| Autor: |
Miller, Justin E., Agdanowski, Matthew P., Dolinsky, Joshua L., Sawaya, Michael R., Cascio, Duilio, Rodriguez, Jose A., Yeates, Todd O. |
| Předmět: |
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| Zdroj: |
Acta Crystallographica: Section D, Structural Biology; Apr2024, Vol. 80 Issue 4, p270-278, 9p |
| Abstrakt: |
Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron‐density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine‐learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X‐ray and micro‐electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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