neoantigen

neoantigen predicts candidate tumor-specific neoantigen peptides from somatic mutations by evaluating MHC binding and integrating multiple biological data layers.

Key Features:

• Comprehensive Mutation Analysis: Analyzes somatic mutations beyond point mutations, including insertions, deletions, and structural variants, to identify diverse neoantigen candidates.
• MHC Binding Prediction: Predicts which mutated peptides have the potential to bind Major Histocompatibility Complex (MHC) molecules and be visible to antitumor T cells.
• Integration of Additional Data Layers: Assesses wild-type binding capability, incorporates allele-specific RNA expression levels, and integrates single nucleotide polymorphism (SNP) information to refine neoantigen predictions.
• Combination of Mutations Analysis: Evaluates combinations of mutations and filters out infeasible peptide candidates to prioritize viable neoantigens.

Scientific Applications:

• Personalized vaccine target identification: Identifies and prioritizes potential neoantigen targets for personalized vaccine development.
• Tumor–immune interaction analysis: Enhances understanding of mutation-driven antigen presentation and tumor-immune interactions.
• Clinical trial design support: Supports selection and prioritization of neoantigen candidates for neoantigen-based therapy clinical trials.

Methodology:

Employs a computational framework that integrates multiple biological data layers and applies algorithms to assess MHC binding potential and generate lists of candidate neoantigens.