Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, FL 32611, USA.
Department of Laboratory Medicine and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae403.
Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody-antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.
抗体在免疫防御中发挥着关键作用，并且是重要的治疗药物。亲和力成熟过程中，抗体通过体细胞突变进化，以实现对靶抗原更高的特异性和亲和力，这对于有效的免疫反应至关重要。尽管抗体具有重要意义，但由于传统湿实验室技术的限制，评估抗体-抗原结合亲和力仍然具有挑战性。为了解决这个问题，我们引入了 AntiFormer，这是一种基于图的大型语言模型，旨在预测抗体结合亲和力。AntiFormer 将序列信息纳入基于图的框架中，从而可以精确预测结合亲和力。通过广泛的评估，AntiFormer 与现有方法相比表现出卓越的性能，提供了更准确的预测结果，同时减少了计算时间。将 AntiFormer 应用于严重急性呼吸综合征冠状病毒 2 患者样本中，揭示了具有强大中和能力的抗体，为治疗开发和疫苗接种策略提供了新的见解。此外，对流感疫苗接种后的个体样本进行分析，阐明了年轻和老年成年人之间抗体反应的差异。AntiFormer 确定了接种疫苗后具有增强结合亲和力的特定克隆型，特别是在年轻个体中，这表明免疫反应动力学存在与年龄相关的差异。此外，我们的研究结果强调了大型克隆型类别在驱动亲和力成熟和免疫调节中的重要性。总体而言，AntiFormer 是一种有前途的方法，可以加速基于抗体的诊断和治疗，缩小传统方法和复杂抗体成熟过程之间的差距。
