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

Satinder Kaur, Josue Gomez-Blanco, Ahmad A. Z. Khalifa, Swathi Adinarayanan, Ruben Sanchez-Garcia, Daniel Wrapp, Jason S. McLellan, Khanh Huy Bui and Javier Vargas ()
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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

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-21509-5

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