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Discovery of novel inhibitory peptides on matrix metalloproteinases and elastase for skin antiaging using batch
Rongchao Wang1, Lihua Yang1, Lei Du1
1State Key Laboratory of Bioreactor Engineering, Department of Food Science and Engineering, School of Biotechnology, East China University of Science and Technology, Shanghai, P. R. China.
Background:
Skin aging is linked to the overactivity of matrix metalloproteinases (MMPs) and elastase, making their inhibition a promising approach for antiaging. This study aimed to discover novel antiaging peptides from Chlorella proteins using high-throughput virtual screening.
Methods:
Batch molecular docking protocol with a custom Python script for 3D peptide structure modeling and AutoDock Vina was applied to predict inhibitory peptides on MMPs and elastase from 1,965 peptides theoretically resistant to gastrointestinal digestion. The top candidates were synthesized for activity assay, and MD simulation illustrated the binding mechanism of potent peptides.
Results:
Seventeen peptides with a binding energy < -7.0 kcal/mol showed IC50 ≤ 150 μM. Peptide DGSY acted high potency against MMP-1 (IC50 = 32.6 μM), and HDISHW inhibited MMP-9 and elastase at the lowest IC50 (20.1, 16.5 μM). GAASF inhibited all three enzymes (IC50 = 54.0, 41.9, 62.5 μM). MD simulations confirmed the stability of these peptide-protein complexes, which coincided with the in vitro activity well.
Conclusion:
The virtual strategy efficiently identified multifunctional antiaging peptides and could accelerate the discovery of bioactive peptides for cosmetic and therapeutic use. Additionally, its efficiency makes it useful for building high-quality training sets in deep learning models for bioactive structure discovery.
