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Mass Spectrometry Coupled Experiments and Protein Structure Modeling Methods

机译:质谱耦合实验和蛋白质结构建模方法

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With the accumulation of next generation sequencing data, there is increasing interest in the study of intra-species difference in molecular biology, especially in relation to disease analysis. Furthermore, the dynamics of the protein is being identified as a critical factor in its function. Although accuracy of protein structure prediction methods is high, provided there are structural templates, most methods are still insensitive to amino-acid differences at critical points that may change the overall structure. Also, predicted structures are inherently static and do not provide information about structural change over time. It is challenging to address the sensitivity and the dynamics by computational structure predictions alone. However, with the fast development of diverse mass spectrometry coupled experiments, low-resolution but fast and sensitive structural information can be obtained. This information can then be integrated into the structure prediction process to further improve the sensitivity and address the dynamics of the protein structures. For this purpose, this article focuses on reviewing two aspects: the types of mass spectrometry coupled experiments and structural data that are obtainable through those experiments; and the structure prediction methods that can utilize these data as constraints. Also, short review of current efforts in integrating experimental data in the structural modeling is provided.
机译:随着下一代测序数据的积累,人们对研究分子生物学中种内差异的兴趣越来越高,尤其是在疾病分析方面。此外,蛋白质的动力学被认为是其功能的关键因素。尽管蛋白质结构预测方法的准确性很高,但只要有结构模板,大多数方法仍对可能改变整体结构的关键点的氨基酸差异不敏感。同样,预测的结构固有地是静态的,并且不提供有关结构随时间变化的信息。仅通过计算结构预测来解决灵敏度和动态性是一项挑战。但是,随着各种质谱耦合实验的快速发展,可以获得低分辨率但快速而敏感的结构信息。然后可以将此信息整合到结构预测过程中,以进一步提高灵敏度并解决蛋白质结构的动力学问题。为此,本文重点介绍两个方面:质谱联结实验的类型和可通过这些实验获得的结构数据。以及可以利用这些数据作为约束的结构预测方法。此外,简要回顾了当前在结构模型中整合实验数据的工作。

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