Classifying RNA-binding proteins based on electrostatic properties.
Classifying RNA-binding proteins based on electrostatic properties.
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DOI：
10.1371/journal.pcbi.1000146
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发表时间：
2008-08-08
影响因子：
4.3
通讯作者：
Mandel-Gutfreund Y
中科院分区：
文献类型：
--
作者：
Shazman S;Mandel-Gutfreund Y
蛋白质结构可以为蛋白质的生物学功能提供新的见解，并可以设计更好的实验来了解其生物学作用。此外，破译蛋白质与其他分子的相互作用有助于理解蛋白质在细胞过程中的功能。在这项研究中，我们应用机器学习方法根据RNA结合蛋白的三维结构对其进行分类。该方法是基于表征蛋白质表面上的静电补丁的独特属性。使用从静电补丁中提取的一般蛋白质特征和特定属性的集合，我们训练了一个支持向量机（SVM）来区分RNA结合蛋白和其他不结合核酸的带正电荷蛋白。具体而言，该方法被应用于具有RNA识别基序（RRM）的蛋白质，并成功地从参与蛋白质-蛋白质相互作用的RRM结构域中分类RNA结合蛋白。总的来说，该方法在分类RNA结合蛋白方面达到了88%的准确率，但它不能区分RNA和DNA结合蛋白。然而，通过应用多类SVM方法，我们能够根据RNA靶点对RNA结合蛋白进行分类，具体来说，它们是否结合核糖体RNA（rRNA），转移RNA（tRNA）或信使RNA（mRNA）。最后，我们在这里提出了一种创新的方法，不依赖于序列或结构同源性，并可应用于识别新的RNA结合蛋白与独特的折叠和/或结合基序。所有生物体中的基因表达在转录和转录后水平上都受到一系列复杂事件的调控。RNA结合蛋白在转录后事件中起关键作用，包括剪接、稳定性、转运和翻译。如今，越来越多的证据表明，许多其他细胞过程可能是由RNA介导的。因此，识别与RNA相互作用的新蛋白质对于揭示这些相互作用所涉及的细胞过程至关重要。在目前的研究中，我们提出了一个成功的计算方法来分类RNA结合蛋白质，并区分它们与其他蛋白质的基础上的结构和静电特性。我们测试的方法上一个独特的蛋白质结构域，RNA识别基序（RRM），介导RNA和蛋白质的相互作用。我们表明，我们可以区分RNA结合RRM从蛋白质结合RRM。此外，我们证明，我们可以分类已知的RNA结合蛋白的基础上，他们的RNA靶（mRNA，rRNA，或tRNA）。我们的方法不依赖于任何种类的进化信息，因此可以应用于识别具有新的RNA识别模式的RNA结合蛋白。
Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs. Gene expression in all living organisms is regulated by a complex set of events at both transcriptional and posttranscriptional levels. RNA-binding proteins play a key role in posttranscriptional events including splicing, stability, transport, and translation. Nowadays, there is increasing evidence that many other cellular processes may be mediated by RNA. Identifying new proteins involved in interaction with RNA is thus essential to unraveling the cellular processes in which these interactions are involved. In the current study we present a successful computational approach for classifying RNA-binding proteins and distinguishing them from other proteins based on structural and electrostatic properties. We test the method on a unique protein domain, the RNA recognition motif (RRM), which mediates both RNA and protein interactions. We show that we can discriminate RNA-binding RRMs from protein-binding RRMs. Further, we demonstrate that we can classify known RNA-binding proteins based on their RNA target (mRNA, rRNA, or tRNA). Our method does not rely on any kind of evolutionary information and thus can be applied to identify RNA-binding proteins with novel modes of RNA recognition.
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