. 2011 Mar;173(3):483-96.
Epub 2010 Nov 19.
Exploring the Spatial and Temporal Organization of a Cell's Proteome
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Exploring the Spatial and Temporal Organization of a Cell's Proteome
J Struct Biol
2011 Mar .
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To increase our current understanding of cellular processes, such as cell signaling and division, knowledge is needed about the spatial and temporal organization of the proteome at different organizational levels. These levels cover a wide range of length and time scales: from the atomic structures of macromolecules for inferring their molecular function, to the quantitative description of their abundance, and spatial distribution in the cell. Emerging new experimental technologies are greatly increasing the availability of such spatial information on the molecular organization in living cells. This review addresses three fields that have significantly contributed to our understanding of the proteome's spatial and temporal organization: first, methods for the structure determination of individual macromolecular assemblies, specifically the fitting of atomic structures into density maps generated from electron microscopy techniques; second, research that visualizes the spatial distributions of these complexes within the cellular context using cryo electron tomography techniques combined with computational image processing; and third, methods for the spatial modeling of the dynamic organization of the proteome, specifically those methods for simulating reaction and diffusion of proteins and complexes in crowded intracellular fluids. The long-term goal is to integrate the varied data about a proteome's organization into a spatially explicit, predictive model of cellular processes.
Copyright © 2010 Elsevier Inc. All rights reserved.
Bridging molecular and cellular biology. (Upper panels) Structural information gathered at different levels of organization, ranging from atomic structures of macromolecules (left), to the molecular architectures of larger cellular components (middle) and the higher order proteome organization (right). The right scheme shows a model of the intracellular organization of
E. coli (Reprinted with permission of David S. Goodsell, The Scripps Research Institute, La Jolla, CA.). (Lower panels) Selection of experimental and computational methods that can provide spatial information of the proteome organization at the various scales of resolution. (SAXS: Small-angle X-ray scattering. TEM: Transmission electron microscopy).
Pseudo-atomic models of the yeast 60S (A) and 40S (B) ribosomal subunits determined by combining cryoEM density fitting (at ~9 Å resolution) with RNA and protein homology modeling. YUP.SCX  and Flex-EM  were applied to refine the models. Ribosomal RNA is represented as tubes. 5S and 5.8S rRNA are shown as magenta and green tubes, respectively. Ribosomal proteins, with known homologs and placement, are shown as orange (A) and pink (B) cartoons. Density accounting for proteins that are unique to eukaryotes with no structural homolog available or that have not been localized within the context of the ribosome is represented in yellow (A) and slate (B). The boundary of each cryoEM map is shown as a gray mesh. (Figure is reproduced from Reference ).
The molecular model of ribosome-bound EF4 (rbEF4) in the process of back-translocating tRNAs. (A) The model of rbEF4 based on Flex-EM refinement of the unbound EF4 crystal structure (PDB 3CB4 ) in the cryoEM density (mesh), which was computationally segmented from the density of the entire 70S–EF4 complex (at ~11 Å resolution). (B) The individual domains (corresponding to the EF-G domain nomenclature, ) are colored blue (I), green (II), yellow (III), dark orange (V) lighter orange (V’) and red (CTD). (B) The EF4 X-ray structure (gray cartoon) superimposed on the rbEF4 structure by aligning domain I to illustrate the domain rearrangement between the two. (C,D) The contrasting interaction of domain IV in rbEF-G  (PDB 2OM7) and domain V’ in rbEF4 with the tRNA intermediate (A/L-tRNA, purple ladder representation) is shown (rbEF-G superimposed on the cryoEM density of rbEF4). Domain IV and the G’ sub-domain of EF-G (orange) have no equivalent in EF4 (d, colored as in a). The absence of domain IV in EF4 is a prerequisite for allowing the tRNA to access the A-site during the back-translocation reaction (from the P-site to the A-site). The contacts with the A/L-tRNA (via sub-domain V’ and the CTD), revealed by flexible fitting, allow EF4–GTP to form a ternary complex with tRNA on the ribosome. (Figure is reproduced from Reference ).
Visual proteomics of the human pathogen
Leptospira interrogans. A. Score distributions obtained from artificial tomograms of the observed (blue), true positive (green) false positive hits (red). The arrowhead marks the threshold that accounts for a specificity of 40%. B. Centered slices through a artificial Leptospira cell shown perpendicular (left) and parallel (right) to the electron optical axis, as well as before (top) and after (bottom) simulating the image formation process. Numbers account for protein complexes as described below. C. Centered slice through a tomographic reconstruction, oriented perpendicular to the electron optical axis. D. The templates assigned into this volume are shown surface rendered. 70S ribosomes (1) are shown in dark brown, 2 RNA-polymerase II (2) in green, ATP-synthase (3) in bright brown, GroEL in bright blue, GroEL-ES (4) in dark blue, Hsp (5) in red, cytoplasmic membrane in transparent blue and the cell wall in transparent brown). (Figure is adapted from Reference .)
By a combination of pattern recognition and classification algorithms, the following TAP-identified complexes from
M. pneumoniae, matching to existing electron microscopy and x-ray and tomogram structures (A), were placed in a whole-cell tomogram (B): the structural core of pyruvate dehydrogenase in blue (~23 nm), the ribosome in yellow (~26 nm), RNA polymerase in purple (~17 nm), and GroEL homo-multimer in red (~20 nm). Cell dimensions are ~300 nm by 700 nm. The cell membrane is shown in light blue. The rod, a prominent structure filling the space of the tip region, is depicted in green. Its major structural elements are HMW2 (Mpn310) in the core and HMW3 (Mpn452) in the periphery, stabilizing the rod (42).The individual complexes (A) are not to scale, but they are shown to scale within the bacterial cell (B). (Figure is reproduced from Reference ).
(A) Smoldyn simulations of the chemotaxis pathway of the bacterium Escherichia coli. Each points represents the position of a molecule, the color of the point indicates its specific type. The simulations show differential localization of CheZ and its oligomeric forms. Upper left panel: In the absence of CheA long kinase activity and phosphorylated CheY, all CheZ dimers are unbound and freely diffusing in the cytoplasm. Middle left panel: Upon sudden increase of kinase activity, the level of CheYp rises in the entire cell. Lower left panel: After 1 min at constant kinase activity, the increase of CheYp has led to the formation of oligomeric CheZ2Yp clusters at the inner face of the polar receptor cluster shown as black circles. Panel A is reproduced from Reference . (B) Hard-sphere representation of the E. coli cytoplasm at 50% volume fraction occupied. Spheres represent various macromolecules and their complexes. The particle-size distribution was estimated from the relative abundance, mass, and stoichiometries of protein complexes using an experimentally derived proteome catalog from E. coli K12. Panel B is reproduced from reference  (C) Atomically detailed model the E. coli cytoplasmic environment that includes 50 of the most abundant types of macromolecules at experimentally measured concentrations. Rendering shows the cytoplasm model at the end of a Brownian dynamics simulation performed. Panel C is reproduced from Reference .
Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.
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Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Cryoelectron Microscopy / methods
Electron Microscope Tomography / methods
Image Processing, Computer-Assisted / methods*