Mining for encrypted peptide antibiotics in the human proteome

Discovery of Antimicrobial Peptides in Urinary Tract Infections

Antimicrobial peptides (AMPs) play a pivotal role in the innate immune system as a frontline defense against microbial threats. AMPs can serve as biomarkers and alternative antibiotics, overcoming mortality related to multidrug-resistant pathogens in urinary tract infections (UTIs). While the relevance of AMPs in UTIs has been validated and AMP drugs approved by the US Food and Drug Administration are in clinical use, information about their modification status, regulation, and mechanism of action remains sparse. Only a small fraction of sequences with potential AMP activity, predicted on the basis of known AMP characteristics, have been validated. Elucidation of the global profile of AMPs in the bladder, kidney, and urine under UTI conditions would facilitate an in-depth, disease-specific understanding of the innate immune system and the development of tailored AMP biomarkers and antibiotics. This mini-review focuses on a comprehensive strategy for global profiling and validation of AMPs in UTIs that incorporates AMP data repositories, prediction algorithms, and proteomics for healthy individuals and UTI patients. PATIENT SUMMARY: Short protein molecules called peptides that have antimicrobial activity show promise for the treatment of urinary tract infections. More research and testing of naturally occurring and synthetic peptides with this activity are needed to fully understand how they can help in patient care.

Synergistic collaboration between AMPs and non-direct antimicrobial cationic peptides

Non-direct antimicrobial cationic peptides (NDACPs) are components of the animal innate immune system. But their functions and association with antimicrobial peptides (AMPs) are incompletely understood. Here, we reveal a synergistic interaction between the AMP AW1 and the NDACP AW2, which are co-expressed in the frog Amolops wuyiensis. AW2 enhances the antibacterial activity of AW1 both in vitro and in vivo, while mitigating the development of bacterial resistance and eradicating biofilms. AW1 and AW2 synergistically damage bacterial membranes, facilitating cellular uptake and interaction of AW2 with the intracellular target bacterial genomic DNA. Simultaneously, they trigger the generation of ROS in bacteria, contributing to cell death upon reaching a threshold level. Moreover, we demonstrate that this synergistic antibacterial effect between AMPs and NDACPs is prevalent across diverse animal species. These findings unveil a robust and previously unknown correlation between AMPs and NDACPs as a widespread antibacterial immune defense strategy in animals.

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.

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.

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.

Molecular farming for sustainable production of clinical-grade antimicrobial peptides

Antimicrobial peptides (AMPs) are emerging as next-generation therapeutics due to their broad-spectrum activity against drug-resistant bacterial strains and their ability to eradicate biofilms, modulate immune responses, exert anti-inflammatory effects and improve disease management. They are produced through solid-phase peptide synthesis or in bacterial or yeast cells. Molecular farming, i.e. the production of biologics in plants, offers a low-cost, non-toxic, scalable and simple alternative platform to produce AMPs at a sustainable cost. In this review, we discuss the advantages of molecular farming for producing clinical-grade AMPs, advances in expression and purification systems and the cost advantage for industrial-scale production. We further review how 'green' production is filling the sustainability gap, streamlining patent and regulatory approvals and enabling successful clinical translations that demonstrate the future potential of AMPs produced by molecular farming. Finally, we discuss the regulatory challenges that need to be addressed to fully realize the potential of molecular farming-based AMP production for therapeutics.

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.

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.

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

Encrypted peptides have been recently described as a new class of antimicrobial molecules. They have been proposed to play a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these peptides are found embedded in proteins unrelated to the immune system, suggesting that immunological responses may extend beyond traditional host immunity proteins. To test this idea, here we synthesized and tested representative peptides derived from non-immune proteins for their ability to exert antimicrobial and immunomodulatory properties. Our experiments revealed that 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 IL-6, TNF-α, and 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 play 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.

Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy

Vancomycin-resistant enterococci (VRE) are major opportunistic pathogens and the causative agents of serious diseases, such as urinary tract infections and endocarditis. VRE strains mainly include species of Enterococcus faecium and E. faecalis which can colonise the gastrointestinal tract (GIT) of patients and, following growth and persistence in the gut, can transfer to blood resulting in systemic dissemination in the body. Advancements in genomics have revealed that hospital-associated VRE strains are characterised by increased numbers of mobile genetic elements, higher numbers of antibiotic resistance genes and often lack active CRISPR-Cas systems. Additionally, comparative genomics have increased our understanding of dissemination routes among patients and healthcare workers. Since the efficiency of currently available antibiotics is rapidly declining, new measures to control infection and dissemination of these persistent pathogens are urgently needed. These approaches include combinatory administration of antibiotics, strengthening colonisation resistance of the gut microbiota to reduce VRE proliferation through commensals or probiotic bacteria, or switching to non-antibiotic bacterial killers, such as bacteriophages or bacteriocins. In this review, we discuss the current knowledge of the genomics of VRE isolates and state-of-the-art therapeutic advances against VRE infections.

Polyproline peptide targets Klebsiella pneumoniae polysaccharides to collapse biofilms

Hypervirulent Klebsiella pneumoniae is known for its increased extracellular polysaccharide production. Biofilm matrices of hypervirulent K. pneumoniae have increased polysaccharide abundance and are uniquely susceptible to disruption by peptide bactenecin 7 (bac7 (1-35)). Here, using confocal microscopy, we show that polysaccharides within the biofilm matrix collapse following bac7 (1-35) treatment. This collapse led to the release of cells from the biofilm, which were then killed by the peptide. Characterization of truncated peptide analogs revealed that their interactions with polysaccharide were responsible for the biofilm matrix changes that accompany bac7 (1-35) treatment. Ultraviolet photodissociation mass spectrometry with the parental peptide or a truncated analog bac7 (10-35) reveal the important regions for bac7 (1-35) complexing with polysaccharides. Finally, we tested bac7 (1-35) using a murine skin abscess model and observed a significant decrease in the bacterial burden. These findings unveil the potential of bac7 (1-35) polysaccharide interactions to collapse K. pneumoniae biofilms.

Antimicrobial peptides designed by computational analysis of proteomes

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

Metagenomic evidence for antibiotic-associated actinomycetes in the Karamay Gobi region

Due to the misuse of antibiotics, there is an increasing emergence and spread of multidrug-resistant (MDR) bacteria, leading to a human health crisis. To address clinical antibiotic resistance and prevent/control pathogenic microorganisms, the development of novel antibiotics is essential. This also offers a new approach to discovering valuable actinobacterial flora capable of producing natural bioactive products. In this study, we employed bioinformatics and macro-genome sequencing to collect 15 soil samples from three different locations in the Karamay Gobi region. First, we assessed the diversity of microorganisms in soil samples from different locations, analyzing the content of bacteria, archaea, actinomycetes, and fungi. The biodiversity of soil samples from outside the Gobi was found to be higher than that of soil samples from within and in the center of the Gobi. Second, through microbial interaction network analysis, we identified actinomycetes as the dominant group in the system. We have identified the top four antibiotic genes, such as Ecol_fabG_TRC, Efac_liaR_DAP, tetA (58), and macB, by CARD. These genes are associated with peptide antibiotics, disinfecting agents and antiseptics, tetracycline antibiotics, and macrolide antibiotics. In addition, we also obtained 40 other antibiotic-related genes through CARD alignment. Through in-depth analysis of desert soil samples, we identified several unstudied microbial species belonging to different families, including Erythrobacteriaceae, Solirubrobacterales, Thermoleophilaceae, Gaiellaceae, Nocardioidaceae, Actinomycetia, Egibacteraceae, and Acidimicrobiales. These species have the capability to produce peptide antibiotics, macrolide antibiotics, and tetracycline antibiotics, as well as disinfectants and preservatives. This study provides valuable theoretical support for future in-depth 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.

Computational Design of Peptides for Biomaterials Applications

Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.

