Generative modeling of protein ensembles guided by crystallographic electron densities
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Abstract (arXiv)
Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray crystallography experiments. Fitting an ensemble of protein structures to these measurements is a challenging, ill-posed inverse problem. We propose a non-i.i.d. ensemble guidance approach to solve this problem using existing protein structure generative models and demonstrate that it accurately recovers complicated multi-modal alternate protein backbone conformations observed in certain single crystal measurements.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 0 of its 17 references that are in the map. First posted on arXiv 17 Dec 2024.
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Citing papers per year: 2025: 5; 2026: 2.
7 papers.
- 2025Multiscale guidance of protein structure prediction with heterogeneous cryo-EM datacited by 8
- 2025Dual Ascent Diffusion for Inverse Problemscited by 3
- 2025Representing local protein environments with machine learning force fieldscited by 2
- 2026Inference-time optimization for experiment-grounded protein ensemble generationcited by 2
- 2025Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priorscited by 1
- 2025Seek and You Shall Foldcited by 0
- 2026Fold'EM: Direct atomic structure inference from Cryo-EM particlescited by 0
- 2026Fold'EM: Direct atomic structure inference from Cryo-EM particles1 Oct 2026
- 2026Inference-time optimization for experiment-grounded protein ensemble generation27 Feb 2026
- 2025Seek and You Shall Fold17 Nov 2025
- 2025Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors28 Jul 2025
- 2025Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data4 Jun 2025
- 2025Representing local protein environments with machine learning force fields29 May 2025
- 2025Dual Ascent Diffusion for Inverse Problems23 May 2025