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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

Mining human microbiomes reveals an untapped source of peptide antibiotics

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

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance

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

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

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

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

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

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

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

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

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

Discovery of antimicrobial peptides in the global microbiome with machine learning

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

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

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

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

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

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides

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

Challenges in computational discovery of bioactive peptides in 'omics data

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

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

Background and Aim

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

Materials and Methods

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

Results

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

Conclusion

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

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

BACKGROUND

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

RESULTS

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

SIGNIFICANCE

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

Computational peptide discovery with a genetic programming approach

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

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

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

Recombinant production of antimicrobial peptides in plants

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

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

INTRODUCTION

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Antibacterial and Antiviral Properties of Chenopodin-Derived Synthetic Peptides

Antimicrobial peptides have been developed based on plant-derived molecular scaffolds for the treatment of infectious diseases. Chenopodin is an abundant seed storage protein in quinoa, an Andean plant with high nutritional and therapeutic properties. Here, we used computer- and physicochemical-based strategies and designed four peptides derived from the primary structure of Chenopodin. Two peptides reproduce natural fragments of 14 amino acids from Chenopodin, named Chen1 and Chen2, and two engineered peptides of the same length were designed based on the Chen1 sequence. The two amino acids of Chen1 containing amide side chains were replaced by arginine (ChenR) or tryptophan (ChenW) to generate engineered cationic and hydrophobic peptides. The evaluation of these 14-mer peptides on Staphylococcus aureus and Escherichia coli showed that Chen1 does not have antibacterial activity up to 512 µM against these strains, while other peptides exhibited antibacterial effects at lower concentrations. The chemical substitutions of glutamine and asparagine by amino acids with cationic or aromatic side chains significantly favoured their antibacterial effects. These peptides did not show significant hemolytic activity. The fluorescence microscopy analysis highlighted the membranolytic nature of Chenopodin-derived peptides. Using molecular dynamic simulations, we found that a pore is formed when multiple peptides are assembled in the membrane. Whereas, some of them form secondary structures when interacting with the membrane, allowing water translocations during the simulations. Finally, Chen2 and ChenR significantly reduced SARS-CoV-2 infection. These findings demonstrate that Chenopodin is a highly useful template for the design, engineering, and manufacturing of non-toxic, antibacterial, and antiviral peptides.

Innovative Biomedical and Technological Strategies for the Control of Bacterial Growth and Infections

Antibiotics comprise one of the most successful groups of pharmaceutical products. Still, they have been associated with developing bacterial resistance, which has become one of the most severe problems threatening human health today. This context has prompted the development of new antibiotics or co-treatments using innovative tools to reverse the resistance context, combat infections, and offer promising antibacterial therapy. For the development of new alternatives, strategies, and/or antibiotics for controlling bacterial growth, it is necessary to know the target bacteria, their classification, morphological characteristics, the antibiotics currently used for therapies, and their respective mechanisms of action. In this regard, genomics, through the sequencing of bacterial genomes, has generated information on diverse genetic resources, aiding in the discovery of new molecules or antibiotic compounds. Nanotechnology has been applied to propose new antimicrobials, revitalize existing drug options, and use strategic encapsulating agents with their biochemical characteristics, making them more effective against various bacteria. Advanced knowledge in bacterial sequencing contributes to the construction of databases, resulting in advances in bioinformatics and the development of new antimicrobials. Moreover, it enables in silico antimicrobial susceptibility testing without the need to cultivate the pathogen, reducing costs and time. This review presents new antibiotics and biomedical and technological innovations studied in recent years to develop or improve natural or synthetic antimicrobial agents to reduce bacterial growth, promote well-being, and benefit users.

