Biomedical subjects

Dan C Marinescu

Publications and source records attributed to Dan C Marinescu.

3 recordsLinked to original sources

PubMed2005

A model-based parallel origin and orientation refinement algorithm for cryoTEM and its application to the study of virus structures.

We present a model-based parallel algorithm for origin and orientation refinement for 3D reconstruction in cryoTEM. The algorithm is based upon the Projection Theorem of the Fourier Transform. Rather than projecting the current 3D model and searching for the best match between an experimental view and the calculated projections, the algorithm computes the Discrete Fourier Transform (DFT) of each projection and searches for the central section ("cut") of the 3D DFT that best matches the DFT of the projection. Factors that affect the efficiency of a parallel program are first reviewed and then the performance and limitations of the proposed algorithm are discussed. The parallel program that implements this algorithm, called PO(2)R, has been used for the refinement of several virus structures, including those of the 500 Angstroms diameter dengue virus (to 9.5 Angstroms resolution), the 850 Angstroms mammalian reovirus (to better than 7A), and the 1800 Angstroms paramecium bursaria chlorella virus (to 15 Angstroms).

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PubMed2005

Features of reovirus outer capsid protein mu1 revealed by electron cryomicroscopy and image reconstruction of the virion at 7.0 Angstrom resolution.

Reovirus is a useful model for addressing the molecular basis of membrane penetration by one of the larger nonenveloped animal viruses. We now report the structure of the reovirus virion at approximately 7.0 A resolution as obtained by electron cryomicroscopy and three-dimensional image reconstruction. Several features of the myristoylated outer capsid protein mu1, not seen in a previous X-ray crystal structure of the mu1-sigma3 heterohexamer, are evident in the virion. These features appear to be important for stabilizing the outer capsid, regulating the conformational changes in mu1 that accompany perforation of target membranes, and contributing directly to membrane penetration during cell entry.

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PubMedDate not supplied

Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization.

Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10 A requires a large set of projection images. Several automatic and semi-automatic particle detection algorithms have been developed along the years. Here we present a general technique designed to automatically identify the projection images of particles. The method is based on Markov random field modelling of the projected images and involves a pre-processing of electron micrographs followed by image segmentation and post-processing. The image is modelled as a coupling of two fields--a Markovian and a non-Markovian. The Markovian field represents the segmented image. The micrograph is the non-Markovian field. The image segmentation step involves an estimation of coupling parameters and the maximum á posteriori estimate of the realization of the Markovian field i.e, segmented image. Unlike most current methods, no bootstrapping with an initial selection of particles is required.

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