# Difference between revisions of "2020Ramirez LocalDeblur"

## Citation

Ram\irez-Aportela, E.; Vilas, J. L.; Glukhova, A.; Melero, R.; Conesa, P.; Mart\inez, M.; Maluenda, D.; Mota, J.; Jiménez, A.; Vargas, J.; Marabini, R.; Sexton, P. M.; Carazo, J. M. & Sorzano, C. O. S. Automatic local resolution-based sharpening of cryo-EM maps. Bioinformatics, 2020, 36, 765-772

## Abstract

Recent technological advances and computational developments have allowed the reconstruction of 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 modelling. However, sharpening approaches, in general, neglects the local quality of the map, which is clearly suboptimal. 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. The source code (LocalDeblur) can be found at https://github.com/I2PC/scipion/blob/master/software/em/xmipp/ and can be run using Scipion (http://scipion.cnb.csic.es) (release numbers greater than or equal 1.2.1). Supplementary Data are available at Bioinformatics Online.