Study of the Antimicrobial Activity of the Human Peptide SQQ30 against Pathogenic Bacteria

Given the continuous increase in antibiotic resistance, research has been driven towards the isolation of new antimicrobial molecules. Short, charged, and very hydrophobic antimicrobial peptides have a direct action against biological membranes, which are less prone to developing resistance. Using a bioinformatic tool, we chose the SQQ30 peptide, isolated from the human SOGA1 protein. The antimicrobial activity of this peptide against various Gram-negative and Gram-positive bacterial strains and against a fungal strain was studied. A mechanism of action directed against biological membranes was outlined. When administered in combination with the antibiotic ciprofloxacin and with the TRS21 (buforin II), another antimicrobial peptide, SQQ30 can be used with a lower MIC, showing additivity and synergism, respectively. Particularly interesting is the ability of SQQ30 to bind LPS in Gram-negative strains, preventing the eukaryotic cell from releasing inflammatory mediators. Our study indicates SQQ30 as a novel and promising antimicrobial agent.

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

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

Evaluation of antibiotic use and analysis of ciprofloxacin and gentamicin residue in fish samples from farms in Lagos, Nigeria

Antibiotic use in aquaculture for increase yield has been established over time; however, consumption of such fish may lead to inadvertent intake of sub therapeutic doses of antibiotics. Therefore, this study aimed to investigate the extent of antibiotic use in fish farming and ciprofloxacin and gentamicin residues in fish that are consumed in Lagos, Nigeria. It was conducted in two parts: a survey of 60 fish producers to assess their use of antibiotics and an analysis of ciprofloxacin and gentamicin residues in fish organs using high-performance liquid chromatography (HPLC). The survey found that twenty-nine (48%) of the farms used antibiotics, with oxytetracycline being the most common (20%), followed by ciprofloxacin (15%); gentamicin (5%); neomycin (8.33%); and probiotics (13%). HPLC analysis of fish liver and fillet samples showed that Lagos Island had the lowest residual ciprofloxacin dose (0.3014ug/g), while Lagos mainland had the greatest residual ciprofloxacin dosage (113.78765ug/g). The mean gentamicin residue in flesh of fish from farms in Lagos Island LGA was 0.37ug/g while that obtained for liver of fish samples from Surulere LGA was 2.12ug/g). The largest (5.3240ug/g) and lowest (0.2661ug/g) residual gentamicin antibiotic concentrations were found in fishes harvested from farms located in Surulere and Lagos Island, respectively. The residue levels were within the permitted range set by the WHO, but continuous regulatory surveillance is necessary to prevent antimicrobial resistance spread and improved food safety. The study has revealed that fish produced in the study area contained residues of the commonly used antibiotics hence farmers should be encouraged to consult veterinarians in cases of disease treatment for judicious use of antibiotics while self-medication and purchase of antibiotics from vendors should be discouraged.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

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

Influence of the linkage between long alkyl tails and cationic groups on membrane activity of nano-sized hyperbranched polyquaterniums

The recurrent emergence of serious pathogens necessitates novel insights and highly efficient antibacterial agents. However, the innate inability of metal ions and reactive oxygen species (ROS) to differentiate between bacteria and mammalian cells presents a challenge, limiting the selectivity crucial for an ideal antimicrobial solution. Herein, we present a systematic exploration involving two variants of nano-sized hyperbranched polyquaterniums (NHBPQs) - one featuring a lengthy alkyl tail linked to the ammonium unit at the N-atom center (NHBPQ-A), and the other in a segregated configuration (NHBPQ-B). The exterior alkyl chain chains act as a barrier to the cationic group's non-specific adsorption due to spatial site resistance, causing NHBPQ-A in broad-spectrum cytotoxicity. Conversely, the distinct molecular configuration of NHBPQ-B in the segregated state affords greater flexibility, allowing the cationic groups to be released and interact non-specifically, finally resulting in selective bactericidal activity. Leveraging this selectivity, the optimized NHBPQ-B exhibits robust anti-infectious performance in a model of methicillin-resistant Staphylococcus aureus (MRSA)-infected wounds. This work establishes a promising avenue for biocompatible NHBPQs, holding significant potential in addressing MRSA infections and ameliorating both genetically encoded and phenotypic antibiotic resistance mechanisms.

