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Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. In light of this, artificial intelligence in the form of machine learning has been viewed as a potential counter to delay the spread of AMR. With the aid of AI, there are possibilities to predict and identify AMR in bacteria efficiently. Furthermore, a combination of machine learning algorithms and lab testing can help to accelerate the process of discovering new antimicrobials. To date, many machine learning algorithms for antimicrobial-resistance discovery had been created and vigorously validated. Most of these algorithms produced accurate results and outperformed the traditional methods which relied on sequence comparison within a database. This mini-review will provide an updated overview of antimicrobial design workflow using the latest machine-learning antimicrobial discovery algorithms in the last 5 years. With this review, we hope to improve upon the current AMR identification and antimicrobial development techniques by introducing the use of AI into the mix, including how the algorithms could be made more effective.

Biofilms are complex microbial communities in which planktonic and dormant bacteria are enveloped in extracellular polymeric substances (EPS) such as exopolysaccharides, proteins, lipids, and DNA. These multicellular structures present resistance to conventional antimicrobial treatments, including antibiotics. The formation of biofilms raises considerable concern in healthcare settings, biofilms can exacerbate infections in patients and compromise the integrity of medical devices employed during treatment. Similarly, certain bacterial species contribute to bulking, foaming, and biofilm development in water environments such as wastewater treatment plants, water reservoirs, and aquaculture facilities. Additionally, food production facilities provide ideal conditions for establishing bacterial biofilms, which can serve as reservoirs for foodborne pathogens. Efforts to combat antibiotic resistance involve exploring various strategies, including bacteriophage therapy. Research has been conducted on the effects of phages and their individual proteins to assess their potential for biofilm removal. However, challenges persist, prompting the examination of refined approaches such as drug-phage combination therapies, phage cocktails, and genetically modified phages for clinical applications. This review aims to highlight the progress regarding bacteriophage-based approaches for biofilm eradication in different settings.

Recent years have witnessed a dramatic increase in bacterial antimicrobial resistance and a decline in the development of novel antibiotics. New therapeutic strategies are urgently needed to combat the growing threat posed by multidrug resistant bacterial infections. The Dsb disulfide bond forming pathways are potential targets for the development of antimicrobial agents because they play a central role in bacterial pathogenesis. In particular, the DsbA/DsbB system catalyses disulfide bond formation in a wide array of virulence factors, which are essential for many pathogens to establish infections and cause disease. These redox enzymes are well placed as antimicrobial targets because they are taxonomically widespread, share low sequence identity with human proteins, and many years of basic research have provided a deep molecular understanding of these systems in bacteria. In this review, we discuss disulfide bond catalytic pathways in bacteria and their significance in pathogenesis. We also review the use of different approaches to develop inhibitors against Dsb proteins as potential anti-virulence agents, including fragment-based drug discovery, high-throughput screening and other structure-based drug discovery methods.

The link between the antimicrobial and anticancer activity of peptides has long been studied, and the number of peptides identified with both activities has recently increased considerably. In this work, we hypothesized that designed peptides with a wide spectrum of selective antimicrobial activity will also have anticancer activity, and tested this hypothesis with newly designed peptides. The spectrum of peptides, used as partial or full design templates, ranged from cell-penetrating peptides and putative bacteriocin to those from the simplest animals (placozoans) and the Chordata phylum (anurans). We applied custom computational tools to predict amino acid substitutions, conferring the increased product of bacteriostatic activity and selectivity. Experiments confirmed that better overall performance was achieved with respect to that of initial templates. Nine of our synthesized helical peptides had excellent bactericidal activity against both standard and multidrug-resistant bacteria. These peptides were then compared to a known anticancer peptide polybia-MP1, for their ability to kill prostate cancer cells and dermal primary fibroblasts. The therapeutic index was higher for seven of our peptides, and anticancer activity stronger for all of them. In conclusion, the peptides that we designed for selective antimicrobial activity also have promising potential for anticancer applications.

