The interaction of a protein with its environment can be understood and controlled via its 3D structure. Experimental methods for protein structure determination, such as X-ray crystallography or cryogenic electron microscopy, shed light on biological processes but introduce challenging inverse problems. Learning-based approaches have emerged as accurate and efficient methods to solve these inverse problems for 3D structure determination, but are specialized for a predefined type of measurement. Here, we introduce a versatile framework to turn raw biophysical measurements of varying types into 3D atomic models. Our method combines a physics-based forward model of the measurement process with a pretrained generative model providing a task-agnostic, data-driven prior. Our method outperforms posterior sampling baselines on both linear and non-linear inverse problems. In particular, it is the first diffusion-based method for refining atomic models from cryo-EM density maps.

通过结合物理学模型和先前训练的生成模型，我们引入了一个多功能框架，将不同类型的生物物理测量转化为三维原子模型，该方法在线性和非线性反问题上优于后验采样基线，特别是首次利用扩散方法从低温电子显微镜密度图中改进原子模型。

使用基于扩散的先验解决蛋白质空间中的逆问题