Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides

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.

Authors

Institutions

Publication Details

Journal
Nutrients
Published
2026-10-12
DOI
https://doi.org/10.3390/nu18203339
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides

Zhiyao Zhao, D. Wang, Jingjie Zhang, Chi Yan et al.
Nutrients
Computational Drug Discovery Methods
article

Deep Learning-Driven Identification and Experimental Validation of COX-2 and ERK1/2 as Anti-Inflammatory Targets of Avenanthramides

Zhiyao Zhao, D. Wang, Jingjie Zhang, Chi Yan, Yonghui Yu, Zeran Wang, Tengyu Li
article en

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.

NutrientsVol. 18(20)
Beijing Technology and Business University (CN)
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.