Expert opinion on drug discovery · 2025
Discovery of novel inhibitory peptides on matrix metalloproteinases and elastase for skin antiaging using batch molecular docking strategy.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Rongchao, Yang Lihua, Du Lei, Zhao Li, Chen Siyu, Li Weihu, Dai Daoxin, Shi Binhai, Xie Jingli
Аннотация
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.
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.