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Density modification of cryo-EM maps.

Authors
  • Terwilliger, Thomas C1
  • Sobolev, Oleg V2
  • Afonine, Pavel V2
  • Adams, Paul D2
  • Read, Randy J3
  • 1 New Mexico Consortium, Los Alamos, NM 87544, USA. , (Mexico)
  • 2 Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
  • 3 Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, The Keith Peters Building, Hills Road, Cambridge CB2 0XY, United Kingdom. , (United Kingdom)
Type
Published Article
Journal
Acta crystallographica. Section D, Structural biology
Publication Date
Oct 01, 2020
Volume
76
Issue
Pt 10
Pages
912–925
Identifiers
DOI: 10.1107/S205979832001061X
PMID: 33021493
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Density modification uses expectations about features of a map such as a flat solvent and expected distributions of density in the region of the macromolecule to improve individual Fourier terms representing the map. This process transfers information from one part of a map to another and can improve the accuracy of a map. Here, the assumptions behind density modification for maps from electron cryomicroscopy are examined and a procedure is presented that allows the incorporation of model-based information. Density modification works best in cases where unfiltered, unmasked maps with clear boundaries between the macromolecule and solvent are visible, and where there is substantial noise in the map, both in the region of the macromolecule and the solvent. It also is most effective if the characteristics of the map are relatively constant within regions of the macromolecule and the solvent. Model-based information can be used to improve density modification, but model bias can in principle occur. Here, model bias is reduced by using ensemble models that allow an estimation of model uncertainty. A test of model bias is presented that suggests that even if the expected density in a region of a map is specified incorrectly by using an incorrect model, the incorrect expectations do not strongly affect the final map. open access.

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