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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Predicting protein-binding RNA nucleotides with consideration of binding partners
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Predicting protein-binding RNA nucleotides with consideration of binding partners

机译：考虑结合伴侣预测蛋白质结合RNA核苷酸

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In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in most performance measures. To the best of our knowledge, this is the first sequence-based prediction of protein-binding nucleotides in RNA which considers the binding partner of RNA. The new model will provide valuable information for designing biochemical experiments to find putative protein-binding sites in RNA with unknown structure. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译：近年来，已经开发了几种计算方法来预测蛋白质中的RNA结合位点。这些方法大多数都不考虑蛋白质的相互作用伴侣，因此即使蛋白质与不同的RNA结合，它们也可以预测给定蛋白质序列的相同RNA结合位点。与预测蛋白质中的RNA结合位点的问题不同，预测RNA中的蛋白质结合位点的问题很少受到关注，这主要是因为它困难得多并且平均显示出较低的准确性。在我们以前的研究中，我们开发了一种从RNA序列预测蛋白质结合核苷酸的方法。为了提高先前方法的预测准确性和实用性，我们开发了一种同时使用RNA和蛋白质序列数据的新方法。在这项研究中，我们确定了RNA和蛋白质分子的有效特征，并开发了一种新的支持向量机（SVM）模型，可从RNA和蛋白质序列数据预测蛋白质结合核苷酸。使用蛋白质和RNA序列数据的新模型的灵敏度为86.5％，特异性为86.2％，阳性预测值（PPV）为72.6％，阴性预测值（NPV）为93.8％，马修斯相关系数（ MCC）为0.69（十倍交叉验证）;在独立测试中，它的灵敏度为58.8％，特异性为87.4％，PPV为65.1％，NPV为84.2％，MCC为0.48。为了进行比较，我们建立了另一个预测模型，该预测模型仅使用RNA序列数据并将其运行在同一数据集上。在10倍交叉验证中，其灵敏度为85.7％，特异性为80.5％，PPV为67.7％，NPV为92.2％，MCC为0.63。在独立测试中，它的灵敏度为67.7％，特异性为78.8％，PPV为57.6％，NPV为85.2％，MCC为0.45。在交叉验证和独立测试中，在大多数性能指标中，同时使用RNA和蛋白质序列的新模型显示出比仅使用RNA序列数据的模型更好的性能。据我们所知，这是RNA中第一个基于序列的蛋白质结合核苷酸预测，其中考虑了RNA的结合伴侣。新模型将为设计生化实验提供有价值的信息，以发现结构未知的RNA中假定的蛋白质结合位点。 （C）2015 Elsevier Ireland Ltd.保留所有权利。

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