Case Report:

From AI Prediction to Experimental Proof

85% Signal Reduction with AIxplore® Epitope & Paratope Mapping

Client

Biotech company

Sector

Therapeutic

Research Domain

Immunology

Pipeline stage

Lead candidat characterization

Key Processes

Antibody and antigen sequence analysis
3D modeling and simulations
Experimental validation

Key Numbers

Signal reduction

Critical residues identified

Context

A biotech company needed to rapidly determine the precise epitope–paratope interactions of a therapeutic antibody candidate. Understanding these molecular details was critical not only to predict therapeutic activity (agonist/antagonist), but also to design diagnostic tools (e.g., sandwich ELISAs), strengthen intellectual property claims, and prepare for antibody engineering such as affinity maturation.

Challenges

1

Large conformational search space due to unknown binding interfaces

2

Need for high-quality structural models of both antibody and antigen

3

Protein flexibility, requiring representative structures for accurate mapping

4

Experimental validation essential to confirm in silico predictions

ProteoGenix Approach

Antibody and antigen sequence analysis
3D modeling of structures
AI-driven docking simulations
Identification of epitope/paratope regions
Experimental validation via mutagenesis and binding assay

Results

AIxplore® Epitope & Paratope Mapping pinpointed a functional hotspot near the antigen’s nucleic acid binding site. Substitution of three critical residues per mutant led to an 85% reduction in binding signal, demonstrating both the accuracy of the AI predictions and the antibody’s likely neutralizing effect. Mapping also highlighted paratope residues, with heavy-chain mutations exerting the strongest impact. These insights provide a solid basis for therapeutic mechanism hypotheses, IP protection, and future engineering strategies

3D Modelization

Key Takeway

By combining AI-driven predictions with experimental validation, ProteoGenix delivered reliable insights into antibody–antigen interactions. This integrated approach reduced risks and timelines, while equipping the client with data to guide therapeutic development, design diagnostic assays, and secure stronger intellectual property protection.