Deep Learning for 3-D reconstruction of heterogeneous molecular structures from Cryo-EM data

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Determining the structure of biomolecules is the goal of structuralbiology and is essential to understand biological mechanismsresponsible for life and in drug discovery. Biological macromoleculescan be thought of as complex machines that perform complexoperations in living cells. These dynamic machines pass throughvarious conformations in the course of their actions and a completeunderstanding of their working requires the determination of multipleconformations.Single particle Electron Cryo-Microscopy (Cryo-EM) has emerged as a unique method to determine molecular structures at near-atomic resolution. Achieving high-resolution estimation of structures of dynamic protein complexes requires large numbers of images and computationally intensive algorithms. Such reconstruction problems have been often approached by devising methods that use information about the imaging procedure and the properties of the object that needs to be reconstructed and many remarkable breakthroughs have been achieved over the years. These computational approaches are called "model-based" methods andhave the advantage to be predictable and stable. However, they can be computationally expensive and do not always derive maximum value from complex data. In particular, they are often unable to resolve complex heterogeneous structures. In contrast "data-driven" methods like deep neural networks have demonstrated, in other contexts, a remarkable ability to improve the quality of biomedical images. The problem with many deep learning approaches is that they are not predictable in the sense that often even small deviations in the inputdata can result in a huge deviation of the output, which can have devastating effects in bio-imaging applications. Moreover, it is often very difficult to interpret what a deep network machine is really optimizing.This project will advance a new family of deep neural networks for the3-D reconstruction of dynamic protein complexes from cryo-electrondata. By working closely with structural biologists, we will put forwardapproaches that systematically embed prior knowledge and constraintsabout the signal and the physics of the data formation process into thedeep neural network architectures. We will also collaborate with Prof.M. Unser and his team from EPFL Switzerland. They will provideexpertise in the area of analysis of stability of deep neural networksand will share their experience in developing AI-based methods formolecule reconstruction from Cryo-EM data. We expect that thisapproach and these collaborations will allow us to introduce stable andinterpretable neural networks able to resolve heterogeneous biologicalstructures at a resolution that current methods cannot. The methodsproduced will be in open-source format, integrated in existingcomputational suites like CCP-EM and made available to the broadestpossible community.