Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data
| Autor: | Jennifer M. Hays, David S. Cafiso, Peter M. Kasson |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
010304 chemical physics Computer science Experimental data Resonance (particle physics) 01 natural sciences Article 03 medical and health sciences Molecular dynamics 0103 physical sciences General Materials Science Physical and Theoretical Chemistry Experimental methods Biological system Conformational ensembles 030304 developmental biology |
| Zdroj: | J Phys Chem Lett |
| ISSN: | 1948-7185 |
| DOI: | 10.1021/acs.jpclett.9b01407 |
| Popis: | 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 syntaxin's 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. |
| Databáze: | OpenAIRE |
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