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利用简单分子描述符评价各种丁酰胆碱酯酶抑制剂的关键结构特征。

Evaluation of the Key Structural Features of Various Butyrylcholinesterase Inhibitors Using Simple Molecular Descriptors.

机构信息

Institute for Medical Research and Occupational Health, Ksaverska cesta 2, HR-10 000 Zagreb, Croatia.

出版信息

Molecules. 2022 Oct 14;27(20):6894. doi: 10.3390/molecules27206894.

DOI:10.3390/molecules27206894
PMID:36296489
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9610766/
Abstract

In this study, we developed several QSAR models based on simple descriptors (such as topological and constitutional) to estimate butyrylcholinesterase (BChE) inhibition potency, p (or pIC), of a set of 297 (289 after exclusion of outliers) structurally different compounds. The models were similar to the best model that we obtained previously for acetylcholinesterase AChE and were based on the valence molecular connectivity indices of second and third order ( and ), the number of aliphatic hydroxyl groups (nOH), AlogP Ghose-Crippen octanol-water partition coeff. (logP), and O-060-atom-centred fragments (Al-O-Ar, Ar-O-Ar, R..O..R and R-O-C=X). The best models with two and three descriptors yielded = 0.787 and S.E. = 0.89, and = 0.827 and S.E. = 0.81, respectively. We also correlated nine scoring functions, calculated for 20 ligands whose complexes with BChE we found in the Protein Data Bank as crystal structures to p (or pIC). The best correlations yielded PLP1 and PLP2 (Piecewise Linear Pairwise potential functions) with = 0.619 and 0.689, respectively. Correlation with certain simple topological and constitutional descriptors yielded better results, e.g., ( = 0.730), on the same set of compounds ( = 20).

摘要

在这项研究中,我们基于简单的描述符(如拓扑和构象)开发了几个 QSAR 模型,以估计 297 种(排除异常值后为 289 种)结构不同的化合物对丁酰胆碱酯酶(BChE)抑制活性的 p(或 pIC)。这些模型与我们之前获得的对乙酰胆碱酯酶 AChE 的最佳模型相似,基于二阶和三阶的价分子连接性指数(和)、脂肪族羟基数量(nOH)、AlogP Ghose-Crippen 辛醇-水分配系数(logP)和 O-060-原子中心片段(Al-O-Ar、Ar-O-Ar、R..O..R 和 R-O-C=X)。具有两个和三个描述符的最佳模型分别产生 = 0.787 和 S.E. = 0.89, = 0.827 和 S.E. = 0.81。我们还将九个评分函数与 20 种配体相关联,这些配体的复合物我们在蛋白质数据库中找到了晶体结构与 BChE 的复合物,以 p(或 pIC)。最佳相关性产生了 PLP1 和 PLP2(分段线性成对势能函数),分别为 = 0.619 和 0.689。与某些简单的拓扑和构象描述符的相关性产生了更好的结果,例如,在同一组化合物( = 20)上, = 0.730。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/635bcca10d33/molecules-27-06894-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/f05149e80411/molecules-27-06894-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/e8954801a4e6/molecules-27-06894-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/c37e7ce01d45/molecules-27-06894-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/635bcca10d33/molecules-27-06894-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/f05149e80411/molecules-27-06894-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/e8954801a4e6/molecules-27-06894-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/c37e7ce01d45/molecules-27-06894-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a61/9610766/635bcca10d33/molecules-27-06894-g004.jpg

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