initial method involves utilizing the pre-computed physicochemical attributes of peptides as the fundamental input data for a machine-learning classification approach. In the second method, these fundamental peptide features are converted into signal images, which are then transmitted to a deep learning neural network. The first and second methods have accuracy of 74% and 92.9%, respectively. The proposed methods were developed to target a single microorganism (gram negative E.coli), however, they offered a framework that could potentially be adapted for other types of antimicrobial, antiviral, and anticancer peptides with further validation. Furthermore, they have the potential to result in significant time and cost reductions, as well as the development of innovative AMP-based treatments. This research contributes to the advancement of deep learning-based AMP drug discovery methodologies by generating potent peptides for drug development and application. This discovery has significant implications for the processing of biological data and the computation of pharmacology.

Host defense peptides in crocodilians - A comprehensive review

Amphibians and reptiles, like all animals, are prone to periodic infections. However, crocodilians stand out for their remarkable ability to remain generally healthy and infection-free despite frequent exposure to a wide variety of microorganisms in their habitats and often sustaining significant injuries. These animals have evolved highly active immune mechanisms that provide rapid and effective defense. This is evidenced by the superior hemolytic capacity of their plasma compared to that of other organisms. To date, several host defense peptides (HDPs) have been identified in crocodilians, including cathelicidins, beta-defensins, hepcidins, leucrocins, hemocidins, and omwaprins. These peptides exhibit potent and broad-spectrum antimicrobial, antibiofilm, antifungal, and anticancer activities. Due to the relatively low but diverse evolutionary rate of crocodilians, the HDPs found in this species offer valuable insights into proteins and mechanisms of action that are highly conserved across many animals related to immune defense. The potential applications of HDPs in modern medicine represent a promising strategy for developing new therapeutic agents. Their novelty and the vast variability with which peptide sequences can be designed and modified expand the field of application for HDPs almost infinitely. This review addresses the urgent need for innovative and more effective drugs to combat the rise of antimicrobialresistant infections and evaluates the potential of crocodilian HDPs. It presents recent advances in the identification of crocodilian HDPs, particularly antimicrobial peptides (AMPs), including previously underexplored topics such as the sequential and structural conformation of different peptide types in crocodilians and the use of bioinformatics tools to enhance native peptides.

Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and the expansion of effective medicinal compounds. The novel functional properties of peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals and biotechnology advance drug development rapidly and reduce costs. Short-chain peptides inhibit a wide range of pathogens and have great potential for targeting diseases. To address the challenges of synthesis and sustainability, artificial intelligence methods, namely machine learning, must be integrated into their production. Learning methods can use complicated computations to select the active and toxic compounds of the drug and its metabolic activity. Through this comprehensive review, we investigated the artificial intelligence method as a potential tool for finding peptide-based drugs and providing a more accurate analysis of peptides through the introduction of predictable databases for effective selection and development.

Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation

Recent technological advancements, including computer-assisted drug discovery, gene-editing techniques, and high-throughput screening approaches, have greatly expanded the palette of methods for the discovery of peptides available to researchers. These emerging strategies, driven by recent advances in bioinformatics and multi-omics, have significantly improved the efficiency of peptide drug discovery when compared with traditional in vitro and in vivo methods, cutting costs and improving their reliability. An added benefit of peptide-based drugs is the ability to precisely target protein-protein interactions, which are normally a particularly challenging aspect of drug discovery. Another recent breakthrough in this field is targeted protein degradation through proteolysis-targeting chimeras. These revolutionary compounds represent a noteworthy advancement over traditional small-molecule inhibitors due to their unique mechanism of action, which allows for the degradation of specific proteins with unprecedented specificity. The inclusion of a peptide as a protein-of-interest-targeting moiety allows for improved versatility and the possibility of targeting otherwise undruggable proteins. In this review, we discuss various novel wet-lab and computational multi-omic methods for peptide drug discovery, provide an overview of therapeutic agents discovered through these cutting-edge techniques, and discuss the potential for the therapeutic delivery of peptide-based drugs.

