Deep learning-guided structural analysis of a novel bacteriophage KPP105 against multidrug-resistant Klebsiella pneumoniae

Abstract

The increasing prevalence of multidrug-resistant bacteria, particularly Klebsiella species, poses a significant global health threat. Bacteriophages have emerged as promising alternatives due to their specificity and efficacy against bacterial targets. Characterizing phages, alongside analyzing their protein structures provide crucial insights into their host specificity, infection mechanisms, and potential applications. In this study, we isolated a novel bacteriophage, KPP105, and conducted comprehensive physiological, genomic, and structural analysis. Physiological assessments revealed that KPP105 maintains stable activity across a wide range of pHs and temperature conditions and exhibits host-specific infection properties. Genomic analysis classified KPP105 as a member of the Demerecviridae family and identified it as a lytic bacteriophage harboring a lytic cassette. Deep learning-based structural analysis of host-interacting proteins, including the receptor-binding protein (RBP) and endolysin derived from KPP105, was performed. Structural similarity analysis indicated that its RBP facilitates interactions with host receptors and exhibits unique sequence patterns distinguishing Klebsiella strains from other bacteria. Structure-based functional analysis provided comprehensive insights into cell wall degradation with various peptidoglycan fragments. In conclusion, this study reports the physiological, genomic, and structural characteristics of the novel lytic bacteriophage KPP105, offering valuable insights into its potential as an alternative agent against multidrug-resistant Klebsiella infections.