Patel Preeti, Patel Vijay K, Singh Avineesh, Jawaid Talha, Kamal Mehnaz, Rajak Harish
Medicinal Chemistry Research Laboratory, Institute of Pharmaceutical Sciences, Guru Ghasidas University, Bilaspur- 495 009, (C.G.), India.
Department of Pharmacology, College of Medicine, Dar Al Uloom University, Al Mizan St, Al Falah, Riyadh-13314, Saudi Arabia.
Curr Comput Aided Drug Des. 2019;15(2):145-166. doi: 10.2174/1573409914666180502113135.
Overexpression of Histone deacetylase 1 (HDAC1) is responsible for carcinogenesis by promoting epigenetic silence of tumour suppressor genes. Thus, HDAC1 inhibitors have emerged as the potential therapeutic leads against multiple human cancers, as they can block the activity of particular HDACs, renovate the expression of several tumour suppressor genes and bring about cell differentiation, cell cycle arrest and apoptosis.
The present research work comprises atom-based 3D-QSAR, docking, molecular dynamic simulations and DFT (density functional theory) studies on a diverse series of hydroxamic acid derivatives as selective HDAC1 inhibitors. Two pharmacophoric models were generated and validated by calculating the enrichment factors with the help of the decoy set. The Four different 3D-QSAR models i.e., PLS (partial least square) model, MLR (multiple linear regression) model, Field-based model and GFA (Genetic function approximation) model were developed using 'PHASE' v3.4 (Schrödinger) and Discovery Studio (DS) 4.1 software and validated using different statistical parameters like internal and external validation.
The results showed that the best PLS model has R2=0.991 and Q2=0.787, the best MLR model has R2= 0.993 and Q2= 0.893, the best Field-based model has R2= 0.974 and Q2= 0.782 and the best GFA model has R2= 0.868 and Q2= 0.782. Cross-validated coefficients, (rcv 2) of 0.967, 0.926, 0.966 and 0.829 was found for PLS model, MLR, Field based and GFA model, respectively, indicated the satisfactory correlativity and prediction. The docking studies were accomplished to find out the conformations of the molecules and their essential binding interactions with the target protein. The trustworthiness of the docking results was further confirmed by molecular dynamics (MD) simulations studies. Density Functional Theory (DFT) study was performed which promptly optimizes the geometry, stability and reactivity of the molecule during receptor-ligand interaction.
Thus, the present research work provides spatial fingerprints which would be beneficial for the development of potent HDAC1 inhibitors.
组蛋白去乙酰化酶1（HDAC1）的过表达通过促进肿瘤抑制基因的表观遗传沉默而导致癌变。因此，HDAC1抑制剂已成为对抗多种人类癌症的潜在治疗先导物，因为它们可以阻断特定HDAC的活性，恢复多种肿瘤抑制基因的表达，并导致细胞分化、细胞周期停滞和凋亡。
本研究工作包括对一系列不同的异羟肟酸衍生物作为选择性HDAC1抑制剂进行基于原子的3D-QSAR、对接、分子动力学模拟和DFT（密度泛函理论）研究。通过借助诱饵集计算富集因子生成并验证了两个药效团模型。使用“PHASE”v3.4（薛定谔）和Discovery Studio（DS）4.1软件开发了四种不同的3D-QSAR模型，即PLS（偏最小二乘）模型、MLR（多元线性回归）模型、基于场的模型和GFA（遗传函数逼近）模型，并使用内部和外部验证等不同统计参数进行验证。
结果表明，最佳PLS模型的R2 = 0.991，Q2 = 0.787；最佳MLR模型的R2 = 0.993，Q2 = 0.893；最佳基于场的模型的R2 = 0.974，Q2 = 0.782；最佳GFA模型的R2 = 0.868，Q2 = 0.782。PLS模型、MLR模型、基于场的模型和GFA模型的交叉验证系数（rcv2）分别为0.967、0.926、0.966和0.829，表明具有令人满意的相关性和预测性。进行对接研究以找出分子的构象及其与靶蛋白的关键结合相互作用。分子动力学（MD）模拟研究进一步证实了对接结果的可信度。进行了密度泛函理论（DFT）研究，该研究在受体-配体相互作用期间迅速优化了分子的几何形状、稳定性和反应性。
因此，本研究工作提供了空间指纹图谱，这将有助于开发有效的HDAC1抑制剂。
