https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2025Leone_Review2025Leone Review - Revision history2026-05-01T07:51:27ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2025Leone_Review&diff=5130&oldid=prevWikiSysop: Created page with "== Citation == Leone, V. and Marinelli, F. 2025. From snapshots to ensembles: Integrating experimental data and dynamics. Current Opinion in Structural Biology. 95, (2025), 103155. == Abstract == Protein function arises from the interplay of structure, dynamics, and biomolecular interactions. Despite advances in cryo-EM and AI-based structure prediction, capturing dynamic and energetic features remains a challenge. Biophysical methods like NMR, EPR, HDX-MS, SAXS, and..."2025-12-30T08:48:07Z<p>Created page with "== Citation == Leone, V. and Marinelli, F. 2025. From snapshots to ensembles: Integrating experimental data and dynamics. Current Opinion in Structural Biology. 95, (2025), 103155. == Abstract == Protein function arises from the interplay of structure, dynamics, and biomolecular interactions. Despite advances in cryo-EM and AI-based structure prediction, capturing dynamic and energetic features remains a challenge. Biophysical methods like NMR, EPR, HDX-MS, SAXS, and..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
<br />
Leone, V. and Marinelli, F. 2025. From snapshots to ensembles: Integrating experimental data and dynamics. Current Opinion in Structural Biology. 95, (2025), 103155.<br />
<br />
== Abstract ==<br />
<br />
Protein function arises from the interplay of structure, dynamics,<br />
and biomolecular interactions. Despite advances in<br />
cryo-EM and AI-based structure prediction, capturing dynamic<br />
and energetic features remains a challenge. Biophysical<br />
methods like NMR, EPR, HDX-MS, SAXS, and cryo-EM provide<br />
valuable but often indirect signals. Connecting these to<br />
molecular mechanisms requires integrative approaches that<br />
combine experiments with physics-based simulations,<br />
revealing both stable structures and transient, functionally<br />
important intermediates. This review highlights recent advances<br />
in integrative modeling using the maximum entropy<br />
principle to build dynamic ensembles from diverse data while<br />
addressing uncertainty and bias. These methods help resolve<br />
heterogeneity and interpret low-resolution data. We conclude<br />
by exploring how integrative modeling, enhanced sampling,<br />
and AI-driven tools enable new insights into slow, large-scale<br />
conformational changes.<br />
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== Keywords ==<br />
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== Links ==<br />
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https://www.sciencedirect.com/science/article/pii/S0959440X25001733<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop