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Computational prediction of RNA-protein interaction partners and interfaces

机译:RNA-蛋白质相互作用伙伴和界面的计算预测

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摘要

RNA-protein interactions play important roles in fundamental cellular processes involved in human diseases, viral replication and defense against pathogens in plants, animals and microbes. However, the detailed recognition mechanisms underlying these interactions are poorly understood. To gain a better understanding of the molecular recognition code for RNA-protein interactions, this dissertation has three related goals: i) to develop methods for predicting RNA-protein interaction partners; ii) to develop an approach for predicting interfacial residues in both the RNA and protein components of RNA-protein complexes; and iii) to develop computational tools and resources for investigating RNA-protein interactions.First, we present machine learning classifiers for predicting RNA-protein interaction partners. The classifiers use the amino acid composition of proteins and the ribonucleotide composition of RNAs as input to predict whether a given RNA-protein pair interacts. We show that protein and RNA sequences alone (i.e., in the absence of any structural information) contain enough signal to allow reliable prediction of interaction partners.Second, we present RPISeq, a webserver that predicts the interaction probabilities of input RNA-protein pairs, using the above-mentioned machine learning classifiers. A comprehensive database of RNA-protein interactions, RPIntDB, is integrated with the webserver to allow users to search for homologous proteins and their known interacting RNA partners.Finally, we perform an analysis of contiguous interfacial amino acids and ribonucleotides in RNA-protein complexes for which structures are known. We generate a dataset of bipartite RNA-protein motifs that can be used to predict interfacial residues in both the RNA and protein sequences of a given RNA-protein pair simultaneously. We show that taking binding partner information into account leads to higher precision in the prediction of RNA-binding residues in proteins.Taken together, these studies have increased our understanding of how RNA and proteins interact.
机译:RNA-蛋白质相互作用在涉及人类疾病,病毒复制和防御植物,动物和微生物病原体的基本细胞过程中起重要作用。但是,对这些相互作用的详细识别机制了解甚少。为了更好地理解RNA-蛋白质相互作用的分子识别代码,本文具有三个相关的目标:i)开发预测RNA-蛋白质相互作用伙伴的方法; ii)开发一种预测RNA蛋白质复合物的RNA和蛋白质成分中的界面残基的方法; iii)开发用于研究RNA-蛋白质相互作用的计算工具和资源。首先,我们介绍了用于预测RNA-蛋白质相互作用伙伴的机器学习分类器。分类器使用蛋白质的氨基酸组成和RNA的核糖核苷酸组成作为输入,以预测给定的RNA-蛋白质对是否相互作用。我们展示了单独的蛋白质和RNA序列(即在没有任何结构信息的情况下)包含足够的信号,可以可靠地预测相互作用的伙伴。其次,我们展示了RPISeq,这是一个网络服务器,可预测输入RNA-蛋白对的相互作用概率,使用上述机器学习分类器。网络服务器集成了一个全面的RNA-蛋白质相互作用数据库RPIntDB,可让用户搜索同源蛋白质及其已知的相互作用RNA伴侣。最后,我们对RNA-蛋白质复合物中的连续界面氨基酸和核糖核苷酸进行了分析哪些结构是已知的。我们生成了两部分RNA-蛋白质基序的数据集,可用于同时预测给定RNA-蛋白质对的RNA和蛋白质序列中的界面残基。我们发现,将结合伴侣信息纳入考量可提高蛋白质中RNA结合残基的预测精度,这些研究加深了我们对RNA和蛋白质相互作用的了解。

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    Muppirala, Usha;

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  • 年度 2013
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  • 原文格式 PDF
  • 正文语种 en
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