Publication details
High-throughput characterization of enzymes from genomic and proteomic projects-multivariate statistical approach
| Authors | |
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| Year of publication | 2006 |
| Type | Appeared in Conference without Proceedings |
| MU Faculty or unit | |
| Citation | |
| 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. The obtained dataset, completed with phycical-chemical properties of halogenated hydrocarbons tested, was submitted to quantitave structure-activity relationship. The concept have been verified on screening of haloalkane dehalogenases. Members of this enzyme family cleave carbon-halogen bond in halogenated hydrocarbons. Haloalkane dehalogenases can be used in bioremediation, as industrial biocatalysts or as biosensors. To increase their applicability, the outcome from principle component and phylogenetic analysis can be combine to look for novel enzymes with promising biotechnological properties. Quantitave structure-activity relationship brought detail view into explanation of activity or inactivity of particular substrates tested. Such information is helpful in process of rational design and improvement of haloalkane dehalogenases planned to be applied in biotechnology. The methodology described here and applied on haloalkane dehalogenase family has potential to extend systematic knowledge of particular enzymes and protein families saving time, money and experimental capacity without loosing information. References: 1. S. Wold, K. Esbensen & P. Geladi, Chemometrics and Intelligent Laboratory Systems, 2 (1987) 37-52. 2. S. Marvanova, Y. Nagata, M. Wimmerova, J. Sykorova, K. Hynkova & J. Damborsky, Journal of Microbiological Methods, 44 (2001) 149-157. |
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