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SAFlex: A structural alphabet extension to integrate protein structural flexibility and missing data information

机译:SAFlex:结构字母扩展整合了蛋白质结构的灵活性和缺失的数据信息

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摘要

In this paper, we describe SAFlex (Structural Alphabet Flexibility), an extension of an existing structural alphabet (HMM-SA), to better explore increasing protein three dimensional structure information by encoding conformations of proteins in case of missing residues or uncertainties. An SA aims to reduce three dimensional conformations of proteins as well as their analysis and comparison complexity by simplifying any conformation in a series of structural letters. Our methodology presents several novelties. Firstly, it can account for the encoding uncertainty by providing a wide range of encoding options: the maximum a posteriori, the marginal posterior distribution, and the effective number of letters at each given position. Secondly, our new algorithm deals with the missing data in the protein structure files (concerning more than 75% of the proteins from the Protein Data Bank) in a rigorous probabilistic framework. Thirdly, SAFlex is able to encode and to build a consensus encoding from different replicates of a single protein such as several homomer chains. This allows localizing structural differences between different chains and detecting structural variability, which is essential for protein flexibility identification. These improvements are illustrated on different proteins, such as the crystal structure of an eukaryotic small heat shock protein. They are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility.
机译:在本文中,我们描述了SAFlex(结构字母灵活性),它是现有结构字母(HMM-SA)的扩展,通过在残基缺失或不确定情况下编码蛋白质构象,更好地探索增加的蛋白质三维结构信息。 SA旨在通过简化一系列结构字母中的任何构象来减少蛋白质的三维构象及其分析和比较复杂性。我们的方法提出了一些新颖性。首先,它可以通过提供广泛的编码选项来解决编码不确定性:最大后验概率,边缘后验分布以及每个给定位置的有效字母数。其次,我们的新算法在严格的概率框架中处理蛋白质结构文件中的缺失数据(涉及蛋白质数据库中超过75%的蛋白质)。第三,SAFlex能够从单个蛋白质的不同复制品(例如,多个同聚物链)进行编码并建立共识编码。这样可以定位不同链之间的结构差异,并检测结构变异性,这对于蛋白质柔韧性鉴定至关重要。这些改进在不同的蛋白质上得到了说明,例如真核小热激蛋白的晶体结构。他们有望探索不断增加的蛋白质冗余数据,并获得对其灵活性的有用量化。

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