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首页> 外文会议>International Conference on Intelligent Computing;ICIC 2008 >Prediction of RNA-Binding Residues in Proteins Using the Interaction Propensities of Amino Acids and Nucleotides
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Prediction of RNA-Binding Residues in Proteins Using the Interaction Propensities of Amino Acids and Nucleotides

机译：使用氨基酸和核苷酸的相互作用倾向预测蛋白质中的RNA结合残基

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Recently several machine learning approaches have been attempted to predict RNA-binding residues in amino acid sequences. None of these consider interacting partners (i.e., RNA) for a given protein when predicting RNA-binding amino acids, so they always predict the same RNA-binding residues for a given protein even if the protein may bind to different RNA molecules. In this study, we present a support vector machine (SVM) classifier that takes an RNA sequence as well as a protein sequence as input and predicts potential RNA-binding residues in the protein. The interaction propensity between an amino acid and nucleotide obtained from the extensive analysis of the representative protein-RNA complexes in the Protein Data Bank (PDB) was encoded in the feature vector of the SVM classifier. Four biochemical properties of an amino acid (the side chain pKa value, hydrophobicity index, molecular mass, and accessible surface area) were also encoded in the feature vector. On a dataset of 145 protein sequences and 78 RNA sequences, the SVM classifier achieved a sensitivity of 72.30% and specificity of 78.03%.
机译：最近，已经尝试了几种机器学习方法来预测氨基酸序列中的RNA结合残基。在预测RNA结合氨基酸时，这些方法都不考虑给定蛋白质的相互作用伴侣（即RNA），因此即使蛋白质可能结合不同的RNA分子，他们也总是预测给定蛋白质的相同RNA结合残基。在这项研究中，我们提出了一种支持向量机（SVM）分类器，该分类器将RNA序列以及蛋白质序列作为输入，并预测蛋白质中潜在的RNA结合残基。通过对蛋白质数据库（PDB）中代表性蛋白质-RNA复合物的广泛分析获得的氨基酸与核苷酸之间的相互作用倾向被编码在SVM分类器的特征向量中。特征向量中还编码了氨基酸的四个生化特性（侧链pKa值，疏水性指数，分子量和可及表面积）。在145个蛋白质序列和78个RNA序列的数据集上，SVM分类器实现了72.30％的灵敏度和78.03％的特异性。

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