Immunoinformatics approach to engineer a multi-epitope vaccine against SdrG in skin commensal Staphylococcus...

The human skin serves as a dynamic ecosystem for beneficial commensal bacteria such as Staphylococcus epidermidis, which play a crucial role in maintaining skin barrier integrity and modulating immune responses. Remarkably, recent research has demonstrated that the skin can function as a natural vaccination site, producing specific antibodies against commensal microbes without inducing inflammation. However, S. epidermidis can transition into an opportunistic pathogen in clinical settings, forming resilient biofilms on medical implants and exhibiting increasing resistance to antibiotics (MRSE), posing a significant healthcare challenge. To address this challenge, advanced immunoinformatics strategies were leveraged to design a novel multi-epitope vaccine targeting the SdrG protein, a key mediator of S. epidermidis biofilm formation. The vaccine's binding dynamics with Toll-like receptor 4 (TLR4) were evaluated through computational analyses, including molecular docking and 500-nanosecond molecular dynamics (MD) simulations. Stability assessments via Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and Radius of Gyration (Rg) confirmed that the vaccine-TLR4 complex achieved structural equilibrium, with TLR4 maintaining rigidity while the vaccine exhibited adaptive flexibility for optimal binding. The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method revealed a strong binding affinity, with a peak free energy of -52.73 kcal/mol and an average of -24.72 ± 9.5989 kcal/mol over the last 50 ns, indicating a thermodynamically favorable interaction. Furthermore, in silico cloning validated the vaccine's expressibility, with successful integration into the pET-Sangamo-His vector (8560 bp) for optimal E. coli production. These findings underscore the vaccine's potential to elicit a robust immune response by stably engaging TLR4, a critical step in innate immune activation. By combining computational precision with immunological insights, this study lays a foundation for developing an effective prophylactic strategy against S. epidermidis biofilm-associated infections.