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Human cytochrome P450 (P450) 27A1 catalyzes the hydroxylation of cholesterol and vitamin D derivatives. P450 27A1 is localized in the mitochondria and is reduced by its redox partner protein adrenodoxin twice for each catalytic cycle. The reliance on adrenodoxin is conserved across all human mitochondrial P450 enzymes. This study examines the adrenodoxin interaction with P450 27A1 and draws comparisons with studies of other P450 enzymes to determine if differences exist. The P450-adrenodoxin complex structure was examined by chemical crosslinking and analyzed by mass spectrometry. The effect of adrenodoxin concentration on P450 27A1 function was assessed by studying effects on steady state enzyme kinetics parameters and equilibrium substrate binding. The results suggest that adrenodoxin binds to P450 27A1 at a proximal site like other P450 enzymes but differs in the specific residues involved. Furthermore, the presence of adrenodoxin and/or substrate decreases the number of interprotein and intraprotein crosslinks observed, indicating that these components change the conformation of the P450 enzyme. Increased adrenodoxin concentration causes the P450 and vitamin D3 kcat value to increase, the kcat/Km value to decrease, and the substrate Kd to remain constant. These results suggest adrenodoxin alters enzyme efficiency beyond electron transfer without affecting substrate loading. The adrenodoxin effects on P450 27A1 kinetics and equilibrium constants differ from those of other human mitochondrial P450 enzymes. In total, these structural and functional studies suggest that while the general adrenodoxin binding site and function is conserved across P450 enzymes, the details and additional effects of this interaction vary.
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NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling
Abstract Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challenging to construct robust prediction models for this important property. Most of the existing models, including the one recently published by Nature Catalysis (Li et al.), are suffering from the overfitting issue. In this study, we proposed a novel protocol to construct Kcat prediction models, introducing an intermedia step to separately develop substrate and protein processors. The substrate processor leverages analyzing Simplified Molecular Input Line Entry System (SMILES) strings using a graph neural network model, attentive FP, while the protein processor abstracts protein sequence information utilizing long short-term memory architecture. This protocol not only mitigates the impact of data imbalance in the original dataset but also provides greater flexibility in customizing the general-purpose Kcat prediction model to enhance the prediction accuracy for specific enzyme classes. Our general-purpose Kcat prediction model demonstrates significantly enhanced stability and slightly better accuracy (R2 value of 0.54 versus 0.50) in comparison with Li et al.’s model using the same dataset. Additionally, our modeling protocol enables personalization of fine-tuning the general-purpose Kcat model for specific enzyme categories through focused learning. Using Cytochrome P450 (CYP450) enzymes as a case study, we achieved the best R2 value of 0.64 for the focused model. The high-quality performance and expandability of the model guarantee its broad applications in enzyme engineering and drug research & development.
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- Award ID(s):
- 1955260
- PAR ID:
- 10627175
- Publisher / Repository:
- OXFORD
- Date Published:
- Journal Name:
- Briefings in Bioinformatics
- Volume:
- 26
- Issue:
- 3
- ISSN:
- 1467-5463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1093/bib/bbaf212
