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

Plant Antimicrobial Peptides (PAMPs): Features, Applications, Production, Expression, and Challenges

The quest for an extraordinary array of defense strategies is imperative to reduce the challenges of microbial attacks on plants and animals. Plant antimicrobial peptides (PAMPs) are a subset of antimicrobial peptides (AMPs). PAMPs elicit defense against microbial attacks and prevent drug resistance of pathogens given their wide spectrum activity, excellent structural stability, and diverse mechanism of action. This review aimed to identify the applications, features, production, expression, and challenges of PAMPs using its structure-activity relationship. The discovery techniques used to identify these peptides were also explored to provide insight into their significance in genomics, transcriptomics, proteomics, and their expression against disease-causing pathogens. This review creates awareness for PAMPs as potential therapeutic agents in the medical and pharmaceutical fields, such as the sensitive treatment of bacterial and fungal diseases and others and their utilization in preserving crops using available transgenic methods in the agronomical field. PAMPs are also safe to handle and are easy to recycle with the use of proteases to convert them into more potent antimicrobial agents for sustainable development.

In Silico Design and In Vitro Evaluation of Some Novel AMPs Derived From Human LL-37 as Potential Antimicrobial Agents for Keratitis

The human body produces two classes of antimicrobial peptides (AMPs), namely defensins and cathelicidins. In this study, a novel decapeptide (Catoid) and its dimer (Dicatoid) based on human cathelicidin (LL-37) have been designed by bioinformatics tools to be used in the treatment of bacterial keratitis. After the selection and synthesis of peptide sequences, their antimicrobial activities against the standard and resistant strains of Pseudomonas aeruginosa and Staphylococcus aureus were evaluated. This test was performed with LL-37, gentamicin, ciprofloxacin, amikacin, and penicillin for a more accurate comparison. Furthermore, the cytotoxicity levels of the specified compounds on fibroblast cells and bovine corneal endothelial cells were investigated. The results demonstrated that the designed peptides had a superior antimicrobial activity on P. aeruginosa, compared to LL-37; however, Catoid had a better effect on the S. aureus strain. Additionally, a significant achievement is the very low toxicity level of Catoid and Dicatoid on the human skin fibroblast cell line and bovine corneal endothelial cells, compared to that of LL-37 as the initial design model.

An Efficient Evaluation System Accelerates α-Helical Antimicrobial Peptide Discovery and Its Application to Global Human Genome Mining

Antimicrobial peptides (AMPs), as an important part of the innate immune system of an organism, is a kind of promising drug candidate for novel antibiotics due to their unique antibacterial mechanism. However, the discovery of novel AMPs is facing a great challenge due to the complexity of systematic experiments and the poor predictability of antimicrobial activity. Here, a novel and comprehensive screening system, the Multiple Descriptor Multiple Strategy (MultiDS), was proposed based on 59 physicochemical and structural parameters, three strategies, and four algorithms for the mining of α-helical AMPs. This approach was applied to mine the encrypted peptide antibiotics from the global human genome, including introns and exons. A library of approximately 70 billion peptides with 15–25 amino acid residues was screened by the MultiDS system and generated a list of peptides with the Multiple Descriptor Index (MD index) scores, which was the core part of the MultiDS system. Sixty peptides with top MD scores were chemically synthesized and experimentally tested their antimicrobial activity against 10 kinds of Gram-positive bacteria, Gram-negative bacteria (including drug-resistant pathogens). A total of fifty-nine out of 60 (98.3%) peptides exhibited antimicrobial activity (MIC ≤ 64 μg/mL), and 24 out of 60 (40%) peptides showed high activity (MIC ≤ 2 μg/mL), validating the MultiDS system was an effective and predictive screening tool with high hit rate and superior antimicrobial activity. For further investigation, AMPs S1, S2, and S3 with the highest MD scores were used to treat the skin infection mouse models in vivo caused by Escherichia coli, drug-resistance Escherichia coli, and Staphylococcus aureus, respectively. All of S1, S2, and S3 showed comparable therapeutic effects on promoting infection healing to or even better than the positive drug levofloxacin. A mechanism study discovered that rapid bactericidal action was caused by cell membrane disruption and content leakage. The MultiDS system not only provides a high-throughput approach that allows for the mining of candidate AMPs from the global genome sequence but also opens up a new route to accelerate the discovery of peptide antibiotics.

