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Article

Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators

1
Curtin Health and Innovation Research Institute, Curtin Medical School, Curtin University, Bentley, WA 6102, Australia
2
School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia
3
Curtin Institute for Computation, Curtin University, Bentley, WA 6102, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Shaoyong Lu
Molecules 2022, 27(1), 176; https://doi.org/10.3390/molecules27010176
Received: 9 December 2021 / Revised: 23 December 2021 / Accepted: 24 December 2021 / Published: 28 December 2021
(This article belongs to the Special Issue Computational Strategy for Drug Design)
γ-Secretase is an intramembrane aspartyl protease that is important in regulating normal cell physiology via cleavage of over 100 transmembrane proteins, including Amyloid Precursor Protein (APP) and Notch family receptors. However, aberrant proteolysis of substrates has implications in the progression of disease pathologies, including Alzheimer’s disease (AD), cancers, and skin disorders. While several γ-secretase inhibitors have been identified, there has been toxicity observed in clinical trials associated with non-selective enzyme inhibition. To address this, γ-secretase modulators have been identified and pursued as more selective agents. Recent structural evidence has provided an insight into how γ-secretase inhibitors and modulators are recognized by γ-secretase, providing a platform for rational drug design targeting this protease. In this study, docking- and pharmacophore-based screening approaches were evaluated for their ability to identify, from libraries of known inhibitors and modulators with decoys with similar physicochemical properties, γ-secretase inhibitors and modulators. Using these libraries, we defined strategies for identifying both γ-secretase inhibitors and modulators incorporating an initial pharmacophore-based screen followed by a docking-based screen, with each strategy employing distinct γ-secretase structures. Furthermore, known γ-secretase inhibitors and modulators were able to be identified from an external set of bioactive molecules following application of the derived screening strategies. The approaches described herein will inform the discovery of novel small molecules targeting γ-secretase. View Full-Text
Keywords: γ-secretase; virtual screening; molecular docking; pharmacophore model; Alzheimer’s disease γ-secretase; virtual screening; molecular docking; pharmacophore model; Alzheimer’s disease
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MDPI and ACS Style

Ioppolo, A.; Eccles, M.; Groth, D.; Verdile, G.; Agostino, M. Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators. Molecules 2022, 27, 176. https://doi.org/10.3390/molecules27010176

AMA Style

Ioppolo A, Eccles M, Groth D, Verdile G, Agostino M. Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators. Molecules. 2022; 27(1):176. https://doi.org/10.3390/molecules27010176

Chicago/Turabian Style

Ioppolo, Alicia, Melissa Eccles, David Groth, Giuseppe Verdile, and Mark Agostino. 2022. "Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators" Molecules 27, no. 1: 176. https://doi.org/10.3390/molecules27010176

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