Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning

Xiao Wang, Eman Alnabati, Tunde W. Aderinwale, Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi and Daisuke Kihara ()
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Xiao Wang: Purdue University
Eman Alnabati: Purdue University
Tunde W. Aderinwale: Purdue University
Sai Raghavendra Maddhuri Venkata Subramaniya: Purdue University
Genki Terashi: Purdue University
Daisuke Kihara: Purdue University

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps.

Date: 2021
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DOI: 10.1038/s41467-021-22577-3

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