In doing so, the algorithm aggregated structural information from all conformations into a single, optimized density map that resolves high-resolution details in α-helices and β-sheets even in the flexible domains. On a dataset from tri-snRNP spliceosome particles (EMPIAR- 10073), 3DFlex learned a wide range of non-rigid motions, including the bending of subunits across a span of more than 20 Å (Fig. Results from empirical datasets demonstrated the potential of 3DFlex to uncover the structure and motion of flexible proteins. This is in contrast to other recent methods such as cryoDRGN 2, 3DVA 3 and e2gmm 4, which can model conformational landscapes but do not directly improve the resolution of moving parts. Because of the way 3DFlex models deformations, it naturally aggregates structural information across the conformational landscape of the target protein to improve the resolution of the density map in flexible regions. It preserves local geometry (for example, the relative positions and orientations of side chains). The algorithm considers the fact that most conformational variability is a result of physical processes that tend to transport density over space. The parameters of the model are jointly learned from image data using a specialized training algorithm, with little prior knowledge about the flexibility of the molecule. As the input latent coordinate is varied, the generated deformations span all conformations captured by the model. The deformation field ‘bends’ the canonical density of the protein through convection to produce a version of the protein in a particular conformation. It represents a flexible molecule in terms of a single, high-resolution canonical 3D density map plus a neural network that takes in a latent coordinate and generates a deformation field that models the flexible motion of the protein. We developed 3D flexible refinement (3DFlex), a deep neural network model for profiling continuously flexible protein molecules.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |