原位翻译后修饰(PTM)串扰是指发生在蛋白质的相同残基位点上的不同类型的PTM之间的相互作用。串扰位点通常具有与具有单一PTM类型的那些不同的特性。针对后者特征的研究已经广泛进行,而对前者特征的研究很少。例如,已经研究了丝氨酸磷酸化(pS)和丝氨酸ADP核糖基化(SADPr)的特征,而它们的原位交叉茎(pSADPr)是未知的。在这项研究中,我们收集了3,250人pSADPr,7,520SADPr,151,227pS和80,096个未修饰的丝氨酸位点,并探索了pSADPr位点的特征。我们发现,与pS或未修饰的丝氨酸位点相比,pSADPr位点的特征与SADPr的特征更相似。此外,串扰位点可能被一些激酶家族磷酸化(例如,AGC,CAMK,STE和TKL)而不是其他(例如,CK1和CMGC)。此外,我们构建了三个分类器来预测pS数据集的pSADPr位点,SADPr数据集和蛋白质序列分开。我们在十倍交叉验证和独立测试数据集中构建并评估了五个深度学习分类器。我们还使用分类器作为基础分类器来开发一些基于堆叠的集成分类器以提高性能。最佳分类器的AUC值为0.700、0.914和0.954,用于识别来自SADPr的pSADPr位点,PS和未修饰的丝氨酸位点,分别。通过分离pSADPr和SADPr位点获得最低的预测精度,这与观察到pSADPr的特征比其他特征更类似于SADPr的观察结果一致。最后,我们开发了一种在线工具,用于基于CNNOH分类器广泛预测人类pSADPr位点,被称为EdeepSADPr。它可以通过http://edeepsadpr免费获得。bioinfogo.org/。我们希望我们的调查将促进对串扰的全面了解。
The in situ post-translational modification (PTM) crosstalk refers to the interactions between different types of PTMs that occur on the same residue site of a protein. The crosstalk sites generally have different characteristics from those with the single PTM type. Studies targeting the latter\'s features have been widely conducted, while studies on the former\'s characteristics are rare. For example, the characteristics of serine phosphorylation (pS) and serine ADP-ribosylation (SADPr) have been investigated, whereas those of their in situ crosstalks (pSADPr) are unknown. In this study, we collected 3,250 human pSADPr, 7,520 SADPr, 151,227 pS and 80,096 unmodified serine sites and explored the features of the pSADPr sites. We found that the characteristics of pSADPr sites are more similar to those of SADPr compared to pS or unmodified serine sites. Moreover, the crosstalk sites are likely to be phosphorylated by some kinase families (e.g., AGC, CAMK, STE and TKL) rather than others (e.g., CK1 and CMGC). Additionally, we constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. We also used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. The best classifiers had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites, respectively. The lowest prediction accuracy was achieved by separating pSADPr and SADPr sites, which is consistent with the observation that pSADPr\'s characteristics are more similar to those of SADPr than the rest. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/. We expect our investigation will promote a comprehensive understanding of crosstalks.