Model-based local density sharpening of cryo-EM maps

被引:163
|
作者
Jakobi, Arjen J. [1 ,2 ,3 ]
Wilmanns, Matthias [2 ]
Sachse, Carsten [1 ]
机构
[1] European Mol Biol Lab, Struct & Computat Biol, Heidelberg, Germany
[2] DESY, European Mol Biol Lab, Hamburg Unit, Hamburg, Germany
[3] Hamburg Ctr Ultrafast Imaging, Hamburg, Germany
来源
ELIFE | 2017年 / 6卷
关键词
ANGSTROM RESOLUTION; ELECTRON-MICROSCOPY; BETA-GALACTOSIDASE; REFINEMENT; CRYSTAL; COMPLEX; CONTRAST; SYSTEM; CRYSTALLOGRAPHY; VALIDATION;
D O I
10.7554/eLife.27131
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Atomic models based on high-resolution density maps are the ultimate result of the cryo-EM structure determination process. Here, we introduce a general procedure for local sharpening of cryo-EM density maps based on prior knowledge of an atomic reference structure. The procedure optimizes contrast of cryo-EM densities by amplitude scaling against the radially averaged local falloff estimated from a windowed reference model. By testing the procedure using six cryo-EM structures of TRPV1, beta-galactosidase, gamma-secretase, ribosome-EF-Tu complex, 20S proteasome and RNA polymerase Ill, we illustrate how local sharpening can increase interpretability of density maps in particular in cases of resolution variation and facilitates model building and atomic model refinement.
引用
收藏
页数:26
相关论文
共 50 条
  • [1] Automatic local resolution-based sharpening of cryo-EM maps
    Ramirez-Aportela, Erney
    Luis Vilas, Jose
    Glukhova, Alisa
    Melero, Roberto
    Conesa, Pablo
    Martinez, Marta
    Maluenda, David
    Mota, Javier
    Jimenez, Amaya
    Vargas, Javier
    Marabini, Roberto
    Sexton, Patrick M.
    Maria Carazo, Jose
    Sorzano, Carlos Oscar S.
    BIOINFORMATICS, 2020, 36 (03) : 765 - 772
  • [2] Cryo-EM density maps adjustment for subtraction, consensus and sharpening
    Gimenez, E. Fernandez
    Martinez, M.
    Sanchez-Garcia, R.
    Marabini, R.
    Ramirez-Aportela, E.
    Conesa, P.
    Carazo, J. M.
    Sorzano, C. O. S.
    JOURNAL OF STRUCTURAL BIOLOGY, 2021, 213 (04)
  • [3] Quantifying the local resolution of cryo-EM density maps
    Kucukelbir A.
    Sigworth F.J.
    Tagare H.D.
    Nature Methods, 2014, 11 (1) : 63 - 65
  • [4] Density modification of cryo-EM maps
    Terwilliger, Thomas C.
    Sobolev, Oleg V.
    Afonine, Pavel V.
    Adams, Paul D.
    Read, Randy J.
    ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 2020, 76 : 912 - 925
  • [6] Improvement of cryo-EM maps by density modification
    Terwilliger, Thomas C.
    Ludtke, Steven J.
    Read, Randy J.
    Adams, Paul D.
    Afonine, Pavel, V
    NATURE METHODS, 2020, 17 (09) : 923 - +
  • [7] Improvement of cryo-EM maps by density modification
    Thomas C. Terwilliger
    Steven J. Ludtke
    Randy J. Read
    Paul D. Adams
    Pavel V. Afonine
    Nature Methods, 2020, 17 : 923 - 927
  • [8] CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning
    Dai, Muzhi
    Dong, Zhuoer
    Xu, Kui
    Zhang, Qiangfeng Cliff
    JOURNAL OF MOLECULAR BIOLOGY, 2023, 435 (09)
  • [9] ANALYSES OF SUBNANOMETER RESOLUTION CRYO-EM DENSITY MAPS
    Baker, Matthew L.
    Baker, Mariah R.
    Hryc, Corey F.
    DiMaio, Frank
    METHODS IN ENZYMOLOGY, VOL 483: CRYO-EM, PART C: ANALYSES, INTERPRETATION, AND CASE STUDIES, 2010, 483 : 1 - 29
  • [10] Automated Threshold Selection for Cryo-EM Density Maps
    Pfab, Jonas
    Si, Dong
    ACM-BCB'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, 2019, : 161 - 166