Literature DB >> 12767152

Quantitative structure-activity relationships for the enantioselectivity of oxirane ring-opening catalyzed by epoxide hydrolases.

Simona Funar-Timofei1, Takahiro Suzuki, Joachim A Paier, Andreas Steinreiber, Kurt Faber, Walter M F Fabian.   

Abstract

The enantioselective ring-opening catalyzed by epoxide hydrolases originating from seven different sources of a series of 2,2-disubstituted oxiranes containing alkyl chains of different lengths, unsaturated (alkenyl, alkinyl) and aromatic groups as well as electronegative heteroatoms at various positions within the side chain was analyzed by quantitative structure-activity relationships. Models for the enantioselectivity were derived with the aid of multiple linear regression analysis (MLR) using several steric and electronic (quantum chemical) descriptors. On the basis of the models derived by MLR nonlinear modeling with artificial neural networks (ANN) was also done. Good predictive performance was observed for both modeling approaches. The models also indicate that different steric and/or electronic features account for the enantioselectivities observed for the individual epoxide hydrolases.

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Year:  2003        PMID: 12767152     DOI: 10.1021/ci020047z

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  4 in total

1.  Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase.

Authors:  Maciej Szaleniec; Małgorzata Witko; Ryszard Tadeusiewicz; Jakub Goclon
Journal:  J Comput Aided Mol Des       Date:  2006-06-16       Impact factor: 3.686

2.  Learning epistatic interactions from sequence-activity data to predict enantioselectivity.

Authors:  Julian Zaugg; Yosephine Gumulya; Alpeshkumar K Malde; Mikael Bodén
Journal:  J Comput Aided Mol Des       Date:  2017-12-12       Impact factor: 3.686

3.  Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code.

Authors:  Jessica H Hartman; Steven D Cothren; Sun-Ha Park; Chul-Ho Yun; Jerry A Darsey; Grover P Miller
Journal:  Bioorg Med Chem       Date:  2013-04-22       Impact factor: 3.641

4.  Counter propagation artificial neural networks modeling of an enantioselectivity of artificial metalloenzymes.

Authors:  Sylwester Mazurek; Thomas R Ward; Marjana Novic
Journal:  Mol Divers       Date:  2008-03-04       Impact factor: 2.943

  4 in total

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