Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Evaluation of bacteriophage ϕ11 host recognition protein and its host-binding peptides for diagnosing/targeting Staphylococcus aureus infections

BACKGROUND

Evaluating the potential of using both synthetic and biological products as targeting agents for the diagnosis, imaging, and treatment of infections due to particularly antibiotic-resistant pathogens is important for controlling infections. This study examined the interaction between Gp45, a receptor-binding protein of the ϕ11 lysogenic phage, and its host Staphylococcus aureus (S. aureus), a common cause of nosocomial infections.

METHODS

Using molecular dynamics and docking simulations, this study identified the peptides that bind to S. aureus wall teichoic acids via Gp45. It compared the binding affinity of Gp45 and the two highest-scoring peptide sequences (P1 and P3) and their scrambled forms using microscopy, spectroscopy, and ELISA.

RESULTS

It was found that rGp45 (recombinant Gp45) and chemically synthesised P1 had a higher binding affinity for S. aureus compared with all other peptides, except for Escherichia coli. Furthermore, rGp45 had a capture efficiency of > 86%; P1 had a capture efficiency of > 64%.

CONCLUSION

These findings suggest that receptor-binding proteins such as rGp45, which provide a critical initiation of the phage life cycle for host adsorption, might play an important role in the diagnosis, imaging, and targeting of bacterial infections. Studying such proteins could accordingly enable the development of effective strategies for controlling infections.

Important structural features of antimicrobial peptides towards specific activity: Trends in the development of efficient therapeutics

Proteins and peptides, as polypeptide chains, have usually got unique conformational structures for effective biological activity. Antimicrobial peptides (AMPs) are a group of bioactive peptides, which have been increasingly studied during recent years for their promising antibacterial, antifungal, antiviral and anti-inflammatory activity, as well as, other esteemed bioactivities. Numerous AMPs have been separated from a wide range of natural resources, or produced in vitro through chemical synthesis and recombinant protein expression. Natural AMPs have had limited clinical application due to several drawbacks, such as their short half-life due to protease degradation, lack of activity at physiological salt concentrations, toxicity to mammalian cells, and the absence of suitable methods of delivery for the AMPs that are targeted and sustained. The creation of synthetic analogs of AMPs would both avoid the drawbacks of the natural analogs and maintain or even increase the antimicrobial effectiveness. The structure-activity relationship of discovered AMPs or their derivatives facilitates the development of synthetic AMPs. This review discovered that the relationship between the activity of AMPs and their positive net charge, hydrophobicity, and amino acid sequence and the relationship between AMPs' function and other features like their topology, glycosylation, and halogenation.

In silico optimization of analogs derived pro-adrenomedullin peptide to evaluate antimicrobial potential

Diverse computational approaches have been widely used to assist in designing antimicrobial peptides with enhanced activities. This tactic has also been used to address the need for new treatment alternatives to combat resistant bacterial infections. Herein, we have designed eight variants from a natural peptide, pro-adrenomedullin N-terminal 20 peptide (PAMP), using an in silico pattern insertion approach, the Joker algorithm. All the variants show an α-helical conformation, but with differences in the helix percentages according to circular dichroism (CD) results. We found that the C-terminal portion of PAMP may be relevant for its antimicrobial activities, as revealed by the molecular dynamics, CD, and antibacterial results. The analogs showed variable antibacterial potential, but most were not cytotoxic. Nevertheless, PAMP2 exhibited the most potent activities against human and animal-isolated bacteria, showing cytotoxicity only at a substantially higher concentration than its minimal inhibitory concentration (MIC). Our results suggest that the enhanced activity in the profile of PAMP2 may be related to their particular physicochemical properties, along with the adoption of an amphipathic α-helical arrangement with the conserved C-terminus portion. Finally, the peptides designed in this study can constitute scaffolds for the design of improved sequences.

QSAR Reveals Decreased Lipophilicity of Polar Residues Determines the Selectivity of Antimicrobial Peptide Activity

Antimicrobial resistance has increased rapidly, causing daunting morbidity and mortality rates worldwide. Antimicrobial peptides (AMPs) have emerged as promising alternatives to traditional antibiotics due to their broad range of targets and low tendency to elicit resistance. However, potent antimicrobial activity is often accompanied by excessive cytotoxicity toward host cells, leading to a halt in AMP therapeutic development. Here, we present multivariate analyses that correlate 28 peptide properties to the activity and toxicity of 46 diverse African-derived AMPs and identify the negative lipophilicity of polar residues as an essential physiochemical property for selective antimicrobial activity. Twenty-seven active AMPs are identified, of which the majority are of scorpion or frog origin. Of these, thirteen are novel with no previously reported activities. Principal component analysis and quantitative structure-activity relationships (QSAR) reveal that overall hydrophobicity, lipophilicity, and residue side chain surface area affect the antimicrobial and cytotoxic activity of an AMP. This has been well documented previously, but the present QSAR analysis additionally reveals that a decrease in the lipophilicity, contributed by those amino acids classified as polar, confers selectivity for a peptide to pathogen over mammalian cells. Furthermore, an increase in overall peptide charge aids selectivity toward Gram-negative bacteria and fungi, while selectivity toward Gram-positive bacteria is obtained through an increased number of small lipophilic residues. Finally, a conservative increase in peptide size in terms of sequence length and molecular weight also contributes to improved activity without affecting toxicity. Our findings suggest a novel approach for the rational design or modification of existing AMPs to increase pathogen selectivity and enhance therapeutic potential.

A novel diterpenic derivative produced by Streptomyces chrestomyceticus ADP4 is a potent inhibitor of biofilm and virulence factors in Candida albicans and C. auris

AIM

Isolation, identification, structural and functional characterization of potent anti-Candida compound with specific antagonistic activities against significant human pathogens, Candida albicans and C. auris.

