Abstract text (incl. figure legends and references)
Introduction: Single-particle cryo electron microscopy (cryo-EM) allows 3D reconstruction of multiple conformations of purified biomolecular complexes from their 2D images. The elucidation of different conformations is the key to understand the molecular mechanisms behind the biological functions of the complexes and the key to novel drug discovery. The standard cryo-EM data analysis procedures involve many rounds of 2D and 3D classifications to disentangle and interpret the combined conformational, orientational, and translational heterogeneity. Gradual conformational transitions give raise to many intermediate conformational states. Continuous conformational heterogeneity in cryo-EM data (a mixture of many intermediate conformational states), due to such gradual conformational transitions, is both an obstacle for high-resolution 3D reconstruction of different states and an opportunity to obtain the information about multiple coexisting states at once.
Objective: We aim at developing new methods for analyzing continuous conformational heterogeneity in cryo-EM data, which will be fast, user-friendly, and allow obtaining the full conformational landscape.
Methods: HEMNMA method [1], that we specifically developed for analyzing continuous conformational heterogeneity in cryo-EM data, determines the conformation, orientation, and position of the complex in each single particle image by analyzing images using normal modes (motion directions simulated for a given atomic structure or EM map), which in turn allows determining the full conformational space of the complex but at the price of high computational cost. To speed up HEMNMA, we recently combined it with a deep learning approach. This deep learning extension of HEMNMA is referred to as DeepHEMNMA [2].
Results: With a synthetic dataset, we have shown that DeepHEMNMA is more than 40 times faster than HEMNMA [2]. With an experimental dataset, we have shown that DeepHEMNMA reveals the conformational heterogeneity that is out of reach of standard methods [2].
Conclusion: DeepHEMNMA is a fast and user-friendly software that allows obtaining the full conformational landscape of biomolecules. It is available in ContinuousFlex, open-source software package that we are developing. ContinuousFlex provides user-friendly graphical interface to several methods for analyzing continuous conformational heterogeneity in vitro [1-3] and in situ [4-5], and it is available as a plugin for Scipion [6-7]. In this talk, I will present DeepHEMNMA and its performance using synthetic and experimental cryo-EM images. Also, I will briefly introduce ContinuousFlex.
[1] https://doi.org/10.1016/j.str.2014.01.004
[2] https://doi.org/10.3389/fmolb.2022.965645
[3] https://doi.org/10.1016/j.jmb.2022.167483
[4] https://doi.org/10.3389/fmolb.2021.663121
[5] https://doi.org/10.1016/j.jmb.2021.167381
[6] https://doi.org/10.1016/j.jsb.2022.107906
[7] https://pypi.org/project/scipion-em-continuousflex