Reprogramming biological peptides to combat infectious diseases

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.

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.

Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning

The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2-4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.

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.

Antibacterial micro/nanomotors: advancing biofilm research to support medical applications

Multi-drug resistant (MDR) bacterial infections are gradually increasing in the global scope, causing a serious burden to patients and society. The formation of bacterial biofilms, which is one of the key reasons for antibiotic resistance, blocks antibiotic penetration by forming a physical barrier. Nano/micro motors (MNMs) are micro-/nanoscale devices capable of performing complex tasks in the bacterial microenvironment by transforming various energy sources (including chemical fuels or external physical fields) into mechanical motion or actuation. This autonomous movement provides significant advantages in breaking through biological barriers and accelerating drug diffusion. In recent years, MNMs with high penetrating power have been used as carriers of antibiotics to overcome bacterial biofilms, enabling efficient drug delivery and improving the therapeutic effectiveness of MDR bacterial infections. Additionally, non-antibiotic antibacterial strategies based on nanomaterials, such as photothermal therapy and photodynamic therapy, are continuously being developed due to their non-invasive nature, high effectiveness, and non-induction of resistance. Therefore, multifunctional MNMs have broad prospects in the treatment of MDR bacterial infections. This review discusses the performance of MNMs in the breakthrough and elimination of bacterial biofilms, as well as their application in the field of anti-infection. Finally, the challenges and future development directions of antibacterial MNMs are introduced.

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.

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.

Deep learning tools to accelerate antibiotic discovery

INTRODUCTION

As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity.

AREAS COVERED

This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics.

EXPERT OPINION

Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.

Combating Antimicrobial Resistance in the Post-Genomic Era: Rapid Antibiotic Discovery

Constantly evolving drug-resistant "superbugs" have caused an urgent demand for novel antimicrobial agents. Natural products and their analogs have been a prolific source of antimicrobial agents, even though a high rediscovery rate and less targeted research has made the field challenging in the pre-genomic era. With recent advancements in technology, natural product research is gaining new life. Genome mining has allowed for more targeted excavation of biosynthetic potential from natural sources that was previously overlooked. Researchers use bioinformatic algorithms to rapidly identify and predict antimicrobial candidates by studying the genome before even entering the lab. In addition, synthetic biology and advanced analytical instruments enable the accelerated identification of novel antibiotics with distinct structures. Here, we reviewed the literature for noteworthy examples of novel antimicrobial agents discovered through various methodologies, highlighting the candidates with potent effectiveness against antimicrobial-resistant pathogens.

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.

Dual-Mechanism Glycolipidpeptide with High Antimicrobial Activity, Immunomodulatory Activity, and Potential Application for Combined Antibacterial Therapy

Bacterial drug resistance is becoming increasingly serious, and it is urgent to develop effective antibacterial drugs. Antimicrobial peptides (AMPs), as potential candidates against bacteria, have a broad prospect for development. Herein, a series of AMPs with biological characteristics (net positive charge, amphiphilicity, and α-helix), an AXA motif recognized by membrane bound serine protease type I signal peptidases (SPase I), an FLPII motif to reduce hemolysis, and a monosaccharide motif to improve the stability and activity were designed and synthesized, and among which, the glycolipidpeptide GLP6 (glycosylated LP6 lipopeptide) had excellent antibacterial and immunomodulatory activity, good stability and biocompatibility, and excellent biofilm eradication and membrane penetrating activity. The positively charged spherical aggregates formed by self-assembly of GLP6 could encapsulate tetracycline (TC) to form GLP6@TC with a sustained-release effect, which could enhance the sensitivity of bacteria to the antibiotic and realize combined sterilization. The results of acute peritonitis and bacterial keratitis showed that GLP6@TC had a good combined antibacterial effect and the ability to inhibit interleukin-2 (IL-2), which could significantly reduce the inflammatory response while treating bacterial infection, and it had great potential for application. The results of computer molecular docking showed the AXA motif could effectively bind to SPase I, which was consistent with the results of biological experiments. In general, the study could provide a perspective for the design of AMPs and combined antibacterial therapy.

