Mechanism of synergistic enhancement of α-secretase (ADAM10) activity by EGCG and FA

Enhancing the activity of α-secretase has emerged as a potential therapeutic strategy for treating Alzheimer's disease (AD). The exploration of small molecules that can enhance α-secretase activity and their mechanisms provides insights for future AD treatments and the development of novel activators. In this study, ADAM10, a major α-secretase, is used as a model and is bound with the ligands (−)-epigallocatechin-3-gallate (EGCG) and ferulic acid (FA) in a 1:2 ratio (ADAM10:EGCG/FA = 1:2/2) and equimolar ratio (ADAM10:EGCG:FA = 1:1:1) to investigate the effects on ADAM10 activation and reveal the synergistic mechanism of the EGCG and FA combination. The activity of ADAM10 was enhanced by the combination of EGCG and FA, compared to that achieved with EGCG or FA alone, where EGCG plays a dominant role, whereas FA plays a supportive role. The combined use of EGCG induces strong hydrophobic interactions between ADAM10 and FA, causing FA to dissociate from the S1 domain, thereby preventing the inhibition of ADAM10 activity by pure FA. The presence of FA allows EGCG to bind more precisely within the active cavity of ADAM10, thereby increasing the binding strength. Overall, the combination of EGCG and FA significantly increased the distance between the S1 domain and the cysteine-rich C-terminus, further opening up the cavity containing the active sites, consequently exposing more active sites and enhancing the activity of ADAM10. The combination treatment of EGCG and FA synergistically increased the activity of ADAM10 by opening larger activity cavity and consequently exposing more active sites.

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Journal
ChemPhysMater
Published
2025-04-03
DOI
https://doi.org/10.1016/j.chphma.2025.03.002
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Mechanism of synergistic enhancement of α-secretase (ADAM10) activity by EGCG and FA

Wenyue Xu, Hongqi Ai, Yvning Guan
ChemPhysMater
Computational Drug Discovery Methods
article

Mechanism of synergistic enhancement of α-secretase (ADAM10) activity by EGCG and FA

Wenyue Xu, Hongqi Ai, Yvning Guan
article en

Abstract

Enhancing the activity of α-secretase has emerged as a potential therapeutic strategy for treating Alzheimer's disease (AD). The exploration of small molecules that can enhance α-secretase activity and their mechanisms provides insights for future AD treatments and the development of novel activators. In this study, ADAM10, a major α-secretase, is used as a model and is bound with the ligands (−)-epigallocatechin-3-gallate (EGCG) and ferulic acid (FA) in a 1:2 ratio (ADAM10:EGCG/FA = 1:2/2) and equimolar ratio (ADAM10:EGCG:FA = 1:1:1) to investigate the effects on ADAM10 activation and reveal the synergistic mechanism of the EGCG and FA combination. The activity of ADAM10 was enhanced by the combination of EGCG and FA, compared to that achieved with EGCG or FA alone, where EGCG plays a dominant role, whereas FA plays a supportive role. The combined use of EGCG induces strong hydrophobic interactions between ADAM10 and FA, causing FA to dissociate from the S1 domain, thereby preventing the inhibition of ADAM10 activity by pure FA. The presence of FA allows EGCG to bind more precisely within the active cavity of ADAM10, thereby increasing the binding strength. Overall, the combination of EGCG and FA significantly increased the distance between the S1 domain and the cysteine-rich C-terminus, further opening up the cavity containing the active sites, consequently exposing more active sites and enhancing the activity of ADAM10. The combination treatment of EGCG and FA synergistically increased the activity of ADAM10 by opening larger activity cavity and consequently exposing more active sites.

ChemPhysMaterVol. 4(4)
University of Jinan (CN)
Natural Science Foundation of Shandong Province
Openalex Percentile: Top 92%
Computational Drug Discovery Methods
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Mechanism of synergistic enhancement of α-secretase (ADAM10) activity by EGCG and FA — Wenyue Xu, Hongqi Ai, et al. · ChemPhysMater (2025) | TGRS Research Map | TGRS