Iqbal Muhammad Waleed, Shahab Muhammad, Ullah Zakir, Zheng Guojun, Anjum Irfan, Shazly Gamal A, Mengistie Atrsaw Asrat, Sun Xinxiao, Yuan Qipeng
State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
Department of Basic Medical Sciences, Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, 44000, Pakistan.
Sci Rep. 2025 Jan 13;15(1):1836. doi: 10.1038/s41598-024-83767-9.
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.
酪氨酸蛋白激酶Src在有利条件下的细胞增殖和生长中起关键作用，但其过表达和基因突变可导致各种炎症性疾病的进展。由于先前发现的抑制剂（如达沙替尼和博舒替尼）存在特异性和选择性问题，我们采用了机器学习与基于结构的药物重定位相结合的策略，以寻找新型、靶向且无毒的Src激酶抑制剂。使用已有的针对Src激酶的化合物的生物活性数据，训练了包括随机森林（RF）、k近邻（K-NN）、决策树和支持向量机（SVM）在内的不同机器学习模型。这些模型的性能评估表明SVM是最佳模型，通过筛选1040种FDA批准的药物库，该模型进一步筛选出51种高效化合物。随后采用分子对接和分子动力学模拟来评估所提出化合物的结合亲和力和稳定性。奥利司他、阿卡波糖和阿法替尼被确定为有效的先导化合物，通过均方根偏差（RMSD）、均方根波动（RMSF）、回转半径（RoG）和氢键分析验证，它们表现出稳定的构象和更强的相互作用。分子力学/广义玻恩表面积（MMGBSA）分析通过为奥利司他（-33.4743±3.8908）、阿卡波糖（-19.5455±5.4702）和阿法替尼（-36.4944±5.4929）提供比对照达沙替尼（-13.7785±5.8058）更低的结合自由能，验证了它们的结合亲和力。最后，毒性分析表明奥利司他和阿卡波糖可能是更安全的治疗药物，因为阿法替尼显示出明显的毒性问题而被排除。我们的研究支持在药物发现领域利用先进的计算方法，并建议对所提出的Src激酶抑制剂进行进一步的实验验证，以更安全地用于治疗炎症性疾病。本研究的最终目的是推动针对与Src过表达相关的炎症性疾病的有效治疗方法的开发。
