In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design

PISAD: De novo peptide design for target protein with iterative stochastic searching algorithm and docking assessment

Rapid identification of peptides that bind specifically to a target protein is essential for disease diagnostics and drug development. However, de novo design of peptides without any prior structural knowledge remains a long-standing challenge. Herein, we present Peptide Iterative design with Stochastic Algorithm and Docking (PISAD), a de novo peptide design method, which combines an iterative stochastic searching algorithm with docking assessment. The searching algorithm simulates the evolution of peptide sequences by introducing mutations and crossovers iteratively. After every round of evolution, the peptide sequences undergo docking assessments with the target protein based on the structural prediction of AlphaFold2. We demonstrated PISAD's efficacy by designing peptides targeting four proteins, namely ARF6, ARF1, TGF-β1, and IL-6. For each target, PISAD managed to output peptides with ideal binding affinity within only four iterations of evolutions, and no more than 1250 sequences were assessed. Particularly, the best-performing peptide achieved a KD value of 3.4 nM with ARF6, which has been further experimentally validated. These results demonstrate the efficiency and accuracy of PISAD, which may serve as a universal tool for rapid de novo design of peptides targeting specific proteins.

Fingolimod as a potent anti-Staphylococcus aureus: pH-dependent cell envelope damage and eradication of biofilms/persisters

BACKGROUND

The urgent need for new antibacterial drugs has driven interest in repurposing therapies to combat Gram-positive biofilms and persisters. Fingolimod, an Food and Drug Administration (FDA)-approved drug for multiple sclerosis, shows bactericidal activity, particularly against Methicillin-resistant Staphylococcus aureus (MRSA) and biofilm-related infections. With a well-documented safety profile and strong translational potential, it aligns with World Health Organization's goals for antimicrobial repurposing. However, the action mode and mechanism of Fingolimod against gram-positive bacteria remain elusive.

METHODS

This study utilized clinical Staphylococcus aureus (S. aureus), Enterococcus faecalis (E. faecalis), Streptococcus agalactiae (S. agalactiae). And their susceptibility to Fingolimod and other antibiotics was tested via Minimum Inhibitory Concentration (MIC) assays. Biofilm inhibition and hemolytic activity were evaluated using crystal violet staining, Confocal Laser Scanning Microscopy (CLSM), and hemolysis assays, respectively, while the effect of phospholipids on Fingolimod efficacy was assessed with checkerboard assays. Membrane permeability and integrity were measured using SYTOX green staining and transmission electron microscopy. Whole-genome sequencing was performed on Fingolimod-resistant S. aureus isolates to identify Single Nucleotide Polymorphisms (SNPs) linked to resistance.

RESULTS

Our data indicated that Fingolimod exerted bactericidal activity against a wide spectrum of gram-positive bacteria, including S. aureus, E. faecalis, S. agalactiae. Moreover, Fingolimod could significantly eliminate the persisters, inhibit biofilm formation and eradicate in-vitro mature biofilms of S. aureus. The mechanism by which Fingolimod rapidly eradicated S. aureus involved a pH-dependent disruption of bacterial cell permeability and envelope integrity. Concomitantly, exogenous supplementation of phospholipids in the culture medium resulted in a dose-dependent increase in the MIC of Fingolimod. Specifically, the addition of 64 μg/mL of cardiolipin (CL) and phosphatidylethanolamine (PE) completely nullified the bactericidal activity of Fingolimod at a concentration of 4 times the MIC. After four months of Fingolimod exposure, the MIC values of S. aureus showed a slight increase, indicating that it is not prone to developing drug resistance.

CONCLUSION

Fingolimod exhibits bactericidal activity against diverse gram-positive bacteria, with remarkable effects on S. aureus (including MRSA), disrupting bacterial cell structural integrity in a pH-dependent way and eradicating biofilms and persisters of S. aureus.

Tutorial: guidelines for the use of machine learning methods to mine genomes and proteomes for antibiotic discovery

Genomes and proteomes constitute a rich reservoir of molecular diversity. However, they have remained underexplored because of a lack of appropriate tools. In recent years, computational approaches have been developed to mine this unexplored biological information, or dark matter, accelerating the discovery of new antibiotic molecules. Such efforts have yielded a wide range of new molecules. These include peptides released via predicted proteolytic cleavage of larger proteins, termed 'encrypted peptides', which have been found to be widespread in nature. Molecules encoded by and translated from small open reading frames within genomic sequences have also been uncovered, further expanding the landscape of bioactive compounds. Here, we discuss computational approaches, including machine learning and artificial intelligence (AI) tools, which have been used to date to identify antimicrobial compounds, with a special emphasis on peptides. We also propose potential avenues for future exploration in this rapidly evolving field. Moreover, we provide an overview of the experimental methods commonly used to validate these computational predictions. We anticipate that efforts combining cutting-edge AI and experimental approaches for biological sequence mining will reveal new insights into host immunity and continue to accelerate discoveries in the fields of antibiotics and infectious diseases.

