In pursuit of next-generation therapeutics: Antimicrobial peptides against superbugs, their sources, mechanism of action, nanotechnology-based delivery, and clinical applications

Antimicrobial Peptide CFH3 from Human Saliva: A Novel Inhibitor of Staphylococcus hemolyticus and Its Application in Chewing Gum

Staphylococcus hemolyticus is a primary pathogen in oral and maxillofacial space infections, against which antimicrobial peptides in human oral saliva may offer protection. In this study, an antimicrobial peptide, CFH3 (RPYFPVAVGK), derived from human complement factor H of saliva, was investigated through bioinformatics analysis. CFH3 exhibited a minimum bactericidal concentration (MBC) of 7.8 μg/mL against S. hemolyticus, eliminating bacteria at 1 × MBC by targeting intracellular DNA and proteins without cell membrane disruption. At 2 × MBC, CFH3 induced cell membrane disruption and leakage of cellular contents. CFH3 exhibited high biosafety, demonstrating low toxicity to human liver cells (LO2) with a 100% viability rate at 2 × MBC. Besides, its hemolysis rate remained below 5% even at 8 × MBC. A chewing gum containing 0.02% CFH3 was successfully formulated, releasing 62.53 ± 0.76% (312.64 ± 3.79 μg/mL) of the compound within 20 min of chewing. Furthermore, in artificial saliva, 125 μg/mL CFH3 significantly inhibited S. hemolyticus. Overall, these findings provide valuable insights into the functional role of peptides derived from oral saliva and their potential application in oral health and the food industry.

Leveraging protein language models for robust antimicrobial peptide detection

Antimicrobial peptides (AMPs) are promising candidates for addressing the global challenge of antibiotic resistance due to their broad-spectrum antimicrobial properties. Traditional AMP identification methods, while effective, are labor-intensive and time-consuming. Recent advancements in deep learning and large language models (LLMs), especially protein language models (PLMs) present a transformative approach for AMP prediction. In this study, we propose PLAPD, a novel framework leveraging a pre-trained ESM2 protein language model for AMP classification. Besides, PLAPD combines local feature extraction via convolutional layers and global feature extraction with a residual Transformer module. We benchmarked PLAPD against state-of-the-art AMP prediction models using a dataset comprising 8,268 peptide sequences, achieving superior performance in Accuracy (0.87), Precision (0.9359), Specificity (0.9456), MCC (0.7486), and AUC (0.9225). The results highlight the potential of PLAPD as a high-throughput and accurate tool for AMP discovery.

An insight into cell-penetrating peptides: perspectives on design, optimization, and targeting in drug delivery systems

The authors carried out a comprehensive review of the application of peptides known as cell-penetrating peptides (CPPs) in various drug delivery systems (DDS), with the prospect of achieving novel solutions and ideas to overcome the challenges of DDS, by making them more able to penetrate cells and biological membranes. A conceptual search was conducted in relevant literature databases (Scopus, PubMed, Web of Science, and Google Scholar) up to 1 April 2025 using keywords such as drug delivery systems, cell-penetrating peptides, CPPs, complexes, conjugates, nanoparticles, dendrimers, exosomes, liquid crystalline, liposomes, micelles, nanospheres and lipid nanoparticles. The studies demonstrate that the antimicrobial effect of drugs, including curcumin, gentamicin, and antifungal drugs like imidazoacridinone derivatives, can be enhanced when they are conjugated or complexed with CPPs. CPPs possess positive charges, which make them suitable for gene therapy applications by facilitating the delivery of plasmids and siRNAs with negative charges in modern delivery systems. Medicinal formulations containing CPPs in combination with liquid crystals or nanostructured lipid carriers (NLCs) increase drugs penetration to the skin. Additionally, several investigations showed that CPPs could have a positive impact on the pharmacokinetic and pharmacodynamic of chemotherapy agents, reducing their side effects. CPPs have significant potential in enhancing penetration, bioavailability, targeting, and optimization of DDS. By using computer modeling and designing CPPs with more desirable features and conducting more clinical studies, new methods for treating diseases and better formulations can be achieved.

Therapeutic peptides: chemical strategies fortify peptides for enhanced disease treatment efficacy

Therapeutic peptides, as a unique form of medication composed of orderly arranged sequences of amino acids, are valued for their high affinity, specificity, low immunogenicity, and economical production costs. Currently, more than 100 peptides have already secured market approval. Over 150 are actively undergoing clinical trials, while an additional 400-600 are in the preclinical research stage. Despite this, their clinical application is limited by factors such as salt sensitivity, brief residence in the bloodstream, inadequate cellular uptake, and high structural flexibility. By employing suitable chemical methods to modify peptides, it is possible to regulate important physicochemical factors such as charge, hydrophobicity, conformation, amphiphilicity, and sequence that affect the physicochemical properties and biological activity of peptides. This can overcome the inherent deficiencies of peptides, enhance their pharmacokinetic properties and biological activity, and promote continuous progress in the field of research. A diverse array of modified peptides is currently being developed and investigated across numerous therapeutic fields. Drawing on the latest research, this review encapsulates the essential physicochemical factors and significant chemical modification strategies that influence the properties and biological activity of peptides as pharmaceuticals. It also assesses how physicochemical factors affect the application of peptide drugs in disease treatment and the effectiveness of chemical strategies in disease therapy. Concurrently, this review discusses the prospective advancements in therapeutic peptide development, with the goal of offering guidance for designing and optimizing therapeutic peptides and to delve deeper into the therapeutic potential of peptides for disease intervention.