METHODS AND RESULTS

The compound (55B3) was purified from the metabolites produced by Streptomyces chrestomyceticus ADP4 by employing column chromatography. The structure of 55B3 was determined from the analyses of spectral data that included LCMS, nuclear magnetic resonance, FTIR, and UV spectroscopies. It was identified as a novel derivative of diterpenic aromatic acid, 3-(dictyotin-11'-oate-15'α, 19'β-olide)-4-(dictyotin-11'-oate-15″α, 19″β-olide)-protocatechoic acid. The compound displayed potent antifungal and anti-biofilm activities against C. albicans ATCC 10231 (Minimum Inhibitory Concentration, MIC90:14.94 ± 0.17 μgmL-1 and MBIC90: 16.03 ± 1.1 μgmL-1) and against C. auris CBS 12372 (MIC90: 21.75 ± 1.5 μgmL-1 and Minimum Biofilm Inhibitory Concentration, MBIC90: 18.38 ± 1.78 μgmL-1). Further, pronounced inhibition of important virulence attributes of Candida spp., e.g. yeast-to-hyphae transition, secretory aspartyl proteinase and phospholipase B by 55B3 was noted at subinhibitory concentrations. A plausible mechanism of anti-Candida action of the compound appeared to be the inhibition of ergosterol biosynthesis, which was inhibited by 64 ± 3% at the MIC90 value. The non-cytotoxic attribute of the compound was noted in the liver cell line (HepG2 cells).

CONCLUSION

The present work led to the discovery of a novel diterpenic derivative produced by S. chrestomyceticus ADP4. The compound displayed potent anti-Candida activity, particularly against the two most significant human pathogens, C. albicans and C. auris, which underlined its significance as a potential drug candidate for infections involving these pathogens.

Structure-aware machine learning strategies for antimicrobial peptide discovery

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Antimicrobial peptides as promising antibiotic adjuvants to combat drug-resistant pathogens

The widespread antimicrobial resistance (AMR) calls for the development of new antimicrobial strategies. Antibiotic adjuvant rescues antibiotic activity and increases the life span of the antibiotics, representing a more productive, timely, and cost-effective strategy in fighting drug-resistant pathogens. Antimicrobial peptides (AMPs) from synthetic and natural sources are considered new-generation antibacterial agents. Besides their direct antimicrobial activity, growing evidence shows that some AMPs effectively enhance the activity of conventional antibiotics. The combinations of AMPs and antibiotics display an improved therapeutic effect on antibiotic-resistant bacterial infections and minimize the emergence of resistance. In this review, we discuss the value of AMPs in the age of resistance, including modes of action, limiting evolutionary resistance, and their designing strategies. We summarise the recent advances in combining AMPs and antibiotics against antibiotic-resistant pathogens, as well as their synergistic mechanisms. Lastly, we highlight the challenges and opportunities associated with the use of AMPs as potential antibiotic adjuvants. This will shed new light on the deployment of synergistic combinations to address the AMR crisis.

Alternative therapeutic strategies to treat antibiotic-resistant pathogens

Resistance threatens to render antibiotics - which are essential for modern medicine - ineffective, thus posing a threat to human health. The discovery of novel classes of antibiotics able to overcome resistance has been stalled for decades, with the developmental pipeline relying almost entirely on variations of existing chemical scaffolds. Unfortunately, this approach has been unable to keep pace with resistance evolution, necessitating new therapeutic strategies. In this Review, we highlight recent efforts to discover non-traditional antimicrobials, specifically describing the advantages and limitations of antimicrobial peptides and macrocycles, antibodies, bacteriophages and antisense oligonucleotides. These approaches have the potential to stem the tide of resistance by expanding the physicochemical property space and target spectrum occupied by currently approved antibiotics.

Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides

Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an in silico approach to design cationic AMPs de novo, followed by their experimental testing. The antibacterial potential of de novo designed cationic AMPs, along with their synergistic properties in combination with conventional antibiotics was examined. Furthermore, the effects of bacterial inoculum density and metabolic state on the antibacterial activity of AMPs were evaluated. Finally, the impact of several potent AMPs on E. coli cell envelope and genomic DNA integrity was determined. Collectively, this comprehensive analysis provides insights into the unique characteristics of cationic AMPs.

Evaluation of novel compounds as anti-bacterial or anti-virulence agents

Antimicrobial resistance is a global threat, leading to an alarming increase in the prevalence of bacterial infections that can no longer be treated with available antibiotics. The World Health Organization estimates that by 2050 up to 10 million deaths per year could be associated with antimicrobial resistance, which would equal the annual number of cancer deaths worldwide. To overcome this emerging crisis, novel anti-bacterial compounds are urgently needed. There are two possible approaches in the fight against bacterial infections: a) targeting structures within bacterial cells, similar to existing antibiotics; and/or b) targeting virulence factors rather than bacterial growth. Here, for the first time, we provide a comprehensive overview of the key steps in the evaluation of potential new anti-bacterial and/or anti-virulence compounds. The methods described in this review include: a) in silico methods for the evaluation of novel compounds; b) anti-bacterial assays (MIC, MBC, Time-kill); b) anti-virulence assays (anti-biofilm, anti-quorum sensing, anti-adhesion); and c) evaluation of safety aspects (cytotoxicity assay and Ames test). Overall, we provide a detailed description of the methods that are an essential tool for chemists, computational chemists, microbiologists, and toxicologists in the evaluation of potential novel antimicrobial compounds. These methods are cost-effective and have high predictive value. They are widely used in preclinical studies to identify new molecular candidates, for further investigation in animal and human trials.