Resistance to antibiotics is one of the main current threats to human health and every year multi-drug resistant bacteria are infecting millions of people worldwide, with many dying as a result. Ever since their discovery, some 40 years ago, the antimicrobial peptides (AMPs) of innate defense have been hailed as a potential alternative to conventional antibiotics due to their relatively low potential to elicit resistance. Despite continued effort by both academia and start-ups, currently there are still no antibiotics based on AMPs in use. In this study, we discuss what we know and what we do not know about these agents, and what we need to know to successfully translate discovery to application. Understanding the complex mechanics of action of these peptides is the main prerequisite for identifying and/or designing or redesigning novel molecules with potent biological activity. However, other aspects also need to be well elucidated, i.e., the (bio)synthetic processes, physiological and pathological contexts of their activity, and a quantitative understanding of how physico-chemical properties affect activity. Research groups worldwide are using biological, biophysical, and algorithmic techniques to develop models aimed at designing molecules with the necessary blend of antimicrobial potency and low toxicity. Shedding light on some open questions may contribute toward improving this process.

Scorpion venoms have proven to be excellent sources of antimicrobial agents. However, although many of them have been functionally characterized, they remain underutilized as pharmacological agents, despite their evident therapeutic potential. In this review, we discuss the physicochemical properties of short scorpion venom antimicrobial peptides (ssAMPs). Being generally short (13–25 aa) and amidated, their proven antimicrobial activity is generally explained by parameters such as their net charge, the hydrophobic moment, or the degree of helicity. However, for a complete understanding of their biological activities, also considering the properties of the target membranes is of great relevance. Here, with an extensive analysis of the physicochemical, structural, and thermodynamic parameters associated with these biomolecules, we propose a theoretical framework for the rational design of new antimicrobial drugs. Through a comparison of these physicochemical properties with the bioactivity of ssAMPs in pathogenic bacteria such as Staphylococcus aureus or Acinetobacter baumannii, it is evident that in addition to the net charge, the hydrophobic moment, electrostatic energy, or intrinsic flexibility are determining parameters to understand their performance. Although the correlation between these parameters is very complex, the consensus of our analysis suggests that there is a delicate balance between them and that modifying one affects the rest. Understanding the contribution of lipid composition to their bioactivities is also underestimated, which suggests that for each peptide, there is a physiological context to consider for the rational design of new drugs.

Peptides from frogs are promising antibiotic candidates.Frog-derived synthetic peptides selectively targeted Gram-negative pathogens, sparing beneficial microbiota and human cells.Structure-guided modifications improved the antimicrobial potency by optimizing hydrophobicity and net charge.Lead peptides effectively reduced bacterial loads in murine models of Pseudomonas aeruginosa and Acinetobacter baumannii infections without toxicity. Novel antibiotics are urgently needed since bacteria are becoming increasingly resistant to existing antimicrobial drugs. Furthermore, available antibiotics are broad spectrum, often causing off-target effects on host cells and the beneficial microbiome. To overcome these limitations, we used structure-guided design to generate synthetic peptides derived from Andersonin-D1, an antimicrobial peptide (AMP) produced by the odorous frog Odorrana andersonii. We found that both hydrophobicity and net charge were critical for its bioactivity, enabling the design of novel, optimized synthetic peptides. These peptides selectively targeted Gram-negative pathogens in single cultures and complex microbial consortia, showed no off-target effects on human cells or beneficial gut microbes, and did not select for bacterial resistance. Notably, they also exhibited in vivo activity in two preclinical murine models. Overall, we present synthetic peptides that selectively target pathogenic infections and offer promising preclinical antibiotic candidates. [Display omitted] Synthetic peptides, inspired by the natural defenses of amphibians, demonstrate selective activity against Gram-negative pathogens while sparing the gut microbiota and Gram-positive strains. Rationally designed peptides showed remarkable potency, exhibiting no signs of resistance or toxicity. These results underscore the potential of peptide-based antibiotics to tackle multidrug-resistant bacterial infections. Peptides represent highly promising scaffolds for drug development, offering tunable properties and versatility regarding their targets. Synthetic peptides are at the forefront of innovation in combating antibiotic resistance due to their modular nature, which enables precise design optimization. Currently in the preclinical phase [Technology Readiness Level (TRL) 3 or 4], these peptide molecules have shown robust efficacy in both in vitro and animal models, underscoring their potential as next-generation therapeutics. Recent advancements in rational peptide design, enhanced by machine learning and structure-guided approaches, are significantly improving antimicrobial peptide (AMP) potency, stability, and selectivity. Narrow-spectrum AMPs, designed to target specific pathogens, not only reduce the risk of antimicrobial resistance, but also help preserve the microbiome. Furthermore, consortia-based experiments evaluating peptides in complex bacterial communities are refining their application against multidrug-resistant infections. With continuous investment and technological innovation, AMPs are on track to enter clinical trials within the next decade. Addressing scalability and regulatory challenges will be critical to unlocking their full potential as precision-based therapies, ultimately providing an adaptable and effective solution for tackling resistant infections.

