On Pre-trained Language Models for Antibody (2301.12112v2)

Abstract: Antibodies are vital proteins offering robust protection for the human body from pathogens. The development of general protein and antibody-specific pre-trained LLMs both facilitate antibody prediction tasks. However, there have been limited studies that comprehensively explore the representation capability of distinct pre-trained LLMs on different antibody tasks. To investigate the problem, we aim to answer several key questions in this paper, such as how pre-trained LLMs perform in antibody tasks with different specificity and how introducing specific biological mechanisms to the pre-training process can benefit the model. Additionally, we evaluate if the learned antibody pre-trained representations can be applied to real-world antibody problems, like drug discovery and immune process understanding. Previously, no benchmark available largely hindered the study to answer these questions. To aid in our investigation, we provide an AnTibody Understanding Evaluation (ATUE) benchmark. We comprehensively evaluate the performance of protein pre-trained LLMs by empirical study along with conclusions and new insights. Our ATUE and code are released at https://github.com/dqwang122/EATLM.