Deciphering the structure and mechanism of action of computer-designed mastoparan peptides

Mastoparans are cationic peptides with multifunctional pharmacological properties. Mastoparan-R1 and mastoparan-R4 were computationally designed based on native mastoparan-L from wasps and have improved therapeutic potential for the control of bacterial infections. Here, we evaluated whether these peptides maintain their activity against Escherichia coli strains under a range of salt concentrations. We found that mastoparan-R1 and mastoparan-R4 preserved their activity under the conditions tested, including having antibacterial activities at physiological salt concentrations. The overall structure of the peptides was investigated using circular dichroism spectroscopy in a range of solvents. No significant changes in secondary structure were observed (random coil in aqueous solutions and α-helix in hydrophobic and anionic environments). The three-dimensional structures of mastoparan-R1 and mastoparan-R4 were elucidated through nuclear magnetic resonance spectroscopy, revealing amphipathic α-helical segments for Leu3-Ile13 (mastoparan-R1) and Leu3-Ile14 (mastoparan-R4). Possible membrane-association mechanisms for mastoparan-R1 and mastoparan-R4 were investigated through surface plasmon resonance and leakage studies with synthetic POPC and POPC/POPG (4:1) lipid bilayers. Mastoparan-L had the highest affinity for both membrane systems, whereas the two analogs had weaker association, but improved selectivity for lysing anionic membranes. This finding was also supported by molecular dynamics simulations, in which mastoparan-R1 and mastoparan-R4 were found to have greater interactions with bacteria-like membranes compared with model mammalian membranes. Despite having a few differences in their functional and structural profiles, the mastoparan-R1 analog stood out with the highest activity, greater bacteriostatic potential, and selectivity for lysing anionic membranes. This study reinforces the potential of mastoparan-R1 as a drug candidate.

Virtual screening, activity evaluation, and stability of pancreatic lipase inhibitors in the gastrointestinal degradation of nattokinase

Nattokinase is an alkaline serine protease secreted by natto during fermentation. Despite its good thrombolytic effect, it is intolerant to gastrointestinal conditions and is easily digested and degraded into polypeptides, oligopeptides, and amino acids. However, whether these peptides inhibit fat-digesting enzymes and other biological activities remains unknown. To explore the bioactivity of peptides produced through nattokinase degradation, nattokinase was subjected to simulated digestion in the gastrointestinal tract, and 41 small peptides were obtained through the enzymolysis of gastric enzymes, pancreases, and chymotrypsin. Four pancreatic lipase (PL) inhibitory peptides (SW, ASF, GAY, and PGGTY) were selected based on their activity scores, water solubility, and toxicity predictions. The molecular docking results revealed that hydrogen bonds and electrostatic interactions were the main forces for inhibiting PL activity. The results of enzyme activity verification revealed that all four peptides inhibited PL activity. Among them, GAY exhibited the strongest inhibitory effect, with an inhibitory rate of 10.93 % at a concentration of 1 mg/mL. Molecular dynamics simulations confirmed that the GAY-1ETH complex demonstrated good stability. Natto foods containing nattokinase own the activity of inhibiting fat-digesting enzymes and show antiobesity potentials.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Identification and characterization of a novel decalin derivative with anti-Candida activity from Streptomyces chrestomyceticus strain ADP4

A novel decalin derivative, trans-1-oxo-2,4-diacetylaminodecalin (1) with anti-Candida activity, had been isolated from Streptomyces chrestomyceticus strain ADP4. The structure of the compound was determined from the analysis of spectral data (LCMS/MS, UV, FTIR, 1D- and 2D-NMR). The anti-Candida activity of 1 was specific to Candida albicans and Candida auris. Further, it displayed inhibition of the early-stage biofilm of C. albicans. In-silico analysis of the compound revealed its drug likeness properties without any violations and PAINS alert when investigated for ADME properties. Along with the overall bioavailability, compound 1 did not show any predicted bioaccumulation and mutagenicity in the analysis by TEST software. Non-cytotoxic property was further confirmed by in-vitro assay on the HepG2 cell line.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

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

A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Bioactive Peptides

Neurodegenerative disorders, which include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), represent a significant and growing global health challenge. Current therapies predominantly focus on symptom management rather than altering disease progression. In this review, we discuss the major therapeutic strategies in practice for these disorders, highlighting their limitations. For AD, the mainstay treatments are cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists. For PD, dopamine replacement therapies, including levodopa, are commonly used. HD is managed primarily with symptomatic treatments, and reusable extends survival in ALS. However, none of these therapies halts or substantially slows the neurodegenerative process. In contrast, this review highlights emerging research into bioactive peptides as potential therapeutic agents. These naturally occurring or synthetically designed molecules can interact with specific cellular targets, potentially modulating disease processes. Preclinical studies suggest that bioactive peptides may mitigate oxidative stress, inflammation, and protein misfolding, which are common pathological features in neurodegenerative diseases. Clinical trials using bioactive peptides for neurodegeneration are limited but show promising initial results. For instance, hemiacetal, a γ-secretase inhibitor peptide, has shown potential in AD by reducing amyloid-beta production, though its development was discontinued due to side effects. Despite these advancements, many challenges remain, including identifying optimal peptides, confirming their mechanisms of action, and overcoming obstacles related to their delivery to the brain. Future research should prioritize the discovery and development of novel bioactive peptides and improve our understanding of their pharmacokinetics and pharmacodynamics. Ultimately, this approach may lead to more effective therapies for neurodegenerative disorders, moving beyond symptom management to potentially modify the course of these devastating diseases.

Harnessing the power of artificial intelligence to advance cell therapy

Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.

