Our platform enables:

• Intelligent Neoantigen Prediction: Our platform utilizes advanced neural networks trained on vast immunopeptidome datasets to predict MHC-peptide binding affinity, stability, and cell-surface presentation with exceptional accuracy. Beyond simple binding, our AI integrates T-cell receptor (TCR) cross-reactivity modeling to prioritize neoantigens with the highest potential for inducing a robust and specific immune response.

• Immunogenic Prioritization Engine: We move beyond raw data by incorporating tumor-specific factors, such as gene expression levels and proteasomal processing efficiency. This multi-layered filtering system significantly reduces "false positives," ensuring that only the most immunogenic and clinically relevant neoantigens move forward into the development pipeline.

• High-Fidelity Antigen Synthesis & Characterization: Leveraging our Recombinant Protein Platform, we produce high-purity neoantigen peptides and peptide-MHC (pMHC) complexes. Each candidate is characterized for structural stability and binding kinetics using SPR/BLI to ensure they meet the stringent biophysical criteria required for therapeutic use.

• Multi-Dimensional Immunogenicity Validation: We conduct comprehensive functional screening through ELISA, ELISpot, and FACS-based multimer staining. These assays measure the actual activation and expansion of antigen-specific T-cells, providing laboratory proof of the AI-predicted immunogenicity.

• Integrated In Vivo Verification: To support translational proof-of-concept, we validate neoantigen candidates in humanized mouse models. By evaluating T-cell infiltration and anti-tumor efficacy in a physiological context, we provide the critical data necessary to transition from computational prediction to clinical development.