Integrated computational and experimental benchmarking of Bacillus phage endolysins reveals the relationship between peptidoglycan-fragment recognition descriptors and antibacterial performance.
Protein-based antibacterials such as bacteriophage endolysins offer a targeted therapeutic strategy against Gram-positive pathogens. However, prioritizing the most effective candidates from the large sequence diversity available remains a significant challenge. Here we present a standardized computational-experimental benchmarking framework that evaluates seven phage-derived endolysin variants (E1, E2, E3, E7, E10, E12, and E15) identified from Bacillus genomes. We combined molecular docking and residue-level interaction mapping against muramyl dipeptide (MDP), a minimal conserved peptidoglycan motif, with 1000-ns molecular dynamics simulations, MM/PBSA binding free-energy estimation, and matched functional inhibition assays against Staphylococcus aureus and Micrococcus luteus. Computational analyses revealed generally favorable MDP recognition across variants, albeit with notable differences in contact patterns and complex stability profiles. Experimental screening identified E2 as the most potent antibacterial agent against both species, while E7 and E1 performed strongly in selected computational metrics. Integrated analysis showed only modest correlations between computational descriptors of fragment recognition/stability and observed antibacterial performance. This study establishes a practical comparative benchmarking platform for endolysin candidate prioritization, nominates E2 and E7 as promising candidates for further development, and highlights E1 as a potential structural scaffold for rational engineering, while explicitly demonstrating both the utility and the current limitations of using minimal peptidoglycan fragments as proxies for full cell-wall recognition in lysin benchmarking.