Ahmed Fatma S, Aly Saleh, Liu Xiangrong
Department of Computer Science and Technology, Xiamen University, Xiamen 361005, China.
Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae518.
Antibody-mediated immunity is crucial in the vertebrate immune system. Nanobodies, also known as VHH or single-domain antibodies (sdAbs), are emerging as promising alternatives to full-length antibodies due to their compact size, precise target selectivity, and stability. However, the limited availability of nanobodies (Nbs) for numerous antigens (Ags) presents a significant obstacle to their widespread application. Understanding the interactions between Nbs and Ags is essential for enhancing their binding affinities and specificities. Experimental identification of these interactions is often costly and time-intensive. To address this issue, we introduce NABP-BERT, a deep-learning model based on the BERT architecture, designed to predict NANOBODY®-Ag binding solely from sequence information. Furthermore, we have developed a general pretrained model with transfer capabilities suitable for protein-related tasks, including protein-protein interaction tasks. NABP-BERT focuses on the surrounding amino acid contexts and outperforms existing methods, achieving an AUROC of 0.986 and an AUPR of 0.985.
抗体介导的免疫在脊椎动物免疫系统中至关重要。纳米抗体，也称为VHH或单域抗体（sdAbs），因其紧凑的尺寸、精确的靶点选择性和稳定性，正成为全长抗体有前景的替代物。然而，针对众多抗原（Ag）的纳米抗体（Nb）可用性有限，这对其广泛应用构成了重大障碍。了解纳米抗体与抗原之间的相互作用对于提高它们的结合亲和力和特异性至关重要。通过实验鉴定这些相互作用通常成本高昂且耗时。为解决此问题，我们引入了NABP-BERT，这是一种基于BERT架构的深度学习模型，旨在仅从序列信息预测纳米抗体®-抗原结合。此外，我们还开发了一种具有迁移能力的通用预训练模型，适用于蛋白质相关任务，包括蛋白质-蛋白质相互作用任务。NABP-BERT关注周围氨基酸上下文，性能优于现有方法，曲线下面积（AUROC）达到0.986，精确率-召回率曲线下面积（AUPR）达到0.985。