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).

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Biologically Active Peptides from Venoms: Applications in Antibiotic Resistance, Cancer, and Beyond

Peptides are potential therapeutic alternatives against global diseases, such as antimicrobial-resistant infections and cancer. Venoms are a rich source of bioactive peptides that have evolved over time to act on specific targets of the prey. Peptides are one of the main components responsible for the biological activity and toxicity of venoms. South American organisms such as scorpions, snakes, and spiders are important producers of a myriad of peptides with different biological activities. In this review, we report the main venom-derived peptide families produced from South American organisms and their corresponding activities and biological targets.

Leveraging the therapeutic, biological, and self-assembling potential of peptides for the treatment of viral infections

Peptides and peptide-based materials have an increasing role in the treatment of viral infections through their use as active pharmaceutical ingredients, targeting moieties, excipients, carriers, or structural components in drug delivery systems. The discovery of peptide-based therapeutic compounds, coupled with the development of new stabilization and formulation strategies, has led to a resurgence of antiviral peptide therapeutics over the past two decades. The ability of peptides to bind cell receptors and to facilitate membrane penetration and subsequent intracellular trafficking enables their use in various antiviral systems for improved targeting efficiency and treatment efficacy. Importantly, the self-assembly of peptides into well-defined nanostructures provides a vast library of discrete constructs and supramolecular biomaterials for systemic and local delivery of antiviral agents. We review here the recent progress in exploiting the therapeutic, biological, and self-assembling potential of peptides, peptide conjugates, and their supramolecular assemblies in treating human viral infections, with an emphasis on the treatment strategies for Human Immunodeficiency Virus (HIV).

Phytochemical Analysis and Antioxidant, Antibacterial, and Antifungal Effects of Essential Oil of Black Caraway (Nigella sativa L.) Seeds against Drug-Resistant Clinically Pathogenic Microorganisms

Nigella sativa (NS) is a plant that has long been utilized in traditional medicine as a treatment for certain diseases. The aim of this work was to valorize the essential oil (EO) of this species by phytochemical analysis and antimicrobial and antioxidant evaluation. EO was extracted by hydrodistillation from the seeds of Nigella sativa (EO-NS). Phytochemical content of EO-NS was evaluated by use of gas chromatography coupled to mass spectrometry (GC-MS/MS). Antioxidant ability was in vitro determined by use of three assays: 2.2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing power (FRAP), and total antioxidant capacity (TAC) relative to two synthetic antioxidants: BHT and quercetin. Antimicrobial effect was evaluated against four clinically important bacterial strains (Staphylococcus aureus, ATCC 6633; Escherichia coli, K12; Bacillus subtilis, DSM 6333; and Proteus mirabilis, ATCC 29906) and against four fungal strains (Candida albicans, ATCC 10231; Aspergillus niger, MTCC 282; Aspergillus flavus, MTCC 9606; and Fusarium oxysporum, MTCC 9913). Fifteen constituents that accounted for the majority of the mass of the EO-NS were identified and quantified by use of GC-MSMS. The main component was O-cymene (37.82%), followed by carvacrol (17.68%), α-pinene (10.09%), trans-sabinene hydrate (9.90%), and 4-terpineol (7.15%). EO-NS exhibited significant antioxidant activity with IC50, EC50, and total antioxidant capacity (TAC) of $0.017\pm 0.0002$ , $0.1196\pm 0.012$ , and $114.059\pm 0.97$  mg EAA/g, respectively. Additionally, EO-NS exhibited promising antibacterial activity on all strains under investigation, especially on E. coli K12 resulting in inhibition diameter of $38.67\pm 0.58$  mm and a minimum inhibitory concentration (MIC) of $1.34\pm 0.00$  μg/mL. Also, EO-NS had significant antifungal efficacy, with a percentage of inhibition of $67.45\pm 2.31$ % and MIC of $2.69\pm 0.00$  μg/mL against F. oxysporum, MTCC 9913 and with a diameter of inhibition $42\pm 0.00$  mm and MIC of $0.67\pm 0.00$  μg/mL against C. albicans. To minimize development of antibiotic-resistant bacteria, EO-NS can be utilized as a natural, alternative to synthetic antibiotics and antioxidants to treat free radicals implicated in microbial infection-related inflammatory reactions.

