Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin

TL;DR

This study develops an AI model using sequence data to predict antibody activity against influenza A hemagglutinin, demonstrating high accuracy for known antibodies and promising potential for accelerating antibody discovery.

Contribution

The paper introduces a novel AI framework for predicting antibody-antigen interactions solely from sequence data, enhancing antibody discovery efficiency.

Findings

Achieved AUROC ≥ 0.91 for known antibodies against seen HAs

Achieved AUROC of 0.9 for unseen HAs

AUROC of 0.73 for novel antibody activity prediction

Abstract

Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved modalities for treating autoimmune diseases, infectious diseases, and cancers. However, discovery and development of therapeutic antibodies remains a time-consuming and expensive process. Recent advancements in machine learning (ML) and artificial intelligence (AI) have shown significant promise in revolutionizing antibody discovery and optimization. In particular, models that predict antibody biological activity enable in-silico evaluation of binding and functional properties; such models can prioritize antibodies with the highest likelihoods of success in costly and time-intensive laboratory testing procedures. We here explore an AI model for predicting the binding and receptor blocking activity of antibodies against influenza A hemagglutinin (HA) antigens. Our present model is developed with the MAMMAL framework for biologics discovery to predict antibody-antigen interactions using only sequence information. To evaluate the model's performance, we tested it under various data split conditions to mimic real-world scenarios. Our models achieved an AUROC $\geq$ 0.91 for predicting the activity of existing antibodies against seen HAs and an AUROC of 0.9 for unseen HAs. For novel antibody activity prediction, the AUROC was 0.73, which further declined to 0.63-0.66 under stringent constraints on similarity to existing antibodies. These results demonstrate the potential of AI foundation models to transform antibody design by reducing dependence on extensive laboratory testing and enabling more efficient prioritization of antibody candidates. Moreover, our findings emphasize the critical importance of diverse and comprehensive antibody datasets to improve the generalization of prediction models, particularly for novel antibody development.