Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model

TL;DR

This paper introduces BALM, a bio-inspired language model trained on extensive antibody sequences, which significantly improves antibody structure and function prediction, outperforming existing methods and accelerating therapeutic antibody development.

Contribution

The paper presents BALM and BALMFold, novel deep learning models specifically designed for antibody prediction, leveraging large-scale antibody sequence data to enhance accuracy and efficiency.

Findings

BALM achieves exceptional performance in antigen-binding prediction tasks.

BALMFold outperforms AlphaFold2, IgFold, ESMFold, and OmegaFold in antibody structure prediction.

The models reduce the need for labor-intensive experimental trials.

Abstract

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% non-redundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold, and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials.