The findings highlighted a promising application of deep evolutionary machine learning techniques for screening a range of novel AMPs for selective antimicrobial agents.
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).
Yong-Qiang Liu, Jie Hu, Ning Zhang et al.· Synthetic and Systems Biotec...· 0 citations
The emergence of antibiotic-resistant pathogens such as Staphylococcus aureus demands accelerated antimicrobial discovery strategies. Artificial intelligence (AI) enables large-scale inference of candidate antimicrobial peptides (AMPs), yet experimental validation remains essential to determine whether predictions translate into biological function. Genome-guided mining, rather than unconstrained or randomly generated sequence exploration, offers a biologically grounded search space derived from organisms shaped by ecological and evolutionary pressures. Here, we evaluate this principle using Malassezia furfur, a skin-associated yeast that coexists with bacterial colonizers such as S. aureus, as a genomic source for AI-prioritized antimicrobial candidates. Candidate fragments were generated from two M. furfur genomes, filtered by physicochemical properties, prioritized with deep-learning AMP predictors, synthesized, and experimentally characterized. Selected peptides underwent cross-kingdom antimicrobial screening against S. aureus, combining kinetic growth and ultrastructural assays, complemented by in silico structural prediction, lipid-membrane interaction analysis, and human keratinocyte cytotoxicity evaluation. AI-guided genomic mining enriched biologically motivated sequence space for peptides with measurable antimicrobial activity, while revealing biases and generalizability limits of AI-based AMP inference. Closing the loop between genome-derived candidate generation, AI-based inference, synthesis, and functional characterization, this study provides an experimental assessment of model-guided AMP discovery and a reproducible route from computational prediction to validated antimicrobial candidates.
S. Ojeda, P. Ávila, S. Castellanos et al.· bioRxiv· 0 citations
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.
H. S. Mahrosh, M. Christodoulides, A. Jamil· Probiotics and Antimicrobial...· 0 citations
The escalating crisis of multidrug-resistant bacteria necessitates innovative antibiotic discovery platforms. Conventional antimicrobial peptide (AMP) mining often relies on complete biosynthetic gene clusters (BGCs), leaving fragmented genomic resources underexplored. Here, we present an evolution-inspired approach to reconstruct and predict AMPs from partial BGCs. Applying this strategy to 954 Paenibacillus genomes identifies five polymyxin-like peptides, NP001-NP005, with broad in vitro activity. Crucially, in murine models of polymyxin-resistant infection, NP001 reduced bacterial burdens by up to 1,000-fold in a thigh infection model and improved survival (50% vs. 0%) in a lethal peritonitis model. Structural simulations and biophysical assays revealed that NP001 maintains high affinity for bacterial membranes and effectively binds to MCR-1-modified lipid A, a key colistin-resistance mechanism. Moreover, Leu at position 10 of NP001 plays a key role in antibacterial activity against MCR-1-resistant bacteria. Our work establishes a generalizable framework for AMP discovery and introduces a promising therapeutic candidate, NP001, which effectively counteracts polymyxin-resistant pathogens.
The aim of this review is to provide a systematic synthesis of the methodological framework for AI-based mining of microbiome-derived AMPs, thereby offering theoretical references and technical guidance for the efficient identification of novel AMPs.
Xiaoya Guo, Nannan Mo, Tengteng Fu et al.· Sheng wu gong cheng xue bao...· 0 citations
The widespread use of antibiotics, particularly ciprofloxacin, has accelerated the emergence of antibiotic-resistant bacteria, posing a major public health challenge. Limited investment and declining pharmaceutical interest have slowed the discovery of new antibiotics, contributing to the increasing prevalence of multidrug-resistant (MDR) infections, especially those caused by ESKAPE pathogens. Uropathogenic
Escherichia coli
(UPEC), a leading cause of urinary tract infections (UTIs), develops multidrug resistance through mechanisms including drug target modification and reduced antibiotic susceptibility. In the present study, a series of ciprofloxacin-sulfonamide hybrid molecules (CIS1-CIS15) was designed, in which each pharmacophore retains its established mechanism of action. Ciprofloxacin inhibits DNA gyrase and topoisomerase IV, whereas sulfonamides inhibit dihydropteroate synthase (DHPS) by competing with para-aminobenzoic acid (PABA) in the bacterial folate biosynthetic pathway. Accordingly, the designed hybrids were computationally evaluated against both enzymatic targets to investigate their potential dual-target antibacterial activity. Molecular docking and molecular dynamics (MD) simulations were performed to assess the binding affinity and stability of the ciprofloxacin-bearing sulfonamide derivatives (CIS1-CIS15) against DNA gyrase and
Staphylococcus aureus
DHPS. Principal component analysis (PCA) and free-energy landscape (FEL) analysis were used to characterize the conformational flexibility and low-energy states of the protein–ligand complexes, while MD simulations validated the stability of the highest-scoring docked complexes. Among the designed derivatives, CIS4 (ciprofloxacin-sulfamethoxazole), CIS5 (ciprofloxacin–sulfaguanidine), and CIS12 (ciprofloxacin-sulfamerazine) exhibited the strongest binding affinity toward both targets while maintaining conformational stability throughout the simulations. Structural analysis of the pre- and post-MD trajectories identified Arg52, Lys39, Arg88, Lys94, Lys120, Glu201, and Lys203 as key residues involved in ligand recognition. Overall, CIS4, CIS5, and CIS12 emerged as the most promising dual-target inhibitor of bacterial DHPS and DNA gyrase. These findings provide valuable structural insights for the rational design of novel fluoroquinolone-based antibacterial agents and demonstrate the utility of computational approaches in identifying potential therapeutics against drug-resistant bacterial pathogens.
Unknown authors· Frontiers in Bioinformatics· 0 citations
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