A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition

The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies. Communicated by Ramaswamy H. Sarma

小分子抑制蛋白质-蛋白质相互作用（protein-protein interactions, PPIs）是极具潜力的药物研发策略。本研究旨在开发一套标准化研究流程，用于筛选可靶向抑制PPIs的候选小分子。为此，我们引入了基于知识的蛋白质-蛋白质相互作用抑制流程（Knowledge-based Protein-Protein Interaction Inhibition pipeline, KPI），以提升PPI抑制剂的发现效率。随后，我们对收录的中东抗病毒药用植物中的植物化学成分进行筛选，以识别潜在的新型冠状病毒肺炎（COVID-19）关键PPIs抑制剂。本研究依次开展了以下研究内容：1）明确PPIs中蛋白质的结合伴侣与结合界面；2）开展盲态配体-蛋白质抑制（ligand-protein inhibition, LPI）模拟；3）开展局部配体-蛋白质抑制模拟；4）模拟配体存在与缺失状态下蛋白质与其结合伴侣的相互作用；5）开展分子动力学模拟。此外，我们还对参与LPI过程的药效团基团进行了表征。最终筛选得到芦荟苷（Aloin）、染料木素（Genistein）、新葡萄糖芸苔素（Neoglucobrassicin）和芦丁（Rutin）作为靶向COVID-19相关PPIs的新型候选抑制剂。本研究同时提出可将KPI应用于药物重定位研究。本文由Ramaswamy H. Sarma转交刊发。