@article{22268,
  abstract     = {AlphaFold3 predicts highly accurate protein structures from sequence but tends to collapse to a single dominant conformation, even when the underlying structure is inherently heterogeneous. Moreover, its predictions are oblivious to experimental conditions that can alter local sequence conformation. In this work, we show that AlphaFold3 can be guided to match data obtained by nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography and cryogenic electron microscopy (cryo-EM) experiments and combinations thereof. Our approach can also incorporate data that explicitly report on dynamics, such as site-resolved order parameters. We demonstrate that this methodology generates compact structural ensembles whose ensemble-averaged observables agree with experiment, with fewer distance restraint violations than traditionally resolved NMR structures and with unmodeled alternate conformations uncovered in electron density. This methodology paves the way for experimentally aware predictive models that generate structural ensembles consistent with the measurements, potentially over multiple modalities, and that can be further refined toward thermodynamically grounded ensembles by incorporating energetics.},
  author       = {Maddipatla, Sai A and Sellam, Nadav E and Bojan, Meital I and Masalitin, Vova and Vedula, Sanketh and Schanda, Paul and Marx, Ailie and Bronstein, Alexander},
  issn         = {1546-1696},
  journal      = {Nature Biotechnology},
  publisher    = {Springer Nature},
  title        = {{Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles}},
  doi          = {10.1038/s41587-026-03166-5},
  year         = {2026},
}

@inproceedings{22722,
  abstract     = {The local structure of a protein strongly impacts its function and interactions
with other molecules. Representing local biomolecular environments remains a
key challenge while applying machine learning approaches over protein structures. The structural and chemical variability of these environments makes them
challenging to model, and performing representation learning on these objects
remains largely under-explored. In this work, we propose representations for
local protein environments that leverage intermediate features from machine learning force fields (MLFFs). We extensively benchmark state-of-the-art MLFFs,
comparing their performance across latent spaces and downstream tasks, and
show that their embeddings capture local structural (e.g., secondary motifs) and
chemical features (e.g., amino acid identity and protonation state), organizing
protein environments into a structured manifold. We show that these representations enable zero-shot generalization and transfer across diverse downstream
tasks. As a case study, we build a physics-informed, uncertainty-aware chemical shift predictor that achieves state-of-the-art accuracy in biomolecular NMR
spectroscopy. Our results establish MLFFs as general-purpose, reusable representation learners for protein modeling, opening new directions in representation learning for structured physical systems. Code and data are available at
https://github.com/mb012/MLFF_representation.
},
  author       = {Bojan, Meital I and Vedula, Sanketh and Maddipatla, Sai A and Sellam, Nadav E and Rzayev, Anar and Napoli, Federico and Schanda, Paul and Bronstein, Alexander},
  booktitle    = {14th International Conference on Learning Representations},
  location     = {Rio de Janeiro, Brazil},
  pages        = {100760--100799},
  title        = {{Representing local protein environments with machine learning force fields}},
  volume       = {2026},
  year         = {2026},
}

@inproceedings{21327,
  abstract     = {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.},
  author       = {Maddipatla, Sai A and Sellam, Nadav E and Bojan, Meital I and Vedula, Sanketh and Schanda, Paul and Marx, Ailie and Bronstein, Alexander},
  booktitle    = {Proceedings of the 42nd International Conference on Machine Learning},
  issn         = {2640-3498},
  location     = {Vancouver, Canada},
  pages        = {42366 -- 42393},
  publisher    = {ML Research Press},
  title        = {{Inverse problems with experiment-guided AlphaFold}},
  volume       = {267},
  year         = {2025},
}