Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids
| Autor: | van Zundert, G. C P, Bonvin, A. M J J, Sub NMR Spectroscopy, NMR Spectroscopy |
|---|---|
| Přispěvatelé: | Sub NMR Spectroscopy, NMR Spectroscopy |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Models Molecular PowerFit Computer science Fisher z-transformation computer.software_genre Core-weighted 03 medical and health sciences Software Imaging Three-Dimensional Structural Biology Image Processing Computer-Assisted Leverage (statistics) Cross-correlation business.industry Fisher transformation Resolution (electron density) Cryoelectron Microscopy Modeling Rigid body Ribosome 030104 developmental biology Graphics software Data mining Cross correlation business computer Model building Algorithm Ribosomes Algorithms |
| Zdroj: | Journal of Structural Biology Journal of Structural Biology, 195(2), 252 Sygma NARCIS Journal of Structural Biology, 195(2), 252. Academic Press Inc. |
| ISSN: | 1047-8477 |
| DOI: | 10.1016/j.jsb.2016.06.011 |
| Popis: | Cryo-electron microscopy provides fascinating structural insight into large macromolecular machines at increasing detail. Despite significant advances in the field, the resolution of the resulting three-dimensional images is still typically insufficient for . de novo model building. To bridge the resolution gap and give an atomic interpretation to the data, high-resolution models are typically placed into the density as rigid bodies. Unfortunately, this is often done manually using graphics software, a subjective method that can lead to over-interpretation of the data. A more objective approach is to perform an exhaustive cross-correlation-based search to fit subunits into the density. Here we show, using five experimental ribosome maps ranging in resolution from 5.5 to 6.9. Å, that cross-correlation-based fitting is capable of successfully fitting subunits correctly in the density for over 90% of the cases. Importantly, we provide indicators for the reliability and ambiguity of a fit, using the Fisher z-transformation and its associated confidence intervals, giving a formal approach to identify over-interpreted regions in the density. In addition, we quantify the resolution requirement for a successful fit as a function of the subunit size. For larger subunits the resolution of the data can be down-filtered to 20. Å while still retaining an unambiguous fit. We leverage this information through the use of multi-scale image pyramids to accelerate the search up to 30-fold on CPUs and 40-fold on GPUs at a negligible loss in success rate. We implemented this approach in our rigid-body fitting software PowerFit, which can be freely downloaded from . https://github.com/haddocking/powerfit. |
| Databáze: | OpenAIRE |
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