Computational Biomedicine, Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Physics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; JARA Institute Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich GmbH, Jülich, Germany.
Computational Biomedicine, Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Physics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
Biochem Biophys Res Commun. 2018 Mar 29;498(2):366-374. doi: 10.1016/j.bbrc.2018.01.160. Epub 2018 Feb 2.
Membrane receptors constitute major targets for pharmaceutical intervention. Drug design efforts rely on the identification of ligand binding poses. However, the limited experimental structural information available may make this extremely challenging, especially when only low-resolution homology models are accessible. In these cases, the predictions may be improved by molecular dynamics simulation approaches. Here we review recent developments of multiscale, hybrid molecular mechanics/coarse-grained (MM/CG) methods applied to membrane proteins. In particular, we focus on our in-house MM/CG approach. It is especially tailored for G-protein coupled receptors, the largest membrane receptor family in humans. We show that our MM/CG approach is able to capture the atomistic details of the receptor/ligand binding interactions, while keeping the computational cost low by representing the protein frame and the membrane environment in a highly simplified manner. We close this review by discussing ongoing improvements and challenges of the current implementation of our MM/CG code.
膜受体是药物干预的主要靶点。药物设计工作依赖于配体结合构象的识别。然而，可用的有限的实验结构信息可能会使这变得极具挑战性，特别是当只有低分辨率的同源模型可用时。在这些情况下，通过分子动力学模拟方法可以提高预测结果。本文综述了多尺度、混合的力学/粗粒化（MM/CG）方法在膜蛋白中的最新进展。特别是，我们专注于我们内部的 MM/CG 方法。它特别针对 G 蛋白偶联受体进行了定制，G 蛋白偶联受体是人类最大的膜受体家族。我们表明，我们的 MM/CG 方法能够捕捉到受体/配体结合相互作用的原子细节，同时通过以高度简化的方式表示蛋白质框架和膜环境，保持低的计算成本。本文通过讨论我们的 MM/CG 代码当前实现的正在进行的改进和挑战来结束这篇综述。
