Molecular Diversity ( IF 3.9 ) Pub Date : 2021-07-30 , DOI: 10.1007/s11030-021-10280-w Ankit Ganeshpurkar 1 , Ravi Singh 1 , Shalini Shivhare 1 , Divya 1 , Devendra Kumar 1 , Gopichand Gutti 1 , Ravibhushan Singh 2 , Ashok Kumar 1 , Sushil Kumar Singh 1
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Structure-based drug design (SBDD) is an important in silico technique, used for the identification of enzyme inhibitors. Acetylcholinesterase (AChE), obtained from Electrophorus electricus (ee), is widely used for the screening of AChE inhibitors. It shares structural homology with the AChE of human and other organisms. Till date, the three-dimensional crystal structure of enzyme from ee is not available that makes it challenging to use the SBDD approach for the identification of inhibitors. A homology model was developed for eeAChE in the present study, followed by its structural refinement through energy minimisation. The docking protocol was developed using a grid dimension of 84 × 66 × 72 and grid point spacing of 0.375 Å for eeAChE. The protocol was validated by redocking a set of co-crystallised inhibitors obtained from mouse AChE, and their interaction profiles were compared. The results indicated a poor performance of the Autodock scoring function. Hence, a batch of machine learning-based scoring functions were developed. The validation results displayed an accuracy of 81.68 ± 1.73% and 82.92 ± 3.05% for binary and multiclass classification scoring function, respectively. The regression-based scoring function produced \(r^{2} ,\;Q^{2}_{f1}\) and \(Q^{2}_{f2}\) values of 0.94, 0.635 and 0.634, respectively.
Graphic abstract
中文翻译：
改进的机器学习评分函数用于识别 Electrophorus 的乙酰胆碱酯酶抑制剂
基于结构的药物设计 (SBDD) 是一项重要的计算机技术，用于鉴定酶抑制剂。乙酰胆碱酯酶 (AChE)，从Electrophoruselectricus获得(ee)，广泛用于筛选 AChE 抑制剂。它与人类和其他生物的 AChE 具有结构同源性。迄今为止，无法获得来自 ee 的酶的三维晶体结构，这使得使用 SBDD 方法鉴定抑制剂具有挑战性。在本研究中为 eeAChE 开发了同源模型，然后通过能量最小化对其结构进行了细化。对接协议是使用 84 × 66 × 72 的网格尺寸和 0.375 Å 的 eeAChE 网格点间距开发的。该协议通过重新对接从小鼠 AChE 获得的一组共结晶抑制剂进行了验证，并比较了它们的相互作用曲线。结果表明 Autodock 评分功能表现不佳。因此，开发了一批基于机器学习的评分功能。验证结果显示，二元和多类分类评分函数的准确度分别为 81.68 ± 1.73% 和 82.92 ± 3.05%。产生的基于回归的评分函数\(r^{2} ,\;Q^{2}_{f1}\)和\(Q^{2}_{f2}\)值分别为 0.94、0.635 和 0.634。
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