Molecular Recipe for γ‑Secretase Modulation from Computational Analysis of 60 Active Compounds
journal contributionposted on 24.12.2018, 12:19 by Ning Tang, Arun K. Somavarapu, Kasper P. Kepp
γ-secretase is a membrane protease complex that catalyzes the cleavage of the amyloid precursor protein to produce the infamous Aβ peptides involved in Alzheimer’s disease (AD). Major efforts aim to modulate this cleavage to reduce the formation of longer, more toxic Aβ peptides, yet the molecular basis of this modulation remains unknown. We studied the quantitative structure–activity relations using a carefully curated data set of 60 experimental EC50 values (the GSL60 data set). To ensure adequate optimization, we used 10 different methods to build the models, Y-randomization, 10-fold repeated cross-validation, and explicit external validation on a secondary data set. Neural network optimization best reproduced experimental log EC50. We find that only four descriptors, the number of hydrogen-bond acceptor sites, the topology of the drug, the dehydration energy, and the binding energy to γ-secretase, define most of the potency of γ-secretase modulators. We explain this as a compromise between the binding free energy to the protein and required hydrogen bond networks in the actual modulatory sites. Our model suggests that many molecules can modulate cleavage simply by contributing their binding energy to stabilize the compact ternary complex with C99. This result is in line with a mechanism, referred to here as FIST (Fit, Stay, Trim), where stronger binding to the semiopen state leads to longer retention time and maximal C99 trimming to produce shorter innocent Aβ peptides, whereas AD-causing PSEN1 mutations favor the open state by reducing hydrophobic packing, retention time, and trimming and modulators strengthen interactions in the ternary complex to increase the C99 retention time and trimming, ultimately producing more short, nonpathogenic Aβ peptides. Our results may aid the development of new γ-secretase modulators with optimal hydrogen bonds, shape, and hydrophobicity but more importantly provide a structural–chemical model of the modulation of Aβ production.
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hydrogen-bond acceptor sitesamyloid precursor proteinretention timeγ- secretase modulatorsEC 50 valuescleavagebinding energylog EC 50Neural network optimizationAD-causing PSEN 1 mutations favorβ peptidesGSL 60 dataFISTMajor efforts aimCompounds γ- secretaseC 99 retention timemodulationmodelternaryhydrogen bond networks