Revolutionizing agroindustry: Towards the industrial application of antimicrobial peptides against pathogens and pests

Antibiotics are essential chemicals for medicine and agritech. However, all antibiotics are small molecules that pathogens evolve antimicrobial resistance (AMR). Alternatively, antimicrobial peptides (AMPs) offer potential to overcome or evade AMR. AMPs provide broad-spectrum activity, favourable biosafety profiles, and a rapid and efficient mechanism of action with low resistance incidence. These properties have driven innovative applications, positioning AMPs as promising contributors to advancements in various industrial sectors. This review evaluates the multifaceted nature of AMPs and their biotechnological applications in underexplored sectors. In the food industry, the application of AMPs helps to suppress the growth of microorganisms, thereby decreasing foodborne illnesses, minimizing food waste, and prolonging the shelf life of products. In animal husbandry and aquaculture, incorporating AMPs into the diet reduces the load of pathogenic microorganisms and enhances growth performance and survival rates. In agriculture, AMPs provide an alternative to decrease the use of chemical pesticides and antibiotics. We also review current methods for obtaining AMPs, including chemical synthesis, recombinant DNA technology, cell-free protein synthesis, and molecular farming, are also reviewed. Finally, we look to the peptide market to assess its status, progress, and transition from the discovery stage to benefits for society and high-quality products. Overall, our review exemplifies the other side of the coin of AMPs and how these molecules provide similar benefits to conventional antibiotics and pesticides in the agritech sector.

Development of targeted antimicrobial peptides for Escherichia coli: Combining phage display and rational design for food safety application

The growing demand for minimally processed foods has heightened the risk of pathogenic contamination. Balancing antimicrobial efficacy with the preservation of probiotic activity remains a significant challenge. In this study, we employed phage display peptide library screening, combined with next-generation sequencing to identify the HIMPIQA domain, which selectively targets pathogenic Escherichia coli (E. coli) in just one screening round. Using the yXy(PX)4yXy template, we designed antimicrobial peptides (AMPs) where 'y' represents hydrophobic amino acids, and 'X' represents positively charged residues. The peptide WK, combining HIMPIQA with a Trp/Lys-based antimicrobial domain, demonstrated high specificity and antibacterial activity. Molecular docking revealed WK's binding to FaeG and mechanistic studies showed its ability to disrupt E. coli membranes while maintaining probiotic viability. WK's application as a preservative enhanced the shelf life of yogurt and tomatoes. This innovative approach showcases the potential of phage display in developing targeted AMPs for food preservation and safety.

Frog-derived synthetic peptides display anti-infective activity against Gram-negative pathogens

Novel antibiotics are urgently needed since bacteria are becoming increasingly resistant to existing antimicrobial drugs. Furthermore, available antibiotics are broad spectrum, often causing off-target effects on host cells and the beneficial microbiome. To overcome these limitations, we used structure-guided design to generate synthetic peptides derived from Andersonin-D1, an antimicrobial peptide (AMP) produced by the odorous frog Odorrana andersonii. We found that both hydrophobicity and net charge were critical for its bioactivity, enabling the design of novel, optimized synthetic peptides. These peptides selectively targeted Gram-negative pathogens in single cultures and complex microbial consortia, showed no off-target effects on human cells or beneficial gut microbes, and did not select for bacterial resistance. Notably, they also exhibited in vivo activity in two preclinical murine models. Overall, we present synthetic peptides that selectively target pathogenic infections and offer promising preclinical antibiotic candidates.

A plant peptide with dual activity against multidrug-resistant bacterial and fungal pathogens

Multidrug-resistant (MDR) bacteria pose a major threat to public health, and additional sources of antibacterial candidates are urgently needed. Noncanonical peptides (NCPs), derived from noncanonical small open reading frames, represent small biological molecules with important roles in biology. However, the antibacterial activity of NCPs remains largely unknown. Here, we discovered a plant-derived noncanonical antibacterial peptide (NCBP1) against both Gram-positive and Gram-negative bacteria. NCBP1 is composed of 11 amino acid residues with cationic surface potential and favorable safety and stability. Mechanistic studies revealed that NCBP1 displayed antibacterial activity by targeting phosphatidylglycerol and cardiolipin in bacterial membrane, resulting in membrane damage and dysfunction. Notably, NCBP1 showed promising efficacy in mice. Furthermore, NCBP1 effectively inhibited the growth of plant fungal pathogens and enhanced disease resistance in maize. Our results demonstrate the unexplored antimicrobial potential of plant-derived NCPs and provide an accessible source for the discovery of antimicrobial substances against MDR bacterial and fungal pathogens.

Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability

Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models for stapled AMPs using deep and machine learning, tested their accuracy with an independent data set and wet lab experiments, and characterised stapled loop structures using structural, sequence and amino acid descriptors. AlphaFold improved stapled peptide structure prediction. The support vector machine model performed best, while two deep learning models achieved the highest accuracy of 1.0 on an external test set. Designed cysteine- and lysine-stapled peptides inhibited various bacteria with low concentrations and showed good serum stability and low haemolytic activity. This study highlights the potential of the deep learning method in peptide modification and design.

Paving the way for new antimicrobial peptides through molecular de-extinction

Molecular de-extinction has emerged as a novel strategy for studying biological molecules throughout evolutionary history. Among the myriad possibilities offered by ancient genomes and proteomes, antimicrobial peptides (AMPs) stand out as particularly promising alternatives to traditional antibiotics. Various strategies, including software tools and advanced deep learning models, have been used to mine these host defense peptides. For example, computational analysis of disulfide bond patterns has led to the identification of six previously uncharacterized β-defensins in extinct and critically endangered species. Additionally, artificial intelligence and machine learning have been utilized to uncover ancient antibiotics, revealing numerous candidates, including mammuthusin, and elephasin, which display inhibitory effects toward pathogens in vitro and in vivo. These innovations promise to discover novel antibiotics and deepen our insight into evolutionary processes.

