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rdf:type
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Description
| - Hight throughput genomic and proteomic methods release a lot of novel enzymes every year which require systematic characterization and cataloguing of their properties. Here, we illustrate a novel approach, using multivariate statistics to characterize and describe a protein family with broad substrate specificity. Principle of the approach consists in multivariate statistic method, principle component analysis [1], which enabled firstly to choose sufficient set of 30 substrates from 194 halogenated compounds respecting maximum variability in physical-chemical properties [2]. Quick and reliable enzymatic assay follows the selection and produces an activity data of particular proteins with selected substrates. Third step is application of principal component analysis on enzyme activity data matrix to describe the difference in substrate specifities. In paralell, kinetic constants Km and kcat were measured.
- Hight throughput genomic and proteomic methods release a lot of novel enzymes every year which require systematic characterization and cataloguing of their properties. Here, we illustrate a novel approach, using multivariate statistics to characterize and describe a protein family with broad substrate specificity. Principle of the approach consists in multivariate statistic method, principle component analysis [1], which enabled firstly to choose sufficient set of 30 substrates from 194 halogenated compounds respecting maximum variability in physical-chemical properties [2]. Quick and reliable enzymatic assay follows the selection and produces an activity data of particular proteins with selected substrates. Third step is application of principal component analysis on enzyme activity data matrix to describe the difference in substrate specifities. In paralell, kinetic constants Km and kcat were measured. (en)
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Title
| - High-throughput characterization of enzymes from genomic and proteomic projects-multivariate statistical approach
- High-throughput characterization of enzymes from genomic and proteomic projects-multivariate statistical approach (en)
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skos:prefLabel
| - High-throughput characterization of enzymes from genomic and proteomic projects-multivariate statistical approach
- High-throughput characterization of enzymes from genomic and proteomic projects-multivariate statistical approach (en)
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skos:notation
| - RIV/00216224:14310/06:00017056!RIV11-MSM-14310___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/00216224:14310/06:00017056
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - haloalkane dehalogenase; substrate specificity; screening; multivariate statistics (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Chaloupková, Radka
- Damborský, Jiří
- Fořtová, Andrea
- Prokop, Zbyněk
- Sato, Yukari
- Monincová, Marta
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http://linked.open...n/vavai/riv/zamer
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http://localhost/t...ganizacniJednotka
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