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Identification of protein-RNA interaction sites using the information of spatial adjacent residues

机译:使用空间相邻残基的信息识别蛋白质-RNA相互作用位点

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Background Protein-RNA interactions play an important role in numbers of fundamental cellular processes such as RNA splicing, transport and translation, protein synthesis and certain RNA-mediated enzymatic processes. The more knowledge of Protein-RNA recognition can not only help to understand the regulatory mechanism, the site-directed mutagenesis and regulation of RNA–protein complexes in biological systems, but also have a vitally effecting for rational drug design. Results Based on the information of spatial adjacent residues, novel feature extraction methods were proposed to predict protein-RNA interaction sites with SVM-KNN classifier. The total accuracies of spatial adjacent residue profile feature and spatial adjacent residues weighted accessibility solvent area feature are 78%, 67.07% respectively in 5-fold cross-validation test, which are 1.4%, 3.79% higher than that of sequence neighbour residue profile feature and sequence neighbour residue accessibility solvent area feature. Conclusions The results indicate that the performance of feature extraction method using the spatial adjacent information is superior to the sequence neighbour information approach. The performance of SVM-KNN classifier is little better than that of SVM. The feature extraction method of spatial adjacent information with SVM-KNN is very effective for identifying protein-RNA interaction sites and may at least play a complimentary role to the existing methods.
机译:背景技术蛋白质-RNA相互作用在许多基本细胞过程中发挥重要作用,例如RNA剪接,运输和翻译,蛋白质合成以及某些RNA介导的酶促过程。对蛋白质-RNA识别的更多了解不仅有助于理解生物系统中RNA-蛋白质复合物的调控机制,定点诱变和调控,而且对合理的药物设计也具有至关重要的作用。结果基于空间相邻残基的信息,提出了一种新的特征提取方法,以SVM-KNN分类器预测蛋白质-RNA的相互作用位点。在5倍交叉验证试验中,空间相邻残基谱特征和空间相邻残基加权可及溶剂面积特征的总准确度分别为78%,67.07%,比序列相邻残基谱特征高1.4%,3.79%和序列相邻残基可及性溶剂面积特征。结论结果表明,利用空间邻近信息的特征提取方法的性能优于序列邻近信息方法。 SVM-KNN分类器的性能略优于SVM。利用SVM-KNN进行空间邻近信息的特征提取方法对于识别蛋白质-RNA相互作用位点非常有效,并且至少可以对现有方法起到补充作用。

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