Integrated computational approaches for advancing antimicrobial peptide development

The increasing prevalence of antimicrobial resistance has intensified the need for novel antimicrobial drugs. Antimicrobial peptides (AMPs) are promising alternative antibiotics due to their broad-spectrum activity and slower resistance development. However, the time-consuming, costly development and challenge of systematic optimization limit their translation into the clinic. Recently, integrating computational methods have led to breakthroughs in the precise design and optimization of AMPs, reduced resource consumption, and accelerated AMP development process. We highlight the application of these integrated approaches in AMP molecule discovery, optimization, and delivery and demonstrate the synergy of these strategies to fuel AMP development.

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/.

Screening and heterologous expression of an antimicrobial peptide SCAK33 with broad-spectrum antimicrobial activity resourced from sea cucumber proteome

Antimicrobial peptides (AMPs) are a family of short defense proteins that are naturally produced by all organisms and have great potential as effective substitutes for small-molecule antibiotics. The present study aims to excavate AMPs from sea cucumbers and achieve their heterologous expression in prokaryotic Escherichia coli. Using MytC as a probe, a cysteine-stabilized peptide SCAK33 with broad-spectrum antimicrobial activity was discovered from the proteome of Apostichopus japonicas. The SCAK33 showed inhibitory effects on both gram positive and gram negative bacteria with MICs of 3-28 μM, and without significant hemolysis activity in rat blood erythrocyte. Especially, it exhibited good antimicrobial activity against Bacillus megaterium, B. subtilis, and Vibrio parahaemolyticus with the MIC of 3, 7, and 7 μM, respectively. After observation by scanning electronic microscopy (SEM) and confocal laser scanning microscope (CLSM), it was found that the cell membrane of bacteria was severely damaged. Furthermore, the recombinant SCAK33 (reSCAK33) was heterologously expressed by fusion with SUMO tag in E. coli BL21(DE3), and the protein yield reached 70 mg/L. The research will supplement the existing quantity of sea cucumber AMPs and provide data support for rapid mining and biological preparation of sea cucumber AMPs.

Venom: A Promising Avenue for Antimicrobial Therapeutics

Venom in medicine is well documented in the chronicles of ancient Greece and the Roman Empire and persisted into the Renaissance and even into the modern era. Venoms were not always associated with detrimental consequences. Since ancient times, the curative capacity of venom has been recognized, portraying venom as a metaphor for pharmacy and medicine. Venom proteins and peptides' antimicrobial potential has not undergone systematic exploration despite the huge literature on natural antimicrobials. In light of the escalating challenge of antimicrobial resistance and the diminishing effectiveness of antibiotics, there is a pressing need for innovative antimicrobials capable of effectively addressing illnesses caused by multidrug-resistant microorganisms. This review adds to our understanding of the effectiveness of different venom components against a host of pathogenic microorganisms. The aim is to illuminate the various antimicrobials present in venom and venom peptides, thereby emphasizing the unexplored medicinal potential for antimicrobial properties. We have presented a concise summary of the molecular examination of the venom peptides' functioning processes, as well as the current clinical and preclinical progress of venom antimicrobial peptides.

Potential therapeutic use of dermaseptin S4 from the frog Phyllomedusa sauvagii and its derivatives against bacterial pathogens in fish

AIM

Dermaseptins are one of the main families of antimicrobial peptides (AMPs) derived from the skin secretions of Hylidae frogs. Among them, dermaseptin S4 (DS4) is characterized by its broad-spectrum of activity against bacteria, protozoa, and fungi. In this study, the physicochemical properties of the native peptide DS4 (1-28) and two derivatives [DS4 (1-28)a and DS4 (1-26)a] isolated from the skin of the frog Phyllomedusa sauvagii were investigated and their antimicrobial properties against two marine pathogenic bacteria (Vibrio harveyi and Vibrio anguillarum) were examined.

