用冷冻电镜数据引导蛋白结构预测，生成更符合真实构象的原子模型。
Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data
- 利用冷冻电镜密度图提供全局与局部约束，指导预训练模型采样
- 在多种动态生物分子系统中成功构建与实验数据一致的原子模型
- 无需重训练，推理阶段即可融合实验数据，适合结构生物学研究者
蛋白质结构预测模型如今仅凭序列就能生成精确的三维结构假设。然而，它们常无法捕捉动态生物分子复合物的构象多样性，通常依赖启发式MSA子采样来生成备选状态。与此同时，冷冻电子显微镜（cryo-EM）已成为揭示近天然构象异质性的强大工具，但将原始实验数据转化为原子模型的流程仍十分繁琐。本文提出CryoBoltz方法，将冷冻电镜密度图与蛋白质结构预测模型所学习的序列和生物物理先验相结合。该方法通过从密度图中提取的全局与局部结构约束，引导预训练生物分子结构预测模型的采样轨迹，使预测结果趋向于与实验数据一致的构象状态。我们展示了这一灵活而强大的推理阶段方法，能够在包括转运蛋白和抗体在内的多种动态生物分子系统中，将原子模型构建到异质性冷冻电镜地图中。代码已公开于https://github.com/ml-struct-bio/cryoboltz。
原文摘要 · Abstract (English)
Protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fail to capture the conformational diversity of dynamic biomolecular complexes, often requiring heuristic MSA subsampling approaches for generating alternative states. In parallel, cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for imaging near-native structural heterogeneity, but is challenged by arduous pipelines to transform raw experimental data into atomic models. Here, we bridge the gap between these modalities, combining cryo-EM density maps with the rich sequence and biophysical priors learned by protein structure prediction models. Our method, CryoBoltz, guides the sampling trajectory of a pretrained biomolecular structure prediction model using both global and local structural constraints derived from density maps, driving predictions towards conformational states consistent with the experimental data. We demonstrate that this flexible yet powerful inference-time approach allows us to build atomic models into heterogeneous cryo-EM maps across a variety of dynamic biomolecular systems including transporters and antibodies. Code is available at https://github.com/ml-struct-bio/cryoboltz .
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