AI/ML-Assisted Computational Design and Immunoinformatics Evaluation of a Multi-Epitope Vaccine Targeting Podoplanin in Glioblastoma Multiforme

Abstract Glioblastoma Multiforme (GBM) is one of the most malignant forms of brain tumor in humans, with limited treatment options and poor overall survival rates. In the present study, we employed an in-silico workflow that integrated immunoinformatics and 3D structural modelling tools to design a multi-epitope vaccine against Podoplanin (PDPN), a transmembrane glycoprotein primarily involved in tumor invasion and metastasis. The differential expression of PDPN in tumor versus normal cells was investigated using transcriptomics datasets. Once the overexpression was confirmed, it was designated as a Tumor-Associated Antigen (TAA). B-cell, CTL, and HTL epitopes were predicted and screened for antigenicity, non-allergenicity, and non-toxicity. Selected epitopes were linked with appropriate adjuvant and linker sequences to construct a vaccine candidate. Codon optimization and in silico cloning was conducted to evaluate the construct’s expression in a mammalian expression vector. The 3D structure of the vaccine candidate was modelled, refined, and validated before molecular docking with immune receptors and immune simulation studies. The results indicated that proposed polypeptide, RasIC -01v, could be a potential vaccine candidate for highly vigorous and dangerous cancer like GBM. Further experimental and immunological validations would be required to validate the commercial feasibility and development of RasIC -01v. Graphical Abstract