Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
Department of Applied Physics, Aalto University, Espoo, Finland.
Phys Chem Chem Phys. 2024 Feb 22;26(8):6903-6915. doi: 10.1039/d3cp03492e.
The identification of interaction between protein and ligand including binding positions and strength plays a critical role in drug discovery. Molecular docking and molecular dynamics (MD) techniques have been widely applied to predict binding positions and binding affinity. However, there are few works that describe the systematic exploration of the MD trajectory evolution in this context, potentially leaving out important information. To address the problem, we build a framework, Moira (molecular dynamics trajectory analysis), which enables automating the whole process ranging from docking, MD simulations and various analyses as well as visualizations. We utilized Moira to analyze 400 MD simulations in terms of their geometric features (root mean square deviation and protein-ligand interaction profiler) and energetics (molecular mechanics Poisson-Boltzmann surface area) for these trajectories. Finally, we demonstrate the performance of different analysis techniques in distinguishing native poses among four poses.
蛋白质与配体相互作用的鉴定,包括结合位置和强度,在药物发现中起着至关重要的作用。分子对接和分子动力学 (MD) 技术已广泛应用于预测结合位置和结合亲和力。然而,在这方面,很少有工作描述 MD 轨迹演化的系统探索,可能会遗漏重要信息。为了解决这个问题,我们构建了一个框架,名为 Moira(分子动力学轨迹分析),它可以实现从对接、MD 模拟到各种分析以及可视化的整个过程自动化。我们利用 Moira 来分析 400 个 MD 模拟的几何特征(均方根偏差和蛋白质-配体相互作用分析器)和能量学(分子力学泊松-玻尔兹曼表面区域)。最后,我们展示了不同分析技术在区分四个构象中的天然构象方面的性能。