Stigmurin encapsulated PLA-PEG ameliorates its therapeutic potential, antimicrobial and antiproliferative activities

In light of growing global challenge posed by antimicrobial resistance, it is very important to explore alternatives that can target pathogenic microorganisms. One such strategy involves the use of antimicrobial peptides (AMPs) and Stigmurin is one such AMP present in Brazilian scorpion Tityus stigmurus which possesses antimicrobial, antiproliferative and antiparasitic activity. The study commenced with successful synthesis and characterization of Stigmurin and its analogues, designated S1 and S2. Studies on Stigmurin and its analogues have demonstrated that analogues exhibit enhanced antimicrobial efficacy but often lead to increased hemolysis, limiting their therapeutic application. To prevent the associated toxicity of these peptides, PLA-PEG di-block copolymer was synthesised to prepare nanoparticles (E-WT, E1, and E2) with an average diameter of approximately 160-180 nm. The core of the research involved evaluating the antimicrobial (Bacillus subtilis), antibiofilm (B. subtilis and Pseudomonas aeruginosa), antiproliferative (HEK293 and RAW264.7) and hemolytic activity of the peptides. In addition to the experimental work, in silico analysis using structural models was conducted to further understand their potential interactions. The findings demonstrated that the analogue peptides exhibit enhanced antimicrobial and antibiofilm activity compared to the wild-type Stigmurin. Moreover, encapsulating the peptides in PLA-PEG nanoparticles maintained the antimicrobial activity against B. subtilis. Further, encapsulation significantly reduced hemolysis as well as cytotoxicity by 10-20%, thereby improving their safety profile.

Antimicrobial peptide biological activity, delivery systems and clinical translation status and challenges

Antibiotic resistance is currently one of the most significant threats to global public health and safety. And studies have found that over the next 25 years, 39 million people will die directly and 169 million indirectly due to antibiotic-resistant diseases. Consequently, the development of new types of antimicrobial drugs is urgently needed. Antimicrobial peptides (AMPs) constitute an essential component of the innate immune response in all organisms. They exhibit a distinctive mechanism of action that endows them with a broad spectrum of biological activities, including antimicrobial, antibiofilm, antiviral, and anti-inflammatory effects. However, AMPs also present certain limitations, such as cytotoxicity, susceptibility to protein hydrolysis, and poor pharmacokinetic properties, which have impeded their clinical application. The development of delivery systems can address these challenges by modifying AMP delivery and enabling precise, controlled release at the site of infection or disease. This review offers a comprehensive analysis of the mechanisms of action and biological advantages of AMPs. and systematically evaluate how emerging drug delivery systems, such as nanoparticles and hydrogels, enhance the stability and bioavailability of AMPs, discussing both their strengths and limitations. Moreover, unlike previous reviews, this review highlight the most recent clinically approved AMP-based drugs and those currently in development, emphasizing the key challenges in translating these drugs into clinical practice. With these perspectives, it is hoped that this review will provide some insights into overcoming translational barriers and advancing AMPs drugs into clinical practice.

deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities

Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years. Although there are many machine learning-based AMP identification tools, most of them do not focus on or only focus on a few functional activities. Predicting the multiple activities of antimicrobial peptides can help discover candidate peptides with broad-spectrum antimicrobial ability. We propose a two-stage AMP predictor deep-AMPpred, in which the first stage distinguishes AMP from other peptides, and the second stage solves the multilabel problem of 13 common functional activities of AMP. deep-AMPpred combines the ESM-2 model to encode the features of AMP and integrates CNN, BiLSTM, and CBAM models to discover AMP and its functional activities. The ESM-2 model captures the global contextual features of the peptide sequence, while CNN, BiLSTM, and CBAM combine local feature extraction, long-term and short-term dependency modeling, and attention mechanisms to improve the performance of deep-AMPpred in AMP and its function prediction. Experimental results demonstrate that deep-AMPpred performs well in accurately identifying AMPs and predicting their functional activities. This confirms the effectiveness of using the ESM-2 model to capture meaningful peptide sequence features and integrating multiple deep learning models for AMP identification and activity prediction.

Harnessing Nanoparticles to Overcome Antimicrobial Resistance: Promises and Challenges

The rise of antimicrobial resistance (AMR) has become a serious global health issue that kills millions of people each year globally. AMR developed in bacteria is difficult to treat and poses a challenge to clinicians. Bacteria develop resistance through a variety of processes, including biofilm growth, targeted area alterations, and therapeutic drug alteration, prolonging the period they remain within cells, where antibiotics are useless at therapeutic levels. This rise in resistance is linked to increased illness and death, highlighting the urgent need for effective solutions to combat this growing challenge. Nanoparticles (NPs) offer unique solutions for fighting AMR bacteria. Being smaller in size with a high surface area, enhancing interaction with bacteria makes the NPs strong antibacterial agents against various infections. In this review, we have discussed the epidemiology and mechanism of AMR development. Furthermore, the role of nanoparticles as antibacterial agents, and their role in drug delivery has been addressed. Additionally, the potential, challenges, toxicity, and future prospects of nanoparticles as antibacterial agents against AMR pathogens have been discussed. The research work discussed in this review links with Sustainable Development Goal 3 (SDG-3), which aims to ensure disease-free lives and promote well-being for all ages.

Caerin 1.9-Titanium Plates Aid Implant Healing and Inhibit Bacterial Growth in New Zealand Rabbit Mandibles

OBJECTIVES

With rising rates of maxillofacial fracture, postoperative infection following rigid internal fixation is an important issue that requires immediate resolution. It is important to explore an alternative antibacterial method apart from conventional antibiotics. A controlled experiment was conducted to evaluate the effectiveness of a caerin 1.9 peptide-coated titanium plate in reducing mandibular infection in New Zealand (NZ) rabbits, aiming to minimise the risk of post-metallic implantation infection.

METHODS

Twenty-two NZ rabbits were randomly divided into 3 groups. The experiment group received caerin 1.9 peptide-coated titanium plates and mixed oral bacteria exposure. The control group received normal titanium plates with mixed oral bacteria exposure. The untreated group served as a control to prove that bacteria in the mouth can cause infection. Weight, temperature, hepatic function, and C-reactive protein levels were measured. Wound and bone conditions were evaluated. Further analysis included local infection, anatomic conditions, histology, and bacterial load.