Discovery, characterization, and redesign of potent antimicrobial thanatin orthologs from Chinavia ubica and Murgantia histrionica targeting E. coli LptA

Podisus maculiventris thanatin has been reported as a potent antimicrobial peptide with antibacterial and antifungal activity. Its antibiotic activity has been most thoroughly characterized against E. coli and shown to interfere with multiple pathways, such as the lipopolysaccharide transport (LPT) pathway comprised of seven different Lpt proteins. Thanatin binds to E. coli LptA and LptD, thus disrupting the LPT complex formation and inhibiting cell wall synthesis and microbial growth. Here, we performed a genomic database search to uncover novel thanatin orthologs, characterized their binding to E. coli LptA using bio-layer interferometry, and assessed their antimicrobial activity against E. coli. We found that thanatins from Chinavia ubica and Murgantia histrionica bound tighter (by 3.6- and 2.2-fold respectively) to LptA and exhibited more potent antibiotic activity (by 2.1- and 2.8-fold respectively) than the canonical thanatin from P. maculiventris. We crystallized and determined the LptA-bound complex structures of thanatins from C. ubica (1.90 Å resolution), M. histrionica (1.80 Å resolution), and P. maculiventris (2.43 Å resolution) to better understand their mechanism of action. Our structural analysis revealed that residues A10 and I21 in C. ubica and M. histrionica thanatin are important for improving the binding interface with LptA, thus overall improving the potency of thanatin against E. coli. We also designed a stapled variant of thanatin that removes the need for a disulfide bond but retains the ability to bind LptA and antibiotic activity. Our discovery presents a library of novel thanatin sequences to serve as starting scaffolds for designing more potent antimicrobial therapeutics.

The natriuretic peptide receptor agonist osteocrin disperses Pseudomonas aeruginosa biofilm

Biofilms are highly tolerant to antimicrobials and host immune defense, enabling pathogens to thrive in hostile environments. The diversity of microbial biofilm infections requires alternative and complex treatment strategies. In a previous work we demonstrated that the human Atrial Natriuretic Peptide (hANP) displays a strong anti-biofilm activity toward Pseudomonas aeruginosa and that the binding of hANP by the AmiC protein supports this effect. This AmiC sensor has been identified as an analog of the human natriuretic peptide receptor subtype C (h-NPRC). In the present study, we evaluated the anti-biofilm activity of the h-NPRC agonist, osteocrin (OSTN), a hormone that displays a strong affinity for the AmiC sensor at least in vitro. Using molecular docking, we identified a pocket in the AmiC sensor that OSTN reproducibly docks into, suggesting that OSTN might possess an anti-biofilm activity as well as hANP. This hypothesis was validated since we observed that OSTN dispersed established biofilm of P. aeruginosa PA14 strain at the same concentrations as hANP. However, the OSTN dispersal effect is less marked than that observed for the hANP (-61% versus -73%). We demonstrated that the co-exposure of P. aeruginosa preformed biofilm to hANP and OSTN induced a biofilm dispersion with a similar effect to that observed with hANP alone suggesting a similar mechanism of action of these two peptides. This was confirmed by the observation that OSTN anti-biofilm activity requires the activation of the complex composed by the sensor AmiC and the regulator AmiR of the ami pathway. Using a panel of both P. aeruginosa laboratory reference strains and clinical isolates, we observed that the OSTN capacity to disperse established biofilms is highly variable from one strain to another. Taken together, these results show that similarly to the hANP hormone, OSTN has a strong potential to be used as a tool to disperse P. aeruginosa biofilms.

TRILL: ORCHESTRATING MODULAR DEEP-LEARNING WORKFLOWS FOR DEMOCRATIZED, SCALABLE PROTEIN ANALYSIS AND ENGINEERING

Deep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the "language of life." However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds. TRILL (TRaining and Inference using the Language of Life) is a platform for creative protein design and discovery. Leveraging several state-of-the-art models such as ESM-2, DiffDock, and RFDiffusion, TRILL allows researchers to generate novel proteins, predict 3-D structures, extract high-dimensional representations of proteins, functionally classify proteins and more. What sets TRILL apart is its ability to enable complex pipelines by chaining together models and effectively merging the capabilities of different models to achieve a sum greater than its individual parts. Whether using Google Colab with one GPU or a supercomputer with hundreds, TRILL allows scientists to effectively utilize models with millions to billions of parameters by using optimized training strategies such as ZeRO-Offload and distributed data parallel. Therefore, TRILL not only bridges the gap between complex deep-learning models and their practical application in the field of biology, but also simplifies the orchestration of these models into comprehensive workflows, democratizing access to powerful methods. Documentation: https://trill.readthedocs.io/en/latest/home.html.

Ice Cores as a Source for Antimicrobials: From Bioprospecting to Biodesign

The golden age has passed for antibiotic discovery, and while some antibiotics are currently in various phases of clinical trials in the United States, many pharmaceutical companies have abandoned antibiotic research. With the need for antibiotics, we should expand our horizon for therapeutic mining and can look toward understudied sources such as ice cores. Ice cores contain microorganisms and genetic material that have been frozen in time for thousands of years. The antibiotics used by these organisms are encoded in their genomes, which can be unlocked, identified, and characterized with modern advances in molecular biology, genetic sequencing, various computational approaches, and established natural product discovery pipelines. While synthetic biology can be used in natural product discovery approaches, synthetic biology and bioengineering efforts can also be leveraged in the selection and biodesign of increased compound yields, potency, and stability. Here, we provide the perspective that ice cores can be a source of novel antibiotic compounds and that the tools of synthetic biology can be used to design better antimicrobials.

Molecular hybridization strategy for tuning bioactive peptide function

The physicochemical and structural properties of antimicrobial peptides (AMPs) determine their mechanism of action and biological function. However, the development of AMPs as therapeutic drugs has been traditionally limited by their toxicity for human cells. Tuning the physicochemical properties of such molecules may abolish toxicity and yield synthetic molecules displaying optimal safety profiles and enhanced antimicrobial activity. Here, natural peptides were modified to improve their activity by the hybridization of sequences from two different active peptide sequences. Hybrid AMPs (hAMPs) were generated by combining the amphipathic faces of the highly toxic peptide VmCT1, derived from scorpion venom, with parts of four other naturally occurring peptides having high antimicrobial activity and low toxicity against human cells. This strategy led to the design of seven synthetic bioactive variants, all of which preserved their structure and presented increased antimicrobial activity (3.1-128 μmol L-1). Five of the peptides (three being hAMPs) presented high antiplasmodial at 0.8 μmol L-1, and virtually no undesired toxic effects against red blood cells. In sum, we demonstrate that peptide hybridization is an effective strategy for redirecting biological activity to generate novel bioactive molecules with desired properties.

The Antimicrobial, Antibiofilm and Anti-Inflammatory Activities of P13#1, a Cathelicidin-like Achiral Peptoid

Cationic antimicrobial peptides (CAMPs) are powerful molecules with antimicrobial, antibiofilm and endotoxin-scavenging activities. These properties make CAMPs very attractive drugs in the face of the rapid increase in multidrug-resistant (MDR) pathogens, but they are limited by their susceptibility to proteolytic degradation. An intriguing solution to this issue could be the development of functional mimics of CAMPs with structures that enable the evasion of proteases. Peptoids (N-substituted glycine oligomers) are an important class of peptidomimetics with interesting benefits: easy synthetic access, intrinsic proteolytic stability and promising bioactivities. Here, we report the characterization of P13#1, a 13-residue peptoid specifically designed to mimic cathelicidins, the best-known and most widespread family of CAMPs. P13#1 showed all the biological activities typically associated with cathelicidins: bactericidal activity over a wide spectrum of strains, including several ESKAPE pathogens; the ability to act in combination with different classes of conventional antibiotics; antibiofilm activity against preformed biofilms of Pseudomonas aeruginosa, comparable to that of human cathelicidin LL-37; limited toxicity; and an ability to inhibit LPS-induced proinflammatory effects which is comparable to that of "the last resource" antibiotic colistin. We further studied the interaction of P13#1 with SDS, LPSs and bacterial cells by using a fluorescent version of P13#1. Finally, in a subcutaneous infection mouse model, it showed antimicrobial and anti-inflammatory activities comparable to ampicillin and gentamicin without apparent toxicity. The collected data indicate that P13#1 is an excellent candidate for the formulation of new antimicrobial therapies.

