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Computational Identification of Promising CRYGD Inhibitors for Cataract Treatment
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Version 1
: Received: 16 August 2024 / Approved: 16 August 2024 / Online: 19 August 2024 (09:15:05 CEST)
How to cite: Dahaghin, M.; Rahmani, A. Computational Identification of Promising CRYGD Inhibitors for Cataract Treatment. Preprints 2024, 2024081282. https://doi.org/10.20944/preprints202408.1282.v1 Dahaghin, M.; Rahmani, A. Computational Identification of Promising CRYGD Inhibitors for Cataract Treatment. Preprints 2024, 2024081282. https://doi.org/10.20944/preprints202408.1282.v1
Abstract
This study employs a comprehensive computational approach to identify and evaluate potential inhibitors of gamma D crystallin (CRYGD), a key protein implicated in cataract formation. A library of compounds, including natural products and FDA-approved drugs, was screened using molecular docking simulations and binding energy calculations. The top-scoring compounds were further assessed for drug-likeness properties and potential toxicity. Molecular dynamics simulations were conducted on the most promising candidates to investigate their interactions with CRYGD at the atomic level. The study identified CNP0185903, CID 3741, and CNP0151361 as the most promising compounds, with CNP0151361 demonstrating the most favorable combination of binding affinity, structural stabilization, and conformational modulation of CRYGD. These findings provide a foundation for the development of novel pharmacological interventions for cataract treatment, potentially offering alternatives or complements to current surgical approaches.
Keywords
Cataract; CRYGD; Molecular docking; Molecular dynamics; Crystallin; Protein aggregation
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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