Natural peptides and their synthetic congeners acting against Acinetobacter baumannii through the membrane and cell wall: latest progress

Acinetobacter baumannii is one of the deadliest Gram-negative bacteria (GNB), responsible for 2-10% of hospital-acquired infections. Several antibiotics are used to control the growth of A. baumannii. However, in recent decades, the abuse and misuse of antibiotics to treat non-microbial diseases have led to the emergence of multidrug-resistant A. baumannii strains. A. baumannii possesses a complex cell wall structure. Cell wall-targeting agents remain the center of antibiotic drug discovery. Notably, the antibacterial drug discovery intends to target the membrane of the bacteria, offering several advantages over antibiotics targeting intracellular systems, as membrane-targeting agents do not have to travel through the plasma membrane to reach the cytoplasmic targets. Microorganisms, insects, and mammals produce antimicrobial peptides as their first line of defense to protect themselves from pathogens and predators. Importantly, antimicrobial peptides are considered potential alternatives to antibiotics. This communication summarises the recently identified peptides of natural origin and their synthetic congeners acting against the A. baumannii membrane by cell wall disruption.

Small and Versatile Cyclotides as Anti-infective Agents

Plants provide an abundant source of potential therapeutic agents, including a diverse array of compounds, such as cyclotides, which are peptides known for their antimicrobial activity. Cyclotides are multifaceted molecules with a wide range of biological activities. Their unique topology forms a head-to-tail cyclic structure reinforced by a cysteine knot, which confers chemical and thermal stability. These molecules can directly target membranes of infectious agents by binding to phosphatidylethanolamine in lipid membranes, leading to membrane permeabilization. Additionally, they function as carriers and cell-penetrating molecules, demonstrating antiviral, antibacterial, antifungal, and nematicidal properties. The structure of cyclotides is also amenable to chemical synthesis, facilitating drug design through residue substitutions or grafting of bioactive epitopes within the cyclotide scaffold to enhance peptide stability. In this review, we explore the multifunctionality of these biomolecules as anti-infective agents, emphasizing their potential as a novel class of antimicrobial drugs.

Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides

INTRODUCTION

Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the structure-activity relationships of AMPs can direct the synthesis of desirable peptide therapeutics.

OBJECTIVES

In this study, the lipopolysaccharide-binding domain (LBD) was identified through machine learning-guided directed evolution, which acts as a functional domain of the anti-lipopolysaccharide factor family of AMPs identified from Marsupenaeus japonicus.

METHODS

LBDA-D was identified as an output of this algorithm, in which the original LBDMj sequence was the input, and the three-dimensional solution structure of LBDB was determined using nuclear magnetic resonance. Furthermore, our study involved a comprehensive series of experiments, including morphological studies and in vitro and in vivo antibacterial tests.

RESULTS

The NMR solution structure showed that LBDB possesses a circular extended structure with a disulfide crosslink at the terminus and two 310-helices and exhibits a broad antimicrobial spectrum. In addition, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that LBDB induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. Finally, coinjection of LBDB, Vibrio alginolyticus and Staphylococcus aureus in vivo improved the survival of M. japonicus, demonstrating the promising therapeutic role of LBDB for treating infectious disease.

CONCLUSIONS

The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.

The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Leveraging large language models for peptide antibiotic design

Large language models (LLMs) have significantly impacted various domains of our society, including recent applications in complex fields such as biology and chemistry. These models, built on sophisticated neural network architectures and trained on extensive datasets, are powerful tools for designing, optimizing, and generating molecules. This review explores the role of LLMs in discovering and designing antibiotics, focusing on peptide molecules. We highlight advancements in drug design and outline the challenges of applying LLMs in these areas.

Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance

Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug discovery. These tools improve antibiotic stewardship and identify effective compounds such as antimicrobial peptides and small molecules. This review explores AI applications in diagnostics, therapy, and drug discovery, emphasizing both strengths and areas needing improvement.

Superficial Fungal Infections and Artificial Intelligence: A Review on Current Advances and Opportunities: REVISION

BACKGROUND

Superficial fungal infections are among the most common infections in world, they mainly affect skin, nails and scalp without further invasion. Superficial fungal diseases are conventionally diagnosed with direct microscopy, fungal culture or histopathology, treated with topical or systemic antifungal agents and prevented in immunocompetent patients by improving personal hygiene. However, conventional diagnostic tests can be time-consuming, also treatment can be insufficient or ineffective and prevention can prove to be demanding. Artificial Intelligence (AI) refers to a digital system having an intelligence akin to a human being. The concept of AI has existed since 1956, but hasn't been practicalised until recently. AI has revolutionised medical research in the recent years, promising to influence almost all specialties of medicine.

OBJECTIVE

An increasing number of articles have been published about the usage of AI in cutaneous mycoses.

METHODS

In this review, the key findings of articles about utilisation of AI in diagnosis, treatment and prevention of superficial fungal infections are summarised. Moreover, the need for more research and development is highlighted.

