Functional and structural in silico characterization of endolysins encoded by the novel lytic coliphage ASEC2201 isolated from a wastewater treatment plant

The increasing prevalence of multidrug-resistant (MDR) Escherichia coli in environmental and clinical settings necessitates alternative antibacterial strategies beyond conventional antibiotics. Bacteriophage-derived endolysins are promising enzybiotics due to their rapid bacteriolytic activity, target specificity, and low resistance potential; however, the diversity and functional attributes of endolysins encoded by environmental phage remain underexplored. This study aimed to characterize the endolysin system of a novel lytic coliphage, ASEC2201, isolated from a wastewater treatment plant, and to evaluate the structural, evolutionary, and antimicrobial features of its encoded endolysins using in silico approaches. Three putative endolysin genes identified from the annotated ASEC2201 genome were analysed using sequence similarity searches, conserved domain identification, and phylogenetic reconstruction. Physicochemical properties, promoter elements, secondary and tertiary structures, catalytic residues, and antimicrobial peptide (AMP) propensity were predicted using established bioinformatic tools. All three endolysins belonged to the lysozyme-like R21 superfamily but displayed significant sequence and evolutionary divergence. Phylogenetic analysis revealed that one endolysin clustered with classical phage lysozymes, while the other two formed a distinct R21-type subclade, indicating functional diversification. Structural modelling confirmed stable, catalytically competent folds with conserved active-site residues. Importantly, all three contained intrinsic AMP-like regions, suggesting a dual antibacterial mechanism involving enzymatic peptidoglycan degradation and membrane-interacting activity. The findings demonstrate that phage ASEC2201 encodes a diversified endolysin arsenal with strong predicted stability and antimicrobial potential. These endolysins represent promising candidates for the development of next-generation therapeutics targeting MDR E. coli , providing a robust computational foundation for future experimental validation.