Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan, China.
Department of Information Technology, University of Information and Communication Technology, Thai Nguyen 1000, Vietnam.
Genomics Proteomics Bioinformatics. 2020 Apr;18(2):208-219. doi: 10.1016/j.gpb.2018.10.010. Epub 2020 Jun 24.
Protein succinylation is a biochemical reaction in which a succinyl group (-CO-CH2-CH2-CO-) is attached to the lysine residue of a protein molecule. Lysine succinylation plays important regulatory roles in living cells. However, studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine succinylation. To facilitate this process, several tools have been proposed for the computational identification of succinylated lysine sites. In this study, we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition. Using experimentally verified lysine succinylated sites collected from public resources, the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo program. These findings enabled the adoption of an effective machine learning method, support vector machine, to train a predictive model with not only the amino acid composition, but also the composition of k-spaced amino acid pairs. After the selection of the best model using a ten-fold cross-validation approach, the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research articles. Finally, the selected model was used to develop a web-based tool, SuccSite, to aid the study of protein succinylation. Two proteins were used as case studies on the website to demonstrate the effective prediction of succinylation sites. We will regularly update SuccSite by integrating more experimental datasets. SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.
蛋白质琥珀酰化是一种生化反应，其中琥珀酰基（-CO-CH2-CH2-CO-）与蛋白质分子的赖氨酸残基结合。赖氨酸琥珀酰化在活细胞中发挥着重要的调节作用。然而，由于实验鉴定赖氨酸琥珀酰化的底物特异性具有一定的难度，该领域的研究受到了限制。为了促进这一过程，已经提出了几种工具来进行赖氨酸琥珀酰化位点的计算鉴定。在这项研究中，我们开发了一种基于氨基酸组成来研究赖氨酸琥珀酰化位点底物特异性的方法。使用从公共资源中收集的实验验证的赖氨酸琥珀酰化位点，通过 Two Sample Logo 程序表示琥珀酰化和非琥珀酰化位点之间在位置特异性氨基酸组成上的显著差异。这些发现使我们能够采用有效的机器学习方法，支持向量机，不仅使用氨基酸组成，还使用 k 间隔氨基酸对的组成来训练预测模型。使用十折交叉验证方法选择最佳模型后，所选模型在独立数据集上的表现明显优于基于从已发表的研究文章中手动提取的现有工具。最后，选择了模型用于开发一个基于网络的工具 SuccSite，以帮助研究蛋白质琥珀酰化。该网站使用两个蛋白质作为案例研究，以展示琥珀酰化位点的有效预测。我们将定期通过整合更多的实验数据集来更新 SuccSite。SuccSite 可在 http://csb.cse.yzu.edu.tw/SuccSite/ 免费访问。