RESULTS

Fifty-four studies were reviewed. Onychomycosis was the most researched superficial fungal infection. AI can be used diagnosing fungi in macroscopic and microscopic images and classify them to some extent. AI can be a tool and be used as a part of something bigger to diagnose superficial mycoses.

CONCLUSION

AI can be used in all three steps of diagnosing, treating and preventing. AI can be a tool complementary to the clinician's skills and laboratory results.

AI Methods for Antimicrobial Peptides: Progress and Challenges

Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure-guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure-guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.

Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties

Encrypted peptides (EPs) have been recently described as a new class of antimicrobial molecules. They have been found in numerous organisms and have been proposed to have a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these EPs are found embedded in proteins unrelated to the immune system, suggesting that immunological responses extend beyond traditional host immunity proteins. To test this idea, we synthesized and analyzed representative peptides derived from non-immune human proteins for their ability to exert antimicrobial and immunomodulatory properties. Most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators, such as interleukin (IL)-6, tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein-1 (MCP-1). Moreover, eight of the peptides identified, collagenin-3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may have a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.

Antimicrobial resistance: a concise update

Antimicrobial resistance (AMR) is a serious threat to global public health, with approximately 5 million deaths associated with bacterial AMR in 2019. Tackling AMR requires a multifaceted and cohesive approach that ranges from increased understanding of mechanisms and drivers at the individual and population levels, AMR surveillance, antimicrobial stewardship, improved infection prevention and control measures, and strengthened global policies and funding to development of novel antimicrobial therapeutic strategies. In this rapidly advancing field, this Review provides a concise update on AMR, encompassing epidemiology, evolution, underlying mechanisms (primarily those related to last-line or newer generation of antibiotics), infection prevention and control measures, access to antibiotics, antimicrobial stewardship, AMR surveillance, and emerging non-antibiotic therapeutic approaches. The Review also discusses the potential roles of artificial intelligence in addressing AMR, including antimicrobial susceptibility testing, AMR surveillance, antimicrobial stewardship, diagnosis, and antimicrobial drug discovery and development. This Review highlights the urgent need for addressing the global effects of AMR and for rapid advancement of relevant technology in this dynamic field.

Venomics AI: a computational exploration of global venoms for antibiotic discovery

The relentless emergence of antibiotic-resistant pathogens, particularly Gram-negative bacteria, highlights the urgent need for novel therapeutic interventions. Drug-resistant infections account for approximately 5 million deaths annually, yet the antibiotic development pipeline has largely stagnated. Venoms, representing a remarkably diverse reservoir of bioactive molecules, remain an underexploited source of potential antimicrobials. Venom-derived peptides, in particular, hold promise for antibiotic discovery due to their evolutionary diversity and unique pharmacological profiles. In this study, we mined comprehensive global venomics datasets to identify new antimicrobial candidates. Using machine learning, we explored 16,123 venom proteins, generating 40,626,260 venom-encrypted peptides (VEPs). Using APEX, a deep learning model combining a peptide-sequence encoder with neural networks for antimicrobial activity prediction, we identified 386 VEPs structurally and functionally distinct from known antimicrobial peptides. Our analyses showed that these VEPs possess high net charge and elevated hydrophobicity, characteristics conducive to bacterial membrane disruption. Structural studies revealed considerable conformational flexibility, with many VEPs transitioning to α-helical conformations in membrane-mimicking environments, indicative of their antimicrobial potential. Of the 58 VEPs selected for experimental validation, 53 displayed potent antimicrobial activity. Mechanistic assays indicated that VEPs primarily exert their effects through bacterial membrane depolarization, mirroring AMP-like mechanisms. In vivo studies using a mouse model of Acinetobacter baumannii infection demonstrated that lead VEPs significantly reduced bacterial burdens without notable toxicity. This study highlights the value of venoms as a resource for new antibiotics. By integrating computational approaches and experimental validation, venom-derived peptides emerge as promising candidates to combat the global challenge of antibiotic resistance.

Computational Approaches for Antimicrobial Peptide Delivery

Peptides constitute alternative molecules for the treatment of infections caused by bacteria, viruses, fungi, and protozoa. However, their therapeutic effectiveness is often limited by enzymatic degradation, chemical and physical instability, and toxicity toward healthy human cells. To improve their pharmacokinetic (PK) and pharmacodynamic (PD) profiles, novel routes of administration are being explored. Among these, nanoparticles have shown promise as potential carriers for peptides, although the design of delivery vehicles remains a slow and painstaking process, heavily reliant on trial and error. Recently, computational approaches have been introduced to accelerate the development of effective drug delivery systems for peptides. Here we present an overview of some of these computational strategies and discuss their potential to optimize drug development and delivery.

Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization

Antimicrobial peptides (AMPs) emerge as a type of promising therapeutic compounds that exhibit broad spectrum antimicrobial activity with high specificity and good tolerability. Natural AMPs usually need further rational design for improving antimicrobial activity and decreasing toxicity to human cells. Although several algorithms have been developed to optimize AMPs with desired properties, they explored the variations of AMPs in a discrete amino acid sequence space, usually suffering from low efficiency, lack diversity, and local optimum. In this work, we propose a novel directed evolution method, named PepZOO, for optimizing multi-properties of AMPs in a continuous representation space guided by multi-objective zeroth-order optimization. PepZOO projects AMPs from a discrete amino acid sequence space into continuous latent representation space by a variational autoencoder. Subsequently, the latent embeddings of prototype AMPs are taken as start points and iteratively updated according to the guidance of multi-objective zeroth-order optimization. Experimental results demonstrate PepZOO outperforms state-of-the-art methods on improving the multi-properties in terms of antimicrobial function, activity, toxicity, and binding affinity to the targets. Molecular docking and molecular dynamics simulations are further employed to validate the effectiveness of our method. Moreover, PepZOO can reveal important motifs which are required to maintain a particular property during the evolution by aligning the evolutionary sequences. PepZOO provides a novel research paradigm that optimizes AMPs by exploring property change instead of exploring sequence mutations, accelerating the discovery of potential therapeutic peptides.

PA-Win2: In Silico-Based Discovery of a Novel Peptide with Dual Antibacterial and Anti-Biofilm Activity

Background: The emergence and prevalence of antibiotic-resistant bacteria (ARBs) have become a serious global threat, as the morbidity and mortality associated with ARB infections are continuously rising. The activation of quorum sensing (QS) genes can promote biofilm formation, which contributes to the acquisition of drug resistance and increases virulence. Therefore, there is an urgent need to develop new antimicrobial agents to control ARB and prevent further development. Antimicrobial peptides (AMPs) are naturally occurring defense molecules in organisms known to suppress pathogens through a broad range of antimicrobial mechanisms. Methods: In this study, we utilized a previously developed deep-learning model to identify AMP candidates from the venom gland transcriptome of the spider Pardosa astrigera, followed by experimental validation. Results: PA-Win2 was among the top-scoring predicted peptides and was selected based on physiochemical features. Subsequent experimental validation demonstrated that PA-Win2 inhibits the growth of Bacillus subtilis, Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, and multidrug-resistant P. aeruginosa (MRPA) strain CCARM 2095. The peptide exhibited strong bactericidal activity against P. aeruginosa, and MRPA CCARM 2095 through the depolarization of bacterial cytoplasmic membranes and alteration of gene expression associated with bacterial survival. In addition, PA-Win2 effectively inhibited biofilm formation and degraded pre-formed biofilms of P. aeruginosa. The gene expression study showed that the peptide treatment led to the downregulation of QS genes in the Las, Pqs, and Rhl systems. Conclusions: These findings suggest PA-Win2 as a promising drug candidate against ARB and demonstrate the potential of in silico methods in discovering functional peptides from biological data.

Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects

The issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about revolutionary changes in this field. These advanced computational methods are capable of processing and analyzing large-scale biomedical data, thereby uncovering complex patterns and mechanisms behind the development of resistance. AI technologies are increasingly applied to predict the resistance of pathogens to various antibiotics based on gene content and genomic composition. This article reviews the latest advancements in AI and ML for predicting antimicrobial resistance in pathogenic microorganisms. We begin with an overview of the biological foundations of microbial resistance and its epidemiological research. Subsequently, we highlight the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks. Furthermore, we explore the major challenges in the field, such as data availability, model interpretability, and cross-species resistance prediction. Finally, we discuss new perspectives and solutions for research into microbial resistance through algorithm optimization, dataset expansion, and interdisciplinary collaboration. With the continuous advancement of AI technology, we will have the most powerful weapon in the fight against pathogenic microbial resistance in the future.

An escape from ESKAPE pathogens: A comprehensive review on current and emerging therapeutics against antibiotic resistance

The rise of antimicrobial resistance has positioned ESKAPE pathogens as a serious global health threat, primarily due to the limitations and frequent failures of current treatment options. This growing risk has spurred the scientific community to seek innovative antibiotic therapies and improved oversight strategies. This review aims to provide a comprehensive overview of the origins and resistance mechanisms of ESKAPE pathogens, while also exploring next-generation treatment strategies for these infections. In addition, it will address both traditional and novel approaches to combating antibiotic resistance, offering insights into potential new therapeutic avenues. Emerging research underscores the urgency of developing new antimicrobial agents and strategies to overcome resistance, highlighting the need for novel drug classes and combination therapies. Advances in genomic technologies and a deeper understanding of microbial pathogenesis are crucial in identifying effective treatments. Integrating precision medicine and personalized approaches could enhance therapeutic efficacy. The review also emphasizes the importance of global collaboration in surveillance and stewardship, as well as policy reforms, enhanced diagnostic tools, and public awareness initiatives, to address resistance on a worldwide scale.

Anti-fungal peptides: an emerging category with enthralling therapeutic prospects in the treatment of candidiasis

Candida infections, particularly invasive candidiasis, pose a serious global health threat. Candida albicans is the most prevalent species causing candidiasis, and resistance to key antifungal drugs, such as azoles, echinocandins, polyenes, and fluoropyrimidines, has emerged. This growing multidrug resistance (MDR) complicates treatment options, highlighting the need for novel therapeutic approaches. Antifungal peptides (AFPs) are gaining recognition for their potential as new antifungal agents due to their diverse structures and functions. These natural or recombinant peptides can effectively target fungal virulence and viability, making them promising candidates for future antifungal development. This review examines infections caused by Candida species, the limitations of current antifungal treatments, and the therapeutic potential of AFPs. It emphasizes the importance of identifying novel AFP targets and their production for advancing treatment strategies. By discussing the therapeutic development of AFPs, the review aims to draw researchers' attention to this promising field. The integration of knowledge about AFPs could pave the way for novel antifungal agents with broad-spectrum activity, reduced toxicity, targeted action, and mechanisms that limit resistance in pathogenic fungi, offering significant advancements in antifungal therapeutics.

