Structure-Based Design and In-Silico Evaluation of Computationally Proposed Curcumin Derivatives as Potential Inhibitors of the Coronaviral PLpro Enzymes
<b>Background/Objectives:</b> Highly pathogenic coronaviruses (CoVs), including SARS-CoV, MERS-CoV, and SARS-CoV-2, continue to pose a significant threat to global public health. Therefore, this situation highlights the urgent need for effective broad-spectrum antiviral agents. Curcumin, a naturally occurring polyphenol known for its antiviral and anti-inflammatory properties, faces limitations such as poor bioavailability and rapid metabolic degradation, restricting its practical therapeutic application. <b>Methods:</b> To address these limitations, this study introduces a novel design strategy aimed at 42 new curcumin derivatives with improved pharmacokinetic profiles, specifically targeting the conserved coronavirus enzyme papain-like protease (PLpro). A comprehensive in silico evaluation was performed, including ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity) analysis, molecular docking, molecular dynamics (MD) simulations, and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations. <b>Results:</b> Extensive pharmacokinetic and toxicological assessments (ADMET analyses) identified 19 derivatives exhibiting optimal drug-like characteristics according to Lipinski's Rule of Five (Ro5). Molecular docking analyses demonstrated that these novel derivatives possess significantly enhanced binding affinities to PLpro enzymes from SARS-CoV, MERS-CoV, and SARS-CoV-2 compared to standard antiviral agents and natural curcumin. Further validation through MD simulations and MM/PBSA calculations confirmed the structural stability and robust interactions of the most promising derivatives within the SARS-CoV PLpro active site. <b>Conclusions:</b> The results of this study provide essential structural and functional insights, reinforcing the potential of these newly developed curcumin derivatives as potent, broad-spectrum antiviral agents effective against current and future coronavirus threats.
Authors
- Hakan Alıcı (ORCID: https://orcid.org/0000-0001-5105-8331)
Institutions
- Zonguldak Bülent Ecevit University (TR)
Publication Details
- Journal
- Pharmaceuticals
- Published
- 2025-05-26
- DOI
- https://doi.org/10.3390/ph18060798
- Citations
- 4
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 6.02
Funders
- Karabük Üniversitesi