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DOI 10.1021/acs.jcim.6c01556

Медицинская библиотека · Journal of chemical information and modeling · 2026

Unlocking the Dynamics of Human SPPL2a: First Atomistic Characterization of an Alzheimer's-Linked Intramembrane Protease.

Dutta Sagnika, Singh Mahender Kumar, Dehury Budheswar

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Аннотация

The failure of γ-secretase inhibitors in clinical trials of Alzheimer's disease (AD) has shifted the focus toward more selective targets, specifically SPPL2a (signal peptide-peptidase-like 2a), which is emerging as a critical therapeutic target in AD because of its role in processing TMEM106B. While recent cryo-EM structures have provided essential static snapshots, the dynamic mechanisms governing substrate entry and inhibitor recognition remain unresolved. Here, we present the first microsecond-time scale, all-atom molecular dynamics characterization of human SPPL2a in its apo and inhibitor-bound states, alongside a comparative analysis of the γ-secretase catalytic subunit, PS1. Our simulations reveal that SPPL2a is highly dynamic and undergoes an inhibitor-induced disorder-to-order transition in the TM6a region, where the helicity increases from 10% to 77%. We characterized the lateral gate dynamics of TM2, revealing that inhibitor binding fundamentally remodels the interhelical contact network and quenches conformational sampling. Moreover, we identified a unique inhibitor-stabilized lipid hotspot at Trp189 and demonstrated that SPPL2a utilizes a distributed polar network for binding energy, in contrast to the concentrated aspartate-driven affinity of PS1. These findings provide a quantitative atomistic blueprint of SPPL2a dynamics, offering a structural basis that can be exploited for the design of selectivity-guided therapeutics to bypass the clinical failures associated with nonspecific intramembrane protease inhibition.

Для цитирования: Dutta Sagnika, Singh Mahender Kumar, Dehury Budheswar. Unlocking the Dynamics of Human SPPL2a: First Atomistic Characterization of an Alzheimer's-Linked Intramembrane Protease.. Journal of chemical information and modeling. 2026. DOI: 10.1021/acs.jcim.6c01556.

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