Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approaches
Abstract Alzheimer’s disease is a neurodegenerative disorder characterized by two hallmarks: amyloid beta plaques and neurofibrillary tangles. The receptor for advanced glycation end products (RAGE) is a multi-ligand receptor involved in the pathophysiology of various diseases including cancer, diab...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01271-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850268628593672192 |
|---|---|
| author | Inderjeet Bhogal Vaishali Pankaj Sudeep Roy |
| author_facet | Inderjeet Bhogal Vaishali Pankaj Sudeep Roy |
| author_sort | Inderjeet Bhogal |
| collection | DOAJ |
| description | Abstract Alzheimer’s disease is a neurodegenerative disorder characterized by two hallmarks: amyloid beta plaques and neurofibrillary tangles. The receptor for advanced glycation end products (RAGE) is a multi-ligand receptor involved in the pathophysiology of various diseases including cancer, diabetes, cardiovascular diseases, and Alzheimer’s disease (AD). Therefore, targeting RAGE could be an effective strategy to block RAGE signaling pathways. The present study aims to identify potential RAGE inhibitors against AD through comprehensive in-silico approaches. A total of 708,580 compounds were screened from numerous databases using structure-based virtual screening and ADMET evaluation. Further, the molecules with good glide scores were assessed by molecular docking studies. Subsequently, the top six ligands were subjected to molecular dynamic (MD) simulations for 100 ns and binding free energy calculations to check their stability with RAGE (PDB: 6XQ3). The per-residue decomposition analysis revealed that specific residues namely, GLY_20, ALA_21, LYS_39, GLU_50, LYS_52, ARG_98, GLN_100, LYS_110, ASN_112, and ARG_198 played a key role in the binding process. Furthermore, the trajectory analysis (DCCM and PCA) analyzed the dominant motions of residues and predicted the stability of protein-ligand complexes. In conclusion, the Hit-6 compound could be a promising candidate for targeting RAGE and deserves further consideration as an anti-Alzheimer drug. |
| format | Article |
| id | doaj-art-8ac94c0840194e5ebce4f080edb9163e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8ac94c0840194e5ebce4f080edb9163e2025-08-20T01:53:23ZengNature PortfolioScientific Reports2045-23222025-05-0115111710.1038/s41598-025-01271-0Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approachesInderjeet Bhogal0Vaishali Pankaj1Sudeep Roy2Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of TechnologyDepartment of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of TechnologyDepartment of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of TechnologyAbstract Alzheimer’s disease is a neurodegenerative disorder characterized by two hallmarks: amyloid beta plaques and neurofibrillary tangles. The receptor for advanced glycation end products (RAGE) is a multi-ligand receptor involved in the pathophysiology of various diseases including cancer, diabetes, cardiovascular diseases, and Alzheimer’s disease (AD). Therefore, targeting RAGE could be an effective strategy to block RAGE signaling pathways. The present study aims to identify potential RAGE inhibitors against AD through comprehensive in-silico approaches. A total of 708,580 compounds were screened from numerous databases using structure-based virtual screening and ADMET evaluation. Further, the molecules with good glide scores were assessed by molecular docking studies. Subsequently, the top six ligands were subjected to molecular dynamic (MD) simulations for 100 ns and binding free energy calculations to check their stability with RAGE (PDB: 6XQ3). The per-residue decomposition analysis revealed that specific residues namely, GLY_20, ALA_21, LYS_39, GLU_50, LYS_52, ARG_98, GLN_100, LYS_110, ASN_112, and ARG_198 played a key role in the binding process. Furthermore, the trajectory analysis (DCCM and PCA) analyzed the dominant motions of residues and predicted the stability of protein-ligand complexes. In conclusion, the Hit-6 compound could be a promising candidate for targeting RAGE and deserves further consideration as an anti-Alzheimer drug.https://doi.org/10.1038/s41598-025-01271-0Alzheimer’s diseaseMolecular dynamicsVirtual screeningMM-GBSARAGE inhibitors |
| spellingShingle | Inderjeet Bhogal Vaishali Pankaj Sudeep Roy Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approaches Scientific Reports Alzheimer’s disease Molecular dynamics Virtual screening MM-GBSA RAGE inhibitors |
| title | Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approaches |
| title_full | Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approaches |
| title_fullStr | Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approaches |
| title_full_unstemmed | Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approaches |
| title_short | Identifying RAGE inhibitors as potential therapeutics for Alzheimer’s disease via integrated in-silico approaches |
| title_sort | identifying rage inhibitors as potential therapeutics for alzheimer s disease via integrated in silico approaches |
| topic | Alzheimer’s disease Molecular dynamics Virtual screening MM-GBSA RAGE inhibitors |
| url | https://doi.org/10.1038/s41598-025-01271-0 |
| work_keys_str_mv | AT inderjeetbhogal identifyingrageinhibitorsaspotentialtherapeuticsforalzheimersdiseaseviaintegratedinsilicoapproaches AT vaishalipankaj identifyingrageinhibitorsaspotentialtherapeuticsforalzheimersdiseaseviaintegratedinsilicoapproaches AT sudeeproy identifyingrageinhibitorsaspotentialtherapeuticsforalzheimersdiseaseviaintegratedinsilicoapproaches |