Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors
Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors
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DOI：
10.3390/ijms21155183
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发表时间：
2020-08-01
影响因子：
5.6
通讯作者：
Gandhi, Neha S.
中科院分区：
文献类型：
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作者：
Boittier, Eric D.;Tang, Yat Yin;Gandhi, Neha S.
人类分化簇38（CD 38）是一种有前途的用于计算药物开发的蛋白质靶点，在许多生理和病理过程中起着至关重要的作用，主要通过上游调节控制细胞质Ca（2+）浓度的因子。最近，CD 38的小分子抑制剂被证明可以减缓与衰老和DNA损伤相关的途径。我们研究了7个对接程序的性能，以模拟蛋白质-配体与CD 38的相互作用。使用包含结晶的生物学相关底物的12个CD 38晶体结构的测试集来评估姿态预测。基于原始和预测之间的中值RMSD，每个程序的排名为维纳、AD 4> PLANTS、Gold、Glide、Molegro > rDock。对接具有已知亲和力的42种化合物以评估亲和力/排序预测的程序的准确性。基于得分能力的排名为：维纳、PLANTS > Glide、Gold > Molegro >> AutoDock 4 >> rDock。在表现最好的四个程序中，Glide是唯一一个没有表现出过度预测配体亲和力（基于配体大小）的评分函数。讨论了影响姿态预测和评分可靠性的因素。一般的限制和已知的偏见的评分功能进行检查，部分辅助使用分子指纹和随机森林分类。这种机器学习方法可用于系统地诊断与较差评分准确性相关的分子特征。
A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca(2+)concentrations. Recently, a small-molecule inhibitor of CD38 was shown to slow down pathways relating to aging and DNA damage. We examined the performance of seven docking programs for their ability to model protein-ligand interactions with CD38. A test set of twelve CD38 crystal structures, containing crystallized biologically relevant substrates, were used to assess pose prediction. The rankings for each program based on the median RMSD between the native and predicted were Vina, AD4 > PLANTS, Gold, Glide, Molegro > rDock. Forty-two compounds with known affinities were docked to assess the accuracy of the programs at affinity/ranking predictions. The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock. Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size. Factors that affect the reliability of pose prediction and scoring are discussed. General limitations and known biases of scoring functions are examined, aided in part by using molecular fingerprints and Random Forest classifiers. This machine learning approach may be used to systematically diagnose molecular features that are correlated with poor scoring accuracy.