E2EDA is an End-To-End Domain Assembly method based on deep learning. In E2EDA, we first develop an EfficientNetV2-based deep learning model (RMNet), which is dedicated to predicting inter-domain orientations. The RMNet uses an attention mechanism to predict inter-domain rigid motion by fusing sequence features, multiple template features, and single-domain features. Then, the predicted rigid motions are converted into inter-domain spatial transformations to directly assemble full-chain models of multi-domain proteins without time-consuming simulation processes. Finally, a scoring strategy, RMscore, is designed to select the best model from multiple assembled models to improve assembly accuracy.
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