Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease
Abstract Parkinson’s disease (PD) is a complex neurodegenerative disorder that affects multiple neurotransmitters, and its exact cause is still unknown. Developing new drugs for PD is a lengthy and expensive process, making it difficult to find new treatments. This study aims to create a detailed da...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86926-8 |
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| author | María C. García Sebastián A. Cuesta José R. Mora Jose L. Paz Yovani Marrero-Ponce Frank Alexis Edgar A. Márquez |
| author_facet | María C. García Sebastián A. Cuesta José R. Mora Jose L. Paz Yovani Marrero-Ponce Frank Alexis Edgar A. Márquez |
| author_sort | María C. García |
| collection | DOAJ |
| description | Abstract Parkinson’s disease (PD) is a complex neurodegenerative disorder that affects multiple neurotransmitters, and its exact cause is still unknown. Developing new drugs for PD is a lengthy and expensive process, making it difficult to find new treatments. This study aims to create a detailed dataset to build strong predictive models with various machine learning algorithms. An ensemble modeling approach was employed to screen the DrugBank database, aiming to repurpose approved medications as potential treatments for Parkinson’s disease (PD). The dataset was constructed using pIC50 values of various compounds targeting the inhibition of leucine-rich repeat kinase 2 (LRRK2). The best ensemble model showed exceptional predictive performance, with five-fold cross-validation and external validation metrics exceeding 0.8 (Q2cv = 0.864 and Q2ext = 0.873). The DrugBank screening resulted in three promising drugs—triamterene, phenazopyridine, and CRA_1801—with predicted pIC50 values greater than 7, warranting further investigation as novel PD treatments. Molecular docking and molecular dynamics simulations were performed to provide a comprehensive understanding of the interactions between LRRK2 and the inhibitors in the data set and best molecules of the screening. Free energy of binding calculation along with hydrogen bond occupancy analysis and RMSD of the ligand in the pocket show CRA_1801 as the best candidate to be repurposed as LRRK2 inhibitor. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-a7ff2feffdcc43f7aec2ab13ead224282025-02-09T12:34:09ZengNature PortfolioScientific Reports2045-23222025-02-0115111310.1038/s41598-025-86926-8Using computer modeling to find new LRRK2 inhibitors for parkinson’s diseaseMaría C. García0Sebastián A. Cuesta1José R. Mora2Jose L. Paz3Yovani Marrero-Ponce4Frank Alexis5Edgar A. Márquez6Departamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de QuitoDepartamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de QuitoDepartamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de QuitoDepartamento Académico de Química Inorgánica, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San MarcosGrupo de Medicina Molecular y Traslacional (MeM&T), Universidad San Francisco de Quito, Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA)Departamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de QuitoGrupo de Investigaciones en Química y Biología, Departamento de Química y Biología, Facultad de Ciencias Básicas, Universidad del NorteAbstract Parkinson’s disease (PD) is a complex neurodegenerative disorder that affects multiple neurotransmitters, and its exact cause is still unknown. Developing new drugs for PD is a lengthy and expensive process, making it difficult to find new treatments. This study aims to create a detailed dataset to build strong predictive models with various machine learning algorithms. An ensemble modeling approach was employed to screen the DrugBank database, aiming to repurpose approved medications as potential treatments for Parkinson’s disease (PD). The dataset was constructed using pIC50 values of various compounds targeting the inhibition of leucine-rich repeat kinase 2 (LRRK2). The best ensemble model showed exceptional predictive performance, with five-fold cross-validation and external validation metrics exceeding 0.8 (Q2cv = 0.864 and Q2ext = 0.873). The DrugBank screening resulted in three promising drugs—triamterene, phenazopyridine, and CRA_1801—with predicted pIC50 values greater than 7, warranting further investigation as novel PD treatments. Molecular docking and molecular dynamics simulations were performed to provide a comprehensive understanding of the interactions between LRRK2 and the inhibitors in the data set and best molecules of the screening. Free energy of binding calculation along with hydrogen bond occupancy analysis and RMSD of the ligand in the pocket show CRA_1801 as the best candidate to be repurposed as LRRK2 inhibitor.https://doi.org/10.1038/s41598-025-86926-8Parkinson’s diseaseMolecular dockingMolecular dynamicsVirtual screeningDrugBank |
| spellingShingle | María C. García Sebastián A. Cuesta José R. Mora Jose L. Paz Yovani Marrero-Ponce Frank Alexis Edgar A. Márquez Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease Scientific Reports Parkinson’s disease Molecular docking Molecular dynamics Virtual screening DrugBank |
| title | Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease |
| title_full | Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease |
| title_fullStr | Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease |
| title_full_unstemmed | Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease |
| title_short | Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease |
| title_sort | using computer modeling to find new lrrk2 inhibitors for parkinson s disease |
| topic | Parkinson’s disease Molecular docking Molecular dynamics Virtual screening DrugBank |
| url | https://doi.org/10.1038/s41598-025-86926-8 |
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