[{"citation":{"apa":"Schneider, J., Guillerm, U., Simoes Pereira, C., &#38; Schanda, P. (2026). Dynamic disorder is crucial for mitochondrial protein import. <i>Protein Science</i>. Wiley. <a href=\"https://doi.org/10.1002/pro.70630\">https://doi.org/10.1002/pro.70630</a>","ista":"Schneider J, Guillerm U, Simoes Pereira C, Schanda P. 2026. Dynamic disorder is crucial for mitochondrial protein import. Protein Science. 35(6), e70630.","ama":"Schneider J, Guillerm U, Simoes Pereira C, Schanda P. Dynamic disorder is crucial for mitochondrial protein import. <i>Protein Science</i>. 2026;35(6). doi:<a href=\"https://doi.org/10.1002/pro.70630\">10.1002/pro.70630</a>","ieee":"J. Schneider, U. Guillerm, C. Simoes Pereira, and P. Schanda, “Dynamic disorder is crucial for mitochondrial protein import,” <i>Protein Science</i>, vol. 35, no. 6. Wiley, 2026.","chicago":"Schneider, Jakob, Undina Guillerm, Caroline Simoes Pereira, and Paul Schanda. “Dynamic Disorder Is Crucial for Mitochondrial Protein Import.” <i>Protein Science</i>. Wiley, 2026. <a href=\"https://doi.org/10.1002/pro.70630\">https://doi.org/10.1002/pro.70630</a>.","short":"J. Schneider, U. Guillerm, C. Simoes Pereira, P. Schanda, Protein Science 35 (2026).","mla":"Schneider, Jakob, et al. “Dynamic Disorder Is Crucial for Mitochondrial Protein Import.” <i>Protein Science</i>, vol. 35, no. 6, e70630, Wiley, 2026, doi:<a href=\"https://doi.org/10.1002/pro.70630\">10.1002/pro.70630</a>."},"acknowledgement":"We gratefully acknowledge research funding by the Austrian Science Fund (FWF), projects 10.55776/PAT1647625 and 10.55776/I6223. We thank Prof. Long Li (Peking University) for providing structural models and EM density for the TOM and TIM23 complexes, used to generate part of Figure 3. Open Access funding provided by Institute of Science and Technology Austria.","status":"public","date_published":"2026-06-01T00:00:00Z","day":"01","article_number":"e70630","type":"journal_article","intvolume":"        35","project":[{"name":"Structure and mechanism of the mitochondrial MIM insertase","grant_number":"I06223","_id":"bdb9578d-d553-11ed-ba76-ed5d39fce6f0"}],"OA_place":"publisher","_id":"21929","issue":"6","article_processing_charge":"Yes (via OA deal)","publication":"Protein Science","publication_status":"published","external_id":{"pmid":["42159315"]},"scopus_import":"1","quality_controlled":"1","language":[{"iso":"eng"}],"department":[{"_id":"GradSch"},{"_id":"PaSc"}],"ddc":["572"],"pmid":1,"volume":35,"file_date_updated":"2026-06-02T07:23:12Z","license":"https://creativecommons.org/licenses/by/4.0/","abstract":[{"lang":"eng","text":"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":[{"id":"64368429-eb97-11eb-a6c2-c980b1f44415","last_name":"Schneider","full_name":"Schneider, Jakob","first_name":"Jakob"},{"first_name":"Undina","full_name":"Guillerm, Undina","last_name":"Guillerm","id":"bb74f472-ae54-11eb-9835-bc9c22fb1183"},{"first_name":"Caroline","full_name":"Simoes Pereira, Caroline","last_name":"Simoes Pereira","id":"87266c4a-96d2-11ef-be2c-fe5633233ec3"},{"full_name":"Schanda, Paul","first_name":"Paul","id":"7B541462-FAF6-11E9-A490-E8DFE5697425","orcid":"0000-0002-9350-7606","last_name":"Schanda"}],"PlanS_conform":"1","title":"Dynamic disorder is crucial for mitochondrial protein import","oa_version":"Published Version","doi":"10.1002/pro.70630","oa":1,"publisher":"Wiley","file":[{"file_name":"2026_ProteinScience_Schneider.pdf","checksum":"e0163459a7238fdcc3fc5e17bedcce9a","date_created":"2026-06-02T07:23:12Z","file_id":"21937","relation":"main_file","date_updated":"2026-06-02T07:23:12Z","content_type":"application/pdf","file_size":3897305,"success":1,"access_level":"open_access","creator":"dernst"}],"has_accepted_license":"1","article_type":"original","publication_identifier":{"issn":["0961-8368"],"eissn":["1469-896X"]},"year":"2026","month":"06","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"OA_type":"hybrid","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2026-05-31T22:02:12Z","corr_author":"1","date_updated":"2026-06-02T07:26:34Z"},{"article_processing_charge":"Yes (in subscription journal)","issue":"23","publication":"Journal of Molecular Biology","publication_status":"published","project":[{"grant_number":"I06223","_id":"bdb9578d-d553-11ed-ba76-ed5d39fce6f0","name":"Structure and mechanism of the mitochondrial MIM insertase"},{"grant_number":"I05812","_id":"eb9c82eb-77a9-11ec-83b8-aadd536561cf","name":"AlloSpace. The emergence and mechanisms of allostery"}],"OA_place":"publisher","_id":"20538","pmid":1,"department":[{"_id":"PaSc"},{"_id":"GradSch"}],"ddc":["540"],"abstract":[{"lang":"eng","text":"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."