The tyrosine residue has been identified as suffering three major post-translational modifications (PTMs) including nitration, sulfation, and phosphorylation, which could be involved in different physiological and pathological processes. Multiple tyrosine residues of the whole protein may be modified concurrently, where PTM of a single tyrosine may affect modification of other neighboring tyrosine residues. Hence, it is significant and beneficial to predict nitration, sulfation, and phosphorylation of tyrosine residues in the whole protein sequence. Here, we introduce elastic net to perform feature selection and develop a predictor named TyrPred for predicting nitrotyrosine, sulfotyrosine, and kinase-specific tyrosine phosphorylation sites on the basis of support vector machine. We critically evaluate the performance of TyrPred and compare it with other existing tools. The satisfying results show that using elastic net to mine important features for training can considerably improve the prediction performance. Feature optimization indicates that evolutionary information is significant and contributes to the prediction model. The online tool is established at http://computbiol.ncu.edu.cn/TyrPred. We anticipate that TyrPred can provide useful complements to the existing approaches in this field.

中文翻译：

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弹性网对酪氨酸翻译后修饰的计算预测和分析

酪氨酸残基已被鉴定为遭受三个主要的翻译后修饰（PTM），包括硝化，硫酸化和磷酸化，这可能与不同的生理和病理过程有关。整个蛋白质的多个酪氨酸残基可以同时被修饰，其中单个酪氨酸的PTM可能会影响其他相邻酪氨酸残基的修饰。因此，预测整个蛋白质序列中酪氨酸残基的硝化，硫酸化和磷酸化具有重要意义。在这里，我们引入弹性网来进行特征选择，并在支持向量机的基础上开发出一个名为TyrPred的预测变量，用于预测硝基酪氨酸，磺基酪氨酸和激酶特异性酪氨酸的磷酸化位点。我们严格评估TyrPred的性能，并将其与其他现有工具进行比较。令人满意的结果表明，使用弹性网挖掘重要特征以进行训练可以大大提高预测性能。特征优化表明进化信息是重要的，并有助于预测模型。在线工具位于http://computbiol.ncu.edu.cn/TyrPred。我们期望TyrPred可以为该领域的现有方法提供有用的补充。ncu.edu.cn/TyrPred。我们期望TyrPred可以为该领域的现有方法提供有用的补充。ncu.edu.cn/TyrPred。我们期望TyrPred可以为该领域的现有方法提供有用的补充。