Integrated Design of a Membrane-Lytic Peptide-Based Intravenous Nanotherapeutic Suppresses Triple-Negative Breast Cancer

Membrane-lytic peptides offer broad synthetic flexibilities and design potential to the arsenal of anticancer therapeutics, which can be limited by cytotoxicity to noncancerous cells and induction of drug resistance via stress-induced mutagenesis. Despite continued research efforts on membrane-perforating peptides for antimicrobial applications, success in anticancer peptide therapeutics remains elusive given the muted distinction between cancerous and normal cell membranes and the challenge of peptide degradation and neutralization upon intravenous delivery. Using triple-negative breast cancer as a model, the authors report the development of a new class of anticancer peptides. Through function-conserving mutations, the authors achieved cancer cell selective membrane perforation, with leads exhibiting a 200-fold selectivity over non-cancerogenic cells and superior cytotoxicity over doxorubicin against breast cancer tumorspheres. Upon continuous exposure to the anticancer peptides at growth-arresting concentrations, cancer cells do not exhibit resistance phenotype, frequently observed under chemotherapeutic treatment. The authors further demonstrate efficient encapsulation of the anticancer peptides in 20 nm polymeric nanocarriers, which possess high tolerability and lead to effective tumor growth inhibition in a mouse model of MDA-MB-231 triple-negative breast cancer. This work demonstrates a multidisciplinary approach for enabling translationally relevant membrane-lytic peptides in oncology, opening up a vast chemical repertoire to the arms race against cancer.

In Vitro Pharmacodynamics and Bactericidal Mechanism of Fungal Defensin-Derived Peptides NZX and P2 against Streptococcus agalactiae

(1) Background: Based on the hazard of Streptococcus agalactiae to human and animal health and the increasing drug resistance, it is urgent to develop new antimicrobial agents with high bactericidal activity and low drug resistance against S. agalactiae. This study aims to investigate in vitro pharmacodynamics and bactericidal mechanism of fungal defensin-derived peptides NZX and P2 against S. agalactiae. (2) Methods: Minimum inhibitory concentration (MIC) and mutant prevention concentration (MPC) were determined by broth dilution method and AGAR plate dilution method. Cell membrane integrity was determined by flow cytometer. Cell morphological changes were observed by scanning electron microscope (SEM) and transmission electron microscope (TEM). (3) Results: MIC values (NZX: 0.11 μM, P2: 0.91 μM) and MPC (NZX: 1.82 μM) showed their higher antibacterial activity and stronger inhibition ability of drug resistance mutation. The bactericidal mechanism was elucidated that P2 caused S. agalactiae ACCC 61733 cells to deform, bound to the cell wall, and perturbed cell membrane, resulting in K⁺ leakage, membrane hyperpolarization, ATP release, and reduced cell contents. Compared with P2, NZX focuses on the cell wall, and it bound to the cell wall causing cells boundary disappearance. (4) Conclusion: NZX and P2 are promising antimicrobial agents for streptococcicosis treatment.

Synthetic molecular evolution of antimicrobial peptides

As we learn more about how peptide structure and activity are related, we anticipate that antimicrobial peptides will be engineered to have strong potency and distinct functions and that synthetic peptides will have new biomedical applications, such as treatments for emerging infectious diseases. As a result of the enormous number of possible amino acid sequences and the low-throughput nature of antimicrobial peptide assays, computational tools for peptide design and optimization are needed for direct experimentation toward obtaining functional sequences. Recent developments in computational tools have improved peptide design, saving labor, reagents, costs, and time. At the same time, improvements in peptide synthesis and experimental platforms continue to reduce the cost and increase the throughput of peptide-drug screening. In this review, we discuss the current methods of peptide design and engineering, including in silico methods and peptide synthesis and screening, and highlight areas of potential improvement.

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

Sense the moment: A highly sensitive antimicrobial activity predictor based on hydrophobic moment

BACKGROUND

Computer-aided identification and design tools are indispensable for developing antimicrobial agents for controlling antibiotic-resistant bacteria. Antimicrobial peptides (AMPs) have aroused intense interest, since they have a broad spectrum of activity, and therefore, several systems for predicting antimicrobial peptides have been developed, using scalar physicochemical properties; however, regardless of the machine learning algorithm, these systems often fail in discriminating AMPs from their shuffled versions, leading to the need for new training methods to overcome this bias. Aiming to solve this bias, here we present "Sense the Moment", a prediction system capable of discriminating AMPs and shuffled versions.

METHODS

The system was trained using 776 entries: 388 from known AMPs and another 388 based on shuffled versions of known AMPs. Each entry contained the geometric average of three hydrophobic moments measured with different scales.

RESULTS

The model showed good accuracy (>80%) and excellent sensitivity (>90%) for AMP prediction, exceeding deep-learning-based methods.

CONCLUSION

Our results demonstrate the system's applicability, aiding in identifying and discarding non-AMPs, since the number of false negatives is lower than false positives.

GENERAL SIGNIFICANCE

The application of this model in virtual screening protocols for identifying and/or creating antimicrobial agents could aid in the identification of potential drugs to control pathogenic microorganisms and in solving the antibiotic resistance crisis.

AVAILABILITY

The system was implemented as a web application, available at .