METHODS AND RESULTS

The results indicate that the peptide DS4 (1-26)a has high-antibacterial activity against the tested strains and low-hemolytic activity (<30% lysis at the highest tested concentration of 100 µg/mL) compared to the other two peptides tested. In addition, all three peptides affect the membrane and cell wall integrity of both pathogenic bacteria, causing leakage of cell contents, with DS4 (1-26)a having the most severe impact. These skills were corroborated by transmission electron microscopy and by the variation of cations in their binding sites due to the effects caused by the AMPs.

CONCLUSIONS

These results suggest that DS4 and its derivatives, in particular the truncated and amidated peptide DS4 (1-26)a could be effective in the treatment of infections caused by these marine pathogenic bacteria. Future studies are required to validate the use of DS4  in vivo for the prevention of bacterial diseases in fish.

Exploring pathological link between antimicrobial and amyloid peptides

Amyloid peptides (AMYs) and antimicrobial peptides (AMPs) are considered as the two distinct families of peptides, characterized by their unique sequences, structures, biological functions, and specific pathological targets. However, accumulating evidence has revealed intriguing pathological connections between these peptide families in the context of microbial infection and neurodegenerative diseases. Some AMYs and AMPs share certain structural and functional characteristics, including the ability to self-assemble, the presence of β-sheet-rich structures, and membrane-disrupting mechanisms. These shared features enable AMYs to possess antimicrobial activity and AMPs to acquire amyloidogenic properties. Despite limited studies on AMYs-AMPs systems, the cross-seeding phenomenon between AMYs and AMPs has emerged as a crucial factor in the bidirectional communication between the pathogenesis of neurodegenerative diseases and host defense against microbial infections. In this review, we examine recent developments in the potential interplay between AMYs and AMPs, as well as their pathological implications for both infectious and neurodegenerative diseases. By discussing the current progress and challenges in this emerging field, this account aims to inspire further research and investments to enhance our understanding of the intricate molecular crosstalk between AMYs and AMPs. This knowledge holds great promise for the development of innovative therapies to combat both microbial infections and neurodegenerative disorders.

Important structural features of antimicrobial peptides towards specific activity: Trends in the development of efficient therapeutics

Proteins and peptides, as polypeptide chains, have usually got unique conformational structures for effective biological activity. Antimicrobial peptides (AMPs) are a group of bioactive peptides, which have been increasingly studied during recent years for their promising antibacterial, antifungal, antiviral and anti-inflammatory activity, as well as, other esteemed bioactivities. Numerous AMPs have been separated from a wide range of natural resources, or produced in vitro through chemical synthesis and recombinant protein expression. Natural AMPs have had limited clinical application due to several drawbacks, such as their short half-life due to protease degradation, lack of activity at physiological salt concentrations, toxicity to mammalian cells, and the absence of suitable methods of delivery for the AMPs that are targeted and sustained. The creation of synthetic analogs of AMPs would both avoid the drawbacks of the natural analogs and maintain or even increase the antimicrobial effectiveness. The structure-activity relationship of discovered AMPs or their derivatives facilitates the development of synthetic AMPs. This review discovered that the relationship between the activity of AMPs and their positive net charge, hydrophobicity, and amino acid sequence and the relationship between AMPs' function and other features like their topology, glycosylation, and halogenation.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Blap-6, a Novel Antifungal Peptide from the Chinese Medicinal Beetle Blaps rhynchopetera against Cryptococcus neoformans

