1. Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography
- Author
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Alexander D. Cameron, So Iwata, Pierre Aller, Robin L. Owen, Gwyndaf Evans, David G. Waterman, Wes Armour, Yilmaz Alguel, Danny Axford, and James Foadi
- Subjects
Models, Molecular ,Physics::Medical Physics ,Nanotechnology ,Crystallography, X-Ray ,010402 general chemistry ,01 natural sciences ,law.invention ,Quantitative Biology::Subcellular Processes ,03 medical and health sciences ,Software ,Structural Biology ,law ,Mathematics::Metric Geometry ,Cluster Analysis ,Insulin ,multi-crystal data sets ,Cluster analysis ,Selection (genetic algorithm) ,Plant Proteins ,030304 developmental biology ,Physics ,Physics::Biological Physics ,Quantitative Biology::Biomolecules ,0303 health sciences ,Computer program ,business.industry ,merging ,scaling ,Macromolecular crystallography ,Temperature ,Process (computing) ,Membrane Proteins ,General Medicine ,Microbeam ,Research Papers ,Synchrotron ,0104 chemical sciences ,multiple crystals ,Muramidase ,BLEND ,business ,Biological system ,Synchrotrons ,clustering - Abstract
A systematic approach to the scaling and merging of data from multiple crystals in macromolecular crystallography is introduced and explained., The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of
- Published
- 2013
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