SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties
SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties
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DOI：
10.1039/c5mb00853k
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发表时间：
2016-01-01
影响因子：
--
通讯作者：
Mollah, Md. Nurul Haque
中科院分区：
文献类型：
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作者：
Hasan, Md. Mehedi;Yang, Shiping;Mollah, Md. Nurul Haque
赖氨酸琥珀酰化是一种新出现的蛋白质翻译后修饰，在真核和原核细胞的细胞过程中起着重要的调控作用。然而，琥珀酰化修饰位点特别难以检测，因为所使用的实验技术通常耗时且昂贵。因此，一个准确的预测琥珀酰化位点的计算方法可以帮助研究人员设计他们的实验和理解琥珀酰化的分子机制。在这项研究中，一个新的计算工具，称为SuccinSite已经开发出来，预测蛋白质琥珀酰化位点，通过纳入三个序列编码，即，k-间隔氨基酸对、二进制和氨基酸索引属性。然后，用这些编码训练随机森林分类器以构建预测器。SuccinSite预测因子在5倍交叉验证集中的AUC得分为0.802，并且在综合独立测试集上的表现明显优于现有预测因子。此外，从训练的随机森林模型中提取信息特征和主要规则（即特征组合），以改进对预测因子的解释。最后，我们还编制了一个数据库，涵盖4411实验验证琥珀酰化蛋白与12 456赖氨酸琥珀酰化位点。综上所述，这些结果表明，SuccinSite将是一个有用的计算资源琥珀酰化位点的预测。Web服务器、数据集、源代码和数据库可在http://systbio.cau.edu.cn/SuccinSite/上免费获得。
Lysine succinylation is an emerging protein post-translational modification, which plays an important role in regulating the cellular processes in both eukaryotic and prokaryotic cells. However, the succinylation modification site is particularly difficult to detect because the experimental technologies used are often time-consuming and costly. Thus, an accurate computational method for predicting succinylation sites may help researchers towards designing their experiments and to understand the molecular mechanism of succinylation. In this study, a novel computational tool termed SuccinSite has been developed to predict protein succinylation sites by incorporating three sequence encodings, i.e., k-spaced amino acid pairs, binary and amino acid index properties. Then, the random forest classifier was trained with these encodings to build the predictor. The SuccinSite predictor achieves an AUC score of 0.802 in the 5-fold cross-validation set and performs significantly better than existing predictors on a comprehensive independent test set. Furthermore, informative features and predominant rules (i.e. feature combinations) were extracted from the trained random forest model for an improved interpretation of the predictor. Finally, we also compiled a database covering 4411 experimentally verified succinylation proteins with 12 456 lysine succinylation sites. Taken together, these results suggest that SuccinSite would be a helpful computational resource for succinylation sites prediction. The web-server, datasets, source code and database are freely available at http://systbio.cau.edu.cn/SuccinSite/.