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Template-free detection of macromolecular complexes in cryo electron tomograms.

Xu M, Beck M, Alber F - Bioinformatics (2011)

Bottom Line: Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states.Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes.However, so far only a small fraction of all protein complexes have been structurally resolved.

View Article: PubMed Central - PubMed

Affiliation: Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.

ABSTRACT

Motivation: Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this information is non-trivial due to the low resolution and low signal-to-noise ratio in tomograms. Current pattern recognition methods identify complexes by matching known structures to the cryo electron tomogram. However, so far only a small fraction of all protein complexes have been structurally resolved. It is, therefore, of great importance to develop template-free methods for the discovery of previously unknown protein complexes in cryo electron tomograms.

Results: Here, we have developed an inference method for the template-free discovery of frequently occurring protein complexes in cryo electron tomograms. We provide a first proof-of-principle of the approach and assess its applicability using realistically simulated tomograms, allowing for the inclusion of noise and distortions due to missing wedge and electron optical factors. Our method is a step toward the template-free discovery of the shapes, abundance and spatial distributions of previously unknown macromolecular complexes in whole cell tomograms.

Contact: alber@usc.edu

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Related in: MedlinePlus

Gaussian Hidden Markov Random Field. GHMRF with observable intensity random field (above in red) and hidden class random field (below in blue). In the hidden field, the Markov property graph is defined by the direct neighbors of voxels in the grid (grey connections, for simplicity only a 2D grid is shown) and also by voxels with similar feature vectors (green dotted connections) (i.e. a green connection is formed if two voxels are defined as neighbors in the feature space).
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Figure 3: Gaussian Hidden Markov Random Field. GHMRF with observable intensity random field (above in red) and hidden class random field (below in blue). In the hidden field, the Markov property graph is defined by the direct neighbors of voxels in the grid (grey connections, for simplicity only a 2D grid is shown) and also by voxels with similar feature vectors (green dotted connections) (i.e. a green connection is formed if two voxels are defined as neighbors in the feature space).

Mentions: In standard GHMRF models, the voxel neighborhood is defined by an undirected graph with voxels as vertices and edges as the cubic grid connecting the vertices (Fig. 3). In our method, this graph is augmented by additional edges between those voxels that share similar feature vectors but are at far distance in the tomogram grid. In other words, for a given voxel i, its neighborhood list 𝒩i includes all direct grid neighbors in the tomogram and all voxels that have a similar density environment even if they are located at far distance (Fig. 3). By augmenting the list of grid neighbors, we are able to connect those voxels in the graph that have the same class label, even if these voxels are part of different copies of the same complex located at different regions of the tomogram. The neighborhood 𝒩i of voxel i is, therefore, defined as(12)where 𝒩mapi is the set of all voxels that are adjacent to i in the tomogram grid and 𝒩feai is the set of voxels that have similar feature vectors.


Template-free detection of macromolecular complexes in cryo electron tomograms.

Xu M, Beck M, Alber F - Bioinformatics (2011)

Gaussian Hidden Markov Random Field. GHMRF with observable intensity random field (above in red) and hidden class random field (below in blue). In the hidden field, the Markov property graph is defined by the direct neighbors of voxels in the grid (grey connections, for simplicity only a 2D grid is shown) and also by voxels with similar feature vectors (green dotted connections) (i.e. a green connection is formed if two voxels are defined as neighbors in the feature space).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117359&req=5

Figure 3: Gaussian Hidden Markov Random Field. GHMRF with observable intensity random field (above in red) and hidden class random field (below in blue). In the hidden field, the Markov property graph is defined by the direct neighbors of voxels in the grid (grey connections, for simplicity only a 2D grid is shown) and also by voxels with similar feature vectors (green dotted connections) (i.e. a green connection is formed if two voxels are defined as neighbors in the feature space).
Mentions: In standard GHMRF models, the voxel neighborhood is defined by an undirected graph with voxels as vertices and edges as the cubic grid connecting the vertices (Fig. 3). In our method, this graph is augmented by additional edges between those voxels that share similar feature vectors but are at far distance in the tomogram grid. In other words, for a given voxel i, its neighborhood list 𝒩i includes all direct grid neighbors in the tomogram and all voxels that have a similar density environment even if they are located at far distance (Fig. 3). By augmenting the list of grid neighbors, we are able to connect those voxels in the graph that have the same class label, even if these voxels are part of different copies of the same complex located at different regions of the tomogram. The neighborhood 𝒩i of voxel i is, therefore, defined as(12)where 𝒩mapi is the set of all voxels that are adjacent to i in the tomogram grid and 𝒩feai is the set of voxels that have similar feature vectors.

Bottom Line: Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states.Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes.However, so far only a small fraction of all protein complexes have been structurally resolved.

View Article: PubMed Central - PubMed

Affiliation: Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.

ABSTRACT

Motivation: Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this information is non-trivial due to the low resolution and low signal-to-noise ratio in tomograms. Current pattern recognition methods identify complexes by matching known structures to the cryo electron tomogram. However, so far only a small fraction of all protein complexes have been structurally resolved. It is, therefore, of great importance to develop template-free methods for the discovery of previously unknown protein complexes in cryo electron tomograms.

Results: Here, we have developed an inference method for the template-free discovery of frequently occurring protein complexes in cryo electron tomograms. We provide a first proof-of-principle of the approach and assess its applicability using realistically simulated tomograms, allowing for the inclusion of noise and distortions due to missing wedge and electron optical factors. Our method is a step toward the template-free discovery of the shapes, abundance and spatial distributions of previously unknown macromolecular complexes in whole cell tomograms.

Contact: alber@usc.edu

Show MeSH
Related in: MedlinePlus