Computational Protocol for Assessing the Optimal Pixel Size to Improve the Accuracy of Single-particle Cryo-electron Microscopy Maps

被引:2
|
作者
Tiwari, Sandhya P. [1 ]
Chhabra, Sahil [4 ,5 ]
Tama, Florence [1 ,2 ,3 ]
Miyashita, Osamu [1 ]
机构
[1] RIKEN Ctr Computat Sci, Computat Struct Biol Div, Kobe, Hyogo 6500047, Japan
[2] Nagoya Univ, Grad Sch Sci, Dept Phys, Nagoya, Aichi 4648601, Japan
[3] Nagoya Univ, Inst Transformat Biomol WPI ITbM, Nagoya, Aichi 4648601, Japan
[4] Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA
[5] Univ Michigan, Michigan Inst Computat Discovery & Engn, Ann Arbor, MI 48109 USA
关键词
CRYO-EM; ANGSTROM RESOLUTION; BETA-GALACTOSIDASE; STRUCTURE VALIDATION; REFINEMENT; MOLPROBITY; VISUALIZATION; SELECTION; GROEL;
D O I
10.1021/acs.jcim.9b01107
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Cryo-electron microscopy (cryo-EM) single-particle analysis has come a long way in achieving atomic-level resolution when imaging biomolecules. To obtain the best possible three-dimensional (3D) structure in cryo-EM, many parameters have to be carefully considered. Here we address the often-overlooked parameter of the pixel size, which describes the magnification of the image produced by the experiment. While efforts are made to refine and validate this parameter in the analysis of cryo-EM experimental data, there is no systematic protocol in place. Since the pixel size parameter can have an impact on the resolution and accuracy of a cryo-EM map, and the atomic resolution 3D structure models derived from it, we propose a computational protocol to estimate the appropriate pixel size parameter. In our protocol, we fit and refine atomic structures against cryo-EM maps at multiple pixel sizes. The resulting fitted and refined structures are evaluated using the GOAP (generalized orientation-dependent, all-atom statistical potential) score, which we found to perform better than other commonly used functions, such as Molprobity and the correlation coefficient from refinement. Finally, we describe the efficacy of this protocol in retrieving appropriate pixel sizes for several examples; simulated data based on yeast elongation factor 2 and experimental data from Gro-EL chaperone, beta-galactosidase, and the TRPV1 ion channel.
引用
收藏
页码:2570 / 2580
页数:11
相关论文
共 35 条
  • [1] Single-particle cryo-electron microscopy of macromolecular complexes
    Skiniotis, Georgios
    Southworth, Daniel R.
    MICROSCOPY, 2016, 65 (01) : 9 - 22
  • [2] A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy
    Zhu, Yanan
    Ouyang, Qi
    Mao, Youdong
    BMC BIOINFORMATICS, 2017, 18
  • [3] Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy
    DiIorio, Megan C. C.
    Kulczyk, Arkadiusz W. W.
    MICROMACHINES, 2023, 14 (01)
  • [4] Conformational heterogeneity and probability distributions from single-particle cryo-electron microscopy
    Tang, Wai Shing
    Zhong, Ellen D.
    Hanson, Sonya M.
    Thiede, Erik H.
    Cossio, Pilar
    CURRENT OPINION IN STRUCTURAL BIOLOGY, 2023, 81
  • [5] The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy
    Bendory, Tamir
    Edidin, Dan
    SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE, 2024, 6 (02): : 254 - 282
  • [6] Prospects and Limitations of High-Resolution Single-Particle Cryo-Electron Microscopy
    Chari, Ashwin
    Stark, Holger
    ANNUAL REVIEW OF BIOPHYSICS, 2023, 52 : 391 - 411
  • [7] A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy
    Yanan Zhu
    Qi Ouyang
    Youdong Mao
    BMC Bioinformatics, 18
  • [8] Molecular goniometers for single-particle cryo-electron microscopy of DNA-binding proteins
    Aksel, Tural
    Yu, Zanlin
    Cheng, Yifan
    Douglas, Shawn M.
    NATURE BIOTECHNOLOGY, 2021, 39 (03) : 378 - 386
  • [9] PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
    Elmlund, Hans
    Elmlund, Dominika
    Bengio, Samy
    STRUCTURE, 2013, 21 (08) : 1299 - 1306
  • [10] CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy
    George, Blesson
    Assaiya, Anshul
    Roy, Robin J.
    Kembhavi, Ajit
    Chauhan, Radha
    Paul, Geetha
    Kumar, Janesh
    Philip, Ninan S.
    COMMUNICATIONS BIOLOGY, 2021, 4 (01)