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キーワード:
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要旨:
Ribosomes and messenger RNA assemble to polysomes during protein
synthesis. Cryoelectron tomography enables detection and identification
of large macromolecular complexes under physiological conditions making
the method uniquely suitable to study the supercomplexes that govern
translation of mRNA into proteins. Here, we describe a method for
automated assignment of polysomes in cryoelectron tomograms using the
positions and orientations of ribosomes, as localized by template
matching on tomographic data, as input. On the basis of a training
dataset of expert-curated polysomes in cryoelectron tomograms, we define
the relative 3D arrangements of neighboring ribosomes in polysomes. This
prior distribution is used in a probabilistic framework for polysome
assignment: the localized ribosomes from a tomogram are represented as a
graph of which the edge weights are defined by the prior distribution. A
Markov Random Field is embedded on the graph structure, and a
message-passing algorithm is used to infer a polysome-label for each
ribosome, i.e., to cluster ribosomes into polysomes. The performance of
the method is assessed based on simulated tomograms and experimental
tomograms indicating that polysome detection is reliable for typical
signal-to-noise ratios of cryoelectron tomograms.
