TY - JOUR
T1 - Automatic local resolution-based sharpening of cryo-EM maps
AU - Ramírez-Aportela, Erney
AU - Vilas, Jose Luis
AU - Glukhova, Alisa
AU - Melero, Roberto
AU - Conesa, Pablo
AU - Martínez, Marta
AU - Maluenda, David
AU - Mota, Javier
AU - Jiménez, Amaya
AU - Vargas, Javier
AU - Marabini, Roberto
AU - Sexton, Patrick M.
AU - Carazo, Jose Maria
AU - Sorzano, Carlos Oscar S.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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. AVAILABILITY AND IMPLEMENTATION: The source code (LocalDeblur) can be found at https://github.com/I2PC/xmipp and can be run using Scipion (http://scipion.cnb.csic.es) (release numbers greater than or equal 1.2.1). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - 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. AVAILABILITY AND IMPLEMENTATION: The source code (LocalDeblur) can be found at https://github.com/I2PC/xmipp and can be run using Scipion (http://scipion.cnb.csic.es) (release numbers greater than or equal 1.2.1). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UR - http://www.scopus.com/inward/record.url?scp=85079077973&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btz671
DO - 10.1093/bioinformatics/btz671
M3 - Article
C2 - 31504163
AN - SCOPUS:85079077973
VL - 36
SP - 765
EP - 772
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 3
ER -