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Classification of Ordered/Disordered Regions of Intrinsically Disordered Proteins Based on Comprehensive Sequence Analysis and Chou's Pseudo Amino Acid Composition Method

机译:基于综合序列分析和Chou's伪氨基酸组成法的内源性无序蛋白有序/无序区域分类

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摘要

Intrinsically disordered proteins (IDPs) are a kind of protein that plays important roles but lack well defined three-dimensional structure. In this paper, comprehensive sequence analysis is performed based on a larger dataset derived from the latest version of Disprot database. The results indicate that there are significant differences between the disordered regions and the ordered regions of IDPs. Further analysis shows that the disordered regions prefer hydrophilic amino acids such as D, E, K, Q, S, T and the ordered regions prefer hydrophobic amino acids such as F, I, L, M, V, W, Y. Then, a classification algorithm for disordered regions and ordered regions is proposed by incorporating the information of sequence composition, sequence order and long range correlation based on Chou's pseudo amino acid composition (PseAAC) method. The results show that the efficiency of the hybrid features can be improved in accordance with the diverse evaluation indices of ACC, MCC and AUC in comparison to the traditional components composition based numerical features.
机译:本质上无序的蛋白质(IDP)是一种蛋白质,起着重要的作用,但缺乏明确的三维结构。在本文中,基于从最新版本的Disprot数据库获得的更大数据集执行全面的序列分析。结果表明,IDP的无序区域和有序区域之间存在显着差异。进一步的分析表明,无序区域更喜欢亲水性氨基酸,例如D,E,K,Q,S,T,有序区域更喜欢疏水性氨基酸,例如F,I,L,M,V,W,Y。提出了一种融合无序区域和有序区域的分类算法,该算法结合了基于周氏伪氨基酸组成(PseAAC)方法的序列组成,序列顺序和远距离相关信息。结果表明,与传统的基于成分组成的数值特征相比,根据ACC,MCC和AUC的不同评估指标,可以提高混合特征的效率。

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