Solving Inverse Problems in Protein Space Using Diffusion-Based Priors

• URL: http://arxiv.org/abs/2406.04239v2
• Date: Wed, 23 Apr 2025 17:35:34 GMT
• Title: Solving Inverse Problems in Protein Space Using Diffusion-Based Priors
• Authors: Axel Levy, Eric R. Chan, Sara Fridovich-Keil, Frédéric Poitevin, Ellen D. Zhong, Gordon Wetzstein,
• Abstract summary: We introduce a versatile framework to turn biophysical measurements, such as cryo-EM density maps, into 3D atomic models.<n>Our method combines a physics-based forward model of the measurement process with a pretrained generative model providing a task-agnostic, data-driven prior.
• Score: 36.726170589634826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Gaussian Parameterization for Direct Atomic Structure Identification in Electron Tomography [0.6198237241838559]
  We reformulate the tomographic inverse problem to solve directly for the locations and properties of individual atoms.<n>This representation imparts a strong physical prior on the learned structure, which we show yields improved robustness to real-world imaging artifacts.
  arXiv  Detail & Related papers  (2025-12-17T02:52:32Z)
• CryoGS: Gaussian Splatting for Cryo-EM Homogeneous Reconstruction [55.2480439325792]
  cryogenic electron microscopy (cryo-EM) facilitates the determination of macromolecular structures at near-atomic resolution.<n>The core computational task in single-particle cryo-EM is to reconstruct the 3D electrostatic potential of a molecule.<n>We introduce cryoGS, a GMM-based method that integrates Gaussian splatting with the physics of cryo-EM image formation.
  arXiv  Detail & Related papers  (2025-08-06T23:24:43Z)
• Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data [33.562685684224995]
  cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for imaging near-native structural heterogeneity.<n>Here, we combine cryo-EM density maps with the rich sequence and biophysical priors learned by protein structure prediction models.<n>Our method, CryoBoltz, guides the sampling trajectory of a pretrained protein structure prediction model using both global and local structural constraints.
  arXiv  Detail & Related papers  (2025-06-04T22:16:27Z)
• Diffusion priors for Bayesian 3D reconstruction from incomplete measurements [0.0]
  We explore the use of diffusion models as priors combined with experimental data within a Bayesian framework. We train diffusion models that generate coarse-grained 3D structures at a medium resolution and integrate these with incomplete and noisy experimental data. We find that posterior sampling with diffusion model priors allows for 3D reconstruction from very sparse, low-resolution and partial observations.
  arXiv  Detail & Related papers  (2024-12-19T14:28:00Z)
• Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation [18.394348744611662]
  We introduce a novel generative model termed Equivariant Blurring Diffusion (EBD) EBD defines a forward process that moves towards the fragment-level coarse-grained structure by blurring the fine atomic details of conformers. We demonstrate the effectiveness of EBD by geometric and chemical comparison to state-of-the-art denoising diffusion models on a benchmark of drug-like molecules.
  arXiv  Detail & Related papers  (2024-10-26T19:17:31Z)
• Structure Language Models for Protein Conformation Generation [66.42864253026053]
  Traditional physics-based simulation methods often struggle with sampling equilibrium conformations. Deep generative models have shown promise in generating protein conformations as a more efficient alternative. We introduce Structure Language Modeling as a novel framework for efficient protein conformation generation.
  arXiv  Detail & Related papers  (2024-10-24T03:38:51Z)
• Diffusion-Driven Generative Framework for Molecular Conformation Prediction [0.66567375919026]
  The rapid advancement of machine learning has revolutionized the precision of predictive modeling in this context. This research introduces a cutting-edge generative framework named method. Method views atoms as discrete entities and excels in guiding the reversal of diffusion.
  arXiv  Detail & Related papers  (2023-12-22T11:49:39Z)
• Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem [141.5628276096321]
  We present an approach that utilizes prior knowledge to regularize the training process over larger parameter spaces. We demonstrate the effectiveness of our method in various scenarios, including multilayer structures with box model parameterization. In contrast to previous methods, our approach scales favorably when increasing the complexity of the inverse problem.
  arXiv  Detail & Related papers  (2023-06-28T11:15:53Z)
• A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction [66.21060114843202]
  We propose a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure.
  arXiv  Detail & Related papers  (2023-05-30T13:58:50Z)
• End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics [61.44793171735013]
  We introduce a novel unified architecture, termed latent variation models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline.
  arXiv  Detail & Related papers  (2023-05-17T17:43:10Z)
• Fast and robust single particle reconstruction in 3D fluorescence microscopy [1.0625549557437526]
  Single particle reconstruction is a powerful technique to improve the axial resolution and the degree of fluorescent labeling. We propose a single particle reconstruction method dedicated to convolutional models in 3D fluorescence microscopy. We demonstrate on synthetic approaches in terms of resolution and reconstruction error, while achieving a low computational cost.
  arXiv  Detail & Related papers  (2023-01-23T14:20:01Z)
• Amortized Inference for Heterogeneous Reconstruction in Cryo-EM [36.911133113707045]
  cryo-electron microscopy (cryo-EM) provides insights into the dynamics of proteins and other building blocks of life. The algorithmic challenge of jointly estimating the poses, 3D structure, and conformational heterogeneity of a biomolecule remains unsolved. Our method, cryoFIRE, performs ab initio heterogeneous reconstruction with unknown poses in an amortized framework. We show that our method can provide one order of magnitude speedup on datasets containing millions of images without any loss of accuracy.
  arXiv  Detail & Related papers  (2022-10-13T22:06:38Z)
• Heterogeneous reconstruction of deformable atomic models in Cryo-EM [30.864688165021054]
  We describe a heterogeneous reconstruction method based on an atomistic representation whose deformation is reduced to a handful of collective motions. We show for each distribution that our approach is able to recapitulate the intermediate atomic models with atomic-level accuracy.
  arXiv  Detail & Related papers  (2022-09-29T22:35:35Z)
• Equivariant Diffusion for Molecule Generation in 3D [74.289191525633]
  This work introduces a diffusion model for molecule computation generation in 3D that is equivariant to Euclidean transformations. Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time.
  arXiv  Detail & Related papers  (2022-03-31T12:52:25Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming [71.82571553927619]
  We propose an end-to-end solution for molecular conformation prediction called ConfVAE. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.
  arXiv  Detail & Related papers  (2021-05-15T15:22:29Z)
• Meshless physics-informed deep learning method for three-dimensional solid mechanics [0.0]
  Deep learning and the collocation method are merged and used to solve partial differential equations describing structures' deformation. We consider different types of materials: linear elasticity, hyperelasticity (neo-Hookean) with large deformation, and von Mises plasticity with isotropic and kinematic hardening.
  arXiv  Detail & Related papers  (2020-12-02T21:40:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.