NEP

NEP predicts antibody-recognized epitopes by analyzing neutralization data across viral strains with aligned antigen sequences to delineate epitope locations, functional vulnerabilities, and resistance mechanisms, and the algorithm was developed and validated using broadly neutralizing HIV-1 antibodies.

Key Features:

• Input Requirements: Requires an alignment of antigen sequences and corresponding neutralization data for the antibody, with an optional unbound antigen structure to support structural mapping.
• Prediction Methodology: Utilizes neutralization data together with antigen sequence variation to predict epitope regions and identify sequence determinants of sensitivity and resistance.
• Output and Structural Mapping: Produces predicted epitope regions and maps them onto provided unbound antigen structural models to localize antibody-epitope interactions.
• Validation: The underlying algorithm was developed and validated using broadly neutralizing HIV-1 antibodies.

Scientific Applications:

• Antibody Optimization: Identifies precise epitopes to guide antibody engineering for improved efficacy and specificity.
• Vaccine Design: Informs epitope-based vaccine strategies by highlighting antigenic sites for targeted immune responses.
• Understanding Resistance Mechanisms: Maps how viral sequence variation affects antibody neutralization to characterize mechanisms of resistance.

Methodology:

Analyzes neutralization data together with aligned antigen sequences to predict epitopes and identify functional vulnerabilities and resistance mechanisms, and maps predictions onto provided unbound antigen structures when available.