Model-directed generation of artificial CRISPR-Cas13a guide RNA sequences improves nucleic acid detection

CRISPR guide RNA sequences deriving exactly from natural sequences may not perform optimally in every application. Here we implement and evaluate algorithms for designing maximally fit, artificial CRISPR-Cas13a guides with multiple mismatches to natural sequences that are tailored for diagnostic applications. These guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared with guides derived directly from natural sequences and illuminate design principles that broaden Cas13a targeting.

Enhancing Antimicrobial Peptide Activity through Modifications of Charge, Hydrophobicity, and Structure

Antimicrobial peptides (AMPs) are emerging as a promising alternative to traditional antibiotics due to their ability to disturb bacterial membranes and/or their intracellular processes, offering a potential solution to the growing problem of antimicrobial resistance. AMP effectiveness is governed by factors such as net charge, hydrophobicity, and the ability to form amphipathic secondary structures. When properly balanced, these characteristics enable AMPs to selectively target bacterial membranes while sparing eukaryotic cells. This review focuses on the roles of positive charge, hydrophobicity, and structure in influencing AMP activity and toxicity, and explores strategies to optimize them for enhanced therapeutic potential. We highlight the delicate balance between these properties and how various modifications, including amino acid substitutions, peptide tagging, or lipid conjugation, can either enhance or impair AMP performance. Notably, an increase in these parameters does not always yield the best results; sometimes, a slight reduction in charge, hydrophobicity, or structural stability improves the overall AMP therapeutic potential. Understanding these complex interactions is key to developing AMPs with greater antimicrobial activity and reduced toxicity, making them viable candidates in the fight against antibiotic-resistant bacteria.

Chemically diverse antimicrobial peptides induce hyperpolarization of the E. coli membrane

The negative membrane potential within bacterial cells is crucial in various essential cellular processes. Sustaining a hyperpolarised membrane could offer a novel strategy to combat antimicrobial resistance. However, it remains uncertain which molecules are responsible for inducing hyperpolarization and what the underlying molecular mechanisms are. Here, we demonstrate that chemically diverse antimicrobial peptides (AMPs) trigger hyperpolarization of the bacterial cytosolic membrane when applied at subinhibitory concentrations. Specifically, these AMPs adopt a membrane-induced amphipathic structure and, thereby, generate hyperpolarization in Escherichia coli without damaging the cell membrane. These AMPs act as selective ionophores for K+ (over Na+) or Cl- (over H2PO4- and NO3-) ions, generating diffusion potential across the membrane. At lower dosages of AMPs, a quasi-steady-state membrane polarisation value is achieved. Our findings highlight the potential of AMPs as a valuable tool for chemically hyperpolarising bacteria, with implications for antimicrobial research and bacterial electrophysiology.

Strategically Engineered Ru(II) Complexes with Enhanced ROS Activity Enabling Potent Sonodynamic Effect against Multidrug-Resistant Biofilms

Sonodynamic therapy (SDT) can generate reactive oxygen species (ROS) to combat multidrug-resistant biofilms, which pose significant challenges to human health. As the key to producing ROS in SDT, the design of sonosensitizers with optimal molecular structures for sufficient ROS generation and activity in complex biofilm matrix is essential. In this study, we propose a π-expansion strategy and synthesize a series of small-molecule metal Ru(II) complexes (Ru1-Ru4) as sonosensitizers (Ru1-Ru4) to enhance the efficacy of SDT. Among these complexes, Ru4 demonstrates remarkable ROS generation capability (∼65.5-fold) that surpasses most commercial sonosensitizers (1.3- to 6.7-fold). Through catalyzing endogenous H2O2 decomposition, Ru4 facilitates the production of abundant O2 as a resource for 1O2 and the generation of new ROS (i.e., •OH) for improving SDT. Furthermore, Ru4 maintains the sustained ROS activity via consuming the interferences (e.g., glutathione) that react with ROS. Due to these unique advantages, Ru4 exhibits potent biofilm eradication ability against methicillin-resistant Staphylococcus aureus (MRSA) both in vitro and in vivo, underscoring its potential use in clinical settings. This work introduces a new approach for designing effective sonosensitizers to eliminate biofilm infections, addressing a critical need in healthcare management.

Chemical modification, structure elucidation and antifungal mechanism studies of a peptide extracted from garlic (Allium sativum L.)

BACKGROUND

Garlic is a promising source of antimicrobial peptide separation, and chemical modification is an effective method for activity improvement. The present study aimed to improve the antifungal activity of a peptide extracted from garlic. Chemical modifications were conducted, and the structure-activity relationship and antifungal mechanism were investigated.