Various Biomimetics, Including Peptides as Antifungals

Biomimetics, which are similar to natural compounds that play an important role in the metabolism, manifestation of functional activity and reproduction of various fungi, have a pronounced attraction in the current search for new effective antifungals. Actual trends in the development of this area of research indicate that unnatural amino acids can be used as such biomimetics, including those containing halogen atoms; compounds similar to nitrogenous bases embedded in the nucleic acids synthesized by fungi; peptides imitating fungal analogs; molecules similar to natural substrates of numerous fungal enzymes and quorum-sensing signaling molecules of fungi and yeast, etc. Most parts of this review are devoted to the analysis of semi-synthetic and synthetic antifungal peptides and their targets of action. This review is aimed at combining and systematizing the current scientific information accumulating in this area of research, developing various antifungals with an assessment of the effectiveness of the created biomimetics and the possibility of combining them with other antimicrobial substances to reduce cell resistance and improve antifungal effects.

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information

The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.

Computationally Designed AMPs with Antibacterial and Antibiofilm Activity against MDR Acinetobacter baumannii

The discovery of new antimicrobials is necessary to combat multidrug-resistant (MDR) bacteria, especially those that infect wounds and form prodigious biofilms, such as Acinetobacter baumannii. Antimicrobial peptides (AMPs) are a promising class of new therapeutics against drug-resistant bacteria, including gram-negatives. Here, we utilized a computational AMP design strategy combining database filtering technology plus positional analysis to design a series of novel peptides, named HRZN, designed to be active against A. baumannii. All of the HRZN peptides we synthesized exhibited antimicrobial activity against three MDR A. baumannii strains with HRZN-15 being the most active (MIC 4 µg/mL). This peptide also inhibited and eradicated biofilm of A. baumannii strain AB5075 at 8 and 16 µg/mL, which is highly effective. HRZN-15 permeabilized and depolarized the membrane of AB5075 rapidly, as demonstrated by the killing kinetics. HRZN 13 and 14 peptides had little to no hemolysis activity against human red blood cells, whereas HRZN-15, -16, and -17 peptides demonstrated more significant hemolytic activity. HRZN-15 also demonstrated toxicity to waxworms. Further modification of HRZN-15 could result in a new peptide with an improved toxicity profile. Overall, we successfully designed a set of new AMPs that demonstrated activity against MDR A. baumannii using a computational approach.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

In silico identification of novel antimicrobial peptides from the venom gland transcriptome of the spider Argiope bruennichi (Scopoli, 1772)

As the emergence and prevalence of antibiotic-resistant strains have resulted in a global crisis, there is an urgent need for new antimicrobial agents. Antimicrobial peptides (AMPs) exhibit inhibitory activity against a wide spectrum of pathogens and can be utilized as an alternative to conventional antibiotics. In this study, two novel AMPs were identified from the venom transcriptome of the spider Argiope bruennichi (Scopoli, 1772) using in silico methods, and their antimicrobial activity was experimentally validated. Aranetoxin-Ab2a (AATX-Ab2a) and Aranetoxin-Ab3a (AATX-Ab3a) were identified by homology analysis and were predicted to have high levels of antimicrobial activity based on in silico analysis. Both peptides were found to have antibacterial effect against Gram-positive and -negative strains, and, in particular, showed significant inhibitory activity against multidrug-resistant Pseudomonas aeruginosa isolates. In addition, AATX-Ab2a and AATX-Ab3a inhibited animal and vegetable fungal strains, while showing low toxicity to normal human cells. The antimicrobial activity of the peptides was attributed to the increased permeability of microbial membranes. The study described the discovery of novel antibiotic candidates, AATX-Ab2a and AATX-Ab3a, using the spider venom gland transcriptome, and validated an in silico-based method for identifying functional substances from biological resources.

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

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

Recent advances in the development of therapeutic peptides

Peptides have unique characteristics that make them highly desirable as therapeutic agents. The physicochemical and proteolytic stability profiles determine the therapeutic potential of peptides. Multiple strategies to enhance the therapeutic profile of peptides have emerged. They include chemical modifications, such as cyclization, substitution with d-amino acids, peptoid formation, N-methylation, and side-chain halogenation, and incorporation in delivery systems. There have been recent advances in approaches to discover peptides having these modifications to attain desirable therapeutic properties. We critically review these recent advancements in therapeutic peptide development.

Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.

Using display technologies to identify macrocyclic peptide antibiotics

Antibiotic resistant bacterial infections are now a leading cause of global mortality. While drug resistance continues to spread, the clinical antibiotic pipeline has become bare. This discord has focused attention on developing new strategies for antimicrobial discovery. Natural macrocyclic peptide-based products have provided novel antibiotics and antibiotic scaffolds targeting several essential bacterial cell envelope processes, but discovery of such natural products remains a slow and inefficient process. Synthetic strategies employing peptide display technologies can quickly screen large libraries of macrocyclic sequences for specific target binding and general antibacterial potential providing alternative approaches for new antibiotic discovery. Here we review cell envelope processes that can be targeted with macrocyclic peptide therapeutics, outline important macrocyclic peptide display technologies, and discuss future strategies for both library design and screening.

Combined charge and hydrophobicity-guided screening of antibacterial peptides: two-level approach to predict antibacterial activity and efficacy

Antibacterial peptides can be a potential game changer in the fight against antibiotic resistance. In order for these peptides to become successful antibiotic alternatives, it is essential that they possess high efficacy in addition to just being antibacterial. In this study, we have developed a two-level SVM-based binary classification approach to predict the antibacterial activity of a given peptide (model 1) and thereafter classify its antibacterial efficacy as high/low (model 2) with respect to minimum inhibitory concentration (MIC) values against Staphylococcus aureus, one of the most common pathogens. Based on charge and hydrophobicity of amino acids, we developed a sequence-based combined charge and hydrophobicity-guided triad (CHT) as a new method for obtaining features of any peptide. Model 1 with a combination of CHT and amino acid composition (AAC) as the feature representation method resulted in the highest accuracy of 96.7%. Model 2 with CHT as the feature representation method yielded the highest accuracy of 70.9%. Thus, CHT is found to be a potential feature representation method for classifying antibacterial peptides based on both activity and efficacy. Furthermore, we have also used an explainable machine learning algorithm to extract various insights from these models. These insights are found to be in excellent agreement with experimental findings reported in the literature, thus enhancing the dependability of the proposed models.

Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.

Rational design, synthesis, antifungal evaluation and docking studies of antifungal peptide CGA-N12 analogues based on the target CtKRE9

Candida tropicalis is a major non-albicans species that causes invasive candidiasis. CGA-N12, an anti-Candida peptide found by our group, disrupted cell wall architecture by inhibiting the activity of the protein killer-resistant 9 (KRE9), a β-1,6-glucan synthase specific to Candida spp. and plants. Herein, a set of CGA-N12 analogues were rationally designed based on the interaction networks between CGA-N12 and C. tropicalis KRE9 (CtKRE9). Seven CGA-N12 analogues with significantly improved antifungal activity against C. tropicalis were screened by reducing the docking energy of CGA-N12 and CtKRE9 and increasing the number of positive charges on CGA-N12 based on a stable three-dimensional model of CtKRE9. CGA-N12 and its analogues exhibited antifungal activity against C. tropicalis and its persist cells; they also inhibited biofilm formation and eradicated preformed biofilms. Compared with fluconazole, they displayed higher activities against the growth of persister cells and more effective preformed biofilm eradication. Among them, CGA-N12-0801, CGA-N12-0902 and CGA-N12-1002 displayed much higher activity and anti-proteinase digestion stability than CGA-N12. Specifically, CGA-N12-0801 was the optimal analogue, with a minimum inhibitory concentration of 3.46 μg/mL and a therapeutic index of 158.07. The results of electronic microscopy observations and KRE9 activity inhibition assays showed that CGA-N12 and its analogues killed C. tropicalis by disrupting the architecture of the cell wall and the integrity of the cell membrane. In conclusion, for the first time, we provide a simple and reliable method for the rational design of antimicrobial peptides and ideal candidates for treating Candida infections that not effectively eliminated by azole drugs.

The biological role of charge distribution in linear antimicrobial peptides

INTRODUCTION

Antimicrobial peptides (AMP) have received particular attention due to their capacity to kill bacteria. Although much is known about them, peptides are currently being further researched. A large number of AMPs have been discovered, but only a few have been approved for topical use, due to their promiscuity and other challenges, which need to be overcome.

AREAS COVERED

AMPs are diverse in structure. Consequently, they have varied action mechanisms when targeting microorganisms or eukaryotic cells. Herein, the authors focus on linear peptides, particularly those that are alpha-helical structured, and examine how their charge distribution and hydrophobic amino acids could modulate their biological activity.

EXPERT OPINION

The world currently needs urgent solutions to the infective problems caused by resistant pathogens. In order to start the race for antimicrobial development from the charge distribution viewpoint, bioinformatic tools will be necessary. Currently, there is no software available that allows to discriminate charge distribution in AMPs and predicts the biological effects of this event. Furthermore, there is no software available that predicts the side-chain length of residues and its role in biological functions. More specialized software is necessary.

A Review on Genotoxic and Genoprotective Effects of Biologically Active Compounds of Animal Origin

Envenomation by animal venoms remains a serious medical and social problem, especially in tropical countries. On the other hand, animal venoms are widely used as a source of biologically active compounds for the development of novel drugs. Numerous derivatives of animal venoms are already used in clinical practice. When analysing the mechanisms of action of animal venoms, attention is usually focused on the main target of the venom's enzymes and peptides such as neurotoxic, cytotoxic or haemorrhagic effects. In the present review, we would like to draw attention to the "hidden" effects of animal venoms and their derivatives in regard to DNA damage and/or protection against DNA damage. Alkaloids and terpenoids isolated from sponges such as avarol, ingenamine G or variolin B manifest the capability to bind DNA in vitro and produce DNA breaks. Trabectidin, isolated from a sea squirt, also binds and damages DNA. A similar action is possible for peptides isolated from bee and wasp venoms such as mastoparan, melectin and melittin. However, DNA lesions produced by the crude venoms of jellyfish, scorpions, spiders and snakes arise as a consequence of cell membrane damage and the subsequent oxidative stress, whereas certain animal venoms or their components produce a genoprotective effect. Current research data point to the possibility of using animal venoms and their components in the development of various potential therapeutic agents; however, before their possible clinical use the route of injection, molecular target, mechanism of action, exact dosage, possible side effects and other fundamental parameters should be further investigated.

Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack of new commercially available antibiotics have made in silico discovery of AMPs increasingly important. Machine learning (ML) has improved the speed and efficiency of AMP discovery while reducing the cost of experimental approaches. Despite various ML platforms developed, there is still a lack of integrative use of ML platforms for AMP discovery from publicly available protein databases. Therefore, our study aims to screen potential AMPs with antibiofilm properties from databases using ML platforms, followed by protein-peptide molecular docking analysis and molecular dynamics (MD) simulations. A total of 5850 peptides classified as non-AMP were screened from UniProtKB and analyzed using various online ML platforms (e.g., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). Eight potential AMP peptides against Klebsiella pneumoniae with antibiofilm, non-toxic and non-hemolytic properties were then docked to MrkH, a transcriptional regulator of type 3 fimbriae involved in biofilm formation. Five of eight peptides bound more strongly than the native MrkH ligand when analyzed using HADDOCK and HPEPDOCK. Following the docking studies, our MD simulated that a Neuropeptide B (Peptide 3) bind strongly to the MrkH active sites. The discovery of putative AMPs that exceed the binding energies of the native ligand underscores the utility of the combined ML and molecular simulation strategies for discovering novel AMPs with antibiofilm properties.