Rationalisation of Antifungal Properties of α-Helical Pore-Forming Peptide, Mastoparan B

The high mortality associated with invasive fungal infections, narrow spectrum of available antifungals, and increasing evolution of antifungal resistance necessitate the development of alternative therapies. Host defense peptides are regarded as the first line of defense against microbial invasion in both vertebrates and invertebrates. In this work, we investigated the effectiveness of four naturally occurring pore-forming antimicrobial peptides (melittin, magainin 2, cecropin A, and mastoparan B) against a panel of clinically relevant pathogens, including Candida albicans, Candida parapsilosis, Candida tropicalis, and Candida glabrata. We present data on the antifungal activities of the four pore-forming peptides, assessed with descriptive statistics, and their cytocompatibility with cultured human cells. Among the four peptides, mastoparan B (MB) displayed potent antifungal activity, whereas cecropin A was the least potent. We show that MB susceptibility of phylogenetically distant non-candida albicans can vary and be described by different intrinsic physicochemical parameters of pore-forming α-helical peptides. These findings have potential therapeutic implications for the design and development of safe antifungal peptide-based drugs.

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.

Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application

A multitude of membrane active peptides exists that divides into subclasses, such as cell penetrating peptides (CPPs) capable to enter eukaryotic cells or antimicrobial peptides (AMPs) able to interact with prokaryotic cell envelops. Peptide membrane interactions arise from unique sequence motifs of the peptides that account for particular physicochemical properties. Membrane active peptides are mainly cationic, often primary or secondary amphipathic, and they interact with membranes depending on the composition of the bilayer lipids. Sequences of these peptides consist of short 5–30 amino acid sections derived from natural proteins or synthetic sources. Membrane active peptides can be designed using computational methods or can be identified in screenings of combinatorial libraries. This review focuses on strategies that were successfully applied to the design and optimization of membrane active peptides with respect to the fact that diverse features of successful peptide candidates are prerequisites for biomedical application. Not only membrane activity but also degradation stability in biological environments, propensity to induce resistances, and advantageous toxicological properties are crucial parameters that have to be considered in attempts to design useful membrane active peptides. Reliable assay systems to access the different biological characteristics of numerous membrane active peptides are essential tools for multi-objective peptide optimization.

Helical structure motifs made searchable for functional peptide design

The systematic design of functional peptides has technological and therapeutic applications. However, there is a need for pattern-based search engines that help locate desired functional motifs in primary sequences regardless of their evolutionary conservation. Existing databases such as The Protein Secondary Structure database (PSS) no longer serves the community, while the Dictionary of Protein Secondary Structure (DSSP) annotates the secondary structures when tertiary structures of proteins are provided. Here, we extract 1.7 million helices from the PDB and compile them into a database (Therapeutic Peptide Design database; TP-DB) that allows queries of compounded patterns to facilitate the identification of sequence motifs of helical structures. We show how TP-DB helps us identify a known purification-tag-specific antibody that can be repurposed into a diagnostic kit for Helicobacter pylori. We also show how the database can be used to design a new antimicrobial peptide that shows better Candida albicans clearance and lower hemolysis than its template homologs. Finally, we demonstrate how TP-DB can suggest point mutations in helical peptide blockers to prevent a targeted tumorigenic protein-protein interaction. TP-DB is made available at http://dyn.life.nthu.edu.tw/design/ .

Rationally designed foldameric adjuvants enhance antibiotic efficacy via promoting membrane hyperpolarization

The negative membrane potential of bacterial cells influences crucial cellular processes. Inspired by the molecular scaffold of the antimicrobial peptide PGLa, we have developed antimicrobial foldamers with a computer-guided design strategy. The novel PGLa analogues induce sustained membrane hyperpolarization. When co-administered as an adjuvant, the resulting compounds - PGLb1 and PGLb2 - have substantially reduced the level of antibiotic resistance of multi-drug resistant Escherichia coli, Klebsiella pneumoniae and Shigella flexneri clinical isolates. The observed antibiotic potentiation was mediated by hyperpolarization of the bacterial membrane caused by the alteration of cellular ion transport. Specifically, PGLb1 and PGLb2 are selective ionophores that enhance the Goldman-Hodgkin-Katz potential across the bacterial membrane. These findings indicate that manipulating bacterial membrane electrophysiology could be a valuable tool to overcome antimicrobial resistance.

Real-Time Monitoring the Staged Interactions between Cationic Surfactants and a Phospholipid Bilayer Membrane

The cationic surfactant-lipid interaction directs the development of novel types of nanodrugs or nanocarriers. The membrane action of cationic surfactants also has a wide range of applications. In this work,...

Mining for encrypted peptide antibiotics in the human proteome

The emergence of drug-resistant bacteria calls for the discovery of new antibiotics. Yet, for decades, traditional discovery strategies have not yielded new classes of antimicrobial. Here, by mining the human proteome via an algorithm that relies on the sequence length, net charge, average hydrophobicity and other physicochemical properties of antimicrobial peptides, we report the identification of 2,603 encrypted peptide antibiotics that are encoded in proteins with biological function unrelated to the immune system. We show that the encrypted peptides kill pathogenic bacteria by targeting their membrane, modulate gut and skin commensals, do not readily select for bacterial resistance, and possess anti-infective activity in skin abscess and thigh infection mouse models. We also show, in vitro and in the two mouse models of infection, that encrypted antibiotic peptides from the same biogeographical area display synergistic antimicrobial activity. Our algorithmic strategy allows for the rapid mining of proteomic data and opens up new routes for the discovery of candidate antibiotics.

