Abstract
The resurgence of the Mpox virus (MPXV) highlights the urgent need for scalable, mutation-resistant countermeasures. Slow development timelines hinder traditional empirical vaccine discovery, while conventional vaccinology often yields high false-positive rates because it relies on static, linear sequence-based screening. These classical informatics filters consistently fail to predict physical structural stability or the dynamic presentation of immune cells under physiological conditions, creating a distinct translational gap between computational design and in vitro efficacy. To address these limitations, here we engineered an advanced, AI-augmented vaccine discovery pipeline targeting the most virulent components of the MPXV proteome. We systematically screened eight critical viral targets essential for replication, membrane morphogenesis, and host-cell entry to map highly conserved T-cell and B-cell epitopes. Moving beyond standard heuristic filtering, we implemented a high-fidelity deep learning validation layer: ESM-2 protein language models (pLMs) were deployed to calculate pseudo-perplexity scores across chimeric linker junctions (EAAAK, GPGPG) to guarantee structural naturalness, MHCflurry ensemble models successfully verified active intracellular antigen presentation, and structure-informed graph neural networks (GNNs) simulated the physical node-attention dynamics of the immune synapse. Finally, recurrent neural networks (RNNs)-driven mRNA kinetic modeling optimized translation initiation rates and minimum free energy profiles during in silico cloning. This multi-layered pipeline successfully yielded three structurally refined, multi-epitope vaccine constructs optimized for maximum global population coverage. ESM-2 structural validation confirmed exceptionally low perplexity scores at structural junctions, indicating that the engineered chimaeras have native-like stability and a minimized risk of proteolysis. GNN-based immune synapse modeling demonstrated highly stable binding kinetics and robust binding free energies between the target epitopes and human HLA alleles. Furthermore, RNN kinetic optimizations established that the cDNA sequences are optimized for high-yield expression in heterologous expression hosts without risk of ribosomal stalling or translational bottlenecks. This study establishes a deep learning-augmented framework that bridges the gap between theoretical sequence optimization and biopharmaceutical scalability. By validating the structural naturalness, real-world immunogenicity, and manufacturing kinetics of our vaccine chimeras, this framework presents a robust, proactive paradigm for rapid vaccine architecture against emerging orthopoxvirus threats.