Name
Abstract | ||
|---|---|---|
Motivation: Recent technological advances and computational developments have allowed the reconstruction of Cryo-Electron Microscopy (cryo-EM) maps at near-atomic resolution. On a typical workflow and once the cryo-EM map has been calculated, a sharpening process is usually performed to enhance map visualization, a step that has proven very important in the key task of structural modeling. However, sharpening approaches, in general, neglects the local quality of the map, which is clearly suboptimal. Results: Here, a new method for local sharpening of cryo-EM density maps is proposed. The algorithm, named LocalDeblur, is based on a local resolution-guided Wiener restoration approach of the original map. The method is fully automatic and, from the user point of view, virtually parameter-free, without requiring either a starting model or introducing any additional structure factor correction or boosting. Results clearly show a significant impact on map interpretability, greatly helping modeling. In particular, this local sharpening approach is especially suitable for maps that present a broad resolution range, as is often the case for membrane proteins or macromolecules with high flexibility, all of them otherwise very suitable and interesting specimens for cryo-EM. To our knowledge, and leaving out the use of local filters, it represents the first application of local resolution in cryo-EM sharpening. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btz671 | BIOINFORMATICS |
Field | DocType | Volume |
Sharpening,Computer vision,Data mining,Computer science,Artificial intelligence,Cryo-electron microscopy | Journal | 36 |
Issue | ISSN | Citations |
3 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 0 | 14 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erney Ramirez Aportela | 1 | 4 | 1.46 |
| Jose Luis Vilas | 2 | 0 | 0.68 |
| Alisa Glukhova | 3 | 0 | 0.34 |
| Roberto Melero | 4 | 0 | 1.01 |
| Pablo Conesa | 5 | 0 | 0.34 |
| Marta Martínez | 6 | 7 | 1.50 |
| David Maluenda | 7 | 0 | 0.34 |
| Javier Mota | 8 | 0 | 0.34 |
| Amaya Jiménez | 9 | 0 | 0.34 |
| J Vargas | 10 | 11 | 2.51 |
| Roberto Marabini | 11 | 53 | 10.17 |
| Patrick M. Sexton | 12 | 1 | 0.73 |
| José María Carazo | 13 | 654 | 56.25 |
| C. O. S. Sorzano | 14 | 50 | 11.68 |