In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach
Prostate cancer (PCa) is the second most frequent and the fifth greatest cause of death in men. Although there are already therapies for early-stage PCa, their effectiveness in advanced PCa is limited, primarily because of medication resistance or poor efficacy. To find new therapeutic indications o...
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media Deutschland GmbH
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/45083/ http://umpir.ump.edu.my/id/eprint/45083/1/In%20silico%20identification%20of%20PPAR%CE%B3%20agonists%20from%20diffractaic%20acid.pdf |
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| author | Roney, Miah Uddin, Md. Nazim Ali Khan, Azmat Mohd Fadhlizil Fasihi, Mohd Aluwi Fatima, Sabiha Ahmad, Asrar |
| author_facet | Roney, Miah Uddin, Md. Nazim Ali Khan, Azmat Mohd Fadhlizil Fasihi, Mohd Aluwi Fatima, Sabiha Ahmad, Asrar |
| author_sort | Roney, Miah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Prostate cancer (PCa) is the second most frequent and the fifth greatest cause of death in men. Although there are already therapies for early-stage PCa, their effectiveness in advanced PCa is limited, primarily because of medication resistance or poor efficacy. To find new therapeutic indications or repurpose current medications, this project intends to use computational approaches to investigate possible anti-PCa compounds based on simulated screening of FDA-approved drug bank databases. The techniques used in this study include virtual screening of the drug bank database utilising a matrix or user molecule (Diffractaic acid; DA), integrated network pharmacology, molecular docking, detailed molecular dynamic simulation. The results showed that 18 DA analogues in total were chosen from the drug bank database and put through integrated network pharmacology. The KEGG enrichment analysis indicated that hsa05215: Prostate cancer is one of the most significant PCa enrichment signalling pathways which exhibited 18 protein–protein interactions including PDGFRB, PDGFRA, PPARG, GSK3B, MAP2K1, CREBBP, HSP90AA1, HSP90AB1, CHUK, PIK3CD, BRAF, PIK3CB, MTOR, AR, PIK3CA, PLAU, CDK2, and MAPK1. Subsequent molecular docking study with the best target protein (PPARγ) showed that the DB14929 molecule formed hydrogen bonds with the Gln273, Arg280, Arg288, and Ser342 residues and exhibited a high binding affinity ( − 10.5 kcal/mol) for the PPARγ agonist against PCa. Furthermore, molecular dynamic simulation showed that DB14929 formed a stable protein–ligand complex with RMSD, RMSF, Rg, and SASA values. Furthermore, the dynamic behaviour of the PPARγ protein linked to DB14929 in its conformational space was analysed using the PCA technique, demonstrating the excellent conformational space behaviour. Additionally, the free binding energy value of − 57.15 kcal/mol of DB14929 indicated that it could be an agonist of PPARγ of PCa. |
| first_indexed | 2025-11-15T03:57:45Z |
| format | Article |
| id | ump-45083 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:45Z |
| publishDate | 2025 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-450832025-07-15T03:21:03Z http://umpir.ump.edu.my/id/eprint/45083/ In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach Roney, Miah Uddin, Md. Nazim Ali Khan, Azmat Mohd Fadhlizil Fasihi, Mohd Aluwi Fatima, Sabiha Ahmad, Asrar QD Chemistry QP Physiology R Medicine (General) Prostate cancer (PCa) is the second most frequent and the fifth greatest cause of death in men. Although there are already therapies for early-stage PCa, their effectiveness in advanced PCa is limited, primarily because of medication resistance or poor efficacy. To find new therapeutic indications or repurpose current medications, this project intends to use computational approaches to investigate possible anti-PCa compounds based on simulated screening of FDA-approved drug bank databases. The techniques used in this study include virtual screening of the drug bank database utilising a matrix or user molecule (Diffractaic acid; DA), integrated network pharmacology, molecular docking, detailed molecular dynamic simulation. The results showed that 18 DA analogues in total were chosen from the drug bank database and put through integrated network pharmacology. The KEGG enrichment analysis indicated that hsa05215: Prostate cancer is one of the most significant PCa enrichment signalling pathways which exhibited 18 protein–protein interactions including PDGFRB, PDGFRA, PPARG, GSK3B, MAP2K1, CREBBP, HSP90AA1, HSP90AB1, CHUK, PIK3CD, BRAF, PIK3CB, MTOR, AR, PIK3CA, PLAU, CDK2, and MAPK1. Subsequent molecular docking study with the best target protein (PPARγ) showed that the DB14929 molecule formed hydrogen bonds with the Gln273, Arg280, Arg288, and Ser342 residues and exhibited a high binding affinity ( − 10.5 kcal/mol) for the PPARγ agonist against PCa. Furthermore, molecular dynamic simulation showed that DB14929 formed a stable protein–ligand complex with RMSD, RMSF, Rg, and SASA values. Furthermore, the dynamic behaviour of the PPARγ protein linked to DB14929 in its conformational space was analysed using the PCA technique, demonstrating the excellent conformational space behaviour. Additionally, the free binding energy value of − 57.15 kcal/mol of DB14929 indicated that it could be an agonist of PPARγ of PCa. Springer Science and Business Media Deutschland GmbH 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/45083/1/In%20silico%20identification%20of%20PPAR%CE%B3%20agonists%20from%20diffractaic%20acid.pdf Roney, Miah and Uddin, Md. Nazim and Ali Khan, Azmat and Mohd Fadhlizil Fasihi, Mohd Aluwi and Fatima, Sabiha and Ahmad, Asrar (2025) In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach. 3 Biotech, 15 (7). pp. 1-22. ISSN 2190-572X. (Published) https://doi.org/10.1007/s13205-025-04376-5 https://doi.org/10.1007/s13205-025-04376-5 |
| spellingShingle | QD Chemistry QP Physiology R Medicine (General) Roney, Miah Uddin, Md. Nazim Ali Khan, Azmat Mohd Fadhlizil Fasihi, Mohd Aluwi Fatima, Sabiha Ahmad, Asrar In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach |
| title | In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach |
| title_full | In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach |
| title_fullStr | In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach |
| title_full_unstemmed | In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach |
| title_short | In silico identification of PPARγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach |
| title_sort | in silico identification of pparγ agonists from diffractaic acid analogs in prostate cancer: a comprehensive computational approach |
| topic | QD Chemistry QP Physiology R Medicine (General) |
| url | http://umpir.ump.edu.my/id/eprint/45083/ http://umpir.ump.edu.my/id/eprint/45083/ http://umpir.ump.edu.my/id/eprint/45083/ http://umpir.ump.edu.my/id/eprint/45083/1/In%20silico%20identification%20of%20PPAR%CE%B3%20agonists%20from%20diffractaic%20acid.pdf |