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首页> 外文期刊>Biochimica et biophysica acta. Biomembranes >Membrane proteins structures: A review on computational modeling tools
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Membrane proteins structures: A review on computational modeling tools

机译:膜蛋白结构:计算建模工具综述

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Abstract Background 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. Scope of review 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). Major conclusions 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. General significance 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. Graphical abstract Display Omitted Highlights ? Experimental characterization of membrane proteins is time consuming and expensive. ? Computational approaches are able to provide solutions for experimental problems. ? They rely mostly on molecular detail approaches or machine-learning techniques. ? A review on available computational methods for membrane protein study is provided. ]]>
机译:摘要背景膜蛋白(MPS)在生物体玩法多样和重要功能。它们构成了已知的细菌,古细菌和真核生物基因组的20%至30%。在人类中,因为它们代表了所有已知的药物靶标的50%其重要性被强调。尽管如此,他们的三维(3D)结构的实验测定已经被证明是既耗时又相当昂贵,这导致了计算算法的开发,以补充现有的实验方法,并提供有价值的见解。审查范围本文综述了膜蛋白的重要性,以及如何计算方法是能够克服他们的实验表征相关的挑战。它涵盖了各个MP结构方面,如脂质相互作用,变构和结构预测的基础上的方法,如分子动力学(MD)和机器学习(ML)。主要结论的算法,工具和混合方法,在这两种计算资源的增加和现有数据量一起最近的事态发展导致了日益强大和值得信赖的办法,以模型的国会议员。一般意义即使国会议员是小学和本质上重要的是,他们的3D结构的测定已被证明是一个具有挑战性的努力。计算方法提供了实验方法的可靠选择。在这次审查中,我们侧重于计算技术,以确定MP的三维结构和表征其结合界面。我们也可以总结为国会议员的研究最相关的数据库和软件程序。图形抽象显示省略了亮点?膜蛋白的实验表征是耗时且昂贵的。还计算方法能够用于实验的问题提供解决方案。还他们主要依靠分子细节的方法或机器学习技术。还提供对膜蛋白研究中可用的计算方法进行审查。 ]]>

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