Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase
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Data
2006-06-16Autor
Szaleniec, Maciej
Witko, Małgorzata
Tadeusiewicz, Ryszard
Goclon, Jakub
Metadane
Pokaż pełny rekordStreszczenie
Artificial neural networks (ANNs) are used for
classification and prediction of enzymatic activity of ethylbenzene
dehydrogenase from EbN1 Azoarcus sp. bacterium.
Ethylbenzene dehydrogenase (EBDH) catalyzes
stereo-specific oxidation of ethylbenzene and its derivates
to alcohols, which find its application as building blocks in
pharmaceutical industry. ANN systems are trained based
on theoretical variables derived from Density Functional
Theory (DFT) modeling, topological descriptors, and kinetic
parameters measured with developed spectrophotometric
assay. Obtained models exhibit high degree of
accuracy (100% of correct classifications, correlation between
predicted and experimental values of reaction rates
on the 0.97 level). The applicability of ANNs is demonstrated
as useful tool for the prediction of biochemical
enzyme activity of new substrates basing only on quantum
chemical calculations and simple structural characteristics.
Multi Linear Regression and Molecular Field Analysis
(MFA) are used in order to compare robustness of ANN
and both classical and 3D-quantitative structure–activity
relationship (QSAR) approaches.
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