Exploring the frontiers of therapeutic breadth of antifungal peptides: A new avenue in antifungal drugs

The growing prevalence of fungal infections alongside rising resistance to antifungal drugs poses a significant challenge to public health safety. At the close of the 2000s, major pharmaceutical firms began to scale back on antimicrobial research due to repeated setbacks and diminished economic gains, leaving only smaller companies and research labs to pursue new antifungal solutions. Among various natural sources explored for novel antifungal compounds, antifungal peptides (AFPs) emerge as particularly promising. Despite their potential, AFPs receive less focus than their antibacterial counterparts. These peptides have been sourced extensively from nature, including plants, animals, insects, and especially bacteria and fungi. Furthermore, with advancements in recombinant biotechnology and computational biology, AFPs can also be synthesized in lab settings, facilitating peptide production. AFPs are noted for their wide-ranging efficacy, in vitro and in vivo safety, and ability to combat biofilms. They are distinguished by their high specificity, minimal toxicity to cells, and reduced likelihood of resistance development. This review aims to comprehensively cover AFPs, including their sources-both natural and synthetic-their antifungal and biofilm-fighting capabilities in laboratory and real-world settings, their action mechanisms, and the current status of AFP research.

ONE-SENTENCE SUMMARY

This comprehensive review of AFPs will be helpful for further research in antifungal research.

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.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

Screening and investigation of a short antimicrobial peptide: AVGAV

Bacterial resistance to various drugs is a major problem concerning the field of antibacterial agents. Fortunately, peptides with antibacterial activity can alleviate this problem. In this study, a short peptide (AVGAV) with excellent antibacterial activity was successfully screened from a peptide library by a self-made membrane chromatographic packing. The AVGAV peptide exhibits good biocompatibility and is non-toxic and non-irritating, which ensures that it presents safe antibacterial effects. AVGAV promoted wound healing in a mouse wound bacterial infection model. Most importantly, as a synthetic antimicrobial peptide, AVGAV can alleviate the problem of bacterial resistance, thus improving its application potential. This study provides a solution to the existing and potential problem of bacterial resistance.

Antibiotic identified by AI

Computational approaches are emerging as powerful tools for the discovery of antibiotics. A study now uses machine learning to discover abaucin, a potent antibiotic that targets the bacterial pathogen Acinetobacter baumannii .

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.

Molecular hybridization strategy for tuning bioactive peptide function

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

Model-directed generation of CRISPR-Cas13a guide RNAs designs artificial sequences that improve nucleic acid detection

Generating maximally-fit biological sequences has the potential to transform CRISPR guide RNA design as it has other areas of biomedicine. Here, we introduce model-directed exploration algorithms (MEAs) for designing maximally-fit, artificial CRISPR-Cas13a guides-with multiple mismatches to any natural sequence-that are tailored for desired properties around nucleic acid diagnostics. We find that MEA-designed guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared to guides derived directly from natural sequences, and illuminate interpretable design principles that broaden Cas13a targeting.

Computational exploration of the global microbiome for antibiotic discovery

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

Human gut metagenomic mining reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally mined 444,054 families of putative small proteins from 1,773 human gut metagenomes, identifying 323 peptide antibiotics encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 59% displaying activity against either pathogens or commensals. Since these peptides were unique compared to previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergized with each other, and modulated gut commensals, indicating that they may play a role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. We report the discovery of hundreds of peptide sequences in the human gut.

Computational Peptide Discovery with a Genetic Programming Approach

Background

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search space. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and facilitating the discovery of new peptides.

Results

This study presents the development and use of a variant of the initial POET algorithm, called P O E T R e g e x , which is based on genetic programming, where individuals are represented by a list of regular expressions. The program was trained on a small curated dataset and employed to predict new peptides that can improve the problem of sensitivity in detecting peptides through magnetic resonance imaging using chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET variant and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide.

Conclusions

By combining the power of genetic programming with the flexibility of regular expressions, new potential peptide targets were identified to improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

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.

Activity modulation of the Escherichia coli F1FO ATP synthase by a designed antimicrobial peptide via cardiolipin sequestering

Most antimicrobial peptides (AMPs) exert their microbicidal activity through membrane permeabilization. The designed AMP EcDBS1R4 has a cryptic mechanism of action involving the membrane hyperpolarization of Escherichia coli, suggesting that EcDBS1R4 may hinder processes involved in membrane potential dissipation. We show that EcDBS1R4 can sequester cardiolipin, a phospholipid that interacts with several respiratory complexes of E. coli. Among these, F₁F_O ATP synthase uses membrane potential to fuel ATP synthesis. We found that EcDBS1R4 can modulate the activity of ATP synthase upon partition to membranes containing cardiolipin. Molecular dynamics simulations suggest that EcDBS1R4 alters the membrane environment of the transmembrane F_O motor, impairing cardiolipin interactions with the cytoplasmic face of the peripheral stalk that binds the catalytic F₁ domain to the F_O domain. The proposed mechanism of action, targeting membrane protein function through lipid reorganization may open new venues of research on the mode of action and design of other AMPs.

