Toll-like receptors (TLRs) are transmembrane proteins that are key regulators of innate and adaptive immune responses, particularly TLR4, and they have been identified as potential drug targets for the treatment of disease. Several low-molecular-weight compounds are being considered as new drug targets for various applications, including as immune modulators. Mygalin, a 417 Da synthetic bis-acylpolyamine, is an analog of spermidine that has microbicidal activity. In this study, we investigated the effect of mygalin on the innate immune response based on a virtual screening (VS) and molecular docking analysis. Bone marrow-derived macrophages and the cell lines J774A.1 and RAW 264.7 stimulated with lipopolysaccharide (LPS) were used to confirm the data obtained in silico. Virtual screening and molecular docking suggested that mygalin binds to TLR4 via the protein myeloid differentiation factor 2 (MD-2) and LPS. Macrophages stimulated by mygalin plus LPS showed suppressed gene expression of tumor necrosis factor (TNF-α), interleukine 6 (IL-6), cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), as well as inhibition of signaling protein p65 of the nuclear factor κB (NF-κB), resulting in decreased production of nitric oxide (NO) and TNF-α. These results indicate that mygalin has anti-inflammatory potential, being an attractive option to be explored. In addition, we reinforce the importance of virtual screening analysis to assist in the discovery of new drugs.

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat that requires the discovery of new antimicrobial agents. Environmental microbiomes from understudied regions represent a valuable source of antimicrobial peptide (AMP) candidates. This study aimed to identify and prioritize AMP candidates from South American genomic and metagenomic datasets and to investigate the antimicrobial potential of bioactive secretomes obtained through bacterial co-culture. Methods: A total of 853 genomes and 360 metagenomes were analyzed using a reproducible genome- and metagenome-mining pipeline combined with machine learning-based AMP prediction. Predicted AMP candidates were further characterized using complementary bioinformatic tools to assess physicochemical, structural, hemolytic, toxicological, anti-inflammatory, and anticancer properties. Selected environmental isolates were subjected to bacterial co-culture, followed by SPE-C18 and HPLC fractionation. Antimicrobial activity, antioxidant activity, hemolysis, minimum inhibitory concentration (MIC), and LC-MS/MS peptidomic analyses were performed on bioactive secretome fractions. Results: Genome and metagenome mining identified diverse AMP candidate sequences associated with bacterial genera including Streptomyces, Bacillus, Burkholderia, and Shewanella. Structural predictions revealed a predominance of α-helical conformations among prioritized candidates. Several secretome fractions obtained from co-cultures displayed antimicrobial activity against Gram-positive and Gram-negative bacteria, including methicillin-resistant Staphylococcus aureus (MRSA). Active fractions showed no detectable hemolytic activity and exhibited antioxidant activity in DPPH assays. MIC analyses indicated broad-spectrum activity against Escherichia coli ATCC 11229, Pseudomonas aeruginosa ATCC 27853, Klebsiella pneumoniae, carbapenem-resistant Acinetobacter baumannii, and MRSA, with an apparent MIC of 10,000 mg/L. LC-MS/MS analysis of bioactive fractions identified peptide sequences by de novo sequencing, including KTESHHK, KRVGPRR, GLFPRLGVSPR, and HHAEHLVHFR. Conclusions: Integrated genome mining, bacterial co-culture activation, and peptidomic analyses provide a useful framework for prioritizing antimicrobial peptide candidates from environmental microbiomes. The identification of peptide-containing bioactive fractions with antimicrobial and antioxidant activities highlights the potential of South American bacterial resources for the discovery of novel antimicrobial compounds. Further purification, peptide synthesis, and biological validation will be required to determine the contribution of individual peptides to the observed activities.