title: Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase.
abstract: 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.
authors.affiliation: Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239 Krakow, Poland. ncszalen@cyf-kr.edu.pl
authors.forename: Maciej
authors.initials: M
authors.lastname: Szaleniec
authors.forename: Małgorzata
authors.initials: M
authors.lastname: Witko
authors.forename: Ryszard
authors.initials: R
authors.lastname: Tadeusiewicz
authors.forename: Jakub
authors.initials: J
authors.lastname: Goclon
mesh_terms: D007700:Kinetics; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010088:Oxidoreductases / Q000737:chemistry*; D012044:Regression Analysis
chemical_list: D010088:Oxidoreductases; C121602:ethylbenzene dehydrogenase