In Silico Structure‐Guided Design of Peptide Candidates Targeting γ‐Secretase Subunit Assembly

The γ-secretase complex is a membrane-embedded protease essential for intramembrane cleavage of substrates such as Notch receptors and the amyloid precursor protein (APP), processes central to cancer progression and Alzheimer's disease (AD) pathology. However, catalytic inhibition of γ-secretase disrupts multiple signaling pathways, resulting in dose-limiting toxicities. In this study, we report a structure-guided approach to generate peptides with binding and stability profiles that disrupt the assembly of γ-secretase by targeting the interactions of Presenilin-1 and Nicastrin with APH1. First, molecular docking was performed for 36 248 peptides of varying lengths to assess their affinity scores to the PS1 and NCT interaction regions of APH1. Peptides filtered based on their affinity scores and physicochemical properties were then subjected to global molecular docking. 50-nanosecond molecular dynamics simulations and MM/PBSA analyses were performed on the top 10 potential candidates, identifying those with high dynamic interaction potential. Thus, seven γ-secretase inhibitor candidates with favorable affinity scores capable of providing stable interactions and thereby having the potential to disrupt the APH1:PS1 assembly were identified. This approach, which overcomes the challenges of targeting the transmembrane catalytic domain, is based on the inhibition of subunit assembly and presents promising candidates for future experimental studies.

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Publication Details

Journal
Proteins Structure Function and Bioinformatics
Published
2026-04-02
DOI
https://doi.org/10.1002/prot.70137
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
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article

In Silico Structure‐Guided Design of Peptide Candidates Targeting γ‐Secretase Subunit Assembly

Alper Yılmaz, Selcen Arı Yuka, Kübra Telli
Proteins Structure Function and Bioinformatics
Alzheimer's disease research and treatments
article

In Silico Structure‐Guided Design of Peptide Candidates Targeting γ‐Secretase Subunit Assembly

Alper Yılmaz, Selcen Arı Yuka, Kübra Telli
article en

Abstract

The γ-secretase complex is a membrane-embedded protease essential for intramembrane cleavage of substrates such as Notch receptors and the amyloid precursor protein (APP), processes central to cancer progression and Alzheimer's disease (AD) pathology. However, catalytic inhibition of γ-secretase disrupts multiple signaling pathways, resulting in dose-limiting toxicities. In this study, we report a structure-guided approach to generate peptides with binding and stability profiles that disrupt the assembly of γ-secretase by targeting the interactions of Presenilin-1 and Nicastrin with APH1. First, molecular docking was performed for 36 248 peptides of varying lengths to assess their affinity scores to the PS1 and NCT interaction regions of APH1. Peptides filtered based on their affinity scores and physicochemical properties were then subjected to global molecular docking. 50-nanosecond molecular dynamics simulations and MM/PBSA analyses were performed on the top 10 potential candidates, identifying those with high dynamic interaction potential. Thus, seven γ-secretase inhibitor candidates with favorable affinity scores capable of providing stable interactions and thereby having the potential to disrupt the APH1:PS1 assembly were identified. This approach, which overcomes the challenges of targeting the transmembrane catalytic domain, is based on the inhibition of subunit assembly and presents promising candidates for future experimental studies.

Proteins Structure Function and Bioinformatics
Istanbul Kültür University (TR), Yıldız Technical University (TR), Biotechnology Research Center (IR)
Openalex Percentile: Top 6%
Alzheimer's disease research and treatments
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In Silico Structure‐Guided Design of Peptide Candidates Targeting γ‐Secretase Subunit Assembly — Alper Yılmaz, Selcen Arı Yuka, et al. · Proteins Structure Function and Bioinformatics (2026) | TGRS Research Map | TGRS