Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides
4 affiliations
- 1Key Laboratory of Geriatric Nutrition and Health, Ministry of Education, Beijing Technology and Business University, Beijing 100048, China
- 2China-Canada Joint Lab of Food Nutrition and Health, Beijing Technology and Business University, Beijing 100048, China
- 3Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing 100048, China
- 4School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Abstract
Background: Chronic low-grade inflammation underlies numerous chronic diseases, yet the molecular targets of dietary bioactive compounds remain largely unknown. Methods: We developed a deep learning model integrating heterogeneous network propagation, stacked denoising autoencoders, and a CNN classifier to predict inflammation-related targets of food-derived compounds. The model was validated by cross-validation and on eight compounds from distinct structural categories. Avn A, Avn B, and Avn C were then input into the model to predict their anti-inflammatory targets, followed by network analysis, docking, and cellular assays for validation. Results: The model achieved a mean AUC of 0.9587 and an AUPR of 0.9590, with validation on eight structurally diverse compounds confirming its generalizability. For Avn A, Avn B, and Avn C, 118 overlapping anti-inflammatory targets were predicted. Network analysis and KEGG pathway enrichment identified ERK1/2 as a key node within the MAPK and PI3K-AKT signaling pathways, while COX-2 was prioritized for further validation based on its high prediction score. Molecular docking revealed that Avns bound at the cyclooxygenase channel entrance of COX-2 with binding energies of −7.7 to −8.0 kcal/mol, and at the ATP-binding pockets of ERK1/2 with binding energies of −7.0 to −7.7 kcal/mol. In LPS-stimulated macrophages, Avns dose-dependently inhibited COX-2 enzymatic activity and PGE2 production, attenuated ERK1/2 and c-Fos phosphorylation, and suppressed IL-6 and IL-1β expression. Conclusions: Our deep learning model successfully identified COX-2 and ERK1/2 as key mediators of the anti-inflammatory effects of Avns, revealing a dual mechanism involving COX-2 inhibition and ERK/c-Fos pathway blockade. This study provides new evidence for the molecular targets of Avns and demonstrates the utility of deep learning-based approaches for target discovery of dietary bioactive compounds.
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BibTeX
@article{zhang2026deep,
title = {{Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides}},
author = {Jingjie Zhang and Tengyu Li and Chi Yan and Zeran Wang and Zhiyao Zhao and Yonghui Yu and Ding Wang},
journal = {Nutrients},
year = {2026},
month = oct,
doi = {10.3390/nu18203339},
url = {https://www.mdpi.com/2072-6643/18/20/3339},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides
AU - Jingjie Zhang
AU - Tengyu Li
AU - Chi Yan
AU - Zeran Wang
AU - Zhiyao Zhao
AU - Yonghui Yu
AU - Ding Wang
PY - 2026
DA - 2026/10/12
JO - Nutrients
DO - 10.3390/nu18203339
UR - https://www.mdpi.com/2072-6643/18/20/3339
AB - Background: Chronic low-grade inflammation underlies numerous chronic diseases, yet the molecular targets of dietary bioactive compounds remain largely unknown. Methods: We developed a deep learning model integrating heterogeneous network propagation, stacked denoising autoencoders, and a CNN classifier to predict inflammation-related targets of food-derived compounds. The model was validated by cross-validation and on eight compounds from distinct structural categories. Avn A, Avn B, and Avn C were then input into the model to predict their anti-inflammatory targets, followed by network analysis, docking, and cellular assays for validation. Results: The model achieved a mean AUC of 0.9587 and an AUPR of 0.9590, with validation on eight structurally diverse compounds confirming its generalizability. For Avn A, Avn B, and Avn C, 118 overlapping anti-inflammatory targets were predicted. Network analysis and KEGG pathway enrichment identified ERK1/2 as a key node within the MAPK and PI3K-AKT signaling pathways, while COX-2 was prioritized for further validation based on its high prediction score. Molecular docking revealed that Avns bound at the cyclooxygenase channel entrance of COX-2 with binding energies of −7.7 to −8.0 kcal/mol, and at the ATP-binding pockets of ERK1/2 with binding energies of −7.0 to −7.7 kcal/mol. In LPS-stimulated macrophages, Avns dose-dependently inhibited COX-2 enzymatic activity and PGE2 production, attenuated ERK1/2 and c-Fos phosphorylation, and suppressed IL-6 and IL-1β expression. Conclusions: Our deep learning model successfully identified COX-2 and ERK1/2 as key mediators of the anti-inflammatory effects of Avns, revealing a dual mechanism involving COX-2 inhibition and ERK/c-Fos pathway blockade. This study provides new evidence for the molecular targets of Avns and demonstrates the utility of deep learning-based approaches for target discovery of dietary bioactive compounds.
SN - 2072-6643
ER -