RESULTS

No significant differences were found in temperature, weight, blood alanine aminotransferase, and C-reactive protein levels amongst the 3 groups. The experiment group showed the lowest amount of bacterial RNA in wounds. Additionally, the experiment group had higher peripheral lymphocyte counts compared to the control group and lower neutrophil counts on the third and seventh day postoperatively. Histologic analysis revealed lower levels of inflammatory cell infiltration, bleeding, and areas of necrosis in the experimental group compared with the controls.

CONCLUSIONS

A caerin 1.9-coated titanium plate is able to inhibit bacterial growth in a NZ rabbit mandibular mixed bacteria infection model and is worth further investigation.

A review on the diversity of antimicrobial peptides and genome mining strategies for their prediction

Antibiotic resistance has become one of the most serious threats to human health in recent years. In response to the increasing microbial resistance to the antibiotics currently available, it is imperative to develop new antibiotics or explore new approaches to combat antibiotic resistance. Antimicrobial peptides (AMPs) have shown considerable promise in this regard, as the microbes develop low or no resistance against them. The discovery and development of AMPs still confront numerous obstacles such as finding a target, developing assays, and identifying hits and leads, which are time-consuming processes, making it difficult to reach the market. However, with the advent of genome mining, new antibiotics could be discovered efficiently using tools such as BAGEL, antiSMASH, RODEO, etc., providing hope for better treatment of diseases in the future. Computational methods used in genome mining automatically detect and annotate biosynthetic gene clusters in genomic data, making it a useful tool in natural product discovery. This review aims to shed light on the history, diversity, and mechanisms of action of AMPs and the data on new AMPs identified by traditional as well as genome mining strategies. It further substantiates the various phases of clinical trials for some AMPs, as well as an overview of genome mining databases and tools built expressly for AMP discovery. In light of the recent advancements, it is evident that targeted genome mining stands as a beacon of hope, offering immense potential to expedite the discovery of novel antimicrobials.

Antimicrobial activity of novel symmetrical antimicrobial peptides centered on a hydrophilic motif against resistant clinical isolates: in vitro and in vivo analyses

Antibiotic resistance poses a significant public health threat worldwide. The rise in antibiotic resistance and the sharp decline in effective antibiotics necessitate the development of innovative antibacterial agents. Based on the central symmetric structure of glycine-serine-glycine, combined with tryptophan and arginine, we designed a range of antimicrobial peptides (AMPs) that exhibited broad-spectrum antibacterial activity. Notably, AMP W5 demonstrated a rapid and effective sterilization against methicillin-resistant Staphylococcus aureus (MRSA), displaying both a minimum inhibitory concentration and a minimum bactericidal concentration of 8 µM. Mechanistic studies revealed that AMP W5 killed bacterial cells by disrupting the cytoplasmic membrane integrity, triggering leakage of cell contents. AMP W5 also exhibited excellent biocompatibility in both in vitro and in vivo safety evaluations. AMP W5 treatment significantly reduced skin bacterial load in our murine skin infection model. In conclusion, we designed a novel centrosymmetric AMP representing a promising medical alternative to conventional antibiotics for treating MRSA infections.

IMPORTANCE

Increasing antibiotic resistance and the paucity of effective antibiotics necessitate innovative antibacterial agents. Methicillin-resistant Staphylococcus aureus (MRSA) is a major pathogen causing bacterial infections with high incidence and mortality rates, showing increasing resistance to clinical drugs. Antimicrobial peptides (AMPs) exhibit significant potential as alternatives to traditional antibiotics. This study designed a novel series of AMPs, characterized by a glycine-serine-glycine-centered symmetrical structure, and our results indicated that AMP W5 exhibited a rapid and effective bactericidal effect against MRSA. AMP W5 also demonstrated excellent biocompatibility and a bactericidal mechanism that disrupted membrane integrity, leading to leakage of cellular contents. The notable reduction in skin bacterial load observed in mouse models reinforced the clinical applicability of AMP W5. This study provides a promising solution for addressing the increasing threat of antibiotic-resistant bacteria and heralds new prospects for clinical applications.

The clinical praxis of bacteriocins as natural anti-microbial therapeutics

In recent decades, the excessive use of antibiotics has resulted in a rise in antimicrobial drug resistance (ADR). Annually, a significant number of human lives are lost due to resistant infectious diseases, leading to around 700,000 deaths, and it is estimated that by 2050, there could be up to 10 million casualties. Apart from their possible application as preservatives in the food sector, bacteriocins are gaining acknowledgment as potential clinical treatments. Not only this, these antimicrobial peptides have revealed in modulating the host immune system producing anti-inflammatory and anti-modulatory responses. At the same time, due to the ever-increasing global threat of antibiotic resistance, bacteriocins have gained attraction among researchers due to their potential clinical applications. Bacteriocins as antimicrobial peptides, represent one of the most important natural defense mechanisms among bacterial species, particularly lactic acid bacteria (LAB), that can fight against infection-causing pathogens. In this review, we are highlighting the potential of bacteriocins as novel therapeutics for inhibiting a wide range of clinically relevant and multi-drug-resistant pathogens (MDR). We also highlight the effectiveness and potential applications of current bacteriocin treatments in combating antimicrobial resistance (AMR), thereby promoting human health.

