HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues
HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues
复制标题
DOI：
10.1186/s12864-018-5206-8
复制
发表时间：
2019-04-18
期刊：
影响因子：
4.4
通讯作者：
Tsunoda, Tatsuhiko
中科院分区：
文献类型：
--
作者：
Sharma, Alok;Lysenko, Artem;Tsunoda, Tatsuhiko
背景翻译后修饰被认为是控制蛋白质功能的重要机制，被认为与多种重要疾病有关。然而，使用基于实验室的技术来分析他们的情况仍然具有挑战性。因此，开发准确的计算方法来预测翻译后修饰对于这一领域的研究进展尤为重要。结果本工作探索了使用四个基于半球面曝光的特征来计算预测和甲基化位点。与大多数以前提出的专注于氨基酸共生模式的方法不同，我们能够证明基于蛋白质结构的特征可以提供足够的信息来实现良好的预测性能。该方法具有较高的灵敏度(0.9)、准确度(0.89)和马太相关系数(0.78~0.79)。我们将这些结果与最近发布的pSumo-CD方法进行了比较，并能够证明我们的方法在相同的评估数据集上具有更好的性能。结论所提出的预测因子HseSUMO使用氨基酸的半球状暴露来预测SUMO位点。与最先进的方法相比，该方法在基准数据集上显示了良好的结果。该研究的提取数据可以在https://github.com/YosvanyLopez/HseSUMO.上访问
BackgroundPost-translational modifications are viewed as an important mechanism for controlling protein function and are believed to be involved in multiple important diseases. However, their profiling using laboratory-based techniques remain challenging. Therefore, making the development of accurate computational methods to predict post-translational modifications is particularly important for making progress in this area of research.ResultsThis work explores the use of four half-sphere exposure-based features for computational prediction of sumoylation sites. Unlike most of the previously proposed approaches, which focused on patterns of amino acid co-occurrence, we were able to demonstrate that protein structural based features could be sufficiently informative to achieve good predictive performance. The evaluation of our method has demonstrated high sensitivity (0.9), accuracy (0.89) and Matthew's correlation coefficient (0.78-0.79). We have compared these results to the recently released pSumo-CD method and were able to demonstrate better performance of our method on the same evaluation dataset.ConclusionsThe proposed predictor HseSUMO uses half-sphere exposures of amino acids to predict sumoylation sites. It has shown promising results on a benchmark dataset when compared with the state-of-the-art method.The extracted data of this study can be accessed at https://github.com/YosvanyLopez/HseSUMO.