Periodic Reporting for period 1 - EM-PRIOR (Single Particle Cryo-EM Reconstruction with Convolutional Neural Networks)
Okres sprawozdawczy: 2020-08-01 do 2022-07-31
By tapping into the vast amounts of prior knowledge about protein structures available in public databases, the proposed methods have the potential to not only make existing cryo-EM applications better, but also to enhance the scope of cryo-EM structure determination to many more targets than currently possible. This includes important drug targets, like GPCRs, that currently are outside the size limit of what can be resolved by cryo-EM to high resolution.
In parallel to the above project we’ve developed methods for heterogeneous data processing that involve a novel take on variational autoencoder that involved classical machine learning with deep learning to improve speed and convergence with several orders of magnitude. This first part of this project has been submitted to the NeruIPS conference proceedings. We are in the process of writing the second part of this method.
Additionally, I worked on the development of novel algorithms for the software package RELION that improve the reconstruction pipeline in speed, quality of results and automation.
Heterogeneous reconstruction in cryo-EM has an important impact for understanding the structural diversity and dynamics exhibited by biological molecular systems. In particular, combined with improvements in cryo-electron tomography of in situ samples, these computational methods should enable tremendous insights into the structural diversity associated with different cellular compartments.
Improvements in automated image processing that I introduced in RELION are now being used in cryo-EM facilities around the world for efficient on-the-fly processing of data.