End-to-end cryo-EM complex structure determination with high accuracy and ultra-fast speed

Abstract

While cryogenic-electron microscopy yields high-resolution density maps for complex structures, accurate determination of the corresponding atomic structures still necessitates significant expertise and labour-intensive manual interpretation. Recently, artificial intelligence-based methods have emerged to streamline this process; however, several challenges persist. First, existing methods typically require multi-stage training and inference, causing inefficiencies and inconsistency. Second, these approaches often encounter bias and incur substantial computational costs in aligning predicted atomic coordinates with sequence. Last, due to the limitations of available datasets, previous studies struggle to generalize effectively to complicated and unseen test data. Here, in response to these challenges, we introduce end-to-end and efficient CryoFold (E3-CryoFold), a deep learning method that enables end-to-end training and one-shot inference. E3-CryoFold uses three-dimensional and sequence transformers to extract features from density maps and sequences, using cross-attention modules to integrate the two modalities. Additionally, it uses an SE(3) graph neural network to construct atomic structures based on extracted features. E3-CryoFold incorporates a pretraining stage, during which models are trained on simulated density maps derived from Protein Data Bank structures. Empirical results demonstrate that E3-CryoFold improves the average template modelling score of the generated structures by 400% as compared to Cryo2Struct and significantly outperforms ModelAngelo, while achieving this huge improvement using merely one-thousandth of the inference time required by these methods. Thus, E3-CryoFold represents a robust, streamlined and cohesive framework for cryogenic-electron microscopy structure determination.