TeBaAb: Text-Based Antigen-Conditioned Antibody Redesign via Directed Evolution

Cuong Manh Nguyen, Huy-Hoang Do-Huu, Viet Thanh Duy Nguyen, Truong-Son Hy

Additional Submission Instructions: For the camera-ready version, please include the author names and affiliations, funding disclosures, and acknowledgements.

Track: Track 1: Original Research/Position/Education/Attention Track

Keywords: Antibody Design, Antigen Conditioning, Text-Guided Protein Engineering, Generative Models, Conditional Variational Autoencoder (CVAE), Binding Affinity Prediction, Directed Evolution, Protein Language Model

TL;DR: We present TeBaAb, a text-guided, antigen-conditioned framework that redesigns antibodies using generative modeling and directed evolution to enhance binding affinity.

Abstract: The design of antibodies with high affinity and specificity for target antigens is a cornerstone of therapeutic and diagnostic innovation. Traditional optimization strategies, such as phage or yeast display and directed evolution, remain resource-intensive and limited in their ability to integrate contextual information. Recent AI-driven approaches have accelerated protein engineering, but most rely exclusively on structured inputs, overlooking the potential of natural language as a flexible design interface. In this work, we introduce TeBaAb, a novel text-based antigen-conditioned framework for antibody redesign that combines generative modeling with iterative optimization inspired by directed evolution. TeBaAb integrates a Conditional Variational Autoencoder (CVAE) jointly conditioned on antigen sequences and textual descriptions of antibody properties, coupled with a two-stage binding affinity predictor and an iterative enrichment loop. To support this approach, we curated AbDes, a new dataset of 7,800 text–antibody–antigen pairs with accompanying structural and binding information. Experimental evaluations demonstrate that TeBaAb improves the predicted binding affinity by an average of 15.5% compared to the original antibodies, while preserving structural confidence (RMSPE < 1 angstrom) and generating sequences that are diverse and novel. By enabling text-conditioned antigen-specific antibody design, TeBaAb provides a promising new paradigm for accelerating therapeutic antibody discovery and expanding the antibody design space beyond traditional methods.

Submission Number: 61