Department of Chemistry, University of Michigan, Ann Arbor, 500 S State St, Ann Arbor, Michigan, 48109, USA.
Biophysics Program, University of Michigan, Ann Arbor, 500 S State St, Ann Arbor, Michigan, 48109, USA.
J Comput Aided Mol Des. 2022 Aug;36(8):563-574. doi: 10.1007/s10822-022-00472-3. Epub 2022 Aug 19.
Targeted covalent inhibitors (TCIs) are considered to be an important component in the toolbox of drug discovery and about 30% of currently marketed drugs are TCIs. Although these drugs raise concerns about toxicity, their high potencies and prolonged effects result in less-frequent drug dosing and wide therapeutic margins for patients. This leads to increased interests in developing new computational methods to identify novel covalent inhibitors. The implementation of successful in silico docking algorithms have the potential to provide significant savings of time and money in the discovery of lead compounds. In this paper, we describe the implementation and testing of a covalent docking methodology in Rigid CDOCKER and the optimization of the corresponding physics-based scoring function with an additional customizable covalent bond grid potential which represents the free energy change of bond formation between the ligand and the receptor. We optimize the covalent bond grid potential for different common covalent bond formation reaction in TCIs. The average runtime for docking one covalent compound is 15 minutes which is comparable or faster than other well-established covalent docking methods. We demonstrate comparable top rank accuracy compared with other covalent docking algorithms using the pose prediction benchmark dataset for covalent docking algorithms developed by the Keserű group. Finally, we construct a retrospective virtual screening benchmark dataset containing 8 different receptor targets with different covalent bond formation reactions. To our knowledge, this is the largest dataset for benchmarking covalent docking methods. We show that our new covalent docking algorithm has the ability to identify lead compounds among a large chemical space. The largest AUC value is 0.909 for the target receptor CATK and the warhead chemistry of the covalent inhibitors is addition to the aldehyde functionality.
靶向共价抑制剂 (TCIs) 被认为是药物发现工具包中的重要组成部分，目前市场上约有 30%的药物是 TCIs。尽管这些药物引起了人们对毒性的关注，但它们的高效力和长效性导致患者的药物剂量较少，治疗窗口较宽。这导致人们越来越有兴趣开发新的计算方法来识别新的共价抑制剂。成功实施基于计算机的对接算法有可能在先导化合物的发现中节省大量的时间和金钱。在本文中，我们描述了刚性 CDOCKER 中共价对接方法的实现和测试，并优化了相应的基于物理的评分函数，其中包含一个额外的可定制的共价键网格势能，代表配体和受体之间键形成的自由能变化。我们针对不同常见的 TCI 共价键形成反应对共价键网格势能进行了优化。对接一个共价化合物的平均运行时间为 15 分钟，与其他成熟的共价对接方法相当或更快。我们使用 Keserű 小组开发的共价对接算法的构象预测基准数据集，与其他共价对接算法相比，展示了可比的顶级准确性。最后，我们构建了一个包含 8 个不同受体靶点和不同共价键形成反应的回顾性虚拟筛选基准数据集。据我们所知，这是用于基准测试共价对接方法的最大数据集。我们表明，我们的新共价对接算法有能力在大型化学空间中识别先导化合物。对于目标受体 CATK，最大 AUC 值为 0.909，共价抑制剂的弹头化学是醛基功能团的加成。