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

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

Leveraging artificial intelligence in the fight against infectious diseases

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

Geometric deep learning as a potential tool for antimicrobial peptide prediction

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

Deep learning tools to accelerate antibiotic discovery

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Progress towards the clinical use of antimicrobial peptides: challenges and opportunities

INTRODUCTION

To overcome the challenge of multidrug resistance, natural and synthetic peptides are candidates to become the basis of innovative therapeutics, featuring diverse mechanisms of action. Traditionally, the time elapsed from medical discoveries to their application is long. The urgency derived from the emergence of antibiotic resistance recommends an acceleration of research to put the new weapons in the hands of clinicians.

AREAS COVERED

This narrative review introduces ideas and suggestions of new strategies that may be used as a basis upon which to recommend reduced development times and to facilitate the arrival of new molecules in the fight against microbes.

EXPERT OPINION

Although studies on new innovative antimicrobial treatments are being conducted, sooner rather than later, more clinical trials, preclinical and translational research are needed to promote the development of innovative antimicrobial treatments for multidrug resistant infections. The situation is worrying, no less than that generated by pandemics such as the ones we have just experienced and conflicts such as world wars. Although from the point of view of human perception, resistance to antibiotics may not seem as serious as these other situations, it is possibly the hidden pandemic that most jeopardizes the future of medicine.

Mining for antimicrobial peptides in sequence space

A machine-learning pipeline identifies potent antimicrobial peptides by gradually narrowing down the search space of polypeptide chain sequences.

A pilot study on ultrashort peptide with fluconazole: A promising novel anticandidal combination

Background and Aim

Human infections caused by Candida albicans are common and range in severity from relatively treatable skin and mucosal conditions to systemic, fatal invasive candidiasis. The treatment of fungal infections is challenged by major obstacles, including the scarcity of effective therapeutic options, the toxicity of available medications, and the escalating antifungal resistance. Hence, there exists an urgent need to develop new classes of antimicrobial agents. This study was conducted to investigate the effect of KW-23 peptide against standard and resistant strains of C. albicans alone and in combination with fluconazole.

Materials and Methods

A conjugated ultrashort antimicrobial peptide (KW-23) was designed and synthesized. KW-23 was challenged against standard and multidrug-resistant C. albicans alone and in combination with fluconazole using standard antimicrobial and checkerboard assays. The toxicity of the peptide was examined using hemolytic assays.

Results

KW-23 positively affected the standard and resistant Candidal strains (at 5 and 15 μg/mL respectively), exhibiting potent synergistic antimicrobial activity against the standard strain when combined with fluconazole. The effect of the combination was additive against the resistant strain (0.6 μg/mL). Furthermore, the peptide exhibited negligible toxicity on human erythrocytes.

Conclusion

KW-23 and its combination with fluconazole could be a promising candidate for developing anticandidal agents.

Role of bacterial efflux pumps in antibiotic resistance, virulence, and strategies to discover novel efflux pump inhibitors

The problem of antibiotic resistance among pathogenic bacteria has reached a crisis level. The treatment options against infections caused by multiple drug-resistant bacteria are shrinking gradually. The current pace of the discovery of new antibacterial entities is lagging behind the rate of development of new resistance. Efflux pumps play a central role in making a bacterium resistant to multiple antibiotics due to their ability to expel a wide range of structurally diverse compounds. Besides providing an escape from antibacterial compounds, efflux pumps are also involved in bacterial stress response, virulence, biofilm formation, and altering host physiology. Efflux pumps are unique yet challenging targets for the discovery of novel efflux pump inhibitors (EPIs). EPIs could help rejuvenate our currently dried pipeline of antibacterial drug discovery. The current article highlights the recent developments in the field of efflux pumps, challenges faced during the development of EPIs and potential approaches for their development. Additionally, this review highlights the utility of resources such as natural products and machine learning to expand our EPIs arsenal using these latest technologies.

Advanced delivery systems for peptide antibiotics

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