Solving Inverse Problems in Protein Space Using Diffusion-Based Priors
<|reference_start|>Solving Inverse Problems in Protein Space Using Diffusion-Based Priors: 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.<|reference_end|>
@article{levy2024solving,
    title={Solving Inverse Problems in Protein Space Using Diffusion-Based Priors},
    author={Axel Levy, Eric R. Chan, Sara Fridovich-Keil, Fr'ed'eric Poitevin,
  Ellen D. Zhong, Gordon Wetzstein},
    journal={arXiv preprint arXiv:2406.04239},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04239},
    primaryClass={cs.LG}
}