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

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

Embedded-AMP: A Multi-Thread Computational Method for the Systematic Identification of Antimicrobial Peptides Embedded in Proteome Sequences

Antimicrobial peptides (AMPs) have gained the attention of the research community for being an alternative to conventional antimicrobials to fight antibiotic resistance and for displaying other pharmacologically relevant activities, such as cell penetration, autophagy induction, immunomodulation, among others. The identification of AMPs had been accomplished by combining computational and experimental approaches and have been mostly restricted to self-contained peptides despite accumulated evidence indicating AMPs may be found embedded within proteins, the functions of which are not necessarily associated with antimicrobials. To address this limitation, we propose a machine-learning (ML)-based pipeline to identify AMPs that are embedded in proteomes. Our method performs an in-silico digestion of every protein in the proteome to generate unique k-mers of different lengths, computes a set of molecular descriptors for each k-mer, and performs an antimicrobial activity prediction. To show the efficiency of the method we used the shrimp proteome, and the pipeline analyzed all k-mers between 10 and 60 amino acids in length to predict all AMPs in less than 20 min. As an application example we predicted AMPs in different rodents (common cuy, common rat, and naked mole rat) with different reported longevities and found a relation between species longevity and the number of predicted AMPs. The analysis shows as the longevity of the species is higher, the number of predicted AMPs is also higher. The pipeline is available as a web service.

Ligand and structure-based approaches for the exploration of structure-activity relationships of fusidic acid derivatives as antibacterial agents

Introduction: Fusidic acid (FA) has been widely applied in the clinical prevention and treatment of bacterial infections. Nonetheless, its clinical application has been limited due to its narrow antimicrobial spectrum and some side effects. Purpose: Therefore, it is necessary to explore the structure-activity relationships of FA derivatives as antibacterial agents to develop novel ones possessing a broad antimicrobial spectrum. Methods and result: First, a pharmacophore model was established on the nineteen FA derivatives with remarkable antibacterial activities reported in previous studies. The common structural characteristics of the pharmacophore emerging from the FA derivatives were determined as those of six hydrophobic centers, two atom centers of the hydrogen bond acceptor, and a negative electron center around the C-21 field. Then, seven FA derivatives have been designed according to the reported structure-activity relationships and the pharmacophore characteristics. The designed FA derivatives were mapped on the pharmacophore model, and the Qfit values of all FA derivatives were over 50 and FA-8 possessed the highest value of 82.66. The molecular docking studies of the partial target compounds were conducted with the elongation factor G (EF-G) of S. aureus. Furthermore, the designed FA derivatives have been prepared and their antibacterial activities were evaluated by the inhibition zone test and the minimum inhibitory concentration (MIC) test. The derivative FA-7 with a chlorine group as the substituent group at C-25 of FA displayed the best antibacterial property with an MIC of 3.125 µM. Subsequently, 3D-QSAR was carried on all the derivatives by using the CoMSIA mode of SYBYL-X 2.0. Conclusion: Hence, a computer-aided drug design model was developed for FA, which can be further used to optimize FA derivatives as highly potent antibacterial agents.

Evaluation of the Synthetic Multifunctional Peptide Hp-MAP3 Derivative of Temporin-PTa

In recent years, antimicrobial peptides isolated from amphibian toxins have gained attention as new multifunctional drugs interacting with different molecular targets. We aimed to rationally design a new peptide from temporin-PTa. Hp-MAP3 (NH2-LLKKVLALLKKVL-COOH), net charge (+4), hydrophobicity (0.69), the content of hydrophobic residues (69%), and hydrophobic moment (0.73). For the construction of the analog peptide, the physicochemical characteristics were reorganized into hydrophilic and hydrophobic residues with the addition of lysines and leucines. The minimum inhibitory concentration was 2.7 to 43 μM against the growth of Gram-negative and positive bacteria, and the potential for biofilm eradication was 173.2 μM. Within 20 min, the peptide Hp-MAP3 (10.8 μM) prompted 100% of the damage to E. coli cells. At 43.3 μM, eliminated 100% of S. aureus within 5 min. The effects against yeast species of the Candida genus ranged from 5.4 to 86.6 μM. Hp-MAP3 presents cytotoxic activity against tumor HeLa at a concentration of 21.6 μM with an IC50 of 10.4 µM. Furthermore, the peptide showed hemolytic activity against murine erythrocytes. Structural studies carried out by circular dichroism showed that Hp-MAP3, while in the presence of 50% trifluoroethanol or SDS, an α-helix secondary structure. Finally, Amphipathic Hp-MAP3 building an important model for the design of new multifunctional molecules.

Antimicrobial peptides against polymyxin-resistant Klebsiella pneumoniae: a patent review

The spread of polymyxin-resistant Klebsiella pneumoniae strains represents an emerging health challenge, limiting treatment options for the patients. Thus, the development of new antimicrobials is an urgent requirement. Antimicrobial peptides (AMPs) are a large class of compounds that are part of innate immune response; these peptides are promising compounds in the field of antimicrobial resistance and are present in all organisms. The present review evaluated patents on antimicrobial peptides tested against polymyxin-resistant K. pneumoniae, available on Espacenet as of September 2022. A total of 1313 patents were examined and 1197 excluded as they were out of focus for this review; 104 patents of peptides tested against K. pneumoniae were included; of which only 14 were tested against polymyxin-resistant K. pneumoniae strains. The results indicated that all AMPs evaluated were in the experimental or pre-clinical phase; the clinical phase is pending. Furthermore, a few peptides were tested effectively against polymyxin-resistant K. pneumoniae. Although, the research and patent filing alone are not enough to develop a suitable antimicrobial therapy, they can represent good starting point upon which to develop new antimicrobials. More investment is required to push these pharmaceuticals through the stages of development to introduce them into the market.