RESULTS

The results indicated that the cationic charge induced by Lys residue at the N-terminal was important for the antimicrobial activity, and the modified sequence exhibited significant antifungal activity with low mammalian toxicity and a low tendency of drug resistance (p < 0.05). The structure-activity relationship analysis revealed that the modified active peptide had a predominant α-helical structure and an inner cyclic correlation. Transcriptomic analysis showed that peptide KMLKKLFR (Lys-Met-Leu-Lys-Lyse-Leu-Phe-Arg) affected the rRNA processing and carbon metabolism process of Candida albicans. In addition, the membrane potential study indicated a non-membrane destruction mechanism, and molecular docking analysis and a DNA interaction assay suggested promising inner targets.

CONCLUSION

The results of the present study indicate that chemical modification by amino acid substitution was effective for antimicrobial activity improvement. The present study would benefit future antimicrobial peptide development and suggests that garlic is a great source of antibacterial peptides and peptide template separations for coping with antibiotic resistance. © 2024 Society of Chemical Industry.

Mining human microbiomes reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.

A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection

Development of potent and broad-spectrum antimicrobial peptides (AMPs) could help overcome the antimicrobial resistance crisis. We develop a peptide language-based deep generative framework (deepAMP) for identifying potent, broad-spectrum AMPs. Using deepAMP to reduce antimicrobial resistance and enhance the membrane-disrupting abilities of AMPs, we identify, synthesize, and experimentally test 18 T1-AMP (Tier 1) and 11 T2-AMP (Tier 2) candidates in a two-round design and by employing cross-optimization-validation. More than 90% of the designed AMPs show a better inhibition than penetratin in both Gram-positive (i.e., S. aureus) and Gram-negative bacteria (i.e., K. pneumoniae and P. aeruginosa). T2-9 shows the strongest antibacterial activity, comparable to FDA-approved antibiotics. We show that three AMPs (T1-2, T1-5 and T2-10) significantly reduce resistance to S. aureus compared to ciprofloxacin and are effective against skin wound infection in a female wound mouse model infected with P. aeruginosa. In summary, deepAMP expedites discovery of effective, broad-spectrum AMPs against drug-resistant bacteria.

Computational Design of Pore-Forming Peptides with Potent Antimicrobial and Anticancer Activities

Peptides that form transmembrane barrel-stave pores are potential alternative therapeutics for bacterial infections and cancer. However, their optimization for clinical translation is hampered by a lack of sequence-function understanding. Recently, we have de novo designed the first synthetic barrel-stave pore-forming antimicrobial peptide with an identified function of all residues. Here, we systematically mutate the peptide to improve pore-forming ability in anticipation of enhanced activity. Using computer simulations, supported by liposome leakage and atomic force microscopy experiments, we find that pore-forming ability, while critical, is not the limiting factor for improving activity in the submicromolar range. Affinity for bacterial and cancer cell membranes needs to be optimized simultaneously. Optimized peptides more effectively killed antibiotic-resistant ESKAPEE bacteria at submicromolar concentrations, showing low cytotoxicity to human cells and skin model. Peptides showed systemic anti-infective activity in a preclinical mouse model of Acinetobacter baumannii infection. We also demonstrate peptide optimization for pH-dependent antimicrobial and anticancer activity.

Engineered fungus containing a caterpillar gene kills insects rapidly by disrupting their ecto- and endo-microbiomes

Similar to the physiological importance of gut microbiomes, recent works have shown that insect ectomicrobiotas can mediate defensive colonization resistance against fungal parasites that infect via cuticle penetration. Here we show that engineering the entomopathogenic fungus Metarhizium robertsii with a potent antibacterial moricin gene from silkworms substantially enhances the ability of the fungus to kill mosquitos, locusts, and two Drosophila species. Further use of Drosophila melanogaster as an infection model, quantitative microbiome analysis reveals that engineered strains designed to suppress insect cuticular bacteria additionally disrupt gut microbiomes. An overgrowth of harmful bacteria such as the opportunistic pathogens of Providencia species is detected that can accelerate insect death. In support, quantitative analysis of antimicrobial genes in fly fat bodies and guts indicates that topical fungal infections result in the compromise of intestinal immune responses. In addition to providing an innovative strategy for improving the potency of mycoinsecticides, our data solidify the importance of both the ecto- and endo-microbiomes in maintaining insect wellbeing.

From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review

The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.

Discovery of antimicrobial peptides in the global microbiome with machine learning

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

Deep-learning-enabled antibiotic discovery through molecular de-extinction

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.

Investigation of cytotoxic effect and action mechanism of a synthetic peptide derivative of rabbit cathelicidin against MDA-MB-231 breast cancer cell line

Antimicrobial peptides (AMPs) have sparked significant interest as potential anti-cancer agents, thereby becoming a focal point in pursuing novel cancer-fighting strategies. These peptides possess distinctive properties, underscoring the importance of developing more potent and selectively targeted versions with diverse mechanisms of action against human cancer cells. Such advancements would offer notable advantages compared to existing cancer therapies. This research aimed to examine the toxicity and selectivity of the nrCap18 peptide in both cancer and normal cell lines. Furthermore, the rate of cellular death was assessed using apoptosis and acridine orange/ethidium bromide (AO/EB) double staining at three distinct incubation times. Additionally, the impact of this peptide on the cancer cell cycle and migration was evaluated, and ultimately, the expression of cyclin-dependent kinase 4/6 (CDK4/6) genes was investigated. The results obtained from the study demonstrated significant toxicity and selectivity in cancer cells compared to normal cells. Moreover, a strong progressive increase in cell death was observed over time. Furthermore, the peptide exhibited the ability to halt the progression of cancer cells in the G1 phase of the cell cycle and impede their migration by suppressing the expression of CDK4/6 genes.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides

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

Challenges in computational discovery of bioactive peptides in 'omics data

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

Developing antibacterial peptides as a promising therapy for combating antibiotic-resistant Pseudomonas aeruginosa infections

Background and Aim

Antibiotic-resistant Pseudomonas aeruginosa poses a serious health threat. This study aimed to investigate the antibacterial activity of peptide KW-23 against drug-resistant P. aeruginosa and its potential for enhancing the efficacy of conventional antibiotics.

