DeepHEMNMA for analyzing continuous conformational heterogeneity in single-particle cryo-EM images

Presenter: Dr. Slavica Jonic
IMPMC-UMR 7590 CNRS,
Sorbonne Université,
Paris,
FRANCE

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 understanding molecular mechanisms behind 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 rise 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.

HEMNMA method [1], was specifically developed for analysing continuous conformational heterogeneity in cryo-EM data, determines the conformation, orientation, and position of the complex in each single particle image by analysing 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. Recently, a deep learning extension of HEMNMA, referred to as DeepHEMNMA [2], was proposed, which speeds up HEMNMA by combining it with a deep learning approach. DeepHEMNMA will soon be available in ContinuousFlex, an open-source software package that my team is developing. ContinuousFlex provides a user-friendly graphical interface to several methods for analysing continuous conformational heterogeneity in vitro [1-3] and in situ [4-5]. ContinuousFlex is currently available as a plugin for Scipion [6].

In this talk, DeepHEMNMA will be presented and its performance using synthetic and experimental cryo-EM images. ContinuousFlex will also be briefly introduced.

[1] https://doi.org/10.1016/j.str.2014.01...
[2] https://hal.archives-ouvertes.fr/hal-... (in press)
[3] https://doi.org/10.1016/j.jmb.2022.16...
[4] https://doi.org/10.3389/fmolb.2021.66...
[5] https://doi.org/10.1016/j.jmb.2021.16...
[6] https://github.com/scipion-em/scipion...

About Dr. Jonic:
Slavica Jonic received the BSc and MSc degrees in electrical engineering from the University of Belgrade, Serbia, in 1996 and 1999, respectively; the PhD degree in image processing from the Swiss Federal Institute of Technology in Lausanne - EPFL, Switzerland, in 2003; and a Research Director Habilitation from the University Pierre and Marie Curie – UPMC (current Sorbonne University), France, in 2015. She held Research and Teaching Assistant positions at the University of Belgrade (1996-1999) and the EPFL (2000-2003), and a Research Scientist position at the UPMC (2004-2008). She obtained an Associate Scientist position at the French National Centre for Scientific Research (CNRS) in 2008 and a CNRS Research Director position in 2019. She is currently with the IMPMC – CNRS UMR 7590 laboratory, located at Sorbonne University, since 2004.

Her background is in signal and image processing for biomedical engineering applications. She currently leads a team working in the area of methods development for the reconstruction of structure and dynamics of biological macromolecular complexes from cryo electron microscopy and cryo electron tomography data. Her particular interest is in new methods for studying continuous conformational transitions of complexes, including hybrid methods combining image analysis, molecular mechanics simulation, and deep learning.

Chair: Dr. Sepideh Valimehr, CCeMMP Post Doc, Bio21