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Local computational methods to improve the interpretability and analysis of cryo-EM maps

Author

Listed:
  • Satinder Kaur

    (McGill University 3640 Rue University)

  • Josue Gomez-Blanco

    (McGill University 3640 Rue University)

  • Ahmad A. Z. Khalifa

    (McGill University 3640 Rue University)

  • Swathi Adinarayanan

    (McGill University 3640 Rue University)

  • Ruben Sanchez-Garcia

    (Biocomputing Unit, Centro Nacional de Biotecnología-CSIC C/Darwin 3)

  • Daniel Wrapp

    (The University of Texas at Austin)

  • Jason S. McLellan

    (The University of Texas at Austin)

  • Khanh Huy Bui

    (McGill University 3640 Rue University)

  • Javier Vargas

    (Universidad Complutense de Madrid)

Abstract

Cryo-electron microscopy (cryo-EM) maps usually show heterogeneous distributions of B-factors and electron density occupancies and are typically B-factor sharpened to improve their contrast and interpretability at high-resolutions. However, ‘over-sharpening’ due to the application of a single global B-factor can distort processed maps causing connected densities to appear broken and disconnected. This issue limits the interpretability of cryo-EM maps, i.e. ab initio modelling. In this work, we propose 1) approaches to enhance high-resolution features of cryo-EM maps, while preventing map distortions and 2) methods to obtain local B-factors and electron density occupancy maps. These algorithms have as common link the use of the spiral phase transformation and are called LocSpiral, LocBSharpen, LocBFactor and LocOccupancy. Our results, which include improved maps of recent SARS-CoV-2 structures, show that our methods can improve the interpretability and analysis of obtained reconstructions.

Suggested Citation

  • Satinder Kaur & Josue Gomez-Blanco & Ahmad A. Z. Khalifa & Swathi Adinarayanan & Ruben Sanchez-Garcia & Daniel Wrapp & Jason S. McLellan & Khanh Huy Bui & Javier Vargas, 2021. "Local computational methods to improve the interpretability and analysis of cryo-EM maps," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21509-5
    DOI: 10.1038/s41467-021-21509-5
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    Cited by:

    1. Jiahua He & Tao Li & Sheng-You Huang, 2023. "Improvement of cryo-EM maps by simultaneous local and non-local deep learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Chaehee Park & Jinuk Kim & Seung-Bum Ko & Yeol Kyo Choi & Hyeongseop Jeong & Hyeonuk Woo & Hyunook Kang & Injin Bang & Sang Ah Kim & Tae-Young Yoon & Chaok Seok & Wonpil Im & Hee-Jung Choi, 2022. "Structural basis of neuropeptide Y signaling through Y1 receptor," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Björn O. Forsberg & Pranav N. M. Shah & Alister Burt, 2023. "A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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