# Generative modeling of protein ensembles guided by crystallographic electron densities

arXiv: https://arxiv.org/abs/2412.13223 · HTML: https://mlsanity.com/paper/2412.13223/

## 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.

## Built on by (most cited first, 7 of 7)

- [Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data](https://mlsanity.com/paper/2506.04490/) (2025)
- [Dual Ascent Diffusion for Inverse Problems](https://mlsanity.com/paper/2505.17353/) (2025)
- [Representing local protein environments with machine learning force fields](https://mlsanity.com/paper/2505.23354/) (2025)
- [Inference-time optimization for experiment-grounded protein ensemble generation](https://mlsanity.com/paper/2602.24007/) (2026)
- [Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors](https://mlsanity.com/paper/2507.21260/) (2025)
- [Seek and You Shall Fold](https://mlsanity.com/paper/2511.13244/) (2025)
- [Fold'EM: Direct atomic structure inference from Cryo-EM particles](https://mlsanity.com/paper/2610.01358/) (2026)

## 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.