Friday, December 6, 2024
12/6/2024
Summary In the past decade, we have witnessed revolutionary progress in the attainable resolution of macromolecular assemblies via cryo-electron microscopy (cryo-EM) and in the development of deep learning algorithms (such as AlphaFold) that reliably predict atomic structures that can be fitted to cryo-EM maps. Whereas single- particle cryo-EM today is capable of directly solving the atomic structures of biomolecular assemblies in isolation, cryo-electron tomography (cryo-ET) is widely used in unstained frozen-hydrated samples to capture the 3D organization of supramolecular complexes in their native (organelle, cell, or tissue) environments. The increasing availability of high-quality maps and corresponding atomic models enables the validation of computational strategies, yielding rigorous and reproducible modeling technologies for the future. In this proposal, we have identified research areas for the next five years and beyond, leveraging our computational modeling experience (historically rooted in pre-revolution multi-scale approaches) to offer the biggest value to today’s post-revolution EM community. Our vision is to combine the converging advancements in cryo-EM, cryo-ET, structure prediction, and rigorous validation of modeling methods into a comprehensive research strategy. We will quantitatively measure the fitness of an atomic model in local density regions and characterize the fitness of maps with reliable reference structures. This will lead to new breakthroughs in the flexible fitting and refinement of AlphaFold2 models as well as secondary structure prediction for medium- resolution maps, which have been our key research areas in recent years. Medium- to low-resolution maps are still widely used in EM and can be of significant biological importance. This is particularly true in the case of cryo-ET maps, which are harder to read than single-particle cryo-EM maps because they often exhibit considerable noise, anisotropic resolution, and anisotropic density variations due to the low dose requirements and the missing wedge in the Fourier space. As automated segmentation algorithms in cryo-ET continue to improve, validation of these approaches has become more incumbent. Having a known ground truth on which to base predictions is crucial to reliably testing predicted structures and modeling approaches. We propose a software tool for the realistic simulation of “phantom” cryo-ET maps. We describe current and future applications in the validation of cytoskeletal filament tracing methods. The collaborative efforts supported by this grant will include the refinement of cytoskeletal actin filaments, molecular motors, bacterial chemoreceptor arrays, and hair cell stereocilia. The algorithmic and methodological developments will be distributed freely through the established Internet-based mechanisms used by the Situs and Sculptor packages and as plugins for the popular UCSF Chimera graphics program.
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