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Development of Affinity Rod Scaffolds for Cryo-EM Sample Preparation

$189,496R21FY2019GMNIH

Purdue University, West Lafayette IN

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Abstract

Project Summary Single particle reconstruction (SPR) analysis of cryoelectron microscopy (cryoEM) data is a rapidly growing method for determining the near-atomic molecular structures of biological samples. This burgeoning interest stems from the benefits of the SPR technique, including [1] greatly reduced demand for sample (<100 µg vs. > 10 mg for X-ray diffraction or NMR analysis); [2] the capacity to image large, multi-protein and/or dynamic complexes that are cryofixed in a submicron thick layer of vitreous ice; and [3] improved safety in instances where the sample is of pathogenic origin. Nonetheless, SPR requires large single particle populations, automated class-averaging methods, and advanced computational approaches to achieve near-atomic resolution. Increased sampling is partially addressed by microscope automation, particle picking, and direct detection; however, improvements in sample preparation and reliability are still lacking despite efforts to develop ?affinity grids?, modified graphene, SiN, Au, DNA origami, and engineered 2D protein arrays. This proposal seeks to accelerate the productivity of cryoEM SPR through the development of novel materials that will concentrate the biological sample onto a rod-like scaffold prior to deposition onto the TEM grid. We will achieve this objective by integrating molecular theory, molecular dynamics, and experimental studies in Aims 1 and 2 to design rod-like materials that actively discourage non-specific protein adsorption, while enhancing the capture and random presentation of target proteins on the rod-like scaffold. Materials conferring these properties will then be tested in Aim 3 for their capacity to capture only the target protein from complex mixtures such as cell lysates to elucidate the structures of His-GroEL as a standard and His-p97 in complex with a client protein via cryoEM SPR at near-atomic resolution.

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