Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.
Department of Periodontology, Adhiparasakthi Dental College and Hospital, Melmaruvathur 603319, India.
Molecules. 2022 Apr 25;27(9):2749. doi: 10.3390/molecules27092749.
Despite extensive research on periodontitis and rheumatoid arthritis, the underlying molecular connectivity between these condition remains largely unknown. This research aimed to integrate periodontitis and rheumatoid arthritis gene expression profiles to identify interconnecting genes and focus to develop a common lead molecule against these inflammatory conditions.
Differentially expressed genes (DEGs) of periodontitis and rheumatoid arthritis were identified from the datasets retrieved from the Gene Expression Omnibus database. The network was constructed by merging DEGs, and the interconnecting genes were identified and ranked using GeneMANIA. For the selected top ranked gene, the potential inhibitor was searched using FINDSITE2.0. Subsequently, the molecular docking and molecular dynamics were performed to determine the binding efficiency and protein-ligand complex stability, respectively.
From the network analysis, IFN-induced protein 44-like (IFI44L) was identified as a top ranked gene involved in most of the immunological pathway. With further virtual screening of 6507 molecules, vemurafenib was identified to be the best fit against the IFI44L target. The binding energy and stability of IFI44L with vemurafenib were investigated using molecular docking and molecular dynamics simulation. Docking results show binding energy of -7.7 Kcal/mol, and the simulation results show stability till 100 ns.
The identified IFI44L may represent a common drug target for periodontitis and rheumatoid arthritis. Vemurafenib could be a potent anti-inflammatory drug for both diseases.
尽管对牙周炎和类风湿性关节炎进行了广泛的研究,但这些疾病之间的潜在分子联系在很大程度上仍不清楚。本研究旨在整合牙周炎和类风湿性关节炎的基因表达谱,以识别相互关联的基因,并集中精力开发针对这些炎症性疾病的通用先导分子。
从基因表达综合数据库中检索到的数据集识别牙周炎和类风湿性关节炎的差异表达基因(DEGs)。通过合并 DEGs 构建网络,并使用 GeneMANIA 识别和排名相互关联的基因。对于选定的排名最高的基因,使用 FINDSITE2.0 搜索潜在抑制剂。随后,进行分子对接和分子动力学模拟,分别确定结合效率和蛋白-配体复合物稳定性。
从网络分析中,干扰素诱导蛋白 44 样(IFI44L)被确定为参与大多数免疫途径的顶级基因。通过对 6507 种分子的进一步虚拟筛选,发现vemurafenib 是最适合 IFI44L 靶标的分子。使用分子对接和分子动力学模拟研究 IFI44L 与 vemurafenib 的结合能和稳定性。对接结果显示结合能为-7.7 Kcal/mol,模拟结果显示稳定性可达 100 ns。
鉴定的 IFI44L 可能代表牙周炎和类风湿性关节炎的共同药物靶点。vemurafenib 可能是这两种疾病的有效抗炎药物。