Proline, a unique amino acid whose polymer, polyproline II helix, and its analogues are involved in many biological processes: a review

Proline is a unique amino acid in that its side-chain is cyclised to the backbone, thus giving proline an exceptional rigidity and a considerably restricted conformational space. Polyproline forms two well-characterized helical structures: a left-handed polyproline helix (PPII) and a right-handed polyproline helix (PPI). Usually, sequences made only of prolyl residues are in PPII conformation, but even sequences not rich in proline but which are rich in glycine, lysine, glutamate, or aspartate have also a tendency to form PPII helices. Currently, the only way to study unambiguously PPII structure in solution is to use spectroscopies based on optical activity such as circular dichroism, vibrational circular dichroism and Raman optical activity. The importance of the PPII structure is emphasized by its ubiquitous presence in different organisms from yeast to human beings where proline-rich motifs and their binding domains are believed to be involved in vital biological processes. Some of the domains that are bound by proline-rich motifs include SH3 domains, WW domains, GYF domains and UEV domains, etc. The PPII structure has been demonstrated to be essential to biological activities such as signal transduction, transcription, cell motility, and immune response.

Medium-sized peptides from microbial sources with potential for antibacterial drug development

Covering: 1993 to the end of 2022As the rapid development of antibiotic resistance shrinks the number of clinically available antibiotics, there is an urgent need for novel options to fill the existing antibiotic pipeline. In recent years, antimicrobial peptides have attracted increased interest due to their impressive broad-spectrum antimicrobial activity and low probability of antibiotic resistance. However, macromolecular antimicrobial peptides of plant and animal origin face obstacles in antibiotic development because of their extremely short elimination half-life and poor chemical stability. Herein, we focus on medium-sized antibacterial peptides (MAPs) of microbial origin with molecular weights below 2000 Da. The low molecular weight is not sufficient to form complex protein conformations and is also associated to a better chemical stability and easier modifications. Microbially-produced peptides are often composed of a variety of non-protein amino acids and terminal modifications, which contribute to improving the elimination half-life of compounds. Therefore, MAPs have great potential for drug discovery and are likely to become key players in the development of next-generation antibiotics. In this review, we provide a detailed exploration of the modes of action demonstrated by 45 MAPs and offer a concise summary of the structure-activity relationships observed in these MAPs.

Activity and mechanism of action of antimicrobial peptide ACPs against Candida albicans

AIMS

Candida albicans is the most prevalent pathogenic fungus, exhibiting escalating multidrug resistance (MDR). Antimicrobial peptides (AMPs) represent promising candidates for addressing this issue. In this research, five antimicrobial peptides, ACP1 to ACP5 which named ACPs were studied as alternative fungicidal molecules.

MAIN METHODS

CD assay was used to analyze the 2D structures, Absorbance method was used to test the antimicrobial activity, haemolytic activity, time-kill kinetics, biofilm inhibition and reduction activity, resistance induction activity and assessment against fluconazole-resistant C. albicans. SEM, TEM, CLSM, flow cytometer and FM were carried out to provide insight into the mechanisms of anti-Candida action.

KEY FINDINGS

ACPs possessed an α-helical structure and strong anti-Candida activities, with minimum inhibitory concentrations (MICs) from 3.9 to 15.6 μg/mL. In addition, ACPs did not produce hemolysis at concentrations lower than 10 or 62 × MIC, indicating their low cytotoxicity. Fungicidal kinetics showed that they completely killed C. albicans within 8 h at 2 to 4 × MIC. Notably, ACPs were highly fungicidal against fluconazole-resistant C. albicans and showed low resistance. In addition, they were effective in inhibiting mycelium and biofilm formation. Fluorescence microscopy revealed that while fluconazole had minimal to no inhibitory effect on biofilm-forming cells, ACPs induced apoptosis in all of them. The research on mechanism of action revealed that ACPs disrupted the cell membranes, with ROS increasing and cellular mitochondrial membrane potential decreasing.

SIGNIFICANCE

ACPs could be promising candidates for combating fluconazole-resistant C. albicans infections.

Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides

Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning's strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\%$, thereby significantly accelerating the process of drug-like peptide generation.

CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation

Cyclic peptides are versatile therapeutic agents that boast high binding affinity, minimal toxicity, and the potential to engage challenging protein targets. However, the pharmaceutical utility of cyclic peptides is limited by their low membrane permeability-an essential indicator of oral bioavailability and intracellular targeting. Current machine learning-based models of cyclic peptide permeability show variable performance owing to the limitations of experimental data. Furthermore, these methods use features derived from the whole molecule that have traditionally been used to predict small molecules and ignore the unique structural properties of cyclic peptides. This study presents CycPeptMP: an accurate and efficient method to predict cyclic peptide membrane permeability. We designed features for cyclic peptides at the atom-, monomer-, and peptide-levels and seamlessly integrated these into a fusion model using deep learning technology. Additionally, we applied various data augmentation techniques to enhance model training efficiency using the latest data. The fusion model exhibited excellent prediction performance for the logarithm of permeability, with a mean absolute error of $0.355$ and correlation coefficient of $0.883$. Ablation studies demonstrated that all feature levels contributed and were relatively essential to predicting membrane permeability, confirming the effectiveness of augmentation to improve prediction accuracy. A comparison with a molecular dynamics-based method showed that CycPeptMP accurately predicted peptide permeability, which is otherwise difficult to predict using simulations.

Long-term effects of fecal microbiota transplantation on gut microbiota after Helicobacter pylori eradication with bismuth quadruple therapy: A randomized controlled trial

BACKGROUND

Eradicating Helicobacter pylori infection by bismuth quadruple therapy (BQT) is effective. However, the effect of BQT and subsequent fecal microbiota transplant (FMT) on the gut microbiota is less known.

MATERIALS AND METHODS

This prospective randomized controlled trial was conducted at a tertiary hospital in China from January 2019 to October 2020, with the primary endpoints the effect of BQT on the gut microbiota and the effect of FMT on the gut microbiota after bismuth quadruple therapy eradication therapy. A 14-day BQT with amoxicillin and clarithromycin was administered to H. pylori-positive subjects, and after eradication therapy, patients received a one-time FMT or placebo treatment. We then collected stool samples to assess the effects of 14-day BQT and FMT on the gut microbiota. 16 s rDNA and metagenomic sequencing were used to analyze the structure and function of intestinal flora. We also used Gastrointestinal Symptom Rating Scale (GSRS) to evaluate gastrointestinal symptom during treatment.