Materials and Methods

KW-23 was synthesized from nine amino acids, specifically three tryptophans and three lysines. The purity of the substance was analyzed using reverse-phase high-performance liquid chromatography. The peptide was identified through mass spectrometry using electrospray ionization. The minimum inhibitory concentration (MIC) values of KW-23 in combination with conventional antibiotics against control and multidrug-resistant P. aeruginosa were determined utilizing broth microdilution. The erythrocyte hemolytic assay was used to measure toxicity. The KW-23 effect was analyzed using the time-kill curve.

Results

The peptide exhibited strong antibacterial activity against control and multidrug-resistant strains of P. aeruginosa, with MICs of 4.5 μg/mL and 20 μg/mL, respectively. At higher concentration of 100 μg/mL, KW-23 exhibited a low hemolytic impact, causing no more than 3% damage to red blood. The cytotoxicity assay demonstrates KW-23's safety, while the time-kill curve highlights its rapid and sustained antibacterial activity. The combination of KW-23 and gentamicin exhibited synergistic activity against both susceptible and resistant P. aeruginosa, with fractional inhibitory concentration index values of 0.07 and 0.27, respectively.

Conclusion

The KW-23 synthesized in the laboratory significantly combats antibiotic-resistant P. aeruginosa. Due to its strong antibacterial properties and low toxicity to cells, KW-23 is a promising alternative to traditional antibiotics in combating multidrug-resistant bacteria.

Machine learning for antimicrobial peptide identification and design

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.

An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion

BACKGROUND

As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs.

RESULTS

A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models.

SIGNIFICANCE

The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.

Computational peptide discovery with a genetic programming approach

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases

Biomolecular research traditionally revolves around comprehending the mechanisms through which peptides or proteins facilitate specific functions, often driven by their relevance to clinical ailments. This conventional approach assumes that unraveling mechanisms is a prerequisite for wielding control over functionality, which stands as the ultimate research goal. However, an alternative perspective emerges from physics-based inverse design, shifting the focus from mechanisms to the direct acquisition of functional control strategies. By embracing this methodology, we can uncover solutions that might not have direct parallels in natural systems, yet yield crucial insights into the isolated molecular elements dictating functionality. This provides a distinctive comprehension of the underlying mechanisms.In this context, we elucidate how physics-based inverse design, facilitated by evolutionary algorithms and coarse-grained molecular simulations, charts a promising course for innovating the reverse engineering of biopolymers interacting with intricate fluid phases such as lipid membranes and liquid protein phases. We introduce evolutionary molecular dynamics (Evo-MD) simulations, an approach that merges evolutionary algorithms with the Martini coarse-grained force field. This method directs the evolutionary process from random amino acid sequences toward peptides interacting with complex fluid phases such as biological lipid membranes, offering significant promises in the development of peptide-based sensors and drugs. This approach can be tailored to recognize or selectively target specific attributes such as membrane curvature, lipid composition, membrane phase (e.g., lipid rafts), and protein fluid phases. Although the resulting optimal solutions may not perfectly align with biological norms, physics-based inverse design excels at isolating relevant physicochemical principles and thermodynamic driving forces governing optimal biopolymer interaction within complex fluidic environments. In addition, we expound upon how physics-based evolution using the Evo-MD approach can be harnessed to extract the evolutionary optimization fingerprints of protein-lipid interactions from native proteins. Finally, we outline how such an approach is uniquely able to generate strategic training data for predictive neural network models that cover the whole relevant physicochemical domain. Exploring challenges, we address key considerations such as choosing a fitting fitness function to delineate the desired functionality. Additionally, we scrutinize assumptions tied to system setup, the targeted protein structure, and limitations posed by the utilized (coarse-grained) force fields and explore potential strategies for guiding evolution with limited experimental data. This discourse encapsulates the potential and remaining obstacles of physics-based inverse design, paving the way for an exciting frontier in biomolecular research.

Recombinant production of antimicrobial peptides in plants

Classical plant breeding methods are limited in their ability to confer disease resistance on plants. However, in recent years, advancements in molecular breeding and biotechnological have provided new approaches to overcome these limitations and protect plants from disease. Antimicrobial peptides (AMPs) constitute promising agents that may be able to protect against infectious agents. Recently, peptides have been recombinantly produced in plants at scale and low cost. Because AMPs are less likely than conventional antimicrobials to elicit resistance of pathogenic bacteria, they open up exciting new avenues for agricultural applications. Here, we review recent advances in the design and production of bioactive recombinant AMPs that can effectively protect crop plants from diseases.