Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information
Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information
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DOI：
10.1038/s41598-019-44548-x
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发表时间：
2019-06-04
影响因子：
4.6
通讯作者：
Kurata, Hiroyuki
中科院分区：
文献类型：
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作者：
Hasan, Md Mehedi;Rashid, Md Mamunur;Kurata, Hiroyuki
丝氨酸(S)和苏氨酸(T)上的蛋白质磷酸化已成为控制许多生物过程的关键装置。近年来，微生物体内的磷酸化因其在细胞生长和分裂等多种细胞过程中的重要作用而受到广泛关注。研究人员开发了一种新的机器学习预测器MPSite（微生物磷酸化位点预测器），利用序列特征的增强特征来识别微生物磷酸化位点。通过Wilcoxon秩和测试优化最终的特征向量。然后使用最优特征训练随机森林分类器来构建预测器。使用5倍交叉验证和独立数据集测试的基准调查表明，MPSite能够在s- t磷酸化位点预测上实现稳健的性能。在综合独立数据集上也优于其他现有方法。我们预计MPSite是一个强大的工具，用于蛋白质组范围内微生物磷酸化位点的预测，并促进磷酸化蛋白的假设驱动功能询问。在http://kurata14.bio.kyutech.ac.jp/MPSite/上可以免费获得一个带有管理数据集的web应用程序。
Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S-and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/.