Determination of a protein?s three-dimensional structure is of critical importance in biology, providing insights to biological mechanisms and important targets for drug design. While high- resolution X-ray diffraction data provides an atomic view of cellular components, for many interesting and biologically relevant complexes, it may only be possible to obtain low-resolution structural information. Both cryo-electron microscopy and X-ray crystallography, when applied to large, flexible molecular machines, often produce data of 3-6 resolution. Extracting detailed atomic information from this data, critical in understanding function, the effects of mutation, or in designing drugs is impossible due to the low number of observations and the large conformational space proteins may adopt. I propose to develop computational methods for extracting high-resolution atomic models from this low-resolution data, bridging the ?resolution gap? with computational methods. My proposed research develops and extends our labs? methods for automatically inferring atomic accuracy models, from these ?near-atomic? resolution sources of experimental data. We develop novel conformational sampling methods, guided by experimental data, to infer atomic information both in cases where homologous high-resolution data is available, and where it is not. Additionally, we propose development of methods for estimating model uncertainty; these are critical in understanding to what degree structural conclusions may be made from a particular dataset. Finally, in pushing the resolution limit further, we develop general tools for biomolecular forcefield optimization. These machine-learning tools will allow development of a next-generation forcefield, critical in extending the resolution limit of data from which we can infer atomic details. The overall goal of the proposed research is robust and accessible methods to determine protein structures to atomic accuracy from only sparse experimental data. Combined, the three aims in this proposal will lead to dramatic improvements in our ability to infer atomic interactions from sparse experimental data. This will lead to determination of structures that will reveal key insights into how biomedically important protein complexes perform their function and what goes wrong in human disease.
This project develops computational tools for accurately determining protein structure from low- resolution experimental data. The proposed work will lead to dramatic improvements in our ability to model dynamic structures from sparse data. Obtaining accurate structures from this data will reveal insights into how biomedically important protein complexes perform their function, and what goes wrong in human disease.
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