}],"volume":437,"file_date_updated":"2025-12-30T10:29:08Z","scopus_import":"1","external_id":{"pmid":["41016549"]},"language":[{"iso":"eng"}],"quality_controlled":"1","status":"public","article_number":"169465","day":"01","date_published":"2025-12-01T00:00:00Z","acknowledgement":"A.L.P and G.T were funded by the “New Ideas” program by Vienna Doctoral School in Chemistry. S.K. was funded by the Austrian Science Fund FWF P35098-B. This work was supported financially by the Austrian Science Fund (FWF, grant numbers I06223 and I5812-B, “AlloSpace”). This research was supported by the Scientific Service Units (SSU) of Institute of Science and Technology Austria (ISTA) through resources provided by the Nuclear Magnetic Resonance Facility and the Lab Support Facility (LSF). We thank Celina Sailer for assistance with the analysis of the NMR spectrum of HsTom70.","citation":{"ista":"Knödlstorfer S, Toscano G, Ptaszek AL, Kontaxis G, Napoli F, Schneider J, Maier K, Kapitonova A, Lichtenecker RJ, Schanda P, Konrat R. 2025. A novel HMBC-CC-HMQC NMR strategy for methyl assignment using triple-13C-labeled α-ketoisovalerate integrated with UCBShift 2.0. Journal of Molecular Biology. 437(23), 169465.","apa":"Knödlstorfer, S., Toscano, G., Ptaszek, A. L., Kontaxis, G., Napoli, F., Schneider, J., … Konrat, R. (2025). A novel HMBC-CC-HMQC NMR strategy for methyl assignment using triple-13C-labeled α-ketoisovalerate integrated with UCBShift 2.0. <i>Journal of Molecular Biology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jmb.2025.169465\">https://doi.org/10.1016/j.jmb.2025.169465</a>","short":"S. Knödlstorfer, G. Toscano, A.L. Ptaszek, G. Kontaxis, F. Napoli, J. Schneider, K. Maier, A. Kapitonova, R.J. Lichtenecker, P. Schanda, R. Konrat, Journal of Molecular Biology 437 (2025).","mla":"Knödlstorfer, Sonja, et al. “A Novel HMBC-CC-HMQC NMR Strategy for Methyl Assignment Using Triple-13C-Labeled α-Ketoisovalerate Integrated with UCBShift 2.0.” <i>Journal of Molecular Biology</i>, vol. 437, no. 23, 169465, Elsevier, 2025, doi:<a href=\"https://doi.org/10.1016/j.jmb.2025.169465\">10.1016/j.jmb.2025.169465</a>.","chicago":"Knödlstorfer, Sonja, Giorgia Toscano, Aleksandra L. Ptaszek, Georg Kontaxis, Federico Napoli, Jakob Schneider, Katharina Maier, et al. “A Novel HMBC-CC-HMQC NMR Strategy for Methyl Assignment Using Triple-13C-Labeled α-Ketoisovalerate Integrated with UCBShift 2.0.” <i>Journal of Molecular Biology</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.jmb.2025.169465\">https://doi.org/10.1016/j.jmb.2025.169465</a>.","ieee":"S. Knödlstorfer <i>et al.</i>, “A novel HMBC-CC-HMQC NMR strategy for methyl assignment using triple-13C-labeled α-ketoisovalerate integrated with UCBShift 2.0,” <i>Journal of Molecular Biology</i>, vol. 437, no. 23. Elsevier, 2025.","ama":"Knödlstorfer S, Toscano G, Ptaszek AL, et al. A novel HMBC-CC-HMQC NMR strategy for methyl assignment using triple-13C-labeled α-ketoisovalerate integrated with UCBShift 2.0. <i>Journal of Molecular Biology</i>. 2025;437(23). doi:<a href=\"https://doi.org/10.1016/j.jmb.2025.169465\">10.1016/j.jmb.2025.169465</a>"},"intvolume":"       437","type":"journal_article","month":"12","has_accepted_license":"1","publication_identifier":{"eissn":["1089-8638"],"issn":["0022-2836"]},"article_type":"original","acknowledged_ssus":[{"_id":"NMR"},{"_id":"LifeSc"}],"file":[{"file_name":"2025_JourMolecularBiology_Knoedlstorfer.pdf","checksum":"feb92f9c79032c261165f4ca573f444a","date_created":"2025-12-30T10:29:08Z","file_id":"20915","relation":"main_file","date_updated":"2025-12-30T10:29:08Z","content_type":"application/pdf","file_size":3076611,"success":1,"access_level":"open_access","creator":"dernst"}],"year":"2025","date_created":"2025-10-26T23:01:35Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"hybrid","date_updated":"2025-12-30T10:29:20Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"doi":"10.1016/j.jmb.2025.169465","title":"A novel HMBC-CC-HMQC NMR strategy for methyl assignment using triple-13C-labeled α-ketoisovalerate integrated with UCBShift 2.0","PlanS_conform":"1","author":[{"first_name":"Sonja","full_name":"Knödlstorfer, Sonja","last_name":"Knödlstorfer"},{"id":"334a5e40-8747-11f0-b671-ba1f5154b4b4","last_name":"Toscano","full_name":"Toscano, Giorgia","first_name":"Giorgia"},{"last_name":"Ptaszek","first_name":"Aleksandra L.","full_name":"Ptaszek, Aleksandra L."