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With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA. 中文翻译：随着深度学习的发展，几乎所有单域蛋白质都可以在实验分辨率下进行预测。然而，多结构域蛋白质的结构预测仍然是一个挑战。通过准确预测域间方向实现端到端蛋白质结构域组装并进一步提高全链建模的准确性，同时提高组装效率，将为基于结构的药物发现提供重要的见解。在这项工作中，我们提出了一种基于深度学习的端到端域组装方法，称为E2EDA。我们首先开发 RMNet，这是一种基于 EfficientNetV2 的深度学习模型，它使用注意力机制融合多个特征来预测域间刚性运动。然后，将预测的刚性运动转换为域间空间变换，以直接组装全链模型。最后，评分策略 RMscore 旨在从多个组装模型中选择最佳模型。实验结果表明，E2EDA在基准集（282）上组装的模型的平均TM-score为0.827，优于其他领域组装方法SADA（0.792）和DEMO（0.730）。同时，在我们从 AlphaFold DB 构建的多域数据集上，E2EDA 重新组装的模型的 TM-score 比 AlphaFold2 预测的全链模型高 7.0%，这表明 E2EDA 可以捕获更准确的域间方向提高 AlphaFold2 预测模型的质量。此外，与SADA和AlphaFold2相比，E2EDA在基准测试上的平均运行时间分别降低了64.7%和19.2%，这表明E2EDA可以通过端到端的方法显着提高装配效率。在线服务器位于http://zhanglab-bioinf.com/E2EDA。