Coevolutionary analysis-driven design of a p53 neoantigen multi-epitope peptide vaccine for lung cancer immunotherapy

Neoantigen (NeoAg)-based vaccines have emerged as promising immunotherapeutic approaches for lung cancer (LC) treatment. However, the high mutational variability of p53 protein poses challenges for durable vaccine design. In this study, we implemented a coevolution-guided immunoinformatics approach to design an epitope-based multi-epitope peptide vaccine targeting p53. We analyzed 224 homologous p53 sequences to identify coevolving amino acids using compensation and correlation methodologies. Compensation analysis identified 12 coevolving groups comprising 26 residues (6.6%), whereas correlation analysis revealed 14 groups involving 46 residues (11.7%), resulting in 39 evolutionarily constrained positions that were less prone to immune escape. Both B and T-cell epitopes were predicted and mapped onto these coevolving residues to select regions with high antigenicity and structural stability. Vaccine constructs (non-mutated and mutated (NeoAg) epitopes) were generated, modeled, and validated. In-silico expression of vaccine constructs in E. coli K12 showed promising results. Molecular docking with toll-like receptor 4 (TLR4) demonstrated a strong binding affinity (ΔG = - 15.5 kcal/mol). Furthermore, 200 ns molecular dynamics simulations confirmed the structural stability of both vaccine-TLR4 complexes, while immune simulations predicted robust humoral and cellular immune responses, particularly for the NeoAg construct. Population coverage analysis indicated broad applicability, covering 93.4% of global HLA alleles. This study introduces a novel coevolution-guided immunoinformatics framework for TP53-derived multi-epitope vaccine design, providing a promising strategy for LC immunotherapy. However, experimental validation needed to confirm its efficacy and safety.

The online version contains supplementary material available at 10.1007/s40203-026-00700-w.