REALM: Region-Empowered Antibody Language Model for Antibody Property Prediction | IEEE Conference Publication | IEEE Xplore

REALM: Region-Empowered Antibody Language Model for Antibody Property Prediction


Abstract:

Protein language models (pLM) are beneficial to build antibody property prediction models. However, current pLMs lacks the ability to understand antibody properties becau...Show More

Abstract:

Protein language models (pLM) are beneficial to build antibody property prediction models. However, current pLMs lacks the ability to understand antibody properties because region and structure information is not effectively embedded. We propose the Region-Empowered Antibody Language Model (REALM), a pLM built by multi-task pretraining strategy of residue prediction and region prediction tasks in antibodies, to incorporate not only co-evolution but also region information of antibodies. We demonstrate that our REALM improves the understanding of antibody properties, including hydrophobicity and thermo-stability.
Date of Conference: 03-06 December 2024
Date Added to IEEE Xplore: 10 January 2025
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Conference Location: Lisbon, Portugal

I. Introduction

To reduce the manufacturing costs of antibody drugs, it is crucial to predict physicochemical properties such as hydrophobicity and thermo-stability from antibody sequences. Recent emerging protein language models (pLMs) are beneficial for predicting antibody properties. As the pLM is pretrained on a large amount of protein sequence, the model can predict antibody properties even though it is fine-tuned using only a small amount of data regarding the target task.

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