Design, characterization and structure-function analysis of novel antimicrobial peptides based on the N-terminal CATH-2 fragment

The emergence of multidrug resistance coupled with shrinking antibiotic pipelines has increased the demand of antimicrobials with novel mechanisms of action. Therefore, researchers across the globe are striving to develop new antimicrobial substances to alleviate the pressure on conventional antibiotic therapies. Host-Defence Peptides (HDPs) and their derivatives are emerging as effective therapeutic agents against microbial resistance. In this study, five analogs (DP1-5) of the N-terminal (N-15) fragment of CATH-2 were designed based on the delicate balance between various physicochemical properties such as charge, aliphatic character, amphipathicity and hydrophobicity. By means of in-silico and in-vitro studies a novel peptide (DP1) with the sequence “RFGRFLRKILRFLKK” was found to be more effective and less toxic than the N-terminal CATH-2 peptide. Circular dichroism spectroscopy and differential scanning calorimetry were applied for structural insights. Antimicrobial, haemolytic, and cytotoxic activities were also assessed. The resulting peptide was characterized by low cytotoxicity, low haemolytic activity, and efficient anti-microbial activity. Structurally, it displayed strong helical properties irrespective of the solvent environment and was stable in membrane-mimicking environments. Taken together, the data suggests that DP1 can be explored as a promising therapeutic agent with possible clinical applications.

Emerging Computational Approaches for Antimicrobial Peptide Discovery

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

Deep generative models for peptide design

Computers can already be programmed for superhuman pattern recognition of images and text. For machines to discover novel molecules, they must first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize functions of interest. Machines need to be able to understand, read, write, and eventually create new molecules. Today, this creative process relies on deep generative models, which have gained popularity since powerful deep neural networks were introduced to generative model frameworks. In recent years, they have demonstrated excellent ability to model complex distribution of real-word data (e.g., images, audio, text, molecules, and biological sequences). Deep generative models can generate data beyond those provided in training samples, thus yielding an efficient and rapid tool for exploring the massive search space of high-dimensional data such as DNA/protein sequences and facilitating the design of biomolecules with desired functions. Here, we review the emerging field of deep generative models applied to peptide science. In particular, we discuss several popular deep generative model frameworks as well as their applications to generate peptides with various kinds of properties (e.g., antimicrobial, anticancer, cell penetration, etc). We conclude our review with a discussion of current limitations and future perspectives in this emerging field.

Autonomous Treatment of Bacterial Infections in Vivo Using Antimicrobial Micro- and Nanomotors

The increasing resistance of bacteria to existing antibiotics constitutes a major public health threat globally. Most current antibiotic treatments are hindered by poor delivery to the infection site, leading to undesired off-target effects and drug resistance development and spread. Here, we describe micro- and nanomotors that effectively and autonomously deliver antibiotic payloads to the target area. The active motion and antimicrobial activity of the silica-based robots are driven by catalysis of the enzyme urease and antimicrobial peptides, respectively. These antimicrobial motors show micromolar bactericidal activity in vitro against different Gram-positive and Gram-negative pathogenic bacterial strains and act by rapidly depolarizing their membrane. Finally, they demonstrated autonomous anti-infective efficacy in vivo in a clinically relevant abscess infection mouse model. In summary, our motors combine navigation, catalytic conversion, and bactericidal capacity to deliver antimicrobial payloads to specific infection sites. This technology represents a much-needed tool to direct therapeutics to their target to help combat drug-resistant infections.