Cryptococcus neoformans (C. neoformans) is a pathogenic fungus that can cause life-threatening meningitis, particularly in individuals with compromised immune systems. The current standard treatment involves the combination of amphotericin B and azole drugs, but this regimen often leads to inevitable toxicity in patients. Therefore, there is an urgent need to develop new antifungal drugs with improved safety profiles. We screened antimicrobial peptides from the hemolymph transcriptome of Blaps rhynchopetera (B. rhynchopetera), a folk Chinese medicine. We found an antimicrobial peptide named blap-6 that exhibited potent activity against bacteria and fungi. Blap-6 is composed of 17 amino acids (KRCRFRIYRWGFPRRRF), and it has excellent antifungal activity against C. neoformans, with a minimum inhibitory concentration (MIC) of 0.81 μM. Blap-6 exhibits strong antifungal kinetic characteristics. Mechanistic studies revealed that blap-6 exerts its antifungal activity by penetrating and disrupting the integrity of the fungal cell membrane. In addition to its direct antifungal effect, blap-6 showed strong biofilm inhibition and scavenging activity. Notably, the peptide exhibited low hemolytic and cytotoxicity to human cells and may be a potential candidate antimicrobial drug for fungal infection caused by C. neoformans.

Vesicle protrusion induced by antimicrobial peptides suggests common carpet mechanism for short antimicrobial peptides

Short-cationic alpha-helical antimicrobial peptides (SCHAMPs) are promising candidates to combat the growing global threat of antimicrobial resistance. They are short-sequenced, selective against bacteria, and have rapid action by destroying membranes. A full understanding of their mechanism of action will provide key information to design more potent and selective SCHAMPs. Molecular Dynamics (MD) simulations are invaluable tools that provide detailed insights into the peptide-membrane interaction at the atomic- and meso-scale level. We use atomistic and coarse-grained MD to look into the exact steps that four promising SCHAMPs-BP100, Decoralin, Neurokinin-1, and Temporin L-take when they interact with membranes. Following experimental set-ups, we explored the effects of SCHAMPs on anionic membranes and vesicles at multiple peptide concentrations. Our results showed all four peptides shared similar binding steps, initially binding to the membrane through electrostatic interactions and then flipping on their axes, dehydrating, and inserting their hydrophobic moieties into the membrane core. At higher concentrations, fully alpha-helical peptides induced membrane budding and protrusions. Our results suggest the carpet mode of action is fit for the description of SCHAMPs lysis activity and discuss the importance of large hydrophobic residues in SCHAMPs design and activity.

Structure of the Bacterial Cell Envelope and Interactions with Antimicrobials: Insights from Molecular Dynamics Simulations

Bacteria have evolved over 3 billion years, shaping our intrinsic and symbiotic coexistence with these single-celled organisms. With rising populations of drug-resistant strains, the search for novel antimicrobials is an ongoing area of research. Advances in high-performance computing platforms have led to a variety of molecular dynamics simulation strategies to study the interactions of antimicrobial molecules with different compartments of the bacterial cell envelope of both Gram-positive and Gram-negative species. In this review, we begin with a detailed description of the structural aspects of the bacterial cell envelope. Simulations concerned with the transport and associated free energy of small molecules and ions through the outer membrane, peptidoglycan, inner membrane and outer membrane porins are discussed. Since surfactants are widely used as antimicrobials, a section is devoted to the interactions of surfactants with the cell wall and inner membranes. The review ends with a discussion on antimicrobial peptides and the insights gained from the molecular simulations on the free energy of translocation. Challenges involved in developing accurate molecular models and coarse-grained strategies that provide a trade-off between atomic details with a gain in sampling time are highlighted. The need for efficient sampling strategies to obtain accurate free energies of translocation is also discussed. Molecular dynamics simulations have evolved as a powerful tool that can potentially be used to design and develop novel antimicrobials and strategies to effectively treat bacterial infections.

Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications

Pathogenic bacteria cause the deaths of millions of people every year. With the development of antibiotics, hundreds and thousands of people's lives have been saved. Nevertheless, bacteria can develop resistance to antibiotics, rendering them insensitive to antibiotics over time. Peptides containing specific amino acids can be used as antibacterial agents; however, they can be easily degraded by proteases in vivo. To address these issues, branched peptide dendrimers are now being considered as good antibacterial agents due to their high efficacy, resistance to protease degradation, and low cytotoxicity. The ease with which peptide dendrimers can be synthesized and modified makes them accessible for use in various biological and nonbiological fields. That is, peptide dendrimers hold a promising future as antibacterial agents with prolonged efficacy without bacterial resistance development. Their in vivo stability and multivalence allow them to effectively target multi-drug-resistant strains and prevent biofilm formation. Thus, it is interesting to have an overview of the development and applications of peptide dendrimers in antibacterial research, including the possibility of employing machine learning approaches for the design of AMPs and dendrimers. This review summarizes the synthesis and applications of peptide dendrimers as antibacterial agents. The challenges and perspectives of using peptide dendrimers as the antibacterial agents are also discussed.

Antimicrobial peptides: An alternative to traditional antibiotics

As antibiotic-resistant bacteria and genes continue to emerge, the identification of effective alternatives to traditional antibiotics has become a pressing issue. Antimicrobial peptides are favored for their safety, low residue, and low resistance properties, and their unique antimicrobial mechanisms show significant potential in combating antibiotic resistance. However, the high production cost and weak activity of antimicrobial peptides limit their application. Moreover, traditional laboratory methods for identifying and designing new antimicrobial peptides are time-consuming and labor-intensive, hindering their development. Currently, novel technologies, such as artificial intelligence (AI) are being employed to develop and design new antimicrobial peptide resources, offering new opportunities for the advancement of antimicrobial peptides. This article summarizes the basic characteristics and antimicrobial mechanisms of antimicrobial peptides, as well as their advantages and limitations, and explores the application of AI in antimicrobial peptides prediction amd design. This highlights the crucial role of AI in enhancing the efficiency of antimicrobial peptide research and provides a reference for antimicrobial drug development.

Synergistic interaction of polymyxin B with carvacrol: antimicrobial strategy against polymyxin-resistant Klebsiella pneumoniae

Objective: The antimicrobial activities of the synergistic combination of carvacrol and polymyxin B against polymyxin-resistant Klebsiella pneumoniae were evaluated. Methods: The methods employed checkerboard assays to investigate synergism, biofilm inhibition assessment and membrane integrity assay. In addition, the study included in vivo evaluation using a mouse infection model. Results: The checkerboard method evaluated 48 combinations, with 23 indicating synergistic action. Among these, carvacrol 10 mg/kg plus polymyxin B 2 mg/kg exhibited in vivo antimicrobial activity in a mouse model of infection, resulting in increased survival and a significant decrease in bacterial load in the blood. Conclusion: Polymyxin in synergy with carvacrol represents a promising alternative to be explored in the development of new antimicrobials.

Structure, Function, and Physicochemical Properties of Pore-forming Antimicrobial Peptides

Antimicrobial peptides (AMPs), a class of antimicrobial agents, possess considerable potential to treat various microbial ailments. The broad range of activity and rare complete bacterial resistance to AMPs make them ideal candidates for commercial development. These peptides with widely varying compositions and sources share recurrent structural and functional features in mechanisms of action. Studying the mechanisms of AMP activity against bacteria may lead to the development of new antimicrobial agents that are more potent. Generally, AMPs are effective against bacteria by forming pores or disrupting membrane barriers. The important structural aspects of cytoplasmic membranes of pathogens and host cells will also be outlined to understand the selective antimicrobial actions. The antimicrobial activities of AMPs are related to multiple physicochemical properties, such as length, sequence, helicity, charge, hydrophobicity, amphipathicity, polar angle, and also self-association. These parameters are interrelated and need to be considered in combination. So, gathering the most relevant available information will help to design and choose the most effective AMPs.

