Identification and validation of γ-Linolenic acid as a natural FABP5 inhibitor in hepatocellular carcinoma through deep learning and experimental approaches.
[BACKGROUND] Fatty acid binding protein 5 (FABP5) is implicated in hepatocellular carcinoma (HCC) progression and represents a potential therapeutic target.
APA
An Y, Liu H, Li W (2026). Identification and validation of γ-Linolenic acid as a natural FABP5 inhibitor in hepatocellular carcinoma through deep learning and experimental approaches.. Frontiers in immunology, 17, 1700347. https://doi.org/10.3389/fimmu.2026.1700347
MLA
An Y, et al.. "Identification and validation of γ-Linolenic acid as a natural FABP5 inhibitor in hepatocellular carcinoma through deep learning and experimental approaches.." Frontiers in immunology, vol. 17, 2026, pp. 1700347.
PMID
41685317
Abstract
[BACKGROUND] Fatty acid binding protein 5 (FABP5) is implicated in hepatocellular carcinoma (HCC) progression and represents a potential therapeutic target.
[METHODS] We integrated machine learning-based virtual screening, molecular docking and molecular dynamics simulations to identify natural compounds with high binding affinity to FABP5. Candidate compounds were further validated by in-vitro assays in HCC cell lines, including proliferation, migration/invasion, apoptosis/ferroptosis-related readouts, and mechanistic validation.
[RESULTS] The optimized models enabled efficient screening of natural products and prioritized γ-linolenic acid (GLA) as a top candidate FABP5 inhibitor. Docking and simulations supported stable binding and key residue interactions. Experimentally, GLA inhibited HCC cell proliferation and aggressiveness and promoted cell death-related pathways consistent with anti-tumor activity.
[CONCLUSION] Our deep learning-guided workflow identified γ-linolenic acid as a natural FABP5 inhibitor and supports its potential as a lead compound for HCC therapy.
[METHODS] We integrated machine learning-based virtual screening, molecular docking and molecular dynamics simulations to identify natural compounds with high binding affinity to FABP5. Candidate compounds were further validated by in-vitro assays in HCC cell lines, including proliferation, migration/invasion, apoptosis/ferroptosis-related readouts, and mechanistic validation.
[RESULTS] The optimized models enabled efficient screening of natural products and prioritized γ-linolenic acid (GLA) as a top candidate FABP5 inhibitor. Docking and simulations supported stable binding and key residue interactions. Experimentally, GLA inhibited HCC cell proliferation and aggressiveness and promoted cell death-related pathways consistent with anti-tumor activity.
[CONCLUSION] Our deep learning-guided workflow identified γ-linolenic acid as a natural FABP5 inhibitor and supports its potential as a lead compound for HCC therapy.
MeSH Terms
Humans; Carcinoma, Hepatocellular; Liver Neoplasms; Molecular Docking Simulation; Fatty Acid-Binding Proteins; gamma-Linolenic Acid; Deep Learning; Cell Proliferation; Cell Line, Tumor; Apoptosis; Molecular Dynamics Simulation; Cell Movement
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