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bioRxiv · 10.1101/2025.10.28.685008

Screening of Oyster Peptides for Anti-Muscle Atrophy Based on Machine Learning and Computer Simulation: Guided by Antioxidant Pathways

Zhang, J.·Lin, L.·Yang, J.·Zheng, H.·Fang, T.·Li, W.
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Abstract

Muscle atrophy poses a serious threat to human health, with its primary pathogenic mechanisms closely linked to oxidative stress. This study focuses on the potential of oyster peptides in alleviating dexamethasone (DEX)-induced skeletal muscle atrophy and their underlying antioxidant mechanisms. Utilizing efficient integrated machine learning and computer simulation methods, a systematic screening of active peptides and mechanistic research was conducted. The results revealed that oyster peptides at concentrations of 25-50 g/mL significantly improved the decline in cell viability and myotube atrophy induced by DEX, while downregulating the expression of muscle atrophy markers Atrogin-1 and MuRF1. Through LC-MS/MS, 220 high-activity peptide sequences were identified. Following the replication and extension of the iAnOxPep integrated learning model, 15 potential antioxidant peptides were selected. Among them, the short peptide AWPGPQ demonstrated the strongest binding affinity with Keap1 and PPAR{gamma} targets. Molecular dynamics simulations confirmed its stability, suggesting that AWPGPQ may exert its dual effects--antioxidant and anti-muscle atrophy--by modulating the Keap1-Nrf2 and PPAR{gamma} signaling pathways. This study established a systematic and efficient strategy for screening natural active peptides, providing theoretical support and technical pathways for the discovery of multifunctional short peptide candidates, with significant theoretical value and application prospects.

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Zhang, J., Lin, L., Yang, J., Zheng, H., Fang, T., Li, W.. 2025-10-28. Screening of Oyster Peptides for Anti-Muscle Atrophy Based on Machine Learning and Computer Simulation: Guided by Antioxidant Pathways. https://doi.org/10.1101/2025.10.28.685008

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