Antimicrobial resistance (AMR) represents a major global public health challenge, arising from the ability of microorganisms such as bacteria, fungi, and viruses to develop mechanisms that render them insensitive to conventional antibiotics. A key factor contributing to this problem is the production of β-lactamase enzymes, which confer resistance to multiple antibiotic classes and are associated with multidrug-resistant pathogens such as Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae, responsible for severe and often fatal hospital-acquired infections. In addition, the phospholipid membrane model was generated using CHARMM-GUI to provide insight into peptide-membrane interactions and their potential to induce membrane disruption. In this context, developing new therapeutic strategies, such as antimicrobial peptides (AMPs), has emerged as a promising alternative capable of acting against resistant microorganisms. This study investigates, through in silico analyses using tools such as Fpocket, FTsite, FTmap, and HADDOCK, to evaluate the interactions between five AMPs derived from Neotropical Cereus (mandacaru) species against β-lactamases from K. pneumoniae and A. baumannii. As a result, all five Cereus-derived AMPs showed favorable predicted interactions with β-lactamase interaction sites, and CF267 and CJ149 exhibited stable engagement with the phospholipid bilayer, supporting their potential to disrupt membranes in the simulated system. Docking analyses indicated favorable affinities for all peptides, with CF267 × 3RXX and CJ149 × 3RXX emerging as the most promising complexes due to their highly negative energy scores. These findings suggest favorable predicted interactions with β-lactamase binding regions, indicating potential molecular compatibility against AMR mechanisms in A. baumannii and K. pneumoniae. Upcoming in vitro assays will be essential to validate these predictions.

A microbiota intestinal influencia funções digestivas, imunes e neurais por meio de moléculas microbianas. As fímbrias curli, formadas pela proteína curlina, estão associadas à neuroinflamação e à agregação de proteínas amiloides humanas. Contudo, os mecanismos moleculares que conectam curli e comunicação intestino-cérebro permanecem pouco esclarecidos. Assim, este estudo investigou possíveis interações entre curlina e proteínas hospedeiras humanas, fibronectina e integrina, por meio de análises de docking molecular e predição de cavidades.

Bioactive peptides are an important source of therapeutic molecules and molecular scaffolds involved in defense, signaling, and immune regulation. Despite the extraordinary diversity of Coleoptera, the structural landscape of beetle-derived bioactive peptides remains largely unexplored, limiting our understanding of their evolutionary diversity and biotechnological potential. Here, we performed a large-scale structural survey of predicted toxin-like peptide scaffolds across publicly available Coleoptera transcriptomes by integrating transcriptome mining, peptide maturation prediction, physicochemical characterization, AlphaFold 3 structural modeling, structural similarity analyses, and interpretable machine learning. We identified 291 candidate peptides, of which 155 contained canonical signal peptides and 273 produced mature peptides within the expected size range of known bioactive peptides. Structural analyses revealed that, despite extensive sequence diversity, many candidates were organized into a comparatively restricted repertoire of compact cysteine-rich architectures, indicating that structural similarity is retained across peptides exhibiting substantial primary-sequence variation. Comparative structural analyses further identified recurrent protein architectures shared across multiple beetle lineages, while machine learning prioritization integrated structural and biochemical descriptors to identify high-confidence candidates for future functional characterization. These analyses establish the first structural atlas of predicted toxin-like peptides across Coleoptera and demonstrate that structure-guided transcriptome mining provides a powerful framework for uncovering recurrent bioactive peptide scaffolds that would remain largely undetected using sequence-based approaches alone. Beyond expanding our understanding of peptide evolution in beetles, this resource is a foundation for future structural, functional, and biotechnological exploration of bioactive peptides in underexplored animal groups.

Bioactive peptides are an important source of therapeutic molecules and molecular scaffolds involved in defense, signaling, and immune regulation. Despite the extraordinary diversity of Coleoptera, the structural landscape of beetle-derived bioactive peptides remains largely unexplored, limiting our understanding of their evolutionary diversity and biotechnological potential. Here, we performed a large-scale structural survey of predicted toxin-like peptide scaffolds across publicly available Coleoptera transcriptomes by integrating transcriptome mining, peptide maturation prediction, physicochemical characterization, AlphaFold 3 structural modeling, structural similarity analyses, and interpretable machine learning. We identified 291 candidate peptides, of which 155 contained canonical signal peptides and 273 produced mature peptides within the expected size range of known bioactive peptides. Structural analyses revealed that, despite extensive sequence diversity, many candidates converged toward a comparatively restricted repertoire of compact cysteine-rich architectures, suggesting that structural conservation exceeds primary sequence conservation during peptide diversification. Comparative structural analyses further identified recurrent protein architectures shared across multiple beetle lineages, while machine learning prioritization integrated structural and biochemical descriptors to identify high-confidence candidates for future functional characterization. These analyses establish the first structural atlas of predicted toxin-like peptides across Coleoptera and demonstrate that structure-guided transcriptome mining provides a powerful framework for uncovering evolutionarily conserved bioactive peptide scaffolds that would remain largely undetected using sequence-based approaches alone. Beyond expanding our understanding of peptide evolution in beetles, this resource is a foundation for future structural, functional, and biotechnological exploration of bioactive peptides in underexplored animal groups.