Hierarchical Methods for Large BioMolecular Complexes

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DESCRIPTION (provided by applicant): Large Biomolecular Complexes (LBCs) form the machinery responsible for most biological processes and are relevant to understanding many diseases such as cancer and metabolic disorders. Knowledge of these structures would provide not only the mechanistic descriptions for how macromolecules act in an assembly but also clues in developing therapeutic interventions related to disease. Today, hybrid experimental approaches for LBCs utilizing cryo-electron microscopy (CryoEM), electron tomography (ET) and X-ray crystallography (Xray) or nuclear magnetic resonance spectroscopy (NMR), need to be ably complimented with faster and more accurate computational processing for final ultrastructure elucidation of LBCs at the best level of resolution that is possible. This proposal addresses the development of an enhanced and automated computational processing pipeline, once a volumetric CryoEM map of an LBC has been reconstructed. In particular we propose the development of hierarchical computational representations, algorithms and software, which automates structural feature determination of LBCs as well as speeds up match and fitting techniques between LBCs and relevant proteins and/or nucleic acids. More precisely, our specific aims are: AIM 1: To develop algorithms for determining structural features of LBCs from Cryo-EM maps at three different morphological scales. AIM 2: To develop algorithms for generating hierarchical, volumetric spline approximations of the determined structural features of LBCs to facilitate fast Fourier based correlation search methods. AIM 3: To develop fast correlation search methods using our volumetric spline approximations for LBCs, including structural feature identification, structure fitting, LBC and protein/RNA docking. AIM 4: To implement, test, package and freely distribute our structural feature determination and fast search/fitting techniques.