Nanobodies can provide specific binding to divergent antigens, leading to many promising therapeutic and detection applications in recent years. Traditional technologies of nanobody discovery based on alpaca immunization and phage display are very time-consuming and labour-intensive. Despite recent progress in the study of nanobodies, developing fast and accurate computational tools for nanobody–antigen interaction (NAI) prediction is urgently desirable. Here we propose an ensemble deep learning-based framework named DeepNano-seq to predict general protein–protein interaction (PPI) containing NAI from pure sequence information. Quantitative comparison results show that DeepNano-seq possesses the best cross-species generalization ability among existing PPI algorithms. Nevertheless, several of the most effective PPI methods, including DeepNano-seq, demonstrate suboptimal performance for NAI prediction due to the distinction between NAI and PPI at both the pattern and data levels. Therefore, we organize NAI data from the public database for dedicated NAI modelling. Furthermore, we enhance the prediction pipeline of DeepNano-seq by directing the model’s attention to the antigen-binding sites through a prompt-based approach to present the final DeepNano. The comprehensive evaluation demonstrates that DeepNano performs superiorly in NAI prediction and virtual screening of nanobodies. Overall, DeepNano-seq and DeepNano can offer powerful tools for nanobody discovery.

纳米抗体可以提供与不同抗原的特异性结合，近年来导致了许多有前景的治疗和检测应用。基于羊驼免疫和噬菌体展示的传统纳米抗体发现技术非常耗时且劳动密集。尽管纳米抗体的研究最近取得了进展，但迫切需要开发快速准确的计算工具来预测纳米抗体-抗原相互作用 （NAI）。在这里，我们提出了一个名为 DeepNano-seq 的基于集成深度学习的框架，用于从纯序列信息中预测包含 NAI 的一般蛋白质-蛋白质相互作用 （PPI）。定量对比结果表明，在现有的 PPI 算法中，DeepNano-seq 具有最好的跨物种泛化能力。然而，由于 NAI 和 PPI 在模式和数据层面的区别，包括 DeepNano-seq 在内的几种最有效的 PPI 方法在 NAI 预测方面表现出次优性能。因此，我们从公共数据库中组织 NAI 数据，用于专门的 NAI 建模。此外，我们通过基于提示的方法将模型的注意力引导到抗原结合位点，以呈现最终的 DeepNano，从而增强了 DeepNano-seq 的预测管道。综合评估表明，DeepNano 在 NAI 预测和纳米抗体虚拟筛选方面表现出色。总体而言，DeepNano-seq 和 DeepNano 可以为纳米抗体发现提供强大的工具。