RESULTS

A total of 30 patients were recruited and 15 were assigned to either FMT or placebo groups. After eradication therapy, alpha-diversity was decreased in both groups. At the phylum level, the abundance of Bacteroidetes and Firmicutes decreased, while Proteobacteria increased. At the genus level, the abundance of beneficial bacteria decreased, while pathogenic bacteria increased. Eradication therapy reduced some resistance genes abundance while increased the resistance genes abundance linked to Escherichia coli. While they all returned to baseline by Week 10. Besides, the difference was observed in Week 10 by the diarrhea score between two groups. Compared to Week 2, the GSRS total score and diarrhea score decreased in Week 3 only in FMT group.

CONCLUSIONS

The balance of intestinal flora in patients can be considerably impacted by BQT in the short term, but it has reverted back to baseline by Week 10. FMT can alleviate gastrointestinal symptoms even if there was no evidence it promoted restoration of intestinal flora.

Non-canonical amino acid bioincorporation into antimicrobial peptides and its challenges

The rise of antimicrobial resistance and multi-drug resistant pathogens has necessitated explorations for novel antibiotic agents as the discovery of conventional antibiotics is becoming economically less viable and technically more challenging for biopharma. Antimicrobial peptides (AMPs) have emerged as a promising alternative because of their particular mode of action, broad spectrum and difficulty that microbes have in becoming resistant to them. The AMPs bacitracin, gramicidin, polymyxins and daptomycin are currently used clinically. However, their susceptibility to proteolytic degradation, toxicity profile, and complexities in large-scale manufacture have hindered their development. To improve their proteolytic stability, methods such as integrating non-canonical amino acids (ncAAs) into their peptide sequence have been adopted, which also improves their potency and spectrum of action. The benefits of ncAA incorporation have been made possible by solid-phase peptide synthesis. However, this method is not always suitable for commercial production of AMPs because of poor yield, scale-up difficulties, and its non-'green' nature. Bioincorporation of ncAA as a method of integration is an emerging field geared towards tackling the challenges of solid-phase synthesis as a green, cheaper, and scalable alternative for commercialisation of AMPs. This review focusses on the bioincorporation of ncAAs; some challenges associated with the methods are outlined, and notes are given on how to overcome these challenges. The review focusses particularly on addressing two key challenges: AMP cytotoxicity towards microbial cell factories and the uptake of ncAAs that are unfavourable to them. Overcoming these challenges will draw us closer to a greater yield and an environmentally friendly and sustainable approach to make AMPs more druggable.

Effective antibacterial agents in modern wound dressings: a review

Wound infections are a significant concern in healthcare, leading to long healing times. Traditional approaches for managing wound infections rely heavily on systemic antibiotics, which are associated with the emergence of antibiotic-resistant bacteria. Therefore, the development of alternative antibacterial materials for wound care has gained considerable attention. In today's world, new generations of wound dressing are commonly used to heal wounds. These new dressings keep the wound and the area around it moist to improve wound healing. However, this moist environment can also foster an environment that is favorable for the growth of bacteria. Excessive antibiotic use poses a significant threat to human health and causes bacterial resistance, so new-generation wound dressings must be designed and developed to reduce the risk of infection. Wound dressings using antimicrobial compounds minimize wound bacterial colonization, making them the best way to avoid open wound infection. We aim to provide readers with a comprehensive understanding of the latest advancements in antibacterial materials for wound management.

DeepSS2GO: protein function prediction from secondary structure

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy

Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.

Heterologous expression of the novel dimeric antimicrobial peptide LIG in Pichia pastoris

The antimicrobial peptide (AMP) LI is a fusion product of antimicrobial peptide LL37 produced by human neutrophils and Indolicidin secreted by bovine neutrophils. LI retained the antimicrobial activity of the parental peptides and showed high cell selectivity. In this study, the flexible linker Gly-Ser-Gly (G-S-G) was used to ligate LI into dimeric LIG, and constructed the Pichia pastoris (P. pastoris) expression vector pPIC9K-6×His-3×FLAG-LIG. The total protein expression of P. pastoris GS115 reached the highest level (189.6 mg/L) after 96 h induction with 3 % methanol at the initial pH value of 7.0. Finally, 5.9 mg/L of recombinant LIG (rLIG) was obtained after enterokinase digestion and purification. The rLIG had high antimicrobial activity and low hemolytic activity. Compared with monomer LI, GSG linked dimeric LIG, which had no significant change in antimicrobial activity and had good salt ions stability. In this study, the dimeric antimicrobial peptide LIG was successfully expressed, which provided a new idea for the expression of AMPs in the P. pastoris expression system, and had important significance for the application of AMPs.

FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization

Anti-coronavirus peptides (ACVPs) represent a relatively novel approach of inhibiting the adsorption and fusion of the virus with human cells. Several peptide-based inhibitors showed promise as potential therapeutic drug candidates. However, identifying such peptides in laboratory experiments is both costly and time consuming. Therefore, there is growing interest in using computational methods to predict ACVPs. Here, we describe a model for the prediction of ACVPs that is based on the combination of feature engineering (FE) optimization and deep representation learning. FEOpti-ACVP was pre-trained using two feature extraction frameworks. At the next step, several machine learning approaches were tested in to construct the final algorithm. The final version of FEOpti-ACVP outperformed existing methods used for ACVPs prediction and it has the potential to become a valuable tool in ACVP drug design. A user-friendly webserver of FEOpti-ACVP can be accessed at http://servers.aibiochem.net/soft/FEOpti-ACVP/.

iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model

As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.

