文献类型：期刊文献

英文题名：Discovery of novel inhibitory peptides on matrix metalloproteinases and elastase for skin antiaging using batch molecular docking strategy

作者：Wang, Rongchao[1];Yang, Lihua[1];Du, Lei[1];Zhao, Li[1];Chen, Siyu[2];Li, Weihu[2];Dai, Daoxin[2];Shi, Binhai[2];Xie, Jingli[1,3]

机构：[1]East China Univ Sci & Technol, Sch Biotechnol, Dept Food Sci & Engn, State Key Lab Bioreactor Engn, POB 283,130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Shiseido China Co Ltd, Shiseido China Innovat Ctr, Shanghai, Peoples R China;[3]Shanghai Collaborat Innovat Ctr Biomfg SCICB, Shanghai, Peoples R China

年份：2025

卷号：20

期号：12

起止页码：1713

外文期刊名：EXPERT OPINION ON DRUG DISCOVERY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001625443600001)】；

基金：This work was supported by Shanghai Agricultural Science and Technology Innovation Project [202402080012F0036] and the National Key Research and Development Program of China [2020YFA0907800].

语种：英文

外文关键词：Batch molecular docking; high-throughput screening; antiaging peptide; matrix metalloproteinase; Chlorella

摘要：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 mu M. Peptide DGSY acted high potency against MMP-1 (IC50 = 32.6 mu M), and HDISHW inhibited MMP-9 and elastase at the lowest IC50 (20.1, 16.5 mu M). GAASF inhibited all three enzymes (IC50 = 54.0, 41.9, 62.5 mu M). 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.