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Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data
Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22903 USA;Univ Virginia, Dept Mol Physiol & Biophys, Charlottesville, VA 22903 USA.
Univ Virginia, Dept Mol Physiol & Biophys, Charlottesville, VA 22903 USA;Univ Virginia, Dept Chem, Mccormick Rd, Charlottesville, VA 22903 USA.
Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Molekylär biofysik. Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22903 USA;Univ Virginia, Dept Mol Physiol & Biophys, Charlottesville, VA 22903 USA.
2019 (engelsk)Inngår i: Journal of Physical Chemistry Letters, ISSN 1948-7185, E-ISSN 1948-7185, Vol. 10, nr 12, s. 3410-3414Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Multistructured biomolecular systems play crucial roles in a wide variety of cellular processes but have resisted traditional methods of structure determination, which often resolve only a few low-energy states. High-resolution structure determination using experimental methods that yield distributional data remains extremely difficult, especially when the underlying conformational ensembles are quite heterogeneous. We have therefore developed a method to integrate sparse, multimultimodal spectroscopic data to obtain high-resolution estimates of conformational ensembles. We have tested our method by incorporating double electron-electron resonance data on the soluble N-ethylmaleimide-sensitive factor attachment receptor (SNARE) protein syntaxin-1a into biased molecular dynamics simulations. We find that our method substantially outperforms existing state-of-the-art methods in capturing syntaxins open-closed conformational equilibrium and further yields new conformational states that are consistent with experimental data and may help in understanding syntaxin's function. Our improved methods for refining heterogeneous conformational ensembles from spectroscopic data will greatly accelerate the structural understanding of such systems.

sted, utgiver, år, opplag, sider
American Chemical Society (ACS), 2019. Vol. 10, nr 12, s. 3410-3414
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URN: urn:nbn:se:uu:diva-390681DOI: 10.1021/acs.jpclett.9b01407ISI: 000472804700034PubMedID: 31181934OAI: oai:DiVA.org:uu-390681DiVA, id: diva2:1343053
Tilgjengelig fra: 2019-08-15 Laget: 2019-08-15 Sist oppdatert: 2019-08-15bibliografisk kontrollert

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