Computational exploration of the global microbiome for antibiotic discovery

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

Human gut metagenomic mining reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally mined 444,054 families of putative small proteins from 1,773 human gut metagenomes, identifying 323 peptide antibiotics encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 59% displaying activity against either pathogens or commensals. Since these peptides were unique compared to previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergized with each other, and modulated gut commensals, indicating that they may play a 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. We report the discovery of hundreds of peptide sequences in the human gut.

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning

Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.

Transition between Different Diffusion Modes of Individual Lipids during the Membrane-Specific Action of As-CATH4 Peptides

The cell membrane permeabilization ability of immune defense antimicrobial peptides (AMPs) is widely applied in biomedicine. Although the mechanisms of peptide-membrane interactions have been widely investigated, analyses at the molecular level are still lacking. Herein, the membrane-specific action of a native AMP, As-CATH4, is investigated using a single-lipid tracking method in combination with live cell and model membrane assays conducted at different scales. The peptide-membrane interaction process is characterized by analyzing single-lipid diffusion behaviors. As-CATH4 exhibits potent antimicrobial activity through bacterial membrane permeabilization, with moderate cytotoxicity against mammalian cells. In-plane diffusion analyses of individual lipids show that the lipid molecules exhibit non-Gaussian and heterogeneous diffusion behaviors in both pristine and peptide-treated membranes, which can be decomposed into two Gaussian subgroups corresponding to normal- and slow-diffusive lipids. Assessment of the temporal evolution of these two diffusion modes of lipids reveal that the peptide action states of As-CATH4 include surface binding, transmembrane defect formation, and dynamic equilibrium. The action mechanisms of As-CATH4 at varying concentrations and against different membranes are distinguished. This work resolves the simultaneous mixed diffusion mechanisms of single lipids in biomimetic cell membranes, especially during dynamic membrane permeabilization by AMPs.

Structure-function-guided design of synthetic peptides with anti-infective activity derived from wasp venom

Antimicrobial peptides (AMPs) derived from natural toxins and venoms offer a promising alternative source of antibiotics. Here, through structure-function-guided design, we convert two natural AMPs derived from the venom of the solitary eumenine wasp Eumenes micado into α-helical AMPs with reduced toxicity that kill Gram-negative bacteria in vitro and in a preclinical mouse model. To identify the sequence determinants conferring antimicrobial activity, an alanine scan screen and strategic single lysine substitutions are made to the amino acid sequence of these natural peptides. These efforts yield a total of 34 synthetic derivatives, including alanine substituted and lysine-substituted sequences with stabilized α-helical structures and increased net positive charge. The resulting lead synthetic peptides kill the Gram-negative pathogens Escherichia coli and Pseudomonas aeruginosa (PAO1 and PA14) by rapidly permeabilizing both their outer and cytoplasmic membranes, exhibit anti-infective efficacy in a mouse model by reducing bacterial loads by up to three orders of magnitude, and do not readily select for bacterial resistance.

Leveraging artificial intelligence in the fight against infectious diseases

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

Geometric deep learning as a potential tool for antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.

Isolation and Optimization of a Broad-Spectrum Synthetic Antimicrobial Peptide, Ap920-WI, from Arthrobacter sp. H5 for the Biological Control of Plant Diseases

Antimicrobial peptides (AMPs) are naturally occurring molecules found in various organisms that can help to defend against invading microorganisms and reduce the likelihood of drug resistance development. This study focused on the isolation of new AMPs from the genome library of a Gram-positive bacterium called Arthrobacter sp. H5. To achieve this, we used the Bacillus subtilis expression system and employed bioinformatics techniques to optimize and modify the peptides, resulting in the development of a new synthetic antimicrobial peptide (SAMP). Ap920 is expected to be a new antimicrobial peptide with a high positive charge (+12.5). Through optimization, a new synthetic antimicrobial peptide, Ap920-WI, containing only 15 amino acids, was created. Thereafter, the antimicrobial and antifungal activities of Ap920-WI were determined using minimum inhibitory concentration (MIC) and the concentration for 50% of maximal effect (EC₅₀). The Ap920-WI peptide was observed to target the outer membrane of fungal hyphae, leading to inhibition of growth in Rhizoctonia Solani, Sclerotinia sclerotiorum, and Botrytis cinerea. In plants, Ap920-WI showed significant antifungal activity and inhibited the infestation of S. sclerotiorum on rape leaves. Importantly, Ap920-WI was found to be safe for mammalian cells since it did not show any hemolytic activity against sheep red blood cells. Overall, the study found that the new synthetic antimicrobial peptide Ap920-WI exhibits broad-spectrum activity against microorganisms and may offer a new solution for controlling plant diseases, as well as hold potential for drug development.

