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. 2022 Sep 8;9:965645. doi: 10.3389/fmolb.2022.965645

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Copyright © 2022 Hamitouche and Jonic.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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DeepHEMNMA neural network step. The deep learning neural network is a combination of a ResNet 34 feature extractor (ResNet block) and a 4-layer multilayer perceptron (MLP block). It is trained to map each single-particle image onto the corresponding, HEMNMA-estimated conformational parameters (M normal-mode amplitudes), orientational parameters (3 Euler angles), and positional parameters (2 in-plane shifts) of the particle in the image. DeepHEMNMA converts the Euler-angle representation of the orientation used in HEMNMA into a 4-parameter quaternion representation, which is learned by the neural network internally. The learned quaternion representation of the orientation is then converted back to the Euler-angle representation for the analysis at Stage 3 of DeepHEMNMA.