Department of Biochemical Sciences 'A. Rossi Fanelli', Sapienza Università di Roma, Rome 00185, Italy.
Bioinformatics. 2022 Sep 2;38(17):4233-4234. doi: 10.1093/bioinformatics/btac452.
The primary strategy for predicting the binding mode of small molecules to their receptors and for performing receptor-based virtual screening studies is protein-ligand docking, which is undoubtedly the most popular and successful approach in computer-aided drug discovery. The increased popularity of docking has resulted in the development of different docking algorithms and scoring functions. Nonetheless, it is unlikely that a single approach outperforms the others in terms of reproducibility and precision. In this ground, consensus docking techniques are taking hold.
We have developed DockingPie, an open source PyMOL plugin for individual, as well as consensus docking analyses. Smina, AutoDock Vina, ADFR and RxDock are the four docking engines that DockingPie currently supports in an easy and extremely intuitive way, thanks to its integrated docking environment and its GUI, fully integrated within PyMOL.
https://github.com/paiardin/DockingPie.
Supplementary data are available at Bioinformatics online.
预测小分子与其受体结合模式并进行基于受体的虚拟筛选研究的主要策略是蛋白配体对接,这无疑是计算机辅助药物发现中最流行和最成功的方法。对接的普及导致了不同对接算法和评分函数的发展。然而,就重现性和精度而言,单一方法不太可能优于其他方法。在这一背景下,共识对接技术正在兴起。
我们开发了 DockingPie,这是一个用于个体和共识对接分析的开源 PyMOL 插件。Smina、AutoDock Vina、ADFR 和 RxDock 是 DockingPie 当前支持的四个对接引擎,它通过其集成的对接环境和其 GUI,在 PyMOL 中完全集成,以一种简单且极其直观的方式实现。
https://github.com/paiardin/DockingPie。
补充数据可在 Bioinformatics 在线获得。