Methods for the design and characterization of peptide antibiotics

Multi-drug resistant infections cause the death of millions of people worldwide. Today, there is an urgent need to identify innovative and sustainable alternatives to conventional antibiotics and to develop outside the box strategies to counter drug resistance. Versatile molecules such as antimicrobial peptides (AMPs), which display multiple mechanisms of action, have been explored as templates constituting a new generation of antibiotics. Here, we review recent methodological advances for the design, structural and functional characterization of AMPs. The methodologies outlined here have been validated and well established and may be used as a guide for the discovery, design, development, and reprogramming of peptide antibiotics.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Synthetic Peptide Derived from Scorpion Venom Displays Minimal Toxicity and Anti-infective Activity in an Animal Model

Multidrug-resistant bacteria represent a global health problem increasingly leading to infections that are untreatable with our existing antibiotic arsenal. Therefore, it is critical to identify novel effective antimicrobials. Venoms represent an underexplored source of potential antibiotic molecules. Here, we engineered a peptide (IsCT1-NH₂) derived from the venom of the scorpion Opisthacanthus madagascariensis, whose application as an antimicrobial had been traditionally hindered by its high toxicity. Through peptide design and the knowledge obtained in preliminary studies with single and double-substituted analogs, we engineered IsCT1 derivatives with multiple amino acid substitutions to assess the impact of net charge on antimicrobial activity and toxicity. We demonstrate that increased net charge (from +3 to +6) significantly reduced toxicity toward human erythrocytes. Our lead synthetic peptide, [A]¹[K]³[F]⁵[K]⁸-IsCT1-NH₂ (net charge of +4), exhibited increased antimicrobial activity against Gram-negative and Gram-positive bacteria in vitro and enhanced anti-infective activity in a mouse model. Mechanism of action studies revealed that the increased antimicrobial activity of our lead molecule was due, at least in part, to its enhanced ability to permeabilize the outer membrane and depolarize the cytoplasmic membrane. In summary, we describe a simple method based on net charge tuning to turn highly toxic venom-derived peptides into viable therapeutics.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Probiotic engineering strategies for the heterologous production of antimicrobial peptides

Engineered probiotic bacteria represent an innovative approach for treating and detecting a wide range of diseases including those caused by infectious agents. Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics for combating antibiotic-resistant infections. These molecules can be delivered orally to the gut by using engineered probiotics, which confer protection against AMP degradation, thus enabling numerous applications including treating drug-resistant enteric pathogens and remodeling the microbiota in real time. Here, we provide an update on the current state of the art on AMP-producing probiotics, discuss methods to enhance gut colonization, and end by outlining future perspectives.

Long-Lasting Stable Expression of Human LL-37 Antimicrobial Peptide in Transgenic Barley Plants

Antimicrobial peptides play a crucial role in the innate immune system of multicellular organisms. LL-37 is the only known member of the human cathelicidin family. As well as possessing antibacterial properties, it is actively involved in various physiological responses in eukaryotic cells. Accordingly, there is considerable interest in large-scale, low-cost, and microbial endotoxin-free production of LL-37 recombinant peptides for pharmaceutical applications. As a heterologous expression biofactory, we have previously obtained homologous barley (Hordeum vulgare L.) as an attractive vehicle for producing recombinant human LL-37 in the grain storage compartment, endosperm. The long-term stability of expression and inheritance of transgenes is necessary for the successful commercialization of recombinant proteins. Here, we report the stable inheritance and expression of the LL-37 gene in barley after six generations, including two consecutive seasons of experimental field cultivation. The transgenic plants showed normal growth and remained fertile. Based on the bacteria viability test, the produced peptide LL-37 retained high antibacterial activity.

Unraveling the implications of multiple histidine residues in the potent antimicrobial peptide Gaduscidin-1

