Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Division of Biochemistry, Chemistry Department, Faculty of Science Tanta University, Tanta 31527, Egypt.
Comput Biol Chem. 2024 Jun;110:108037. doi: 10.1016/j.compbiolchem.2024.108037. Epub 2024 Feb 28.
Cancer is the most prevalent disease globally, which presents a significant challenge to the healthcare industry, with breast and lung cancer being predominant malignancies. This study used RNA-seq data from the TCGA database to identify potential biomarkers for lung and breast cancer. Tumor Necrosis Factor (TNFAIP8) and Sulfite Oxidase (SUOX) showed significant expression variation and were selected for further study using structure-based drug discovery (SBDD). Compounds derived from the Euphorbia ammak plant were selected for in-silico study with both TNFAIP8 and SUOX. Stigmasterol had the greatest binding scores (normalized scores of -8.53 kcal/mol and -9.69 kcal/mol) with both proteins, indicating strong stability in their binding pockets throughout the molecular dynamics' simulation. Although Stigmasterol first changed its initial conformation (RMSD = 0.5 nm with the starting conformation) in SUOX, it eventually reached a stable conformation (RMSD of 1.5 nm). The compound on TNFAIP8 showed a persistent shape (RMSD of 0.35 nm), indicating strong protein stability. The binding free energy of the complex was calculated using the MM/GBSA technique; TNFAIP8 had a ΔG of -24.98 kcal/mol, with TYR160 being the most significant residue, contributing -2.52 kcal/mol. On the other hand, the SUOX complex had a binding free energy of -16.87 kcal/mol, with LEU151 being the primary contributor (-1.17 kcal/mol). Analysis of the complexes' free energy landscape unveiled several states with minimum free energy, indicating robust interactions between the protein and ligand. In its conclusion, this work emphasises the favourable ability of Stigmasterol to bind with prospective targets for lung and breast cancer, indicating the need for more experimental study.
癌症是全球最普遍的疾病,对医疗保健行业构成了重大挑战,其中乳腺癌和肺癌是主要的恶性肿瘤。本研究使用 TCGA 数据库的 RNA-seq 数据,鉴定了肺癌和乳腺癌的潜在生物标志物。肿瘤坏死因子 (TNFAIP8) 和亚硫酸氧化酶 (SUOX) 表现出显著的表达变化,并选择使用基于结构的药物发现 (SBDD) 进行进一步研究。从大戟属植物中提取的化合物被选为 TNFAIP8 和 SUOX 的计算机研究。豆甾醇与两种蛋白质的结合评分最高(归一化评分分别为-8.53 kcal/mol 和-9.69 kcal/mol),表明在整个分子动力学模拟过程中,其在结合口袋中具有很强的稳定性。尽管豆甾醇首先在 SUOX 中改变了其初始构象(与起始构象相比 RMSD = 0.5 nm),但它最终达到了稳定的构象(RMSD 为 1.5 nm)。在 TNFAIP8 上的化合物显示出持续的形状(RMSD 为 0.35 nm),表明蛋白质稳定性很强。使用 MM/GBSA 技术计算复合物的结合自由能;TNFAIP8 的ΔG 为-24.98 kcal/mol,其中 TYR160 是最重要的残基,贡献了-2.52 kcal/mol。另一方面,SUOX 复合物的结合自由能为-16.87 kcal/mol,其中 LEU151 是主要贡献者(-1.17 kcal/mol)。对复合物自由能景观的分析揭示了几个具有最小自由能的状态,表明蛋白质和配体之间存在稳健的相互作用。在结论中,本工作强调了豆甾醇与肺癌和乳腺癌潜在靶点结合的有利能力,表明需要进行更多的实验研究。