Proteomics and genomics projects have yielded a detailed inventory of proteins in a cell. However, little is known about how these proteins are spatially and temporally arranged within the cell. Such knowledge is essential to understanding how proteins contribute to the structure and function of a living cell. Just as words must be assembled into sentences, paragraphs, and chapters to make sense, vital cellular functions are performed by structured assemblies (or complexes) of proteins rather than individual molecules. Often, these complexes comprise tens or hundreds of proteins. This proposal describes a set of computational methods that will exploit and integrate several kinds of emerging experimental data to uncover the structure and dynamics of protein complexes, toward the ultimate goal of achieving a mechanistic understanding of the cell. In particular, we will characterize proteome organization on two levels through the following projects. (1) We will develop an efficient computational method to determine the structure of individual protein complexes by simultaneously fitting multiple components to cryo-electron microscopy maps. (2) We will develop advanced pattern mining methods to discover and localize unknown protein complexes in whole-cell cryo- electron tomograms - a prerequisite towards comprehensive visual proteomics. We will also develop methods to study the systems dynamics of protein interaction networks within realistic cellular environments. With these tools, we are poised to model the spatial and temporal organizations of proteome. We will freely provide software packages and source code for all methods to the scientific community.
(Public Health Relevance) Detailed knowledge concerning the spatial and temporal organization of the proteome is essential to understanding how proteins perform their functions in a cell. Proteins need to appear in exactly in the right places at exactly the right times in order to accomplish their roles. Any discrepancies may lead to diseases. In fact, many diseases are known to be caused by malfunctions in proteins or protein interactions. Our proposed methods will significantly contribute to knowledge in this area, so are highly relevant to public health.
|Joseph, Agnel Praveen; Polles, Guido; Alber, Frank et al. (2017) Integrative modelling of cellular assemblies. Curr Opin Struct Biol 46:102-109|
|Frazier, Zachary; Xu, Min; Alber, Frank (2017) TomoMiner and TomoMinerCloud: A Software Platform for Large-Scale Subtomogram Structural Analysis. Structure 25:951-961.e2|
|Pei, Long; Xu, Min; Frazier, Zachary et al. (2016) Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking. BMC Bioinformatics 17:405|
|Tjong, Harianto; Li, Wenyuan; Kalhor, Reza et al. (2016) Population-based 3D genome structure analysis reveals driving forces in spatial genome organization. Proc Natl Acad Sci U S A 113:E1663-72|
|Pandurangan, Arun Prasad; Vasishtan, Daven; Alber, Frank et al. (2015) ?-TEMPy: Simultaneous Fitting of Components in 3D-EM Maps of Their Assembly Using a Genetic Algorithm. Structure 23:2365-76|
|Xu, Min; Alber, Frank (2013) Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms. Bioinformatics 29:i274-82|
|Thalassinos, Konstantinos; Pandurangan, Arun Prasad; Xu, Min et al. (2013) Conformational States of macromolecular assemblies explored by integrative structure calculation. Structure 21:1500-8|
|Xu, Min; Alber, Frank (2012) High precision alignment of cryo-electron subtomograms through gradient-based parallel optimization. BMC Syst Biol 6 Suppl 1:S18|
|Zhou, Xianghong Jasmine; Alber, Frank (2012) Zooming in on genome organization. Nat Methods 9:961-3|
|Xu, Min; Beck, Martin; Alber, Frank (2012) High-throughput subtomogram alignment and classification by Fourier space constrained fast volumetric matching. J Struct Biol 178:152-64|
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