The development of antimicrobial peptides (AMPs) as potential therapeutics requires resolving the foundational principles behind their structure-activity relationships. The role of histidine residues within AMPs remains a mystery despite the fact that several potent peptides containing this amino acid are being considered for further clinical development. Gaduscidin-1 (Gad-1) is a potent AMP from Atlantic cod fish that has a total of five His residues. Herein, the role of His residues and metal-potentiated activity of Gad-1 was studied. The five His residues contribute to the broad-spectrum activity of Gad-1. We demonstrated that Gad-1 can coordinate two Cu²⁺ ions, one at the N-terminus and one at the C-terminus, where the C-terminal binding site is a novel Cu²⁺ binding motif. High affinity Cu²⁺ binding at both sites was observed using mass spectrometry and isothermal titration calorimetry. Electron paramagnetic resonance was used to determine the coordination environment of the Cu²⁺ ions. Cu²⁺ binding was shown to be responsible for an increase in antimicrobial activity and a new mode of action. Along with the traditional AMP mode of action of pore formation, Gad-1 in the presence of Cu²⁺ (per)oxidizes lipids. Importantly, His3, His11, His17, and His21 were found to be important to lipid (per)oxidation. This insight will help further understand the inclusion and role of His residues in AMPs, the role of the novel C-terminal binding site, and can contribute to the field of designing potent AMPs that bind metal ions to potentiate activity.

Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery

Antibiotic resistance is one of the greatest challenges of our time. This global health problem originated from a paucity of truly effective antibiotic classes and an increased incidence of multi-drug-resistant bacterial isolates in hospitals worldwide. Indeed, it has been recently estimated that 10 million people will die annually from drug-resistant infections by the year 2050. Therefore, the need to develop out-of-the-box strategies to combat antibiotic resistance is urgent. The biological world has provided natural templates, called antimicrobial peptides (AMPs), which exhibit multiple intrinsic medical properties including the targeting of bacteria. AMPs can be used as scaffolds and, via engineering, can be reconfigured for optimized potency and targetability toward drug-resistant pathogens. Here, we review the recent development of tools for the discovery, design, and production of AMPs and propose that the future of peptide drug discovery will involve the convergence of computational and synthetic biology principles.

Synthetic Host Defense Peptides Inhibit Venezuelan Equine Encephalitis Virus Replication and the Associated Inflammatory Response

Venezuelan equine encephalitis virus (VEEV), a New World alphavirus of the Togaviridae family of viruses causes periodic outbreaks of disease in humans and equines. Disease following VEEV infection manifests as a febrile illness with flu-like symptoms, which can progress to encephalitis and cause permanent neurological sequelae in a small number of cases. VEEV is classified as a category B select agent due to ease of aerosolization and high retention of infectivity in the aerosol form. Currently, there are no FDA-approved vaccines or therapeutics available to combat VEEV infection. VEEV infection in vivo is characterized by extensive systemic inflammation that can exacerbate infection by potentially increasing the susceptibility of off-site cells to infection and dissemination of the virus. Hence, a therapeutic targeting both the infection and associated inflammation represents an unmet need. We have previously demonstrated that host defense peptides (HDPs), short peptides that are key components of the innate immune response, exhibit antiviral activity against a multitude of viruses including VEEV. In this study, we designed synthetic peptides derived from indolicidin, a naturally occurring HDP, and tested their efficacy against VEEV. Two candidate synthetic peptides inhibited VEEV replication by approximately 1000-fold and decreased the expression of inflammatory mediators such as IL1α, IL1β, IFNγ, and TNFα at both the gene and protein expression levels. Furthermore, an increase in expression levels of genes involved in chemotaxis of leukocytes and anti-inflammatory genes such as IL1RN was also observed. Overall, we conclude that our synthetic peptides inhibit VEEV replication and the inflammatory burden associated with VEEV infection.

Hybrid Antimicrobial Peptide Targeting Staphylococcus aureus and Displaying Anti-infective Activity in a Murine Model

Broad-spectrum antimicrobial peptides (AMPs) kill bacteria indiscriminately, increasing the possibility of an ecological imbalance in the microbiota. To solve this problem, new types of AMPs, which kill pathogenic bacteria without breaking the micro-ecological balance of the body, were proposed. Here, we successfully designed a targeting AMP, S2, which is a fusion peptide composed of a species-specific targeting domain and broad-spectrum AMP domain. In the current study, S2 showed specific killing activity against Staphylococcus aureus, and almost no resistance induced compared to penicillin. Mechanism studies indicated that S2 killed S. aureus by destroying the bacterial membrane. Meanwhile, S2 possessed excellent salt-tolerance properties and biocompatibility. Importantly, S2 exhibited perfect treatment efficacy against an S. aureus subcutaneous infection model and remained nontoxic. In conclusion, this study provides a promising strategy for designing specific AMPs against growing bacterial infections.