},{"full_name":"Kontaxis, Georg","first_name":"Georg","last_name":"Kontaxis"},{"first_name":"Federico","full_name":"Napoli, Federico","last_name":"Napoli","orcid":"0000-0002-9043-136X","id":"d42e08e7-f4fc-11eb-af0a-d71e26138f1b"},{"id":"64368429-eb97-11eb-a6c2-c980b1f44415","last_name":"Schneider","full_name":"Schneider, Jakob","first_name":"Jakob"},{"last_name":"Maier","first_name":"Katharina","full_name":"Maier, Katharina"},{"first_name":"Anna","full_name":"Kapitonova, Anna","last_name":"Kapitonova","id":"9fb2a840-89e1-11ee-a8b7-cc5c7ba62471"},{"last_name":"Lichtenecker","full_name":"Lichtenecker, Roman J.","first_name":"Roman J."},{"full_name":"Schanda, Paul","first_name":"Paul","orcid":"0000-0002-9350-7606","id":"7B541462-FAF6-11E9-A490-E8DFE5697425","last_name":"Schanda"},{"full_name":"Konrat, Robert","first_name":"Robert","last_name":"Konrat"}],"oa_version":"Published Version","publisher":"Elsevier","oa":1},{"day":"30","date_published":"2025-07-30T00:00:00Z","status":"public","arxiv":1,"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. ","citation":{"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.","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.","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.","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.","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.","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."},"intvolume":"       267","type":"conference","publication":"Proceedings of the 42nd International Conference on Machine Learning","publication_status":"published","article_processing_charge":"No","_id":"21327","OA_place":"publisher","project":[{"_id":"eb9c82eb-77a9-11ec-83b8-aadd536561cf","grant_number":"I05812","name":"AlloSpace. The emergence and mechanisms of allostery"},{"grant_number":"I06223","_id":"bdb9578d-d553-11ed-ba76-ed5d39fce6f0","name":"Structure and mechanism of the mitochondrial MIM insertase"}],"conference":{"start_date":"2025-07-13","end_date":"2025-07-19","location":"Vancouver, Canada","name":"ICML: International Conference on Machine Learning"},"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."}],"file_date_updated":"2026-02-19T08:56:10Z","volume":267,"ddc":["000","540"],"department":[{"_id":"PaSc"},{"_id":"AlBr"},{"_id":"GradSch"}],"language":[{"iso":"eng"}],"quality_controlled":"1","external_id":{"arxiv":["2502.09372"]},"page":"42366 - 42393","alternative_title":["PMLR"],"oa_version":"Published Version","title":"Inverse problems with experiment-guided AlphaFold","author":[{"first_name":"Sai A","full_name":"Maddipatla, Sai A","last_name":"Maddipatla","id":"e957f5e5-91c9-11f0-a95f-e090f66ecb4d"},{"id":"ef280fe0-91c9-11f0-a95f-8dea3f5bc513","last_name":"Sellam","full_name":"Sellam, Nadav E","first_name":"Nadav E"},{"first_name":"Meital I","full_name":"Bojan, Meital I","last_name":"Bojan","id":"11d88cf5-91ca-11f0-a95f-edf9f08f47b7"},{"last_name":"Vedula","id":"94f2fe44-70fa-11f0-b76b-92922c09452b","first_name":"Sanketh","full_name":"Vedula, Sanketh"},{"full_name":"Schanda, Paul","first_name":"Paul","id":"7B541462-FAF6-11E9-A490-E8DFE5697425","orcid":"0000-0002-9350-7606","last_name":"Schanda"},{"last_name":"Marx","first_name":"Ailie","full_name":"Marx, Ailie"},{"full_name":"Bronstein, Alexander","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730","last_name":"Bronstein"}],"oa":1,"publisher":"ML Research Press","month":"07","year":"2025","publication_identifier":{"eissn":["2640-3498"]},"has_accepted_license":"1","acknowledged_ssus":[{"_id":"ScienComp"}],"file":[{"date_created":"2026-02-19T08:56:10Z","date_updated":"2026-02-19T08:56:10Z","file_id":"21338","relation":"main_file","file_name":"2025_ICML_Maddipatla.pdf","checksum":"f33230a6d59b7978d4cd72795e4e9059","creator":"dernst","file_size":1924177,"content_type":"application/pdf","access_level":"open_access","success":1}],"date_updated":"2026-02-19T08:56:43Z","corr_author":"1","date_created":"2026-02-18T12:11:17Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"gold","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"}}]