Structural Modeling of γ-Secretase Aβ _n Complex Formation and Substrate Processing

Area of Science:

• Biochemistry
• Structural Biology
• Neuroscience

Background:

• γ-secretase (GSEC) is an intramembrane protease crucial for processing the amyloid precursor protein (APP).
• GSEC's cleavage of APP generates amyloid-β (Aβ) peptides, including Aβ42, which aggregate and are implicated in Alzheimer's disease pathogenesis.
• Understanding GSEC-APP substrate recognition is vital for elucidating Aβ production and developing therapeutic strategies.

Purpose of the Study:

• To elucidate the molecular mechanism of GSEC-APP substrate recognition.
• To generate and validate structural models of GSEC-APP binding and processing.
• To provide a working model for future structural and biochemical studies.

Main Methods:

• Utilized molecular dynamics (MD) simulations to model GSEC-APP binding modes.
• Employed restraint MD simulations to analyze sequential cleavage events.
• Integrated structural data with existing experimental findings (mutations, inhibitors, cross-linking, spectroscopy).

Main Results:

• Identified a primary binding mode where the APP substrate helix resides in a cleft between GSEC's presenilin transmembrane helices 2 and 3.
• Detailed the molecular motions and residue contributions during sequential substrate processing.
• Validated the structural model against a wide range of experimental data, including mutation effects and inhibitor interactions.

Conclusions:

• The proposed structural model provides a plausible mechanism for GSEC-APP substrate binding and sequential cleavage.
• This model aligns with existing experimental observations and offers insights into GSEC's function.
• The findings facilitate the design of APP-selective modulators and guide future research in Alzheimer's disease.