Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH.
Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX.
PLoS Comput Biol. 2019 Feb 27;15(2):e1006678. doi: 10.1371/journal.pcbi.1006678. eCollection 2019 Feb.
We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases' substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations.
我们提出了 CoPhosK 来预测通过质谱 (MS) 检测到的磷酸肽底物的激酶-底物关联。该工具利用朴素贝叶斯框架和已知激酶-底物关联 (KSA) 的先验概率来生成预测结果。通过对 MS 数据进行挖掘，以揭示激酶底物的集体动态特征，通过对磷酸肽强度数据的相关性分析，该工具推断出数据中缺乏此类注释的大量底物的 KSA。我们使用公共 MS 数据，针对依赖于静态信息（例如序列、结构和相互作用）的现有预测 KSA 的方法，对该工具进行了基准测试，包括乳腺癌、结肠癌和卵巢癌模型。基准测试表明，当有静态信息可用时（约 35%的位点），共磷酸化分析可以显著提高预测性能，同时为其余位点提供可靠的预测，从而将实验 MS 数据提供的 KSA 数量增加了两倍，从而全面而可靠地描述了激酶-底物相互作用的范围，远远超出了当前的限制。
