Yasir Muhammad, Park Jinyoung, Han Eun-Taek, Han Jin-Hee, Park Won Sun, Hassan Mubashir, Kloczkowski Andrzej, Chun Wanjoo
Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
Department of Medical Environmental Biology and Tropical Medicine, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
PLoS One. 2024 Dec 27;19(12):e0315245. doi: 10.1371/journal.pone.0315245. eCollection 2024.
The increasing utilization of deep learning models in drug repositioning has proven to be highly efficient and effective. In this study, we employed an integrated deep-learning model followed by traditional drug screening approach to screen a library of FDA-approved drugs, aiming to identify novel inhibitors targeting the TNF-α converting enzyme (TACE). TACE, also known as ADAM17, plays a crucial role in the inflammatory response by converting pro-TNF-α to its active soluble form and cleaving other inflammatory mediators, making it a promising target for therapeutic intervention in diseases such as rheumatoid arthritis. Reference datasets containing active and decoy compounds specific to TACE were obtained from the DUD-E database. Using RDKit, a cheminformatics toolkit, we extracted molecular features from these compounds. We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. Our trained model was subsequently used to predict the TACE inhibitory potential of FDA-approved drugs. From these predictions, Vorinostat was identified as a potential TACE inhibitor. Moreover, molecular docking and molecular dynamics simulation were conducted to validate these findings, using BMS-561392 as a reference TACE inhibitor. Vorinostat, originally an FDA-approved drug for cancer treatment, exhibited strong binding interactions with key TACE residues, suggesting its repurposing potential. Biological evaluation with RAW 264.7 cell confirmed the computational results, demonstrating that Vorinostat exhibited comparable inhibitory activity against TACE. In conclusion, our study highlights the capability of deep learning models to enhance virtual screening efforts in drug discovery, efficiently identifying potential candidates for specific targets such as TACE. Vorinostat, as a newly identified TACE inhibitor, holds promise for further exploration and investigation in the treatment of inflammatory diseases like rheumatoid arthritis.
深度学习模型在药物重新定位中的应用日益广泛,已被证明具有很高的效率和效果。在本研究中,我们采用了一种集成深度学习模型,随后采用传统药物筛选方法,对美国食品药品监督管理局(FDA)批准的药物库进行筛选,旨在鉴定靶向肿瘤坏死因子-α转换酶(TACE)的新型抑制剂。TACE,也称为ADAM17,通过将前肿瘤坏死因子-α转化为其活性可溶性形式并切割其他炎症介质,在炎症反应中起关键作用,使其成为类风湿性关节炎等疾病治疗干预的有希望的靶点。从DUD-E数据库中获得了包含TACE特异性活性和诱饵化合物的参考数据集。使用化学信息学工具包RDKit,我们从这些化合物中提取了分子特征。我们在DeepChem框架内应用了GraphConvMol模型,该模型利用图卷积网络,基于DUD-E数据集构建了一个预测模型。我们训练的模型随后用于预测FDA批准药物的TACE抑制潜力。从这些预测中,伏立诺他被鉴定为潜在的TACE抑制剂。此外,使用BMS-561392作为参考TACE抑制剂进行了分子对接和分子动力学模拟,以验证这些发现。伏立诺他最初是一种FDA批准的癌症治疗药物,与TACE关键残基表现出强烈的结合相互作用,表明其重新利用的潜力。用RAW 264.7细胞进行的生物学评估证实了计算结果,表明伏立诺他对TACE表现出相当的抑制活性。总之,我们的研究突出了深度学习模型在增强药物发现中的虚拟筛选工作方面的能力,能够有效地识别TACE等特定靶点的潜在候选物。伏立诺他作为一种新鉴定的TACE抑制剂,在类风湿性关节炎等炎症性疾病的治疗中具有进一步探索和研究的前景。