Strategies for Antimicrobial Peptides Immobilization on Surfaces to Prevent Biofilm Growth on Biomedical Devices

Nosocomial and medical device-induced biofilm infections affect millions of lives and urgently require innovative preventive approaches. These pathologies have led to the development of numerous antimicrobial strategies, an emergent topic involving both natural and synthetic routes, among which some are currently under testing for clinical approval and use. Antimicrobial peptides (AMPs) are ideal candidates for this fight. Therefore, the strategies involving surface functionalization with AMPs to prevent bacterial attachment/biofilms formation have experienced a tremendous development over the last decade. In this review, we describe the different mechanisms of action by which AMPs prevent bacterial adhesion and/or biofilm formation to better address their potential as anti-infective agents. We additionally analyze AMP immobilization techniques on a variety of materials, with a focus on biomedical applications. Furthermore, we summarize the advances made to date regarding the immobilization strategies of AMPs on various surfaces and their ability to prevent the adhesion of various microorganisms. Progress toward the clinical approval of AMPs in antibiotherapy is also reviewed.

Plant Antimicrobial Peptides as Potential Tool for Topic Treatment of Hidradenitis Suppurativa

Among chronic skin autoinflammatory diseases, Hidradenitis Suppurativa (HS) stands out for its chronicity, highly variable condition, and profound impact on the patients' quality of life. HS is characterized by suppurative skin lesions in diverse body areas, including deep-seated painful nodules, abscesses, draining sinus, and bridged scars, among others, with typical topography. To date, HS is considered a refractory disease and medical treatments aim to reduce the incidence, the infection, and the pain of the lesions. For this purpose, different classes of drugs, including anti-inflammatory molecules, antibiotics and biological drugs are being used. Antimicrobial peptides (AMPs), also called defense peptides, emerge as a new class of therapeutic compounds, with broad-spectrum antimicrobial action, in addition to reports on their anti-inflammatory, healing, and immunomodulating activity. Such peptides are present in prokaryotes and eukaryotes, as part of the innate eukaryotic immune system. It has been proposed that a deregulation in the expression of AMPs in human epithelial tissues of HS patients may be associated with the etiology of this skin disease. In this scenario, plant AMPs stand out for their richness, diversity of types, and broad antimicrobial effects, with potential application for topical systemic use in patients affected by HS.

The Two Weapons against Bacterial Biofilms: Detection and Treatment

Bacterial biofilms are defined as complex aggregates of bacteria that grow attached to surfaces or are associated with interfaces. Bacteria within biofilms are embedded in a self-produced extracellular matrix made of polysaccharides, nucleic acids, and proteins. It is recognized that bacterial biofilms are responsible for the majority of microbial infections that occur in the human body, and that biofilm-related infections are extremely difficult to treat. This is related with the fact that microbial cells in biofilms exhibit increased resistance levels to antibiotics in comparison with planktonic (free-floating) cells. In the last years, the introduction into the market of novel compounds that can overcome the resistance to antimicrobial agents associated with biofilm infection has slowed down. If this situation is not altered, millions of lives are at risk, and this will also strongly affect the world economy. As such, research into the identification and eradication of biofilms is important for the future of human health. In this sense, this article provides an overview of techniques developed to detect and imaging biofilms as well as recent strategies that can be applied to treat biofilms during the several biofilm formation steps.

Improving the Activity of Antimicrobial Peptides Against Aquatic Pathogen Bacteria by Amino Acid Substitutions and Changing the Ratio of Hydrophobic Residues

With the increasing number of drug-resistant bacteria, there is an urgent need for new antimicrobial agents, and antimicrobial peptides (AMPs), which exist in the human non-specific immune system, are one of the most promising candidates. It is an effective optimization strategy to modify antimicrobial peptides (AMPs) according to the distribution of amino acids and hydrophobic characteristics. The addition of bacterial pheromones to the N short peptide can increase the ability to recognize bacteria. In this study, we designed and synthesized AMP1–6 by amino acid substitution of mBjAMP1. Additionally, P-6, S-6, and L-6 were designed and synthesized by adding bacterial pheromones based on 1–6. Functional tests showed that the four AMPs had the ability to kill Gram-negative Vibrio anguillarum, Pseudomonas mendocina, and Vibrio parahaemolyticus, and Gram-positive Micrococcus luteus and Listeria monocytogenes. Additionally, all four AMPs induced permeabilization and depolarization of bacterial cell membranes and increased intracellular reactive oxygen species (ROS) levels. Importantly, they had little or no mammalian cytotoxicity. At the same time, 1–6 and L-6 protected the stability of intestinal flora in Sebastes schlegelii and increased the relative abundance of Lactobacillaceae. In summary, our results indicate that the designed AMPs have broad application prospects as a new type of polypeptide antimicrobial agent.

PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction

New methods for antimicrobial design are critical for combating pathogenic bacteria in the post-antibiotic era. Fortunately, competition within complex communities has led to the natural evolution of antimicrobial peptide (AMP) sequences that have promising bactericidal properties. Unfortunately, the identification, characterization, and production of AMPs can prove complex and time consuming. Here, we report a peptide generation framework, PepVAE, based around variational autoencoder (VAE) and antimicrobial activity prediction models for designing novel AMPs using only sequences and experimental minimum inhibitory concentration (MIC) data as input. Sampling from distinct regions of the learned latent space allows for controllable generation of new AMP sequences with minimal input parameters. Extensive analysis of the PepVAE-generated sequences paired with antimicrobial activity prediction models supports this modular design framework as a promising system for development of novel AMPs, demonstrating controlled production of AMPs with experimental validation of predicted antimicrobial activity.

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.

Antimicrobial Peptides and Their Applications in Biomedical Sector

In a report by WHO (2014), it was stated that antimicrobial resistance is an arising challenge that needs to be resolved. This resistance is a critical issue in terms of disease or infection treatment and is usually caused due to mutation, gene transfer, long-term usage or inadequate use of antimicrobials, survival of microbes after consumption of antimicrobials, and the presence of antimicrobials in agricultural feeds. One of the solutions to this problem is antimicrobial peptides (AMPs), which are ubiquitously present in the environment. These peptides are of concern due to their special mode of action against a wide spectrum of infections and health-related problems. The biomedical field has the highest need of AMPs as it possesses prominent desirable activity against HIV-1, skin cancer, breast cancer, in Behcet's disease treatment, as well as in reducing the release of inflammatory cells such as TNFα, IL-8, and IL-1β, enhancing the production of anti-inflammatory cytokines such as IL-10 and GM-CSF, and in wound healing properties. This review has highlighted all the major functions and applications of AMPs in the biomedical field and concludes the future potential of AMPs.

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.

Schistocins: Novel antimicrobial peptides encrypted in the Schistosoma mansoni Kunitz Inhibitor SmKI-1

BACKGROUND

Here we describe a new class of cryptides (peptides encrypted within a larger protein) with antimicrobial properties, named schistocins, derived from SmKI-1, a key protein in Shistosoma mansoni survival. This is a multi-functional protein with biotechnological potential usage as a therapeutic molecule in inflammatory diseases and to control schistosomiasis.

METHODS

We used our algorithm enCrypted, to perform an in silico proteolysis of SmKI-1 and a screening for potential antimicrobial activity. The selected peptides were chemically synthesized, tested in vitro and evaluated by both structural (CD, NMR) and biophysical (ITC) studies to access their structure-function relationship.

RESULTS

EnCrypted was capable of predicting AMPs in SmKI-1. Our biophysical analyses described a membrane-induced conformational change from random coil-to-α-helix and a peptide-membrane equilibrium for all schistocins. Our structural data allowed us to suggest a well-known mode of peptide-membrane interaction in which electrostatic attraction between the cationic peptides and anionic membranes results in the bilayer disordering. Moreover, the NMR exchange H/D data with the higher entropic contribution observed for the peptide-membrane interaction showed that shistocins have different orientations upon the membrane.

CONCLUSIONS

This work demonstrate the robustness for using the physicochemical features of predicted peptides in the identification of new bioactive cryptides besides the relevance of combining these analyses with biophysical methods to understand the peptide-membrane affinity and improve further algorithms.

GENERAL SIGNIFICANCE

Bioprospecting cryptides can be conducted through data mining of protein databases demonstrating the success of our strategy. The peptides-based agents derived from SmKI-1 might have high impact for system-biology and biotechnology.

Rationally Modified Antimicrobial Peptides from the N-Terminal Domain of Human RNase 3 Show Exceptional Serum Stability

Multidrug resistance against conventional antibiotics poses an important threat to human health. In this context, antimicrobial peptides (AMPs) have been extensively studied for their antibacterial activity and promising results have been shown so far. However, AMPs tend to be rather vulnerable to protease degradation, which offsets their therapeutic appeal. Here, we demonstrate how replacing functional residues in the antimicrobial region of human RNase 3—also named eosinophil cationic protein—by non-natural amino acids increases stability in human serum. These changes were also shown to reduce the hemolytic effect of the peptides in general terms, whereas the antimicrobial activity was reasonably preserved. Digestion profiles enabled us to design new peptides with superior stability and lower toxicity that could become relevant candidates to reach clinical stages.