HiFun: homology independent protein function prediction by a novel protein-language self-attention model

Protein function prediction based on amino acid sequence alone is an extremely challenging but important task, especially in metagenomics/metatranscriptomics field, in which novel proteins have been uncovered exponentially from new microorganisms. Many of them are extremely low homology to known proteins and cannot be annotated with homology-based or information integrative methods. To overcome this problem, we proposed a Homology Independent protein Function annotation method (HiFun) based on a unified deep-learning model by reassembling the sequence as protein language. The robustness of HiFun was evaluated using the benchmark datasets and metrics in the CAFA3 challenge. To navigate the utility of HiFun, we annotated 2 212 663 unknown proteins and discovered novel motifs in the UHGP-50 catalog. We proved that HiFun can extract latent function related structure features which empowers it ability to achieve function annotation for non-homology proteins. HiFun can substantially improve newly proteins annotation and expand our understanding of microorganisms' adaptation in various ecological niches. Moreover, we provided a free and accessible webservice at http://www.unimd.org/HiFun, requiring only protein sequences as input, offering researchers an efficient and practical platform for predicting protein functions.

Exploring the mechanism of photosensitizer conjugation on membrane perturbation of antimicrobial peptide: A multiscale molecular simulation study

Antimicrobial peptides (AMPs) exert their biological functions by perturbation with cellular membrane. Conjugation of AMPs with photosensitizer (PS) is a promising strategy for enhancing the efficacy and reducing systemic toxicity of AMPs. However, it is still elusive how the conjugated PS impacts the perturbation of AMPs on cell membrane from molecular level. Here, we addressed this issue by a multiscale computational strategy on pyropheophorbide-a (PPA) conjugated K6L9 (PPA-K6L9), a PS-AMP conjugate developed by us previously. Our atomistic molecular dynamics (MD) simulations revealed that the porphyrin moiety of PPA enhanced the stability of the conjugate in a lipid bilayer membrane model. Moreover, such moiety also maintained the amphipathic structure of K6L9, which is crucial for membrane pore formation. Coarse-grained MD simulations further showed that the conjugates aggregated in membrane environment and formed more stable toroidal pores with respect to K6L9 alone, suggesting the conjugation of PPA may enhance the membrane-disruption activity of K6L9. Consistent with this, our cellular experiments confirmed that PPA-K6L9 was more toxic to 4 T1 tumor cells than K6L9. This study provides insights into the mechanism by which PS-AMP conjugates disrupt cellular membranes and could aid in the design of more potent AMP conjugates.

Are Antimicrobial Peptides a 21st-Century Solution for Atopic Dermatitis?

Atopic dermatitis (AD) is a chronic inflammatory skin disorder that is the result of various environmental, bacterial and genetic stimuli, which culminate in the disruption of the skin's barrier function. Characterized by highly pruritic skin lesions, xerosis and an array of comorbidities among which skin infections are the most common, this condition results in both a significant loss of quality of life and in the need for life-long treatments (e.g., corticosteroids, monoclonal antibodies and regular antibiotic intake), all of which may have harmful secondary effects. This, in conjunction with AD's rising prevalence, made the development of alternative treatment strategies the focus of both the scientific community and the pharmaceutical industry. Given their potential to both manage the skin microbiome, fight infections and even modulate the local immune response, the use of antimicrobial peptides (AMPs) from more diverse origins has become one of the most promising alternative solutions for AD management, with some being already used with some success towards this end. However, their production and use also exhibit some limitations. The current work seeks to compile the available information and provide a better understanding of the state of the art in the understanding of AMPs' true potential in addressing AD.

Insect-pathogen crosstalk and the cellular-molecular mechanisms of insect immunity: uncovering the underlying signaling pathways and immune regulatory function of non-coding RNAs

Multicellular organisms are constantly subjected to pathogens that might be harmful. Although insects lack an adaptive immune system, they possess highly effective anti-infective mechanisms. Bacterial phagocytosis and parasite encapsulation are some forms of cellular responses. Insects often defend themselves against infections through a humoral response. This phenomenon includes the secretion of antimicrobial peptides into the hemolymph. Specific receptors for detecting infection are required for the recognition of foreign pathogens such as the proteins that recognize glucans and peptidoglycans, together referred to as PGRPs and βGRPs. Activation of these receptors leads to the stimulation of signaling pathways which further activates the genes encoding for antimicrobial peptides. Some instances of such pathways are the JAK-STAT, Imd, and Toll. The host immune response that frequently accompanies infections has, however, been circumvented by diseases, which may have assisted insects evolve their own complicated immune systems. The role of ncRNAs in insect immunology has been discussed in several notable studies and reviews. This paper examines the most recent research on the immune regulatory function of ncRNAs during insect-pathogen crosstalk, including insect- and pathogen-encoded miRNAs and lncRNAs, and provides an overview of the important insect signaling pathways and effector mechanisms activated by diverse pathogen invaders.

Regulation of Host Defense Peptide Synthesis by Polyphenols

The rise of antimicrobial resistance has created an urgent need for antibiotic-alternative strategies for disease control and prevention. Host defense peptides (HDPs), which have both antimicrobial and immunomodulatory properties, are an important component of the innate immune system. A host-directed approach to stimulate the synthesis of endogenous HDPs has emerged as a promising solution to treat infections with a minimum risk for developing antimicrobial resistance. Among a diverse group of compounds that have been identified as inducers of HDP synthesis are polyphenols, which are naturally occurring secondary metabolites of plants characterized by the presence of multiple phenol units. In addition to their well-known antioxidant and anti-inflammatory activities, a variety of polyphenols have been shown to stimulate HDP synthesis across animal species. This review summarizes both the in vitro and in vivo evidence of polyphenols regulating HDP synthesis. The mechanisms by which polyphenols induce HDP gene expression are also discussed. Natural polyphenols warrant further investigation as potential antibiotic alternatives for the control and prevention of infectious diseases.

