Solving Inverse Problems in Protein Space Using Diffusion-Based Priors
Keywords: machine learning for structural biology, protein structure determination, diffusion models, inverse problems
TL;DR: We leverage the data-driven prior learned by a diffusion model to turn measurements (e.g. cryo-EM maps) into 3D protein structures.
Abstract: 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.
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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 7829
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