CNRS Research Director
Combining normal mode analysis, image analysis, and deep learning for in vitro and in situ studies of continuous conformational variability of macromolecular complexes
Date and time: March 24th, 2021
Wednesday, at 8am PT / 11am ET / 4pm GMT / 5pm CET / Feb 18th midnight China
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HEMNMA is a method to analyze continuous conformational variability of purified macromolecular complexes (in vitro), introduced in 2014 (Jin et al., Structure 22:496-506, 2014). Its software with user friendly graphical interface is part of the open-source ContinuousFlex plugin of Scipion 2 and Scipion 3 (Harastani et al., Protein Science 29:223–236, 2020). HEMNMA combines single particle image analysis, normal mode analysis, and dimension reduction techniques to visualize the full distribution of states (conformational landscape) in a low-dimensional space (usually 2D or 3D space), from which one can obtain 3D reconstructions and movies of molecular motions along desired directions. After a brief reminder on HEMNMA, I will present current developments, including combining HEMNMA with deep learning to accelerate the conformational landscape determination and an extension of HEMNMA to analyze continuous conformational variability of macromolecular complexes in cells from in situ cryo electron tomography data.