Purification and Biological Properties of Raniseptins-3 and -6, Two Antimicrobial Peptides from Boana raniceps (Cope, 1862) Skin Secretion

The number of multidrug-resistant pathogenic microorganisms has been growing in recent years, most of which is due to the inappropriate use of the commercial antibiotics that are currently available. The dissemination of antimicrobial resistance represents a serious global public health problem. Thus, it is necessary to search for and develop new drugs that can act as antimicrobial agents. Antimicrobial peptides are a promising alternative for the development of new therapeutic drugs. Anurans' skin glands are a rich source of broad-spectrum antimicrobial compounds and hylids, a large and diverse family of tree frogs, are known as an important source of antimicrobial peptides. In the present study, two novel antimicrobial peptides, named Raniseptins-3 and -6, were isolated from Boana raniceps skin secretion and their structural and biological properties were evaluated. Raniseptins-3 and -6 are cationic, rich in hydrophobic residues, and adopt an α-helix conformation in the presence of SDS (35 mM). Both peptides are active against Gram-negative bacteria and Gram-positive pathogens, with low hemolytic activity at therapeutic concentrations. No activity was observed for yeasts, but the peptides are highly cytotoxic against B16F10 murine melanoma cells and NIH3T3 mouse fibroblast cells. None of the tested compounds showed improvement trends in the MTT and LDH parameters of MHV-3 infected cells at the concentrations tested.

Prediction and bioactivity of small-molecule antimicrobial peptides from Protaetia brevitarsis Lewis larvae

Antimicrobial peptides (AMPs) are widely recognized as promising natural antimicrobial agents. Insects, as the group of animals with the largest population, have great potential as a source of AMPs. Thus, it is worthwhile to investigate potential novel AMPs from Protaetia brevitarsis Lewis larvae, which is a saprophagous pest prevalent in China. In this study, comparing the whole-genome sequence of Protaetia brevitarsis Lewis larvae with the Antimicrobial Peptide Database (APD3) led to the identification of nine peptide templates that were potentially AMPs. Next, based on the peptide templates, 16 truncated sequences were predicted to the AMPs by bioinformatics software and then underwent structural and physicochemical property analysis. Thereafter, candidate small-molecule AMPs were artificially synthesized and their minimal inhibitory concentration (MIC) values were assessed. A candidate peptide, designated FD10, exhibited strong antimicrobial activity against both bacteria and fungi comprising Escherichia coli (MIC: 8 μg/mL), Pseudomonas aeruginosa (MIC: 8 μg/mL), Bacillus thuringiensis (MIC: 8 μg/mL), Staphylococcus aureus (MIC: 16 μg/mL), and Candida albicans (MIC: 16 μg/mL). Additionally, two other candidate peptides, designated FD12 and FD15, exhibited antimicrobial activity against both E. coli (MIC: both 32 μg/mL) and S. aureus (MIC: both 16 μg/mL). Moreover, FD10, FD12, and FD15 killed almost all E. coli and S. aureus cells within 1 h, and the hemolytic effect of FD10 (0.31%) and FD12 (0.40%) was lower than that of ampicillin (0.52%). These findings indicate that FD12, FD15, and especially FD10 are promising AMPs for therapeutic application. This study promoted the development of antibacterial drugs and provided a theoretical basis for promoting the practical application of antimicrobial peptides in the Protaetia brevitarsis Lewis larvae.

Synthesized peptide analogs from Eumenes pomiformis (Hymenoptera: Eumenidae) venom reveals their antibiotic and pesticide activity potential

One natural antimicrobial peptide (EpVP2a, Eumenes pomiformis Venom Peptide 2a) found in the venom of a potter wasp (Eumenes pomiformis) and six analogs were synthesized and tested to compare their antimicrobial, antifungal, pesticide, and hemolytic activity with the wild type. Our results indicated that while the original peptide and the synthetic analogs had no antifungal activity or anti-bacterial activity against Pseudomonas aeruginosa, the original peptide and the analog with substitution of the aspartic acid on the sequence by a lysine (EpVP2a-D2K2) had activity against Escherichia coli, Staphylococcus aureus and Bacillus subtilis. This same analog also shows significant insecticide activity. The analog with substitution of lysine with a slightly smaller ornithine had activity against E. coli and B. subtilis. All analogs show low hemolytic activity compared to the natural peptide. The peptide with a reverse sequence to the natural one (EpVp2a Retro) shows low helix structure which can also explain why it has no antibacterial activity and low hemolytic activity. Circular dichroism spectra show that these peptides form an alpha helix structure and their amino acid positions predict an amphipathic nature.

Antimicrobial peptides for combating drug-resistant bacterial infections

The problem of drug resistance due to long-term use of antibiotics has been a concern for years. As this problem grows worse, infections caused by multiple bacteria are expanding rapidly and are extremely detrimental to human health. Antimicrobial peptides (AMPs) are a good alternative to current antimicrobials with potent antimicrobial activity and unique antimicrobial mechanisms, which have advantages over traditional antibiotics in fighting against drug-resistant bacterial infections. Currently, researchers have conducted clinical investigations on AMPs for drug-resistant bacterial infections while integrating new technologies in the development of AMPs, such as changing amino acid structure of AMPs and using different delivery methods for AMPs. This article introduces the basic properties of AMPs, deliberates the mechanism of drug resistance in bacteria and the therapeutic mechanism of AMPs. The current disadvantages and advances of AMPs in combating drug-resistant bacterial infections are also discussed. This article provides important insights into the research and clinical application of new AMPs for drug-resistant bacterial infections.

