Pattern‐recognition‐based detection of planar objects in three‐dimensional electron‐density maps
Identifieur interne : 002A64 ( Main/Exploration ); précédent : 002A63; suivant : 002A65Pattern‐recognition‐based detection of planar objects in three‐dimensional electron‐density maps
Auteurs : Johan Hattne [Allemagne] ; Victor S. Lamzin [Allemagne]Source :
- Acta Crystallographica Section D [ 1399-0047 ] ; 2008-08-01.
English descriptors
- Teeft :
- Abrahams leslie, Acta, Acta cryst, Activation function, Amino acids, Asymmetric unit, Atomic model, Base pairs, Basic features, Centre, Code 1mur, Completeness, Covariance matrix, Cryst, Default parameters, Density value, Density values, Different resolutions, Different types, Discriminant, Discriminative power, Eigen decomposition, Electron density, False positives, Feature vector, Fewer planes, Fisher projection, Grid, Grid point, Grid points, Hattne, Hattne lamzin detection, Histidine, Ieee trans, Illingworth kittler, Integral probability, Lamzin, Lamzin wilson, Large planes, Linear discriminant, Local electron density, Local shape, Local variance, Lower success rate, Macromolecular structure, Middle right, Model building, Moment invariants, Nature struct, Noise class, Noise level, Nucleic acids, Optimum radius, Overall completeness, Overall noise level, Pattern anal, Pattern recognition, Phase quality, Planar, Planar fragments, Planar groups, Planar object, Planar objects, Planar side chains, Plane detection, Plane estimate, Plane orientation, Pnorm, Protein part, Protein region, Research papers, Research papers table, Search volume, Side chains, Signal class, Signal classes, Solvent content, Spatial moments, Squared distance, Structure determination, Subsequent model building, Test case, Test cases, Training case, Variance.
Abstract
A pattern‐recognition‐based method for the detection of planar objects in protein or DNA/RNA crystal structure determination is described. The procedure derives a set of rotation‐invariant numeric features from local regions in the asymmetric unit of a crystallographic electron‐density map. These features, primarily moments of various orders, capture different aspects of the local shape of objects in the electron density. Feature classification is achieved using a linear discriminant that is trained to optimize the contrast between planar and nonplanar objects. In five selected test cases with X‐ray data spanning 2.0–3.0 Å resolution, the procedure identified the correct location and orientation for almost all of the double‐ring and a majority of the single‐ring planar groups. The accuracy of the location of the plane centres is of the order of 0.5 Å, even in moderately noisy density maps.
Url:
DOI: 10.1107/S0907444908014327
Affiliations:
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The procedure derives a set of rotation‐invariant numeric features from local regions in the asymmetric unit of a crystallographic electron‐density map. These features, primarily moments of various orders, capture different aspects of the local shape of objects in the electron density. Feature classification is achieved using a linear discriminant that is trained to optimize the contrast between planar and nonplanar objects. In five selected test cases with X‐ray data spanning 2.0–3.0 Å resolution, the procedure identified the correct location and orientation for almost all of the double‐ring and a majority of the single‐ring planar groups. The accuracy of the location of the plane centres is of the order of 0.5 Å, even in moderately noisy density maps.</div></front></TEI><affiliations><list><country><li>Allemagne</li></country><region><li>Hambourg</li></region><settlement><li>Hambourg</li></settlement></list><tree><country name="Allemagne"><region name="Hambourg"><name sortKey="Hattne, Johan" sort="Hattne, Johan" uniqKey="Hattne J" first="Johan" last="Hattne">Johan Hattne</name></region><name sortKey="Lamzin, Victor S" sort="Lamzin, Victor S" uniqKey="Lamzin V" first="Victor S." last="Lamzin">Victor S. Lamzin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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