Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control
Abstract Cytochrome P450 1A2, as many isoenzymes, can generate multiple metabolites from a single substrate. A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the present work, caffeine oxidation to alte...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-025-91155-0 |
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| author | Denis Pompon Luis F. Garcia-Alles Philippe Urban |
| author_facet | Denis Pompon Luis F. Garcia-Alles Philippe Urban |
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| description | Abstract Cytochrome P450 1A2, as many isoenzymes, can generate multiple metabolites from a single substrate. A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the present work, caffeine oxidation to alternative bioactive compounds was used to decipher this pluripotency. A model involving two interacting subsites capable of sequentially accommodating one or two caffeine molecules was considered. Molecular dynamics was used to characterize subsite interactions and feed a dedicated geometric encoding of trajectories that was coupled to dimensional reductions and differential machine learning. The two subsites differentially control caffeine orientations and can exchange substrate through a phenylalanine gated mechanism. This exchange can be locked by the presence of a second bound molecule. Complementary roles of subsites in progressively determining the caffeine orientation during its approach to active oxygen were examined. Interestingly, substrate face flipping becomes impaired upon entry into the rather flat active site. This makes the mechanisms that define the orientation of caffeine relative to active oxygen dependent on the substrate face oriented toward heme. Globally, this evidenced that P450 1A2 regioselectivity results from local determinants combined with subsite interactions and caffeine face preselection at a longer distance. |
| format | Article |
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| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-bedec4216a92447fb49a6ef8a1f5d7152025-08-20T01:57:27ZengNature PortfolioScientific Reports2045-23222025-03-0115111310.1038/s41598-025-91155-0Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity controlDenis Pompon0Luis F. Garcia-Alles1Philippe Urban2Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSAToulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSAToulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSAAbstract Cytochrome P450 1A2, as many isoenzymes, can generate multiple metabolites from a single substrate. A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the present work, caffeine oxidation to alternative bioactive compounds was used to decipher this pluripotency. A model involving two interacting subsites capable of sequentially accommodating one or two caffeine molecules was considered. Molecular dynamics was used to characterize subsite interactions and feed a dedicated geometric encoding of trajectories that was coupled to dimensional reductions and differential machine learning. The two subsites differentially control caffeine orientations and can exchange substrate through a phenylalanine gated mechanism. This exchange can be locked by the presence of a second bound molecule. Complementary roles of subsites in progressively determining the caffeine orientation during its approach to active oxygen were examined. Interestingly, substrate face flipping becomes impaired upon entry into the rather flat active site. This makes the mechanisms that define the orientation of caffeine relative to active oxygen dependent on the substrate face oriented toward heme. Globally, this evidenced that P450 1A2 regioselectivity results from local determinants combined with subsite interactions and caffeine face preselection at a longer distance.https://doi.org/10.1038/s41598-025-91155-0Deep learningMolecular dynamicGeometric encodingP450CaffeineRegioselectivity |
| spellingShingle | Denis Pompon Luis F. Garcia-Alles Philippe Urban Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control Scientific Reports Deep learning Molecular dynamic Geometric encoding P450 Caffeine Regioselectivity |
| title | Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control |
| title_full | Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control |
| title_fullStr | Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control |
| title_full_unstemmed | Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control |
| title_short | Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control |
| title_sort | geometry encoded molecular dynamics enables deep learning insights into p450 regiospecificity control |
| topic | Deep learning Molecular dynamic Geometric encoding P450 Caffeine Regioselectivity |
| url | https://doi.org/10.1038/s41598-025-91155-0 |
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