Immunoinformatics and Reverse Vaccinology Driven Predication of a Multi-epitope Vaccine against Borrelia burgdorferi and Validation through in silico Cloning and Immune Simulation

Background Borrelia burgdorferi is regarded as an extremely dangerous bacteria causing infectious disease in humans, resulting in musculoskeletal pain, fatigue, fever and cardiac symptom. Because of all alarming concerns, no such prophylaxis setup has been available against Borrelia burgdorferi till now. In fact, vaccine construction using traditional methods is so expensive and time-consuming. Therefore, considering all concerns, we designed a multi-epitope-based vaccine design against Borrelia burgdorferi using in silico approaches. Objective To design an effective and safe vaccine that can activate cell-mediated and humoral immunity against Borrelia burgdorferi by using various bioinformatics tools. Methods The present study utilized different computational methodologies, covering different ideas and elements in bioinformatics tools. The protein sequence of Borrelia burgdorferi was retrieved from the NCBI database. Different B and T cell epitopes were predicated using the IEDB tool. Efficient B and T cell epitopes were further assessed for vaccine construction using linkers AAY, EAAAK and GPGPG, respectively. Furthermore, the tertiary structure of constructed vaccine was predicated, and its interaction was determined with TLR9 using ClusPro software. In addition, further atomic level detail of docked complex and their immune response were further determined by MD simulation and C-ImmSim tool, respectively. Results A protein with immunogenic potential and good vaccine properties (candidate) was identified based on high binding scores, low percentile rank, non-allergenicity and good immunological properties, which were further used to calculate epitopes. Additionally, molecular docking possesses strong interaction; seventeen H-bonds interactions were reported, such as THR101-GLU264, THR185-THR270, ARG 257-ASP210, ARG 257-ASP 210, ASP259-LYS 174, ASN263-GLU237, CYS 265-GLU 233, CYS 265-TYR 197, GLU267-THR202, GLN 270-THR202, TYR345-ASP 210, TYR345-THR 213, ARG 346-ASN209, SER350-GLU141, SER350-GLU141, ASP 424-ARG220 and ARG426-THR216 with TLR-9. Finally, high expression was determined in E. coli (CAI = (0.9045), and GC content = (72%)). Using the IMOD server, all-atom MD simulations of docked complex affirmed its significant stability. The outcomes of immune simulation indicate that both T and B cells represent a strong response to the vaccination component. Conclusion This type of in-silico technique may precisely decrease valuable time and expenses in vaccine designing against Borrelia burgdorferi for experimental planning in laboratories. Currently, scientists frequently utilize bioinformatics approaches that speed up their vaccine-based lab work.