R基团搜索技术用于PTH类Tau蛋白抑制剂的分子设计

对26个PTH类Tau蛋白抑制剂进行了Topomer CoMFA研究, 建立了拟合及预测能力良好的Topomer CoMFA模型, 获得的模型拟合、 交互验证及外部预测的复相关系数分别为0.976, 0.603和0.795, 估计标准偏差和Fisher验证值F分别为0.110和115.778. 使用ZINC化合物数据集作为结构片段源, 通过三维定量构效关系(3D-QSAR)模型搜索具有特定活性贡献的R基团. 以样本中活性最高的1号分子过滤, R₁和R₂贡献值均提高了20%的片段分别有9个与2个. 以此交替取代1号样本的R₁与R₂, 得到18个新颖化合物并预测其活性, 其中的15个预测活性值优于模板分子. 研究结果表明, Topomer search可有效地用于分子设计, 所设计的分子为阿尔茨海默病(AD)药物的研发提供了新的候选物.

Molecular Design of PTH Derivatives as Tau Protein Inhibitors Using R-Group Search Technology

The microtubule-associated Tau protein aggregates into insoluble paired helical filaments (PHFs) that deposit as neurofibrillary tangles (NFTs) which may cause neurodegeneration in brains of those with Alzheimer’s disease (AD) and other related tauopathies; therefore, the drug discovery targeting on Tau protein has attracted an increasing number of researchers. In the last decade, most of the studies were focused on the approach to search for inhibitors of phosphorylation kniases, while only few put their eyes on the approach to discover inhibitors of Tau self-aggregation process. Phenylthiazolyl-Hydrazide (PTH) with the same core structure is a kind of molecules that directly inhibit Tau self-aggregation process. To illuminate the relationship between the structure of R-groups and molecular activities, and design new molecules with higher activity than the template molecule, topomer CoMFA was employed in three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of 26 phenylthiazolyl-hydrazides (PTH) derivatives as Tau protein inhibitors. The Topomer CoMFA model has good fitting ability and predictive ability. The number of principal components, r2,q2, rpred.2, SEE and F of the Topomer CoMFA model were 5, 0.976, 0.603, 0.795, 0.110 and 115.778, respectively. The model was used to search R-groups with special activity contribution from ZINC database. By No.1 molecule filtering, there were 9 R1-groups and 2 R2-groups with contribution values rising by 20%. We employed the 9 R1-groups and 2 R2-groups to alternately substitutes for the R1 and R2 of sample 1; as a result, we got a total of 18 new compounds and further predicted their activities using the Topomer CoMFA model obtained, and the activity of 15 of them were higher than that of the template molecule. This research showed that Topomer search could be effectively used in molecular design; meanwhile, the molecules designed provide new candidate drugs for drug development of Alzheimer’s disease.