Plants are extensively used in traditional medicine, and several plant antimicrobial peptides have been described as potential alternatives to conventional antibiotics. However, after more than four decades of research no plant antimicrobial peptide is currently used for treating bacterial infections, due to their length, post-translational modifications or  high dose requirement for a therapeutic effect . Here we report the design of antimicrobial peptides derived from a guava glycine-rich peptide using a genetic algorithm. This approach yields guavanin peptides, arginine-rich α-helical peptides that possess an unusual hydrophobic counterpart mainly composed of tyrosine residues. Guavanin 2 is characterized as a prototype peptide in terms of structure and activity. Nuclear magnetic resonance analysis indicates that the peptide adopts an α-helical structure in hydrophobic environments. Guavanin 2 is bactericidal at low concentrations, causing membrane disruption and triggering hyperpolarization. This computational approach for the exploration of natural products could be used to design effective peptide antibiotics. Antimicrobial peptides are considered promising alternatives to antibiotics. Here the authors developed a computational algorithm that starts with peptides naturally occurring in plants and optimizes this starting material to yield new variants which are highly distinct from the parent peptide.

This comprehensive review explores the advancements in the study of antimicrobial peptides (AMPs), highlighting their potential as promising alternatives to conventional antibiotics in the context of growing antibiotic resistance. AMPs are small molecular proteins found ubiquitously in nature, exhibiting broad-spectrum antimicrobial activity, including antibacterial, antiviral, and antifungal effects, and are vital components of the innate immune system. Due to their non-specific membrane-disrupting mechanism, AMPs are emerging as effective candidates for novel anti-infective agents. The integration of AMPs with biomaterials, such as nanoparticles, liposomes, polymers, and hydrogels, enhances their stability and efficacy while offering multifunctional therapeutic benefits. These combinations promote diverse antibacterial mechanisms, including membrane disruption, intracellular metabolic interference, cell wall modulation, and immune system activation. Despite challenges, such as toxicity, stability, and resistance, innovative strategies including computer-aided design and structural modification show promise in optimizing AMPs’ activity, targeting precision, and biocompatibility. The potential for AMPs in clinical applications remains highly promising, with significant opportunities for overcoming antimicrobial resistance through novel AMP-based therapeutic strategies.

Antimicrobial peptides are important candidates for developing new classes of antibiotics because of their potency against antibioticresistant pathogens. Current research focuses on topical applications and it is unclear how to design peptides with systemic efficacy. To address this problem, we designed two potent peptides by combining database-guided discovery with structure-based design. When bound to membranes, these two short peptides with an identical amino acid composition can adopt two distinct amphipathic structures: A classic horizontal helix (horine) and a novel vertical spiral structure (verine). Their horizontal and vertical orientations on membranes were determined by solid-state 15N NMR data. While horine was potent primarily against gram-positive pathogens, verine showed broad-spectrum antimicrobial activity. Both peptides protected greater than 80% mice from infection-caused deaths. Moreover, horine and verine also displayed significant systemic efficacy in different murine models comparable to conventional antibiotics. In addition, they could eliminate resistant pathogens and preformed biofilms. Significantly, the peptides showed no nephrotoxicity to mice after intraperitoneal or intravenous administration for 1 wk. Our study underscores the significance of horine and verine in fighting drug-resistant pathogens.