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M Unser

Publications and source records attributed to M Unser.

25 records · Page 2Linked to original sources

Molecular substructure of a viral receptor-recognition protein. The gp17 tail-fiber of bacteriophage T7.

The bacteriophage T7 tail complex consists of a conical tail-tube surrounded by six kinked tail-fibers, which are oligomers of the viral protein gp17 (Mr 61,400). We have derived a molecular model for the tail-fiber by integrating secondary structure predictions with ultrastructural information obtained by correlation averaging of electron micrographs of negatively stained tail complexes. This model has been further refined by high-resolution scanning transmission electron microscopy of purified fibers, both negatively stained and unstained. Mass measurements made from the latter images establish that the fiber is a trimer of gp17. The proximal half-fiber is a uniform rod, about 2.0 nm in diameter and 16.4 nm long, which we infer to be a triple-stranded coiled-coil, containing three copies of an alpha-helical domain of about 117 residues, starting at Phe151. The distal half-fiber is 15.5 nm long, and is made up of four globules, 3.1 to 4.8 nm in diameter, in rigid linear array: it contains the carboxy-terminal halves (residues approximately 268 to 553) of the constituent gp17 chains, arranged with 3-fold symmetry around its long axis. The amino-terminal domains (residues 1 to 149) link the fiber to the tail-tube. We conclude that the three gp17 chains are quasi-equivalent in the proximal half-fiber, equivalent in the distal half-fiber, and non-equivalent in the kink region that separates the two half-fibers: such localized non-equivalence may represent a general mechanism for the formation of kinked joints in segmented homo-oligomeric proteins.

Amino Acid Sequence↗

A new resolution criterion based on spectral signal-to-noise ratios.

A new criterion for the "useful" resolution of electron micrographs of macromolecular particles is introduced. This criterion is based on estimation of the spatial frequency limit beyond which the spectral signal-to-noise ratio (SSNR) falls below an acceptable baseline. Applicable to both periodic and aperiodic specimens, this approach is particularly apposite for sets of correlation-averaged images. It represents a straightforward and intuitively appealing generalization of the traditional method of estimating the resolution of crystalline specimens from the spectral ranges of periodic reflections in their diffraction patterns. This method allows one to assess how closely the resolution of an averaged image based on N individual images approaches the ultimate resolution obtainable from an indefinitely large number of statistically equivalent images. Inter-relationships between the SSNR and two other measures of resolution, the differential phase residual and the Fourier ring correlation coefficient, are discussed, and their properties compared.

Biopolymers↗

Optimal background estimation in EELS.

In quantitative electron energy loss spectrometry, it is desirable to estimate the background law below core edge energy in a way that provides the maximum signal-to-noise ratio. Assuming an inverse power background model and independently Poisson distributed measurements, it is shown how to achieve this goal by using a maximum likelihood (ML) estimation technique which provides unbiased and minimum mean square error estimates of all parameters of interest. An efficient and computationally stable implementation of this procedure is proposed. Standard logarithmic least squares estimations are then compared with the ML approach and the gain in performance due to optimal processing is quantified.

Electrons↗

Odd men out: a quantitative objective procedure for identifying anomalous members of a set of noisy images of ostensibly identical specimens.

Achievement of an optimal improvement in signal-to-noise ratio from image averaging techniques depends crucially on the assumption that all members of the set of images to be averaged are fundamentally alike. In HREM of biological macromolecules, this assumption may be invalid for such reasons as variations in viewing geometry, non-uniformity of staining, or structural perturbations caused by specimen preparation procedures of radiation damage. Inclusion of data that are compromized these or other factors will degrade the information content of the averaged image. Here we present an algorithm which provides an objective quantitative method for the identification and elimination of anomalous members of a set of pre-aligned images. Based on a statistical criterion of mutual consistency, the algorithm forms an ordered list in which the individual images are ranked from most to least reliable. On specification of the noise statistics--in the formulation given here, of stationary white noise--an acceptability threshold in this ordered list is imposed. The derivation and implementation of this algorithm are presented, its properties discussed, and its application illustrated using both real and model electron micrograph data.

