Speaker
Description
We have developed a manifold-based machine-learning approach for analyzing cryoEM single-particle data. This approach is capable of mapping continuous conformational changes of biological molecules along any user-selected trajectory on the energy landscape, without timing information, supervision, or templates. Our unbiased approach (1) reveals the number of degrees of freedom exercised during the observations, (2) retrieves energy landscapes explored by the biological molecule, (3) determines least-action functional pathways, and (4) compiles 3D movies of the continuous conformational changes associated with functional pathways. These capabilities constitute a powerful platform for quantitative study of a wide range of key biological processes.