Bacterial Natural Compounds with Anti-Inflammatory and Immunomodulatory Properties (Mini Review)

Inflammation is part of the body's complex biological response to harmful stimuli such as damaged cells, pathogens, or irritants. It is a protective response involving blood cells, immune cells, and molecular mediators. The inflammation not only can eliminate the primary cause of cell injury but also clears out necrotic cells, tissue damaged from the original insults and inflammatory process. Furthermore, it can initiate tissue repair. Pro-inflammatory cytokines are produced predominantly by activated macrophages and are involved in the up-regulation of inflammatory reactions. They are involved in further regulating inflammatory reactions. There is ample evidence that some pro-inflammatory cytokines, such as interleukin 1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α), are involved in the pathological pain process. Some of the natural compounds promote cytokines production and inhibit inflammatory responses. The natural compounds which are produced from microorganisms such as omega-3 fatty acid, cyclic peptide, antimicrobial peptide, oligosaccharides, and polysaccharides can reduce inflammation and could be easily incorporated into the diet without any adverse effects. For example, SCFA (short-chain fatty acids), peptide bacteriocin, and polycyclic peptide bacteriocin (nisin) could be used in the treatment of atherosclerosis, orthopedic postoperative infections, and mycobacterium tuberculosis infection, respectively. Also, fatty acids (saturated and unsaturated fatty acids) can be introduced as anti-inflammatory drugs. This review article summarizes bacterial natural compounds with modulating effects on cytokines that are surveyed which may have potential anti-inflammatory drug-like activity.

The wasp venom antimicrobial peptide polybia-CP and its synthetic derivatives display antiplasmodial and anticancer properties

The wasp venom-derived antimicrobial peptide polybia-CP has been previously shown to exhibit potent antimicrobial activity, but it is also highly toxic. Previously, using a physicochemical-guided peptide design strategy, we reversed its toxicity while preserving and even enhancing its antibacterial properties. Here, we report on several additional unanticipated biological properties of polybia-CP and derivatives, namely their ability to target Plasmodium sporozoites and cancer cells. We leverage a physicochemical-guided approach to identify features that operate as functional hotspots making these peptides viable antiplasmodial and anticancer agents. Helical content and net positive charge are identified as key structural and physicochemical determinants for antiplasmodial activity. In addition to helicity and net charge, hydrophobicity-related properties of polybia-CP and derivatives were found to be equally critical to target cancer cells. We demonstrate that by tuning these physicochemical parameters, it is possible to design synthetic peptides with enhanced submicromolar antiplasmodial potency and micromolar anticancer activity. This study reveals novel and previously undescribed functions for Polybia-CP and analogs. Additionally, we demonstrate that a physicochemical-guided rational design strategy can be used for identifying functional hotspots in peptide molecules and for tuning structure-function to generate novel and potent new-to-nature therapies.

A Novel Peptide Antibiotic, Pro10-1D, Designed from Insect Defensin Shows Antibacterial and Anti-Inflammatory Activities in Sepsis Models