Peptide Power: Mechanistic Insights into the Effect of Mitochondria-Targeted Tetrapeptides on Membrane Electrostatics from Molecular Simulations

Mitochondrial dysfunction is implicated in nine of the ten leading causes of death in the US, yet there are no FDA-approved therapeutics to treat it. Synthetic mitochondria-targeted peptides (MTPs), including the lead compound SS-31, offer promise, as they have been shown to restore healthy mitochondrial function and treat a variety of common diseases. At the cellular level, research has shown that MTPs accumulate strongly at the inner mitochondrial membrane (IMM), slow energy sinks (e.g., proton leaks), and improve ATP production. Modulation of electrostatic fields around the IMM has been implicated as a key aspect in the mechanism of action (MoA) of these peptides; however, molecular and mechanistic details have remained elusive. In this study, we employed all-atom molecular dynamics simulations (MD) to investigate the interactions of four MTPs with lipid bilayers and calculate their effect on structural and electrostatic properties. In agreement with previous experimental findings, we observed the modulation of the membrane surface and dipole potentials by MTPs. The simulations reveal that the MTPs achieve a reduction in the dipole potential by acting to disorder both lipid head groups and water layers proximal to the bilayer surface. We also find that MTPs decrease the bilayer thickness and increase the membrane's capacitance. These changes suggest that MTPs may enhance how much potential energy can be stored across the IMM at a given transmembrane potential difference. The MTPs also displace cations away from the bilayer surface, modulating the surface potential and offering an alternative mechanism for how these MTPs reduce mitochondrial energy sinks like proton leaks and mitigate Ca2+ accumulation stress. In conclusion, this study highlights the therapeutic potential of MTPs and underlines how interactions of MTPs with lipid bilayers serve as a fundamental component of their MoA.

Artificial intelligence-driven antimicrobial peptide discovery

Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.

Progress towards the clinical use of antimicrobial peptides: challenges and opportunities

INTRODUCTION

To overcome the challenge of multidrug resistance, natural and synthetic peptides are candidates to become the basis of innovative therapeutics, featuring diverse mechanisms of action. Traditionally, the time elapsed from medical discoveries to their application is long. The urgency derived from the emergence of antibiotic resistance recommends an acceleration of research to put the new weapons in the hands of clinicians.

AREAS COVERED

This narrative review introduces ideas and suggestions of new strategies that may be used as a basis upon which to recommend reduced development times and to facilitate the arrival of new molecules in the fight against microbes.

EXPERT OPINION

Although studies on new innovative antimicrobial treatments are being conducted, sooner rather than later, more clinical trials, preclinical and translational research are needed to promote the development of innovative antimicrobial treatments for multidrug resistant infections. The situation is worrying, no less than that generated by pandemics such as the ones we have just experienced and conflicts such as world wars. Although from the point of view of human perception, resistance to antibiotics may not seem as serious as these other situations, it is possibly the hidden pandemic that most jeopardizes the future of medicine.

No country for old antibiotics! Antimicrobial peptides (AMPs) as next-generation treatment for skin and soft tissue infection

In recent years, the unprecedented rise of bacterial antibiotic resistance together with the lack of adequate therapies have made the treatment of skin infections and chronic wounds challenging, urging the scientific community to focus on the development of new and more efficient treatment strategies. In this context, there is a growing interest in the use of natural molecules with antimicrobial features, capable of supporting wound healing i.e., antimicrobial peptides (AMPs), for the treatment of skin and soft tissue infections. In this review, we give a short overview of the bacterial skin infections as well as some of the classic treatments used for topical application. We then summarize the AMPs classes, stressing the importance of the appropriate selection of the peptides based on their characteristics and physicochemical properties in order to maximize the antibacterial efficacy of the therapeutic systems against multi-drug resistant pathogens. Additionally, the present paper provides a comprehensive and rigorous assessment of the latest clinical trials investigating the efficacy of AMPs in the treatment of skin and soft tissue infections, highlighting the relevant outcomes. Seeking to obtain novel and improved compounds with synergistic activity, while also decreasing some of the known side effects of AMPs, we present two employed strategies using AMPs: (i) AMPs-conjugated nanosystems for systemic and topical drug delivery systems and (ii) antibiotics-peptide conjugates as a strategy to overcome antibiotics resistance. Finally, an important property of some of the AMPs used in wound treatment is highlighted: their ability to help in wound healing by generally promoting cell proliferation and migration, and in some cases re-epithelialization and angiogenesis among others. Thus, as the pursuit of improvement is an ongoing effort, this work presents the advances made in the treatment of skin and soft tissue infections along with their advantages and limitations, while the still remaining challenges are addressed by providing future prospects and strategies to overcome them.

Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.

Relating Molecular Dynamics Simulations to Functional Activity for Gly-Rich Membranolytic Helical Kiadin Peptides

Kiadins are in silico designed peptides with a strong similarity to diPGLa-H, a tandem sequence of PGLa-H (KIAKVALKAL) and with single, double or quadruple glycine substitutions. They were found to show high variability in their activity and selectivity against Gram-negative and Gram-positive bacteria, as well as cytotoxicity against host cells, which are influenced by the number and placing of glycine residues along the sequence. The conformational flexibility introduced by these substitutions contributes differently peptide structuring and to their interactions with the model membranes, as observed by molecular dynamics simulations. We relate these results to experimentally determined data on the structure of kiadins and their interactions with liposomes having a phospholipid membrane composition similar to simulation membrane models, as well as to their antibacterial and cytotoxic activities, and also discuss the challenges in interpreting these multiscale experiments and understanding why the presence of glycine residues in the sequence affected the antibacterial potency and toxicity towards host cells in a different manner.

Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.

CHARMM-GUI Membrane Builder: Past, Current, and Future Developments and Applications

Molecular dynamics simulations of membranes and membrane proteins serve as computational microscopes, revealing coordinated events at the membrane interface. As G protein-coupled receptors, ion channels, transporters, and membrane-bound enzymes are important drug targets, understanding their drug binding and action mechanisms in a realistic membrane becomes critical. Advances in materials science and physical chemistry further demand an atomistic understanding of lipid domains and interactions between materials and membranes. Despite a wide range of membrane simulation studies, generating a complex membrane assembly remains challenging. Here, we review the capability of CHARMM-GUI Membrane Builder in the context of emerging research demands, as well as the application examples from the CHARMM-GUI user community, including membrane biophysics, membrane protein drug-binding and dynamics, protein-lipid interactions, and nano-bio interface. We also provide our perspective on future Membrane Builder development.

Krein support vector machine classification of antimicrobial peptides

Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Kreĭn-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Kreĭn-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.

Advances of Antimicrobial Peptide-Based Biomaterials for the Treatment of Bacterial Infections

Owing to the increase in multidrug-resistant bacterial isolates in hospitals globally and the lack of truly effective antimicrobial agents, antibiotic resistant bacterial infections have increased substantially. There is thus an urgent need to develop new antimicrobial drugs and their related formulations. In recent years, natural antimicrobial peptides (AMPs), AMP optimization, self-assembled AMPs, AMP hydrogels, and biomaterial-assisted delivery of AMPs have shown great potential in the treatment of bacterial infections. In this review, it is focused on the development prospects and shortcomings of various AMP-based biomaterials for treating animal model infections, such as abdominal, skin, and eye infections. It is hoped that this review will inspire further innovations in the design of AMP-based biomaterials for the treatment of bacterial infections and accelerate their commercialization.

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods

There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.

Helminthic host defense peptides: using the parasite to defend the host

Parasitic helminths are destined to share niches with a variety of microbiota that inevitably influence their interaction with the host. To modulate the microbiome for their benefit and defend against pathogenic isolates, helminths have developed host defense peptides (HDPs) and proteins as integral elements of their immunity. These often exert a relatively nonspecific membranolytic activity toward bacteria, sometimes with limited or no toxicity toward host cells. With a few exceptions, such as nematode cecropin-like peptides and antibacterial factors (ABFs), helminthic HDPs are largely underexplored. This review scrutinizes current knowledge on the repertoire of such peptides in helminths and promotes their research as potential leads for an anti-infective solution to the burgeoning problem of antibiotic resistance.