Growth Optimization of Lactobacillus acidophilus for Production of Antimicrobial Peptide Acidocin 4356: Scale up from Flask to Lab-Scale Fermenter

Background:

Antibiotic-resistant bacteria are a major threat to global health. Older antibiotics have become more or less ineffective as a result of widespread microbial resistance and an urgent need has emerged for the development of new antimicrobial strategies. Acidocin 4356 is a novel antimicrobial bacteriocin peptide produced by Lactobacillus acidophilus ATCC 4356 and capable of confronting the Pseudomonas aeruginosa ATCC 27853 infection challenges. According to our previous studies, the production of Acidocin 4356 is in parallel with cellular biomass production.

Objectives:

Given the costly production of Acidocin 4356, the development of a beneficial approach for increasing productivity of the cellular biomass has been targeted in the lab-scale fermenter for scale-up production of this bacteriocin. Therefore, in this study, we developed an inexpensive optimal culture medium based on the whey feedstock, evaluating this medium for scaling-up of the bacteriocin production from flask to fermenter.

Material and Methods:

In the first step, the optimization of the process parameters and medium components was carried out using the Plackett-Burman (PB) design and Response surface methodology (RSM) in flask culture. After optimization of the medium, bacteriocin production in the optimum culture medium was compared with de Man, Rogosa and Sharpe (MRS) medium by analyzing the intensity of the peptide band. Intensity analysis has been conducted on the PAGE band of the peptide using Image J software. Finally, the scale- up of bacteriocin production in the optimum culture medium was evaluated by batch fermentation in a 3-liter fermenter.

Results:

In this study, a medium containing whey (40 g.L^-1) and sodium acetate (5 g.L^-1) was used as basal medium, and the effect of other factors were then evaluated. According to the PB design, three factors of peptone concentration, yeast extract concentrations and cultivation temperature were selected as the most effective factors which improve the growth of L. acidophilus. The condition providing the highest growth capacity for bacteriocin production were predicted based on the results of RSM as following: temperature 40 ° C, yeast (4 g.L^-1), and peptone (8 g.L^-1). Finally, the dry cell weight was obtained after incubation for 12 h as 2.25 g.L^-1. Comparison of cell growth and bacteriocin production between MRS medium and optimized medium confirmed the efficacy of these optimal conditions for the cost-effective production of Acidocin 4356 in the flask. Besides, the scale- up of bacteriocin production has made under optimal condition in the 3-L fermenter.

Conclusions:

In this study, for the first time, scale- up production of Acidocin 4356 was presented by using a low-cost method based on whey feedstock to tackle P. aeruginosa infections.

Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research

Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated. Models are parameterized using a training data set and then applied to a test data set on which predictive performance is evaluated. The application of ML algorithms to the problem of AMR has garnered increasing interest in the past 5 years due to the exponential growth of experimental and clinical data, heavy investment in computational capacity, improvements in algorithm performance, and increasing urgency for innovative approaches to reducing the burden of disease. Here, we review the current state of research at the intersection of ML and AMR with an emphasis on three domains of work. The first is the prediction of AMR using genomic data. The second is the use of ML to gain insight into the cellular functions disrupted by antibiotics, which forms the basis for understanding mechanisms of action and developing novel anti-infectives. The third focuses on the application of ML for antimicrobial stewardship using data extracted from the electronic health record. Although the use of ML for understanding, diagnosing, treating, and preventing AMR is still in its infancy, the continued growth of data and interest ensures it will become an important tool for future translational research programs.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.

Virtual screening of peptides with high affinity for SARS-CoV-2 main protease

The current pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 2,000,000 deaths worldwide. Currently, vaccine development and drug repurposing have been the main strategies to find a COVID-19 treatment. However, the development of new drugs could be the solution if the main strategies fail. Here, a virtual screening of pentapeptides was applied in order to identify peptides with high affinity to SARS-CoV-2 main protease (Mpro). Over 70,000 peptides were screened employing a genetic algorithm that uses a docking score as the fitness function. The algorithm was coupled with a RESTful API to persist data and avoid redundancy. The docking exhaustiveness was adapted to the number of peptides in each virtual screening step, where the higher the number of peptides, the lower the docking exhaustiveness. Two potential peptides were selected (HHYWH and HYWWT), which have higher affinity to Mpro than to human proteases. Albeit preliminary, the data presented here provide some basis for the rational design of peptide-based drugs to treat COVID-19.

Drug Repurposing: In vitro and in vivo Antimicrobial and Antibiofilm Effects of Bithionol Against Enterococcus faecalis and Enterococcus faecium

Widespread antibiotic resistance has been reported in enterococcal pathogens that cause life-threatening infections. Enterococci species rapidly acquire resistance and the pace of new antibiotic development is slow. Drug repurposing is a promising approach in solving this problem. Bithionol (BT) is a clinically approved anthelminthic drug. In this study, we found that BT showed significant antimicrobial and antibiofilm effects against Enterococcus faecalis and vancomycin-resistant Entercococcus faecium in vitro, in a dose-dependent manner, by disrupting the integrity of the bacterial cell membranes. Moreover, BT effectively reduced the bacterial load in mouse organs when combined with conventional antibiotics in a peritonitis infection model. Thus, BT has shown potential as a therapeutic agent against E. faecalis- and vancomycin-resistant E. faecium-related infections.

