Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2 .

中文翻译：

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DLIGAND2：一种改进的基于知识的能量函数，使用距离尺度，有限，理想气体参考状态进行蛋白质-配体相互作用

基于结构的分子对接的性能在很大程度上取决于评分功能的准确性。一种重要的评分功能是从已知的蛋白质三维结构和/或蛋白质-配体复杂结构中获得的基于知识的潜力。这项研究旨在通过将蛋白质原子的表示形式从13 mol2原子类型扩展到167个残基特异性原子类型，来改善基于距离尺度有限理想气体参考（DFIRE）状态（DLIGAND）的基于知识的蛋白质配体潜力。 ，并使用最近更新的包含12,450个单体蛋白质链的数据集进行训练。我们发现更新版本DLIGAND2在预测本机复杂结构或对接生成的姿势的绑定亲和力方面比DLIGAND具有一致的改进。更重要的是，在基于DUD-E诱饵集的前1％预测中，DLIGAND2的富集因子比DLIGAND增加了52％，并且在所有三个基准测试中始终优于Autodock Vina和其他统计能量函数。我们还发现，对于虚拟筛选与DUD-E训练集不相同的新目标，DLIGAND2优于经验和机器学习方法。鉴于最佳性能是无参数的统计潜力，并且在所有性能指标中是最好的，因此DLIGAND2对于重新评估对接软件生成的姿势或在其他计分功能中充当一个术语应很有用。该程序可从https://github.com/sysu-yanglab/DLIGAND2获得。