Background: Phosphorylation of histidine residues plays crucial roles in signaling pathways and cell metabolism in prokaryotes such as bacteria. While evidence has emerged that protein histidine phosphorylation also occurs in more complex organisms, its role in mammalian cells has remained largely uncharted. Thus, it is highly desirable to develop computational tools that are able to identify histidine phosphorylation sites. Result: Here, we introduce PROSPECT that enables fast and accurate prediction of proteome-wide histidine phosphorylation substrates and sites. Our tool is based on a hybrid method that integrates the outputs of two convolutional neural network (CNN)-based classifiers and a random forest-based classifier. Three features, including the one-of-K coding, enhanced grouped amino acids content (EGAAC) and composition of k-spaced amino acid group pairs (CKSAAGP) encoding, were taken as the input to three classifiers, respectively. Our results show that it is able to accurately predict histidine phosphorylation sites from sequence information. Our PROSPECT web server is user-friendly and publicly available at http://PROSPECT.erc.monash.edu/ . Conclusions: PROSPECT is superior than other pHis predictors in both the running speed and prediction accuracy and we anticipate that the PROSPECT webserver will become a popular tool for identifying the pHis sites in bacteria.

中文翻译：

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PROSPECT：用于预测蛋白质组氨酸磷酸化位点的网络服务器

背景：组氨酸残基的磷酸化在细菌等原核生物的信号通路和细胞代谢中起着至关重要的作用。虽然有证据表明蛋白质组氨酸磷酸化也发生在更复杂的生物体中，但它在哺乳动物细胞中的作用在很大程度上仍然未知。因此，非常需要开发能够识别组氨酸磷酸化位点的计算工具。结果：在这里，我们介绍了 PROSPECT，它可以快速准确地预测蛋白质组范围内的组氨酸磷酸化底物和位点。我们的工具基于一种混合方法，该方法集成了两个基于卷积神经网络 (CNN) 的分类器和一个基于随机森林的分类器的输出。三个特征，包括 one-of-K 编码，将增强的分组氨基酸含量（EGAAC）和k-空间氨基酸组对（CKSAAGP）编码的组成分别作为三个分类器的输入。我们的结果表明，它能够从序列信息中准确预测组氨酸磷酸化位点。我们的 PROSPECT 网络服务器易于使用，可在 http://PROSPECT.erc.monash.edu/ 上公开获取。结论：PROSPECT 在运行速度和预测准确性方面均优于其他 pHis 预测器，我们预计 PROSPECT 网络服务器将成为识别细菌中 pHis 位点的流行工具。我们的 PROSPECT 网络服务器易于使用，可在 http://PROSPECT.erc.monash.edu/ 上公开获取。结论：PROSPECT 在运行速度和预测准确性方面均优于其他 pHis 预测器，我们预计 PROSPECT 网络服务器将成为识别细菌中 pHis 位点的流行工具。我们的 PROSPECT 网络服务器易于使用，可在 http://PROSPECT.erc.monash.edu/ 上公开获取。结论：PROSPECT 在运行速度和预测准确性方面均优于其他 pHis 预测器，我们预计 PROSPECT 网络服务器将成为识别细菌中 pHis 位点的流行工具。