Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy.
Dompé Farmaceutici SpA, Via Campo di Pile, I-67100 L'Aquila, Italy.
Int J Mol Sci. 2020 Jul 21;21(14):5152. doi: 10.3390/ijms21145152.
(1) Background: Virtual screening studies on the therapeutically relevant proteins of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) require a detailed characterization of their druggable binding sites, and, more generally, a convenient pocket mapping represents a key step for structure-based in silico studies; (2) Methods: Along with a careful literature search on SARS-CoV-2 protein targets, the study presents a novel strategy for pocket mapping based on the combination of pocket (as performed by the well-known FPocket tool) and docking searches (as performed by PLANTS or AutoDock/Vina engines); such an approach is implemented by the Pockets 2.0 plug-in for the VEGA ZZ suite of programs; (3) Results: The literature analysis allowed the identification of 16 promising binding cavities within the SARS-CoV-2 proteins and the here proposed approach was able to recognize them showing performances clearly better than those reached by the sole pocket detection; and (4) Conclusions: Even though the presented strategy should require more extended validations, this proved successful in precisely characterizing a set of SARS-CoV-2 druggable binding pockets including both orthosteric and allosteric sites, which are clearly amenable for virtual screening campaigns and drug repurposing studies. All results generated by the study and the Pockets 2.0 plug-in are available for download.
(1) 背景:对严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的治疗相关蛋白进行虚拟筛选研究需要对其可成药结合位点进行详细描述,更一般地说,方便的口袋映射是基于结构的计算研究的关键步骤;(2) 方法:除了对 SARS-CoV-2 蛋白靶标的仔细文献搜索外,本研究还提出了一种基于口袋(由著名的 FPocket 工具执行)和对接搜索(由 PLANTS 或 AutoDock/Vina 引擎执行)相结合的口袋映射新策略;这种方法由 VEGA ZZ 程序套件的 Pockets 2.0 插件实现;(3) 结果:文献分析允许在 SARS-CoV-2 蛋白中鉴定出 16 个有前途的结合腔,并且这里提出的方法能够识别它们,显示出的性能明显优于单独的口袋检测;(4) 结论:尽管所提出的策略需要更多的扩展验证,但它在精确描述一组 SARS-CoV-2 可成药结合口袋方面取得了成功,包括正位和变构位点,这些结合口袋显然适用于虚拟筛选和药物再利用研究。研究和 Pockets 2.0 插件生成的所有结果均可下载。