Owing to the challenges faced by conventional therapeutics, novel peptide antibiotics against multidrug-resistant (MDR) gram-negative bacteria need to be urgently developed. We had previously designed Pro9-3 and Pro9-3D from the defensin of beetle Protaetia brevitarsis; they showed high antimicrobial activity with cytotoxicity. Here, we aimed to develop peptide antibiotics with bacterial cell selectivity and potent antibacterial activity against gram-negative bacteria. We designed 10-meric peptides with increased cationicity by adding Arg to the N-terminus of Pro9-3 (Pro10-1) and its D-enantiomeric alteration (Pro10-1D). Among all tested peptides, the newly designed Pro10-1D showed the strongest antibacterial activity against Escherichia coli, Acinetobacter baumannii, and MDR strains with resistance against protease digestion. Pro10-1D can act as a novel potent peptide antibiotic owing to its outstanding inhibitory activities against bacterial film formation with high bacterial cell selectivity. Dye leakage and scanning electron microscopy revealed that Pro10-1D targets the bacterial membrane. Pro10-1D inhibited inflammation via Toll Like Receptor 4 (TLR4)/Nuclear factor-κB (NF-κB) signaling pathways in lipopolysaccharide (LPS)-stimulated RAW264.7 cells. Furthermore, Pro10-1D ameliorated multiple-organ damage and attenuated systemic infection-associated inflammation in an E. coli K1-induced sepsis mouse model. Overall, our results suggest that Pro10-1D can potentially serve as a novel peptide antibiotic for the treatment of gram-negative sepsis.

Non-perfectly Amphipathic α-Helical Structure Containing the XXYXX Sequence Improves the Biological Activity of Bovine αs2-Casein Antimicrobial Peptides

Non-amphiphilic WIQPKTKVIPYVRYL (WI-6) derived from bovine α_s2-casein f (193-207) was modified by a defined mutation method to obtain five engineered peptides with mirror symmetry structures. The five engineered peptide sequences were WF-1 (WFQVKTRVRTKVQFW), FW-2 (FWRRYKKVKKYRRWF), FW-3 (FWQVIKKVKKIVQWF), FK-4 (FKQFYRRVRRYFQKF), and FR-5 (FRQWYRRVRRYWQRF). However, FW-2, FW-3, FK-4, and FR-5 had obvious XXYXX sequences. Among these, FW-3 was demonstrated to have the highest antibacterial activity, which indicates that the non-perfectly amphipathic α-helical structure containing the XXYXX sequence has a better bactericidal effect. Therefore, peptide FW-3 could be widely used as a substitute for antibiotics in food, medicine, and other fields. These findings provide a potential method for designing novel antimicrobial peptides.

An Enhanced Variant Designed From DLP4 Cationic Peptide Against Staphylococcus aureus CVCC 546

Insect defensins are promising candidates for the development of potent antimicrobials against antibiotic-resistant Staphylococcus aureus (S. aureus). An insect defensin, DLP4, isolated from the hemolymph of Hermetia illucens larvae, showed low antimicrobial activity against Gram-positive (G+) pathogens and high cytotoxicity, which limited its effective therapeutic application. To obtain more potent and low cytotoxicity molecules, a series of peptides was designed based on the DLP4 template by changing the conservative site, secondary structure, charge, or hydrophobicity. Among them, a variant designated as ID13 exhibited strong antibacterial activity at low MIC values of 4-8 μg/mL to G+ pathogens (S. aureus: 4 μg/mL; Staphylococcus epidermidis: 8 μg/mL; Streptococcus pneumoniae: 4 μg/mL; Streptococcus suis: 4 μg/mL), which were lower than those of DLP4 (S. aureus: 16 μg/mL; S. epidermidis: 64 μg/mL; S. pneumoniae: 32 μg/mL; S. suis: 16 μg/mL), and cytotoxicity of ID13 (71.4% viability) was less than that of DLP4 (63.8% viability). ID13 could penetrate and destroy the cell membrane of S. aureus CVCC 546, resulting in an increase in potassium ion leakage; it bound to genomic DNA (gDNA) and led to the change of gDNA conformation. After treatment with ID13, perforated, wrinkled, and collapsed S. aureus CVCC 546 cells were observed in electron microscopy. Additionally, ID13 killed over 99.99% of S. aureus within 1 h, 2 × MIC of ID13 induced a post-antibiotic effect (PAE) of 12.78 ± 0.28 h, and 10 mg/kg ID13 caused a 1.8 log10 (CFU/g) (CFU: colony-forming units) reduction of S. aureus in infected mouse thigh muscles and a downregulation of TNF-α, IL-6, and IL-10 levels, which were superior to those of DLP4 or vancomycin. These findings indicate that ID13 may be a promising peptide antimicrobial agent for therapeutic application.