Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code
| Autor: | Grover P. Miller, Sun-Ha Park, Chul-Ho Yun, Jessica H. Hartman, Jerry A. Darsey, Steven D. Cothren |
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| Rok vydání: | 2013 |
| Předmět: |
Stereochemistry
Clinical Biochemistry Pharmaceutical Science Stereoisomerism Models Biological Biochemistry Article Substrate Specificity Drug Discovery Humans Representation (mathematics) Molecular Biology Topology (chemistry) Artificial neural network Chemistry Organic Chemistry Enantioselective synthesis Propranolol Cytochrome P-450 CYP2C19 Molecular Medicine Aryl Hydrocarbon Hydroxylases Neural Networks Computer Enantiomer Biological system Chirality (chemistry) Oxidation-Reduction Algorithms CHELPG |
| Zdroj: | Bioorganic & Medicinal Chemistry. 21:3749-3759 |
| ISSN: | 0968-0896 |
| DOI: | 10.1016/j.bmc.2013.04.044 |
| Popis: | Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters ( k cat , K m , and k cat / K m ), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box–Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters ( k cat and K m ) were more consistent with experimental values than those for catalytic efficiency ( k cat / K m ). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R - and S -propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. |
| Databáze: | OpenAIRE |
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