CNC - Center for Neuroscience and Cell Biology, Rua Larga, FMUC, Polo I, 1°andar, Universidade de Coimbra, 3004-517, Coimbra, Portugal.
Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, Padualaan 8, 3584CH, The Netherlands.
Biochim Biophys Acta Biomembr. 2017 Oct;1859(10):2021-2039. doi: 10.1016/j.bbamem.2017.07.008. Epub 2017 Jul 15.
Membrane proteins (MPs) play diverse and important functions in living organisms. They constitute 20% to 30% of the known bacterial, archaean and eukaryotic organisms' genomes. In humans, their importance is emphasized as they represent 50% of all known drug targets. Nevertheless, experimental determination of their three-dimensional (3D) structure has proven to be both time consuming and rather expensive, which has led to the development of computational algorithms to complement the available experimental methods and provide valuable insights.
This review highlights the importance of membrane proteins and how computational methods are capable of overcoming challenges associated with their experimental characterization. It covers various MP structural aspects, such as lipid interactions, allostery, and structure prediction, based on methods such as Molecular Dynamics (MD) and Machine-Learning (ML).
Recent developments in algorithms, tools and hybrid approaches, together with the increase in both computational resources and the amount of available data have resulted in increasingly powerful and trustworthy approaches to model MPs.
Even though MPs are elementary and important in nature, the determination of their 3D structure has proven to be a challenging endeavor. Computational methods provide a reliable alternative to experimental methods. In this review, we focus on computational techniques to determine the 3D structure of MP and characterize their binding interfaces. We also summarize the most relevant databases and software programs available for the study of MPs.
膜蛋白（MPs）在生物体内发挥着多样化且重要的功能。它们构成了已知细菌、古菌和真核生物基因组的 20%至 30%。在人类中，由于它们代表了所有已知药物靶点的 50%，因此其重要性尤为突出。然而，实验确定其三维（3D）结构既耗时又昂贵，这导致了计算算法的发展，以补充现有的实验方法并提供有价值的见解。
本篇综述强调了膜蛋白的重要性，以及计算方法如何能够克服其实验特性描述相关的挑战。它涵盖了各种 MP 结构方面，如脂质相互作用、变构和结构预测，基于分子动力学（MD）和机器学习（ML）等方法。
算法、工具和混合方法的最新发展，以及计算资源和可用数据量的增加，使得对 MPs 进行建模的方法越来越强大且可靠。
尽管 MPs 在本质上是基本且重要的，但确定其 3D 结构已被证明是一项具有挑战性的工作。计算方法为实验方法提供了可靠的替代方案。在本篇综述中，我们重点介绍了用于确定 MP 3D 结构和表征其结合界面的计算技术。我们还总结了用于研究 MPs 的最相关数据库和软件程序。
