Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination

Cryo-EM is a key technology for determining the structures of proteins, particularly large protein complexes. However, automatically building high-accuracy protein structures from cryo-EM density maps remains a crucial challenge. In this work, we introduce MICA, a fully automatic and multimodal deep learning approach combining cryo-EM density maps with AlphaFold3-predicted structures at both input and output levels to improve cryo-EM protein structure modeling. It first uses a multi-task encoder-decoder architecture with a feature pyramid network to predict backbone atoms, Cα atoms and amino acid types from both cryo-EM maps and AlphaFold3-predicted structures, which are used to build an initial backbone model. This model is further refined using AlphaFold3-predicted structures and density maps to build final atomic structures. MICA significantly outperforms other state-of-the-art deep learning methods in terms of both modeling accuracy and completeness. Additionally, it builds high-accuracy structural models with an average template-based modeling score (TM-score) of 0.93 from recently released high-resolution cryo-EM density maps, showing it can be used for real-world, automated, accurate protein structure determination.冷冻电镜（Cryo-EM）是解析蛋白质尤其是大型蛋白质复合物结构的核心技术，但从冷冻电镜密度图自动构建高精度蛋白质结构仍是一项关键挑战。本研究提出MICA，一种全自动多模态深度学习方法，在输入与输出层面同时融合冷冻电镜密度图与AlphaFold3预测的蛋白质结构，以优化冷冻电镜蛋白质结构建模任务。该方法首先采用搭载特征金字塔网络（Feature Pyramid Network）的多任务编码器-解码器架构，从冷冻电镜密度图与AlphaFold3预测结构中同步预测主链原子、Cα原子与氨基酸类型，进而构建初始主链模型；随后借助AlphaFold3预测结构与密度图对该模型进行精细化优化，最终生成原子级精度的蛋白质结构。MICA在建模精度与结构完整性两方面均显著优于其他当前顶尖的深度学习方法。此外，该方法可从最新发布的高分辨率冷冻电镜密度图中构建平均模板建模评分（TM-score）达0.93的高精度结构模型，证明其可应用于真实场景下的自动化高精度蛋白质结构解析。