Accessing hidden microbial biosynthetic potential from underexplored sources for novel drug discovery

Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently needed to be developed to combat the growing antimicrobial resistance and other public health problems. The advances in next-generation sequencing technologies and powerful computational tools open up new opportunities to explore microbial biosynthetic potential from underexplored sources, with millions of secondary metabolites awaiting discovery. The review highlights challenges associated with discovery of new chemical entities, rich reservoirs provided by untapped taxa, ecological niches or host microbiomes, emerging synthetic biotechnologies to unearth the hidden microbial biosynthetic potential for novel drug discovery at scale and speed.

Antimicrobial Peptides in the Global Microbiome: Biosynthetic Genes and Resistance Determinants

Antimicrobial peptides are a promising new class of antimicrobials that could address the antibiotic resistance crisis, which poses a major threat to human health. These peptides are present in all kingdoms of life, but especially in microorganisms, having multiple origins in diverse taxa. To date, there has been no global study on the diversity of antimicrobial peptides, the hosts in which these occur, and the potential for resistance to these agents. Here, we investigated the diversity and number of antimicrobial peptides in four main habitats (aquatic, terrestrial, human, and engineered) by analyzing 52,515 metagenome-assembled genomes. The number of antimicrobial peptides was higher in the human gut microbiome than in other habitats, and most hosts of antimicrobial peptides were habitat-specific. The relative abundance of genes that confer resistance to antimicrobial peptides varied between habitats and was generally low, except for the built environment and on human skin. The horizontal transfer of potential resistance genes among these habitats was probably constrained by ecological barriers. We systematically quantified the risk of each resistance determinant to human health and found that nearly half of them pose a threat, especially those that confer resistance to multiple AMPs and polymyxin B. Our results help identify the biosynthetic potential of antimicrobial peptides in the global microbiome, further identifying peptides with a low risk of developing resistance.

Advanced delivery systems for peptide antibiotics

Antimicrobial peptides (AMPs) hold promise as alternatives to traditional antibiotics for preventing and treating multidrug-resistant infections. Although they have potent antimicrobial efficacy, AMPs are mainly limited by their susceptibility to proteases and potential off-site cytotoxicity. Designing the right delivery system for peptides can help to overcome such limitations, thus improving the pharmacokinetic and pharmacodynamic profiles of these drugs. The versatility of peptides and their genetically encodable structure make them suitable for both conventional and nucleoside-based formulations. In this review, we describe the main drug delivery procedures developed so far for peptide antibiotics: lipid nanoparticles, polymeric nanoparticles, hydrogels, functionalized surfaces, and DNA- and RNA-based delivery systems.

D- and N-Methyl Amino Acids for Modulating the Therapeutic Properties of Antimicrobial Peptides and Lipopeptides

Here we designed and synthesized analogs of two antimicrobial peptides, namely C10:0-A2, a lipopeptide, and TA4, a cationic α-helical amphipathic peptide, and used non-proteinogenic amino acids to improve their therapeutic properties. The physicochemical properties of these analogs were analyzed, including their retention time, hydrophobicity, and critical micelle concentration, as well as their antimicrobial activity against gram-positive and gram-negative bacteria and yeast. Our results showed that substitution with D- and N-methyl amino acids could be a useful strategy to modulate the therapeutic properties of antimicrobial peptides and lipopeptides, including enhancing stability against enzymatic degradation. The study provides insights into the design and optimization of antimicrobial peptides to achieve improved stability and therapeutic efficacy. TA4(dK), C10:0-A2(6-NMeLys), and C10:0-A2(9-NMeLys) were identified as the most promising molecules for further studies.