Design and Evaluation of a Novel Anti-microbial Peptide from Cathelicidin-2: Selectively Active Against Acinetobacter baumannii

Background

Infections caused by pathogenic microorganisms have increased the need for hospital care and have thus represented a public health problem and a significant financial burden. Classical treatments consisting of traditional antibiotics face several challenges today. Anti-microbial peptides (AMPs) are a conserved characteristic of the innate immune response among different animal species to defend against pathogenic microorganisms.

Objectives

In this study, a new peptide sequence (mCHTL131-140) was designed using the in silico approach.

Methods

Cathelicidin-2 (UniprotID: Q2IAL7) was used as a potential antimicrobial protein, and a novel 10 - 12 amino acids sequence AMP was designed using bioinformatics tools and the AMP databases. Then, the anti-bacterial, anti-biofilm, and anti-fungal properties of the peptide, as well as its hemolytic activity and cytotoxicity towards human fibroblast (HDF) cells, were investigated in vitro.

Results

Online bioinformatics tools indicated that the peptide sequence could have anti-bacterial, anti-viral, anti-fungal, and anti-biofilm properties with little hemolytic properties. The experimental tests confirmed that mCHTL131-140 exhibited the best anti-bacterial properties against Acinetobacter baumannii and had fair anti-fungal properties. Besides, it did not cause red blood cell lysis and showed no cytotoxicity towards HDF cells.

Conclusions

In general, the designed peptide can be considered a promising AMP to control hospital-acquired infections by A. baumannii.

Recent Patents and FDA-Approved Drugs Based on Antiviral Peptides and Other Peptide-Related Antivirals

In spite of existing cases of severe viral infections with a high mortality rate, there are not enough antiviral drugs and vaccines available for the prevention and treatment of such diseases. In addition, the increasing reports of the emergence of viral epidemics highlight, the need for novel molecules with antiviral potential. Antimicrobial peptides (AMPs) with antiviral activity or antiviral peptides (AVPs) have turned into a research hotspot and already show tremendous potential to become pharmaceutically available antiviral medicines. AMPs, a diverse group of bioactive peptides act as a part of our first line of defense against pathogen inactivation. Although most of the currently reported AMPs are either antibacterial or antifungal peptides, the number of antiviral peptides is gradually increasing. Some of the AMPs that are shown as effective antivirals have been deployed against viruses such as influenza A virus, severe acute respiratory syndrome coronavirus (SARS-CoV), HIV, HSV, West Nile Virus (WNV), and other viruses. This review offers an overview of AVPs that have been approved within the past few years and will set out a few of the most essential patents and their usage within the context mentioned above during 2000-2020. Moreover, the present study will explain some of the progress in antiviral drugs based on peptides and peptide-related antivirals.

Antimicrobial Peptides Therapy: An Emerging Alternative for Treating Drug-Resistant Bacteria

Microbial resistance to antibiotics is an ancient and dynamic issue that has brought a situation reminiscent of the pre-antibiotic era to the limelight. Currently, antibiotic resistance and the associated infections are widespread and pose significant global health and economic burden. Thus, the misuse of antibiotics, which has increased resistance, has necessitated the search for alternative therapeutic agents for combating resistant pathogens. Antimicrobial peptides (AMPs) hold promise as a viable therapeutic approach against drug-resistant pathogens. AMPs are oligopeptides with low molecular weight. They have broad-spectrum antimicrobial activities against pathogenic microorganisms. AMPs are nonspecific and target components of microbes that facilitate immune response by acting as the first-line defense mechanisms against invading pathogenic microbes. The diversity and potency of AMPs make them good candidates for alternative use. They could be used alone or in combination with several other biomaterials for improved therapeutic activity. They can also be employed in vaccine production targeting drug-resistant pathogens. This review covers the opportunities and advances in AMP discovery and development targeting antimicrobial resistance (AMR) bacteria. Briefly, it presents an overview of the global burden of the antimicrobial resistance crisis, portraying the global magnitude, challenges, and consequences. After that, it critically and comprehensively evaluates the potential roles of AMPs in addressing the AMR crisis, highlighting the major potentials and prospects.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 10⁶⁵ unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Identification and characterization of a novel peptide from rainbow trout (Oncorhynchus mykiss) with antimicrobial activity against Streptococcus iniae

The overuse and misuse of antibiotics has led to the emergence of antibiotic-resistant bacterial species which remain a challenge to treat therapeutically. Novel and efficacious drugs are desperately needed to combat pathogens. One method to facilitate these discoveries is the use of in silico methods. Computational biology has the power to scan large data sets and screen for potential molecules with antibacterial function. In the current study, an in silico approach was used to identify an antimicrobial peptide (AMP) derived from rainbow trout von Willebrand Factor. The AMP was tested against a panel of aquatic bacterial pathogens and was found to possess antibacterial activity against Streptococcus iniae (S. iniae). Since S. iniae is a zoonotic pathogen, this may be useful in other species as well. The peptide was non-hemolytic and non-cytotoxic at the concentrations tested in rainbow trout cells. Pre-treatment of rainbow trout cells with the peptide did not result in an upregulation of immune genes but stimulating the rainbow trout macrophage/monocyte-like cell line, RTS11, with heat-killed S. iniae, did result in a significant upregulation of the tumor necrosis factor alpha (tnfa) gene. In this study, a new AMP has been identified but its expression, synthesis and role in vivo remains unknown. Nevertheless, the findings presented improve our understanding of fish gill and macrophage responses towards this important zoonotic pathogen.