Antibacterial Photodynamic Gold Nanoparticles for Skin Infection

Damage or injury to the skin creates wounds that are vulnerable to bacterial infection, which in turn retards the process of skin regeneration and wound healing. In patients with severe burns and those with chronic diseases, such as diabetes, skin infection by multidrug-resistant bacteria can be lethal. Therefore, a broad-spectrum therapy to effectively eradicate bacterial infection through a mechanism different from that of antibiotics is much sought after. We successfully synthesized antibacterial photodynamic gold nanoparticles (AP-AuNPs), which are self-assembled nanocomposites of an antibacterial photodynamic peptide and poly(ethylene glycol) (PEG)-stabilized AuNPs. The AP-AuNPs exhibited aqueous and light stability, a satisfactory generation of reactive oxygen species (ROS), and a remarkable antibacterial effect toward both Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli upon light irradiation. Moreover, the synthesized nanocomposites significantly inhibited bacterial growth and biofilm formation in vitro. Photodynamic antibacterial treatment accelerated the wound-healing rate in S. aureus infections, mimicking staphylococcal skin infections. Using a combination of the bactericidal effect of a peptide, the photodynamic effect of a photosensitizer, and the multivalency clustering on AuNPs for maximal antibacterial effect under light irradiation, we synthesized AP-AuNPs as a wound-dressing nanomaterial in skin infections to promote wound healing. Our findings indicate a promising strategy in the management of bacterial infections resulting from damaged skin tissue, an aspect that has not been fully explored by our peers.

Engineering Selectively Targeting Antimicrobial Peptides

The rise of antibiotic-resistant strains of bacterial pathogens has necessitated the development of new therapeutics. Antimicrobial peptides (AMPs) are a class of compounds with potentially attractive therapeutic properties, including the ability to target specific groups of bacteria. In nature, AMPs exhibit remarkable structural and functional diversity, which may be further enhanced through genetic engineering, high-throughput screening, and chemical modification strategies. In this review, we discuss the molecular mechanisms underlying AMP selectivity and highlight recent computational and experimental efforts to design selectively targeting AMPs. While there has been an extensive effort to find broadly active and highly potent AMPs, it remains challenging to design targeting peptides to discriminate between different bacteria on the basis of physicochemical properties. We also review approaches for measuring AMP activity, point out the challenges faced in assaying for selectivity, and discuss the potential for increasing AMP diversity through chemical modifications.

Molecular Dynamics for Antimicrobial Peptide Discovery

Although antimicrobial resistance is an increasingly significant public health concern, there have only been two new classes of antibiotics approved for human use since the 1960s. Understanding the mechanisms of action of antibiotics is critical for novel antibiotic discovery, but novel approaches are needed that do not exclusively rely on experiments. Molecular dynamics simulation is a computational tool that uses simple models of the atoms in a system to discover nanoscale insights into the dynamic relationship between mechanism and biological function. Such insights can lay the framework for elucidating the mechanism of action and optimizing antibiotic templates. Antimicrobial peptides represent a promising solution to escalating antimicrobial resistance, given their lesser tendency to induce resistance than that of small-molecule antibiotics. Simulations of these agents have already revealed how they interact with bacterial membranes and the underlying physiochemical features directing their structure and function. In this minireview, we discuss how traditional molecular dynamics simulation works and its role and potential for the development of new antibiotic candidates with an emphasis on antimicrobial peptides.

Plant synthetic biology for producing potent phyto-antimicrobials to combat antimicrobial resistance

Inappropriate and injudicious use of antimicrobial drugs in human health, hygiene, agriculture, animal husbandry and food industries has contributed significantly to rapid emergence and persistence of antimicrobial resistance (AMR), one of the serious global public health threats. The crisis of AMR versus slower discovery of newer antibiotics put forth a daunting task to control these drug-resistant superbugs. Several phyto-antimicrobials have been identified in recent years with direct-killing (bactericidal) and/or drug-resistance reversal (re-sensitization of AMR phenotypes) potencies. Phyto-antimicrobials may hold the key in combating AMR owing to their abilities to target major microbial drug-resistance determinants including cell membrane, drug-efflux pumps, cell communication and biofilms. However, limited distribution, low intracellular concentrations, eco-geographical variations, beside other considerations like dynamic environments, climate change and over-exploitation of plant-resources are major blockades in full potential exploration phyto-antimicrobials. Synthetic biology (SynBio) strategies integrating metabolic engineering, RNA-interference, genome editing/engineering and/or systems biology approaches using plant chassis (as engineerable platforms) offer prospective tools for production of phyto-antimicrobials. With expanding SynBio toolkit, successful attempts towards introduction of entire gene cluster, reconstituting the metabolic pathway or transferring an entire metabolic (or synthetic) pathway into heterologous plant systems highlight the potential of this field. Through this perspective review, we are presenting herein the current situation and options for addressing AMR, emphasizing on the significance of phyto-antimicrobials in this apparently post-antibiotic era, and effective use of plant chassis for phyto-antimicrobial production at industrial scales along with major SynBio tools and useful databases. Current knowledge, recent success stories, associated challenges and prospects of translational success are also discussed.