Rational Design of Stapled Antimicrobial Peptides to Enhance Stability and In Vivo Potency against Polymicrobial Sepsis

In this work, we sought to develop a TP4-based stapled peptide that can be used to counter polymicrobial sepsis. First, we segregated the TP4 sequence into hydrophobic and cationic/hydrophilic zones and substituted the preferred residue, lysine, as the sole cationic amino acid. These modifications minimized the intensity of cationic or hydrophobic characteristics within small segments. Then, we incorporated single or multiple staples into the peptide chain, bracketing the cationic/hydrophilic segments to improve pharmacological suitability. Using this approach, we were able to develop an AMP with low toxicity and notable in vivo efficacy. IMPORTANCE In our in vitro studies, one dual stapled peptide out of the series of candidates (TP4-3: FIIXKKSXGLFKKKAGAXKKKXIKK) showed significant activity, low toxicity, and high stability (in 50% human serum). When tested in cecal ligation and puncture (CLP) mouse models of polymicrobial sepsis, TP4-3 improved survival (87.5% on day 7). Furthermore, TP4-3 enhanced the activity of meropenem against polymicrobial sepsis (100% survival on day 7) compared to meropenem alone (37.5% survival on day 7). Molecules such as TP4-3 may be well suited for a wide variety of clinical applications.

Strategic modification of low-activity natural antimicrobial peptides confers antibacterial potential in vitro and in vivo

Antimicrobial peptides (AMPs) show great promise for clinical applications, but the utility of naturally occurring AMPs is often limited by their stability. Here, we used a rational design approach to improve the characteristics of a pair of inactive peptides, tilapia piscidin 1 and 2 (TP1 and TP2). From each starting peptide, we generated a series of novel derivatives by substituting residues to adjust cationic charge density, percent hydrophobicity and hydrophilicity/hydrophobicity coefficients. This approach yielded a novel peptide, TP2-5 (KKCIAKAILKKAKKLLKKLVNP), that exhibits significant bactericidal potency, low cytotoxicity and high stability. The designed peptide further showed antibiofilm activity, rapid antibacterial action and a low capacity to induce bacterial resistance. Importantly, we also demonstrated that TP2-5 can protect mice in a Vibrio vulnificus-infected wound model. Therefore, our peptide modification strategy successfully generated a novel AMP with high potential for future clinical application.

Bacterial resistance to antibacterial agents: Mechanisms, control strategies, and implications for global health

The spread of bacterial drug resistance has posed a severe threat to public health globally. Here, we cover bacterial resistance to current antibacterial drugs, including traditional herbal medicines, conventional antibiotics, and antimicrobial peptides. We summarize the influence of bacterial drug resistance on global health and its economic burden while highlighting the resistance mechanisms developed by bacteria. Based on the One Health concept, we propose 4A strategies to combat bacterial resistance, including prudent Application of antibacterial agents, Administration, Assays, and Alternatives to antibiotics. Finally, we identify several opportunities and unsolved questions warranting future exploration for combating bacterial resistance, such as predicting genetic bacterial resistance through the use of more effective techniques, surveying both genetic determinants of bacterial resistance and the transmission dynamics of antibiotic resistance genes (ARGs).

Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences

Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.

Targeted Drug Delivery Systems for Eliminating Intracellular Bacteria

The intracellular survival of pathogenic bacteria requires a range of survival strategies and virulence factors. These infections are a significant clinical challenge, wherein treatment frequently fails because of poor antibiotic penetration, stability, and retention in host cells. Drug delivery systems (DDSs) are promising tools to overcome these shortcomings and enhance the efficacy of antibiotic therapy. In this review, the classification and the mechanisms of intracellular bacterial persistence are elaborated. Furthermore, the systematic design strategies applied to DDSs to eliminate intracellular bacteria are also described, and the strategies used for internalization, intracellular activation, bacterial targeting, and immune enhancement are highlighted. Finally, this overview provides guidance for constructing functionalized DDSs to effectively eliminate intracellular bacteria.