The synergistic effect of using bacteriophages and chitosan nanoparticles against pathogenic bacteria as a novel therapeutic approach

Public health and environmental security are seriously at risk due to the growing contamination of pathogenic microorganisms. Therefore, effective antimicrobials are urgently needed. In our study, the antimicrobial effects of three types of nanoparticles were investigated with phage. The biosynthesis of nanoparticles was confirmed based on the color change and shapes, which tended to be mono-dispersed with a spherical shape with a size range of 20-35 nm for Ag-CS-NPs; 15-30 nm for Phage-CS-NPs (Ph-CS-NPs); and 5-35 nm for Propolis-CS-NPs (Pro-CS-NPs). Nanoparticles displayed peaks between 380-420 nm, 335-380 nm, and below 335 nm for Ag-CS-NPs, Pro-CS-NPs, and Ph-CS NPs, respectively. Throughout the three synthesized nanoparticles, AgCs NPs represented a higher antibacterial effect in combination with phages. It showed MIC against S. sciuri, S. Typhimurium, and P. aeruginosa between 31.2 and 62.2 μg/mL and MBC at 500, 62.5, and 31.2 μg/mL, respectively, while in combination with phages showed MIC at 62.2, 31.2, and 15.6 μg/mL, respectively and MBC at 125, 62.2, and 15.6 μg/mL, respectively. Furthermore, a significant killing efficiency was observed with 16.5-30.1 μg/mL of Ag-CS NPs combined with phages. In conclusion, Ag-CS-NPs with phages present potential bactericidal and inhibitory effects against Gram-positive and Gram-negative bacteria, as well as against the production of biofilms.

SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning

BACKGROUND

Cell-penetrating peptides (CPPs) have received considerable attention as a means of transporting pharmacologically active molecules into living cells without damaging the cell membrane, and thus hold great promise as future therapeutics. Recently, several machine learning-based algorithms have been proposed for predicting CPPs. However, most existing predictive methods do not consider the agreement (disagreement) between similar (dissimilar) CPPs and depend heavily on expert knowledge-based handcrafted features.

RESULTS

In this study, we present SiameseCPP, a novel deep learning framework for automated CPPs prediction. SiameseCPP learns discriminative representations of CPPs based on a well-pretrained model and a Siamese neural network consisting of a transformer and gated recurrent units. Contrastive learning is used for the first time to build a CPP predictive model. Comprehensive experiments demonstrate that our proposed SiameseCPP is superior to existing baseline models for predicting CPPs. Moreover, SiameseCPP also achieves good performance on other functional peptide datasets, exhibiting satisfactory generalization ability.

In Silico and In Vitro Inhibition of SARS-CoV-2 PLpro with Gramicidin D

Finding an effective drug to prevent or treat COVID-19 is of utmost importance in tcurrent pandemic. Since developing a new treatment takes a significant amount of time, drug repurposing can be an effective option for achieving a rapid response. This study used a combined in silico virtual screening protocol for candidate SARS-CoV-2 PL^pro inhibitors. The Drugbank database was searched first, using the Informational Spectrum Method for Small Molecules, followed by molecular docking. Gramicidin D was selected as a peptide drug, showing the best in silico interaction profile with PL^pro. After the expression and purification of PL^pro, gramicidin D was screened for protease inhibition in vitro and was found to be active against PL^pro. The current study's findings are significant because it is critical to identify COVID-19 therapies that are efficient, affordable, and have a favorable safety profile.

Peptide Utility (PU) search server: A new tool for peptide sequence search from multiple databases

Proteins are essential building blocks in humans that have garnered huge attention from researchers worldwide due to their numerous therapeutic applications. To date, different computational tools have been developed to extract pre-existing information on these biological molecules, but most of these tools suffer from limitations such as non-user friendly interface, redundancy of data, etc. To overcome these limitations, a user-friendly interface, the Peptide Utility (PU) webserver (https://chain-searching.herokuapp.com/) has been developed for searching and analyzing homologous and identical protein/peptide sequences that can be searched from approximately 0.4 million sequences (structural and sequence information) in both online and offline modes. The PU web server can also be used to study different types of interactions in PDBSum, identifying the most dominating interface residues, the most prevalent interactions, and the interaction preferences of different residues. The webserver would also pave way for the design of novel therapeutic peptides and folds by identifying conserved residues in the three-dimensional structure space of proteins.

Design of Antimicrobial Peptides with Cell-Selective Activity and Membrane-Acting Mechanism against Drug-Resistant Bacteria

Antimicrobial peptides (AMPs) can combat drug-resistant bacteria with their unique membrane-disruptive mechanisms. This study aimed to investigate the antibacterial effects of several membrane-acting peptides with amphipathic structures and positional alterations of two tryptophan residues. The synthetic peptides exhibited potent antibacterial activities in a length-dependent manner against various pathogenic drug-resistant and susceptible bacteria. In particular, the location of tryptophan near the N-terminus of AMPs simultaneously increases their antibacterial activity and toxicity. Furthermore, the growth inhibition mechanisms of these newly designed peptides involve cell penetration and destabilization of the cell membrane. These findings provide new insights into the design of peptides as antimicrobial agents and suggest that these peptides can be used as substitutes for conventional antibiotics.

Antimicrobial Peptides-Mechanisms of Action, Antimicrobial Effects and Clinical Applications

The growing emergence of antimicrobial resistance represents a global problem that not only influences healthcare systems but also has grave implications for political and economic processes. As the discovery of novel antimicrobial agents is lagging, one of the solutions is innovative therapeutic options that would expand our armamentarium against this hazard. Compounds of interest in many such studies are antimicrobial peptides (AMPs), which actually represent the host's first line of defense against pathogens and are involved in innate immunity. They have a broad range of antimicrobial activity against Gram-negative and Gram-positive bacteria, fungi, and viruses, with specific mechanisms of action utilized by different AMPs. Coupled with a lower propensity for resistance development, it is becoming clear that AMPs can be seen as emerging and very promising candidates for more pervasive usage in the treatment of infectious diseases. However, their use in quotidian clinical practice is not without challenges. In this review, we aimed to summarize state-of-the-art evidence on the structure and mechanisms of action of AMPs, as well as to provide detailed information on their antimicrobial activity. We also aimed to present contemporary evidence of clinical trials and application of AMPs and highlight their use beyond infectious diseases and potential challenges that may arise with their increasing availability.