Wang Meng, Patsenker Jonathan, Li Henry, Kluger Yuval, Kleinstein Steven H
Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
Program in Applied Mathematics, Yale University, New Haven, Connecticut, United States of America.
PLoS Comput Biol. 2025 Mar 31;21(3):e1012153. doi: 10.1371/journal.pcbi.1012153. eCollection 2025 Mar.
Antibodies play a crucial role in the adaptive immune response, with their specificity to antigens being a fundamental determinant of immune function. Accurate prediction of antibody-antigen specificity is vital for understanding immune responses, guiding vaccine design, and developing antibody-based therapeutics. In this study, we present a method of supervised fine-tuning for antibody language models, which improves on pre-trained antibody language model embeddings in binding specificity prediction to SARS-CoV-2 spike protein and influenza hemagglutinin. We perform supervised fine-tuning on four pre-trained antibody language models to predict specificity to these antigens and demonstrate that fine-tuned language model classifiers exhibit enhanced predictive accuracy compared to classifiers trained on pre-trained model embeddings. Additionally, we investigate the change of model attention activations after supervised fine-tuning to gain insights into the molecular basis of antigen recognition by antibodies. Furthermore, we apply the supervised fine-tuned models to BCR repertoire data related to influenza and SARS-CoV-2 vaccination, demonstrating their ability to capture changes in repertoire following vaccination. Overall, our study highlights the effect of supervised fine-tuning on pre-trained antibody language models as valuable tools to improve antigen specificity prediction.
抗体在适应性免疫反应中发挥着关键作用，其对抗原的特异性是免疫功能的一个基本决定因素。准确预测抗体 - 抗原特异性对于理解免疫反应、指导疫苗设计以及开发基于抗体的治疗方法至关重要。在本研究中，我们提出了一种用于抗体语言模型的监督微调方法，该方法在预测与严重急性呼吸综合征冠状病毒2（SARS-CoV-2）刺突蛋白和流感血凝素的结合特异性方面，对预训练的抗体语言模型嵌入进行了改进。我们对四个预训练的抗体语言模型进行监督微调，以预测对这些抗原的特异性，并证明与基于预训练模型嵌入训练的分类器相比，微调后的语言模型分类器具有更高的预测准确性。此外，我们研究了监督微调后模型注意力激活的变化，以深入了解抗体识别抗原的分子基础。此外，我们将监督微调后的模型应用于与流感和SARS-CoV-2疫苗接种相关的B细胞受体（BCR）库数据，证明了它们捕捉接种疫苗后库变化的能力。总体而言，我们的研究突出了监督微调对预训练抗体语言模型的作用，这些模型是提高抗原特异性预测的有价值工具。
