[Artificial intelligence-based mining of antimicrobial peptides in the microbiome].

Artificial intelligence catalyzes antimicrobial peptide design

A snapshot of AI-driven technologies for AMP design is provided and two modes of AI-driven technologies for AMP design are surveyed, one concentrated on identifying whether current data possess antimicrobial activity and the other on generating AMP candidates with potential therapeutic properties (generation-oriented).

Toward microbiota-friendly design and evaluation of antimicrobial peptides: a mini-review.

Advancing antimicrobial peptides: Mechanisms, design, and applications in the post-antibiotic era.

The rapid emergence of multidrug-resistant and extensively drug-resistant bacteria has intensified the need for alternative antimicrobial strategies in the post-antibiotic era. Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immunity, have attracted considerable attention due to their broad-spectrum antimicrobial activity, rapid mechanisms of action, and lower propensity for resistance development. This review summarizes the structural diversity, mechanisms of action, and structure-activity relationships (SAR) of AMPs that underpin their biological activity and guide the rational design of next-generation peptide therapeutics. It further discusses recent advances in peptide engineering, peptidomimetic design, machine learning-assisted discovery, innovative production platforms, and the application of CRISPR-Cas genome editing for production host optimization. In addition, the review highlights synergistic therapeutic strategies, current clinical progress, and the expanding applications of AMPs in medicine, food preservation, agriculture, and aquaculture. Despite these advances, challenges including limited stability, potential toxicity, manufacturing costs, and regulatory barriers continue to hinder widespread clinical translation of AMP-based therapeutics. By integrating recent experimental and computational advances with current translational challenges and future perspectives, this review provides a comprehensive overview of the field and highlights key directions for the rational development and clinical translation of next-generation antimicrobial peptides to combat antimicrobial resistance.

Antimicrobial peptides in anti-infective and immunomodulatory applications: Mechanisms, challenges, and emerging computational design.

This review examines how the diverse molecular mechanisms through which AMPs exert antimicrobial effects-including membrane disruption, intracellular targeting, and immunomodulation-are intrinsically linked to their structural diversity and ecological breadth and critically evaluates engineering strategies that improve developability.

The Activity of Antimicrobial Peptides (AMPs) Identified Via an Integrated in Silico and Pilot in Vitro Screening Approach Towards ESKAPE Pathogens.

Antimicrobial resistance (AMR), driven largely by ESKAPE(E) pathogens, represents a critical global health challenge. The increasing prevalence of multidrug-resistant (MDR) bacteria and the declining effectiveness of conventional antibiotics have created an urgent need for innovative therapeutic agents with novel mechanisms of action. In this study, we aimed to establish a stability-centric computational discovery pipeline to identify and characterize novel antimicrobial peptides (AMPs) with potent and selective bactericidal activity against ESKAPE pathogens, and to validate the lead candidates through experimental in vitro assays. A library of approximately 3,000 AMPs was retrieved from public databases and subjected to in silico screening for physicochemical properties and predicted toxicity. Following filtration, 200 peptides were selected as ligands for molecular docking against essential target proteins from ESKAPE pathogens. Based on docking scores, binding pocket occupancy, and ligand-protein interaction, the top 10 peptides against each bacterium were shortlisted. The four top consensus hits of Vespid Chemotactic Peptide VT1 (VCP-VT1), Taromycin A, CN-AMP1, and Alliumin were selected based on their superior inhibitory profiles. Subsequent peptide-protein docking analyses confirmed their binding modes and interaction patterns. These four candidates were advanced to peptide-protein interaction profiling, followed by pilot in vitro minimum bactericidal concentration (MBC) determination and cytotoxicity assessment on a mammalian cell line. Hierarchical consensus scoring identified VCP-VT1, Taromycin A, CN-AMP1, and Alliumin as the top-ranked binding AMPs. VCP-VT1 was the most potent peptide in preliminary MBC assays, with modest activity against Gram-positive Enterococcus faecalis, Enterococcus faecium and Staphylococcus aureus, and Gram-negative Acinetobacter baumannii and Pseudomonas aeruginosa (MBC50 of 50 µM, MBC > 90 of 100 µM). All peptides showed minimal cytotoxicity against a cultured human cell line and non-toxic ADMET parameters in silico. This reductionist validation of a stability-pre-filtered computational shortlist substantiated VCP-VT1 as a priority bactericidal lead AMP. Its favourable preliminary efficacy and safety margins would warrant further pharmacokinetic optimization and eventual in vivo efficacy studies against recalcitrant multidrug-resistant (MDR) pathogens. This integrated computational-experimental framework provided a generally applicable strategy for accelerating the discovery of peptide-based therapeutics to combat the growing threat of AMR.

In silico identification and biophysical characterization of candidate antimicrobial peptides from the Indian marine microbiome targeting multidrug-resistant ESKAPE pathogens

The global health crisis of antimicrobial resistance necessitates the discovery of new antibacterial agents. Underexplored marine microbiomes, particularly from the biodiverse Indian coast, represent a rich potential source of antimicrobial peptides (AMPs). Targeting the urgent threat of multidrug-resistant ESKAPE pathogens, the present study aimed to computationally identify novel, membrane-active AMPs from these unique metagenomic datasets, with a focus on inhibiting Gram-negative bacteria. In this study, we computationally mined Indian marine high-resolution shotgun metagenomic datasets through quality filtering, de novo assembly, and small open reading frame prediction. An ensemble of six machine learning-based AMP prediction tools identified over 51,000 high-confidence candidate AMPs. Subsequent filtering based on physicochemical properties and AlphaFold3-predicted structures prioritized ten peptides with favourable membrane-active characteristics. Two lead candidates, c_AMP_1 and c_AMP_2, were subjected to all-atom molecular dynamics simulations within Gram-negative membrane mimetic models of Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae. Our simulations indicated distinct membrane interaction modes: c_AMP_1 adopted a stable, surface-associated α-helical orientation, while c_AMP_2 displayed a more flexible, membrane-inserting orientation in the simulations. Analysis of the MD simulations revealed distinct predicted peptide-membrane interaction profiles, characterized by specific hydrogen bonding patterns, peptide tilt angles, and membrane thinning, which collectively suggest differing biophysical interaction modes. Taken together, our work suggests the Indian marine microbiome as a promising reservoir for novel AMP candidates and suggests that an integrated computational pipeline – combining machine learning, structural biology, and biophysical simulation – may help prioritize candidate peptides for future experimental validation against critical pathogens.