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 biophysical measurements, such as cryo-EM density maps, 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 linear and non-linear inverse problems. In particular, it is the first diffusion-based method for refining atomic models from cryo-EM maps and building atomic models from sparse distance matrices.
@article{arxiv.2406.04239,
title = {Solving Inverse Problems in Protein Space Using Diffusion-Based Priors},
author = {Axel Levy and Eric R. Chan and Sara Fridovich-Keil and Frédéric Poitevin and Ellen D. Zhong and Gordon Wetzstein},
journal= {arXiv preprint arXiv:2406.04239},
year = {2025}
}