Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders
Abstract Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhib...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83767-9 |
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| author | Muhammad Waleed Iqbal Muhammad Shahab Zakir ullah Guojun Zheng Irfan Anjum Gamal A. Shazly Atrsaw Asrat Mengistie Xinxiao Sun Qipeng Yuan |
| author_facet | Muhammad Waleed Iqbal Muhammad Shahab Zakir ullah Guojun Zheng Irfan Anjum Gamal A. Shazly Atrsaw Asrat Mengistie Xinxiao Sun Qipeng Yuan |
| author_sort | Muhammad Waleed Iqbal |
| collection | DOAJ |
| description | Abstract Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds. The performance evaluation of these models demonstrated SVM as the best model, which was further utilized to shortlist 51 highly potent compounds by screening an FDA-approved library of 1040 drugs. Molecular docking and molecular dynamic simulation were subsequently employed to evaluate the binding affinity and stability of the proposed compounds. Orlistat, acarbose and afatinib were identified as the potent leads, demonstrating stable conformations and stronger interactions, validated by root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bond analyses. Molecular Mechanics/Generalized Born Surface Area (MMGBSA) analysis validated their binding affinities by providing comparably lower binding free energies for orlistat (− 33.4743 ± 3.8908), acarbose (− 19.5455 ± 5.4702), and afatinib (− 36.4944 ± 5.4929) than the control, dasatinib (− 13.7785 ± 5.8058). Finally, toxicity analysis revealed orlistat and acarbose as the possible safer therapeutics by eliminating afatinib as it showed significant toxicity concerns. Our investigation supports the advance computational methods utilization in the field of drug discovery and suggest further experimental validation of proposed inhibitors of Src kinase for their safer use against inflammatory diseases. The ultimate aim of this study is to advance the development of effective treatments for inflammatory diseases, linked with Src overexpression. |
| format | Article |
| id | doaj-art-7f81c980db3a414bb1e43ca5f56f047c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7f81c980db3a414bb1e43ca5f56f047c2025-01-19T12:19:36ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-024-83767-9Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disordersMuhammad Waleed Iqbal0Muhammad Shahab1Zakir ullah2Guojun Zheng3Irfan Anjum4Gamal A. Shazly5Atrsaw Asrat Mengistie6Xinxiao Sun7Qipeng Yuan8State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical TechnologyState Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical TechnologyState Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical TechnologyState Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical TechnologyDepartment of Basic Medical Sciences, Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat UniversityDepartment of Pharmaceutics, College of Pharmacy, King Saud UniversityDepartment of Biology, Bahir Dar UniversityState Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical TechnologyState Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical TechnologyAbstract Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds. The performance evaluation of these models demonstrated SVM as the best model, which was further utilized to shortlist 51 highly potent compounds by screening an FDA-approved library of 1040 drugs. Molecular docking and molecular dynamic simulation were subsequently employed to evaluate the binding affinity and stability of the proposed compounds. Orlistat, acarbose and afatinib were identified as the potent leads, demonstrating stable conformations and stronger interactions, validated by root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bond analyses. Molecular Mechanics/Generalized Born Surface Area (MMGBSA) analysis validated their binding affinities by providing comparably lower binding free energies for orlistat (− 33.4743 ± 3.8908), acarbose (− 19.5455 ± 5.4702), and afatinib (− 36.4944 ± 5.4929) than the control, dasatinib (− 13.7785 ± 5.8058). Finally, toxicity analysis revealed orlistat and acarbose as the possible safer therapeutics by eliminating afatinib as it showed significant toxicity concerns. Our investigation supports the advance computational methods utilization in the field of drug discovery and suggest further experimental validation of proposed inhibitors of Src kinase for their safer use against inflammatory diseases. The ultimate aim of this study is to advance the development of effective treatments for inflammatory diseases, linked with Src overexpression.https://doi.org/10.1038/s41598-024-83767-9Src kinaseInflammatory diseasesMachine learningDockingMolecular dynamic simulation |
| spellingShingle | Muhammad Waleed Iqbal Muhammad Shahab Zakir ullah Guojun Zheng Irfan Anjum Gamal A. Shazly Atrsaw Asrat Mengistie Xinxiao Sun Qipeng Yuan Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders Scientific Reports Src kinase Inflammatory diseases Machine learning Docking Molecular dynamic simulation |
| title | Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders |
| title_full | Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders |
| title_fullStr | Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders |
| title_full_unstemmed | Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders |
| title_short | Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders |
| title_sort | integrating machine learning and structure based approaches for repurposing potent tyrosine protein kinase src inhibitors to treat inflammatory disorders |
| topic | Src kinase Inflammatory diseases Machine learning Docking Molecular dynamic simulation |
| url | https://doi.org/10.1038/s41598-024-83767-9 |
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