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.

Tuning of a Membrane-Perforating Antimicrobial Peptide to Selectively Target Membranes of Different Lipid Composition

The use of designed antimicrobial peptides as drugs has been impeded by the absence of simple sequence-structure-function relationships and design rules. The likely cause is that many of these peptides permeabilize membranes via highly disordered, heterogeneous mechanisms, forming aggregates without well-defined tertiary or secondary structure. We suggest that the combination of high-throughput library screening with atomistic computer simulations can successfully address this challenge by tuning a previously developed general pore-forming peptide into a selective pore-former for different lipid types. A library of 2916 peptides was designed based on the LDKA template. The library peptides were synthesized and screened using a high-throughput orthogonal vesicle leakage assay. Dyes of different sizes were entrapped inside vesicles with varying lipid composition to simultaneously screen for both pore size and affinity for negatively charged and neutral lipid membranes. From this screen, nine different LDKA variants that have unique activity were selected, sequenced, synthesized, and characterized. Despite the minor sequence changes, each of these peptides has unique functional properties, forming either small or large pores and being selective for either neutral or anionic lipid bilayers. Long-scale, unbiased atomistic molecular dynamics (MD) simulations directly reveal that rather than rigid, well-defined pores, these peptides can form a large repertoire of functional dynamic and heterogeneous aggregates, strongly affected by single mutations. Predicting the propensity to aggregate and assemble in a given environment from sequence alone holds the key to functional prediction of membrane permeabilization.

Coatable and Resistance-Proof Ionic Liquid for Pathogen Eradication

Antibiotic-resistant bacteria infect close to 3 million people, and kill 35,000, each year in the United States. Ionic liquid (IL)-based antimicrobial agents have the potential to diversify our ever-diminishing antibiotic arsenal. Here, we describe an IL with potent submicromolar antimicrobial activity in vitro against clinically relevant Gram-negative and Gram-positive bacterial pathogens as well as anti-infective activity in a mouse model. The IL kills pathogenic bacteria such as Acinetobacter baumannii, Salmonella enterica, and Escherichia coli by disrupting their outer membrane and does not select for bacterial resistance. We show incorporation of our IL into surface coatings to generate a type of antibiofilm material. The IL-loaded ionogel surfaces demonstrate high-antimicrobial and antifouling activity by killing bacteria in both static and dynamic tests. Our IL-based antibiofilm surfaces are low-cost and easy to manufacture, can be formed on glass, latex, plastic, and metal surfaces, such as catheters and other medical devices where high local concentrations of antimicrobials are needed, and may have applications in other clinical and industrial settings.

Investigating Active Learning and Meta-Learning for Iterative Peptide Design

Often the development of novel functional peptides is not amenable to high throughput or purely computational screening methods. Peptides must be synthesized one at a time in a process that does not generate large amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both peptide properties and predictive modeling accuracy. Here, we study the effectiveness of active learning, optimizing experiment order, and meta-learning, transferring knowledge between contexts, to reduce the number of experiments necessary to build a predictive model. We present a multitask benchmark database of peptides designed to advance these methods for experimental design. Each task is a binary classification of peptides represented as a sequence string. We find neither active learning method tested to be better than random choice. The meta-learning method Reptile was found to improve the average accuracy across data sets. Combining meta-learning with active learning offers inconsistent benefits.

Plant compounds for the potential reduction of food waste - a focus on antimicrobial peptides

A large portion of global food waste is caused by microbial spoilage. The modern approach to preserve food is to apply different hurdles for microbial pathogens to overcome. These vary from thermal processes and chemical additives, to the application of irradiation and modified atmosphere packaging. Even though such preservative techniques exist, loss of food to spoilage still prevails. Plant compounds and peptides represent an untapped source of potential novel natural food preservatives. Of these, antimicrobial peptides (AMPs) are very promising for exploitation. AMPs are a significant component of a plant's innate defense system. Numerous studies have demonstrated the potential application of these AMPs; however, more studies, particularly in the area of food preservation are warranted. This review examines the literature on the application of AMPs and other plant compounds for the purpose of reducing food losses and waste (including crop protection). A focus is placed on the plant defensins, their natural extraction and synthetic production, and their safety and application in food preservation. In addition, current challenges and impediments to their full exploitation are discussed.

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.