SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications.
SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications.
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DOI：
10.1038/s41598-018-33951-5
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发表时间：
2018-10-19
影响因子：
4.6
通讯作者：
Chu YW
中科院分区：
文献类型：
--
作者：
Chang CC;Tung CH;Chen CW;Tu CH;Chu YW
大多数用于预测小泛素样修饰物（SUMO）结合位点（称为SUMO化）的现代工具使用算法，蛋白质的化学特征和共有基序。然而，这些工具很少考虑同一蛋白质内其他位点的翻译后修饰（PTM）信息对预测结果准确性的影响。本研究应用随机森林机器学习方法，以及模体筛选模型和特征选择组合机制，开发了一个SUMO化预测系统，称为SUMOgo。在预测方法上，PTM位点被编码为除了结构特征之外的新的功能特征，例如基于序列的二进制编码，编码蛋白质的化学特征，以及编码对PTM重要的二级结构信息。使用阳性测试数据和随机阴性数据的1：1组合进行20个预测循环。SUMOgo的Matthew相关系数达到0.511，高于目前常用的工具。本研究进一步验证了PTM在SUMOgo中的重要作用，并包括CREB结合蛋白（CREBBP）的案例研究。最后一个工具的网址是http://predictor.nchu.edu.tw/SUMOgo。
Most modern tools used to predict sites of small ubiquitin-like modifier (SUMO) binding (referred to as SUMOylation) use algorithms, chemical features of the protein, and consensus motifs. However, these tools rarely consider the influence of post-translational modification (PTM) information for other sites within the same protein on the accuracy of prediction results. This study applied the Random Forest machine learning method, as well as motif screening models and a feature selection combination mechanism, to develop a SUMOylation prediction system, referred to as SUMOgo. With regard to prediction method, PTM sites were coded as new functional features in addition to structural features, such as sequence-based binary coding, encoded chemical features of proteins, and encoded secondary structure information that is important for PTM. Twenty cycles of prediction were conducted with a 1:1 combination of positive test data and random negative data. Matthew’s correlation coefficient of SUMOgo reached 0.511, which is higher than that of current commonly used tools. This study further verified the important role of PTM in SUMOgo and includes a case study on CREB binding protein (CREBBP). The website for the final tool is http://predictor.nchu.edu.tw/SUMOgo.
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DOI：
10.1073/pnas.0408677102
发表时间：
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影响因子：
11.1
作者：
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