@article{21929,
  abstract     = {The import of proteins into mitochondria poses fundamental mechanistic challenges: aggregation-prone precursor proteins must be maintained in aqueous compartments and threaded through narrow pores without becoming stuck or mislocalized. Recent evidence from mitochondrial protein import studies and other chaperone systems underscores the critical role of dynamics in balancing sufficiently tight binding, promiscuity, specificity, and release. Dynamic binding of client precursor proteins to import machinery components arises naturally from the avidity of their interactions. Conformational entropy enhances their stability, while the multivalent nature of these interactions ensures that client transfer to downstream insertases occurs without a substantial energy barrier. Here, we discuss this emerging paradigm of dynamic protein handling, using examples where dynamic structures have been resolved and highlight outstanding questions.},
  author       = {Schneider, Jakob and Guillerm, Undina and Simoes Pereira, Caroline and Schanda, Paul},
  issn         = {1469-896X},
  journal      = {Protein Science},
  number       = {6},
  publisher    = {Wiley},
  title        = {{Dynamic disorder is crucial for mitochondrial protein import}},
  doi          = {10.1002/pro.70630},
  volume       = {35},
  year         = {2026},
}

@article{20538,
  abstract     = {In this study, we describe an integrated approach for methyl group assignment comprising precursor-based selective methyl group labeling, a novel pulse sequence for methyl to backbone coherence transfer and chemical shift predictions using UCBShift 2.0. The utility of this novel α-ketoacid isotopologue is shown by the adaptation of an HMBC-HMQC pulse sequence that simultaneously connects geminal methyl groups of leucine and valine residues to each other and to the protein backbone. By additional 13C,2H-labeling of residues other than valine and leucine residues of the protein, important chemical shift information about neighboring residues (following valine and leucine residues) can be achieved. Thus, different valine and leucine residues in a protein can be characterized as a specific chemical shift vector. Frequency matching with predicted chemical shifts via UCBShift 2.0 using experimental data taken from a subset of the BMRB database revealed a correct assignment performance of about 90%. With applications to proteins of 60.2 kDa and 134 kDa (4 × 33.5 kDa) in size, we demonstrate that the approach provides valuable information even for very large proteins.},
  author       = {Knödlstorfer, Sonja and Toscano, Giorgia and Ptaszek, Aleksandra L. and Kontaxis, Georg and Napoli, Federico and Schneider, Jakob and Maier, Katharina and Kapitonova, Anna and Lichtenecker, Roman J. and Schanda, Paul and Konrat, Robert},
  issn         = {1089-8638},
  journal      = {Journal of Molecular Biology},
  number       = {23},
  publisher    = {Elsevier},
  title        = {{A novel HMBC-CC-HMQC NMR strategy for methyl assignment using triple-13C-labeled α-ketoisovalerate integrated with UCBShift 2.0}},
  doi          = {10.1016/j.jmb.2025.169465},
  volume       = {437},
  year         = {2025},
}

@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},
}