Strategies for improving antimicrobial peptide production

Antimicrobial peptides (AMPs) found in a wide range of animal, insect, and plant species are host defense peptides forming an integral part of their innate immunity. Although the exact mode of action of some AMPs is yet to be deciphered, many exhibit membrane lytic activity or interact with intracellular targets. The ever-growing threat of antibiotic resistance has brought attention to research on AMPs to enhance their clinical use as a therapeutic alternative. AMPs have several advantages over antibiotics such as broad range of antimicrobial activities including anti-fungal, anti-viral and anti-bacterial, and have not reported to contribute to resistance development. Despite the numerous studies to develop efficient production methods for AMPs, limitations including low yield, degradation, and loss of activity persists in many recombinant approaches. In this review, we outline available approaches for AMP production and various expression systems used to achieve higher yield and quality. In addition, recent advances in recombinant strategies, suitable fusion protein partners, and other molecular engineering strategies for improved AMP production are surveyed.

Antimicrobial potential of a ponericin-like peptide isolated from Bombyx mori L. hemolymph in response to Pseudomonas aeruginosa infection

The main effectors in the innate immune system of Bombyx mori L. are antimicrobial peptides (AMPs). Here, we infected B. mori with varied inoculum sizes of Pseudomonas aeruginosa ATCC 25668 cells to investigate changes in morpho-anatomical responses, physiological processes and AMP production. Ultraviolet-visible spectra revealed a sharp change in λ_max from 278 to 285 nm (bathochromic shift) in the hemolymph of infected B. mori incubated for 24 h. Further, Fourier Transform InfraRed studies on the hemolymph extracted from the infected B. mori showed a peak at 1550 cm^-1, indicating the presence of α-helical peptides. The peptide fraction was obtained through methanol, acetic acid and water mixture (90:1:9) extraction, followed by peptide purification using Reverse Phase High Performance Liquid Chromatography. The fraction exhibiting antibacterial properties was collected and characterized by Matrix-Assisted Laser Desorption/Ionization-Time of Flight. A linear α-helical peptide with flexible termini (LLKELWTKMKGAGKAVLGKIKGLL) was found, corresponding to a previously described peptide from ant venom and here denominated as Bm-ponericin-L1. The antibacterial activity of Bm-ponericin-L1 was determined against ESKAPE pathogens. Scanning electron microscopy confirmed the membrane disruption potential of Bm-ponericin-L1. Moreover, this peptide also showed promising antibiofilm activity. Finally, cell viability and hemolytic assays revealed that Bm-ponericin-L1 is non-toxic toward primary fibroblasts cell lines and red blood cells, respectively. This study opens up new perspectives toward an alternative approach to overcoming multiple-antibiotic-resistance by means of AMPs through invertebrates' infection with human pathogenic bacteria.

In Silico Prediction of Anti-Infective and Cell-Penetrating Peptides from Thalassophryne nattereri Natterin Toxins

The therapeutic potential of venom-derived peptides, such as bioactive peptides (BAPs), is determined by specificity, stability, and pharmacokinetics properties. BAPs, including anti-infective or antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs), share several physicochemical characteristics and are potential alternatives to antibiotic-based therapies and drug delivery systems, respectively. This study used in silico methods to predict AMPs and CPPs derived from natterins from the venomous fish Thalassophryne nattereri. Fifty-seven BAPs (19 AMPs, 8 CPPs, and 30 AMPs/CPPs) were identified using the web servers CAMP, AMPA, AmpGram, C2Pred, and CellPPD. The physicochemical properties were analyzed using ProtParam, PepCalc, and DispHred tools. The membrane-binding potential and cellular location of each peptide were analyzed using the Boman index by APD3, and TMHMM web servers. All CPPs and two AMPs showed high membrane-binding potential. Fifty-four peptides were located in the plasma membrane. Peptide immunogenicity, toxicity, allergenicity, and ADMET parameters were evaluated using several web servers. Sixteen antiviral peptides and 37 anticancer peptides were predicted using the web servers Meta-iAVP and ACPred. Secondary structures and helical wheel projections were predicted using the PEP-FOLD3 and Heliquest web servers. Fifteen peptides are potential lead compounds and were selected to be further synthesized and tested experimentally in vitro to validate the in silico screening. The use of computer-aided design for predicting peptide structure and activity is fast and cost-effective and facilitates the design of potent therapeutic peptides. The results demonstrate that toxins form a natural biotechnological platform in drug discovery, and the presence of CPP and AMP sequences in toxin families opens new possibilities in toxin biochemistry research.

Accelerating the discovery of anticancer peptides targeting lung and breast cancers with the Wasserstein autoencoder model and PSO algorithm

In the development of targeted drugs, anticancer peptides (ACPs) have attracted great attention because of their high selectivity, low toxicity and minimal non-specificity. In this work, we report a framework of ACPs generation, which combines Wasserstein autoencoder (WAE) generative model and Particle Swarm Optimization (PSO) forward search algorithm guided by attribute predictive model to generate ACPs with desired properties. It is well known that generative models based on Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN) are difficult to be used for de novo design due to the problems of posterior collapse and difficult convergence of training. Our WAE-based generative model trains more successfully (lower perplexity and reconstruction loss) than both VAE and GAN-based generative models, and the semantic connections in the latent space of WAE accelerate the process of forward controlled generation of PSO, while VAE fails to capture this feature. Finally, we validated our pipeline on breast cancer targets (HIF-1) and lung cancer targets (VEGR, ErbB2), respectively. By peptide-protein docking, we found candidate compounds with the same binding sites as the peptides carried in the crystal structure but with higher binding affinity and novel structures, which may be potent antagonists that interfere with these target-mediated signaling.