Algorithms↗

Normalization procedures and factorial representations for classification of correlation-aligned images: a comparative study.

We have addressed the problem of optimizing procedures of multivariate statistical analysis (MSA) for identifying homogeneous sets of electron micrographs of biological macromolecules, with a view to averaging over consistent sets of images. Using pre-aligned images of negatively stained protein molecules - known a priori to fall into two subtly different classes - we compared how the capacity to discriminate between them was affected by the normalization procedure used, and by the choice of factorial representation. Specifically, these images were analyzed both after being scaled according to constant minimum and maximum (CMM) values, and after imposing constant values of image mean and variance (CMV). The factorial representations compared were correspondence analysis (CA) and the principal components (PC) formalism. When used with PC, CMM normalization was found to give rise to spurious inter-image fluctuations that were more pronounced than the genuine difference between the two kinds of images; even with CA, CMV proved to be a more satisfactory method of normalization. When CMV was used with CA or PC, both factorial representations yielded qualitatively similar results, although according to a quantitative measure of inter-set discrimination, the performance of PC was slightly superior. Even in the best case, however, the two classes of images - as mapped in factorial space - were not fully resolved. The implications of this observation are discussed with regard to potential ambiguities of image classification in practice.

Classification↗

The spectral signal-to-noise ratio resolution criterion: computational efficiency and statistical precision.

This note describes a practical improvement in the computational efficiency of the spectral signal-to-noise ratio (SSNR) resolution criterion for correlation-averaged images. The total set of N images is randomly partitioned into ng subsets, each subset is separately averaged, and a reduced form of the SSNR is computed from these average images. In general, larger values of ng achieve lower statistical uncertainty, while smaller values of ng are computationally more expedient. It is shown that, for negatively stained data, a judicious compromise is achieved with 10 less than or equal to ng less than or equal to 20, regardless of how large N may be.

Microscopy, Interference↗

Segmentation of gated Tl-SPECT images and computation of ejection fraction: a different approach.

BACKGROUND: We describe a set of image processing algorithms and mathematical models that can be advantageously used in schemes for the segmentation of thallium-201-single photon emission computed tomography (SPECT) images and for computation of left ventricular ejection fraction (EF). METHODS: The system consists of two independent blocs for image segmentation and computation of function. The former is based on a multiresolution elliptical coordinate transformation and dynamic contour tracking. Computation of EF is formulated on the basis of both the endocardial and epicardial contours, and we compare this formulation with that using only the endocardial border for images with low signal-to-noise ratios. The accuracy of border detection was validated against manual border tracing on FDG-PET images, simulated Tl-201-SPECT images where the true underlying borders were known, and actual Tl-201-SPECT images. Finally, we compared EFs computed for FDG-PET, technetium-99m-SPECT and Tl-201-SPECT with those obtained from planar gated blood pool imaging. RESULTS: The automatically obtained results always were within the manual uncertainty range. Agreement between myocardial volumes from positron emission tomography and automatically obtained values from the simulated Tl-201-SPECT images was excellent (r = 0.95, n = 32). Agreement between EFs from planar gated blood pool imaging and the other image modalities was good (FDG-PET: y = 5.89 + 1.21x, r = 0.92, see = 6.24, n = 19, Tc-99m-SPECT: y = -3.86 + 1.06x, r = 0.88, see = 7.78, n = 9, Tl-201-SPECT: y = 17.8 + 0.81x, r = 0.77, see = 7.44, n = 26). For noisy input data the combined use of information from epicardial and endocardial contours gives more accurate EF values than the traditional formula on the basis of the endocardial contour only. CONCLUSIONS: Alternate approaches for segmentation and computation of function have been presented and validated. They might also be advantageously incorporated into other existing techniques.

Algorithms↗