Peptidomics analysis of Jiang-Flavor Daqu from high-temperature fermentation to mature and in different preparation season

Jiang-Flavor Daqu (JFDQ) is a grain-type fermented starter for brewing Chinese liquor. Peptides, the metabolites of proteins in JFDQ, are important for the quality and flavor of JFDQ or even the liquor. The peptide variations in the progress of JFDQ preparation were investigated using RPLC-MS/MS. The JFDQ after high-temperature fermenting (HTF_SU) and after ripening (M_SU), as well as the mature JFDQ prepared in spring (M_SP) and in summer (M_SU), were compared respectively. These two groups were investigated from peptides, precursor proteins, abundance, interactions, and potential antimicrobial peptides (pAMPs). A total of 177, 158, and 262 peptides from HTF_SU, M_SP, and M_SU were identified, respectively. Significant differences (P < 0.01) in the abundance of shared peptides were found in different fermentation stage group (HTF_M), and stronger positive correlations were observed in different preparation season group (MSP_MSU). The interactions of the shared peptides in HTF_M and in MSP_MSU were investigated respectively. In addition, 8 pAMPs in HTF_SU, 5 in M_SP, and 22 in M_SU were predicted using CAMPR3, and their core functional regions were analyzed. This systematic study demonstrated the influences of fermentation stage and preparation season on the peptide profiles in JFDQ, which would provide theoretical guidance and be helpful for JFDQ production. SIGNIFICANCE: Peptidomics analysis showed that the peptide profiles of JFDQ varied in different fermentation stages and different preparation seasons, which mainly resulted from the peptides with high abundance, high interaction degrees, and potential antimicrobial activity, as well as the important precursor proteins such as glutens. This systematic study would benefit for the insufficiency of peptide research of JFDQ till now, and provide theoretical guidance for JFDQ production.

Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation

The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides. To elucidate relevant features of the signal peptide sequence that influence secretion efficiency, an evaluation of ∼12,000 different designed signal peptides was performed using a novel miniaturized high-throughput assay. The results were used to train a machine learning model, and a post-hoc explanation of the model is provided. By describing each signal peptide with a selection of 156 physicochemical features, it is now possible to both quantify feature importance and predict the protein secretion levels directed by each signal peptide. Our analyses allow the detection and explanation of the relevant signal peptide features influencing the efficiency of protein secretion, generating a versatile tool for the de novo design and in silico evaluation of signal peptides.

Application of a deep generative model produces novel and diverse functional peptides against microbial resistance

Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic's world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool.

Molecular dynamics simulations to study the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides

The hydrophobic amino acid biphenylalanine (B) plays a key role in the antibacterial activity of ultrashort peptides. In this study, the interactions of tetrapeptide BRBR-NH₂ (BRBR) and pentapeptide BRBRB-NH₂ (BRBRB) with dioleoylphosphatidylcholine/dioleoylphosphatidylglycerol (DOPC/DOPG) mixed model membrane were studied by molecular dynamics simulation to assess the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides. At low peptide concentrations, both peptides presented amphiphilic conformations; residues B of the pentapeptide approached the membrane faster than those of the tetrapeptide and made more contacts with the membrane; BRBRB exhibited stronger membrane affinity than BRBR. However, due to the low peptide concentrations, the effects of these two peptides on the membrane were not significantly different. At high peptide concentrations, the strong affinity of BRBRB made it have more interaction with membrane than BRBR and most residues B of BRBRB inserted into the membrane; BRBRB was more prone to aggregation and caused the membrane more disordered and thinner than BRBR. Hydrophobic residues often act as anchors in the antibacterial activity of ultrashort antimicrobial peptides. Adding a hydrophobic residue B to the C-terminal of BRBR could improve the ability of the peptide to "grasp" the membrane. At high peptide concentrations, the addition of residue B might enhance the antibacterial activity of the peptide. Thus, our results will be helpful in designing efficient antibacterial drugs.