---
OA_place: publisher
OA_type: gold
_id: '21327'
abstract:
- lang: eng
  text: Proteins exist as a dynamic ensemble of multiple conformations, and these
    motions are often crucial for their functions. However, current structure prediction
    methods predominantly yield a single conformation, overlooking the conformational
    heterogeneity revealed by diverse experimental modalities. Here, we present a
    framework for building experiment-grounded protein structure generative models
    that infer conformational ensembles consistent with measured experimental data.
    The key idea is to treat stateof-the-art protein structure predictors (e.g., AlphaFold3)
    as sequence-conditioned structural priors, and cast ensemble modeling as posterior
    inference of protein structures given experimental measurements. Through extensive
    real-data experiments, we demonstrate the generality of our method to incorporate
    a variety of experimental measurements. In particular, our framework uncovers
    previously unmodeled conformational heterogeneity from crystallographic densities,
    and generates high-accuracy NMR ensembles orders of magnitude faster than the
    status quo. Notably, we demonstrate that our ensembles outperform AlphaFold3 (Abramson
    et al., 2024) and sometimes better fit experimental data than publicly deposited
    structures to the Protein Data Bank (PDB, Burley et al. (2017)). We believe that
    this approach will unlock building predictive models that fully embrace experimentally
    observed conformational diversity.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: 'This work was supported by the Israeli Science Foundation (ISF)
  grant number 1834/24. We acknowledge support from the Austrian Science Fund (FWF,
  grant numbers I5812-B and I6223) and the financial support of the Helmsley Fellowships
  Program for Sustainability and Health. This research uses resources of the Institute
  of Science and Technology Austria’s scientific computing cluster. '
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Sai A
  full_name: Maddipatla, Sai A
  id: e957f5e5-91c9-11f0-a95f-e090f66ecb4d
  last_name: Maddipatla
- first_name: Nadav E
  full_name: Sellam, Nadav E
  id: ef280fe0-91c9-11f0-a95f-8dea3f5bc513
  last_name: Sellam
- first_name: Meital I
  full_name: Bojan, Meital I
  id: 11d88cf5-91ca-11f0-a95f-edf9f08f47b7
  last_name: Bojan
- first_name: Sanketh
  full_name: Vedula, Sanketh
  id: 94f2fe44-70fa-11f0-b76b-92922c09452b
  last_name: Vedula
- first_name: Paul
  full_name: Schanda, Paul
  id: 7B541462-FAF6-11E9-A490-E8DFE5697425
  last_name: Schanda
  orcid: 0000-0002-9350-7606
- first_name: Ailie
  full_name: Marx, Ailie
  last_name: Marx
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
citation:
  ama: 'Maddipatla SA, Sellam NE, Bojan MI, et al. Inverse problems with experiment-guided
    AlphaFold. In: <i>Proceedings of the 42nd International Conference on Machine
    Learning</i>. Vol 267. ML Research Press; 2025:42366-42393.'
  apa: 'Maddipatla, S. A., Sellam, N. E., Bojan, M. I., Vedula, S., Schanda, P., Marx,
    A., &#38; Bronstein, A. M. (2025). Inverse problems with experiment-guided AlphaFold.
    In <i>Proceedings of the 42nd International Conference on Machine Learning</i>
    (Vol. 267, pp. 42366–42393). Vancouver, Canada: ML Research Press.'
  chicago: Maddipatla, Sai A, Nadav E Sellam, Meital I Bojan, Sanketh Vedula, Paul
    Schanda, Ailie Marx, and Alex M. Bronstein. “Inverse Problems with Experiment-Guided
    AlphaFold.” In <i>Proceedings of the 42nd International Conference on Machine
    Learning</i>, 267:42366–93. ML Research Press, 2025.
  ieee: S. A. Maddipatla <i>et al.</i>, “Inverse problems with experiment-guided AlphaFold,”
    in <i>Proceedings of the 42nd International Conference on Machine Learning</i>,
    Vancouver, Canada, 2025, vol. 267, pp. 42366–42393.
  ista: 'Maddipatla SA, Sellam NE, Bojan MI, Vedula S, Schanda P, Marx A, Bronstein
    AM. 2025. Inverse problems with experiment-guided AlphaFold. Proceedings of the
    42nd International Conference on Machine Learning. ICML: International Conference
    on Machine Learning, PMLR, vol. 267, 42366–42393.'
  mla: Maddipatla, Sai A., et al. “Inverse Problems with Experiment-Guided AlphaFold.”
    <i>Proceedings of the 42nd International Conference on Machine Learning</i>, vol.
    267, ML Research Press, 2025, pp. 42366–93.
  short: S.A. Maddipatla, N.E. Sellam, M.I. Bojan, S. Vedula, P. Schanda, A. Marx,
    A.M. Bronstein, in:, Proceedings of the 42nd International Conference on Machine
    Learning, ML Research Press, 2025, pp. 42366–42393.
conference:
  end_date: 2025-07-19
  location: Vancouver, Canada
  name: 'ICML: International Conference on Machine Learning'
  start_date: 2025-07-13
corr_author: '1'
date_created: 2026-02-18T12:11:17Z
date_published: 2025-07-30T00:00:00Z
date_updated: 2026-02-19T08:56:43Z
day: '30'
ddc:
- '000'
- '540'
department:
- _id: PaSc
- _id: AlBr
- _id: GradSch
external_id:
  arxiv:
  - '2502.09372'
file:
- access_level: open_access
  checksum: f33230a6d59b7978d4cd72795e4e9059
  content_type: application/pdf
  creator: dernst
  date_created: 2026-02-19T08:56:10Z
  date_updated: 2026-02-19T08:56:10Z
  file_id: '21338'
  file_name: 2025_ICML_Maddipatla.pdf
  file_size: 1924177
  relation: main_file
  success: 1
file_date_updated: 2026-02-19T08:56:10Z
has_accepted_license: '1'
intvolume: '       267'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '07'
oa: 1
oa_version: Published Version
page: 42366 - 42393
project:
- _id: eb9c82eb-77a9-11ec-83b8-aadd536561cf
  grant_number: I05812
  name: AlloSpace. The emergence and mechanisms of allostery
- _id: bdb9578d-d553-11ed-ba76-ed5d39fce6f0
  grant_number: I06223
  name: Structure and mechanism of the mitochondrial MIM insertase
publication: Proceedings of the 42nd International Conference on Machine Learning
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Inverse problems with experiment-guided AlphaFold
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 267
year: '2025'
...