Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods. Hence, this work aimed to carry out the succinylation site prediction based on a deep learning model. The abundance of MS-verified succinylated peptides enabled the investigation of substrate site specificity of succinylation sites through sequence-based attributes, such as position-specific amino acid composition, the composition of k-spaced amino acid pairs (CKSAAP), and position-specific scoring matrix (PSSM). Additionally, the maximal dependence decomposition (MDD) was adopted to detect the substrate signatures of lysine succinylation sites by dividing all succinylated sequences into several groups with conserved substrate motifs. According to the results of ten-fold cross-validation, the deep learning model trained using PSSM and informative CKSAAP attributes can reach the best predictive performance and also perform better than traditional machine-learning methods. Moreover, an independent testing dataset that truly did not exist in the training dataset was used to compare the proposed method with six existing prediction tools. The testing dataset comprised of 218 positive and 2621 negative instances, and the proposed model could yield a promising performance with 84.40% sensitivity, 86.99% specificity, 86.79% accuracy, and an MCC value of 0.489. Finally, the proposed method has been implemented as a web-based prediction tool (CNN-SuccSite), which is now freely accessible at http://csb.cse.yzu.edu.tw/CNN-SuccSite/ .

译文

:琥珀酰化是蛋白质翻译后修饰(PTM)的一种,它可以在多种细胞过程中发挥重要作用。由于从高通量质谱法(MS)获得的位点特异性琥珀酰化肽的数量不断增加,已开发出各种工具来计算识别蛋白质上的琥珀酰化位点。但是,这些工具大多数都基于传统的机器学习方法来预测琥珀酰化位点。因此,这项工作旨在基于深度学习模型进行琥珀酰化位点的预测。经过MS验证的琥珀酰化肽的丰度使得可以通过基于序列的属性(例如位置特异性氨基酸组成,k间距氨基酸对的组成(CKSAAP)和位置特异性)研究琥珀酰化位点的底物位点特异性得分矩阵(PSSM)。另外,通过将所有琥珀酰化序列分成具有保守底物基序的几组,采用最大依赖性分解(MDD)来检测赖氨酸琥珀酰化位点的底物特征。根据十倍交叉验证的结果,使用PSSM和信息丰富的CKSAAP属性训练的深度学习模型可以达到最佳的预测性能,并且其性能也要优于传统的机器学习方法。此外,使用了训练数据集中实际上不存在的独立测试数据集,将所提出的方法与六个现有的预测工具进行了比较。测试数据集包括218个阳性实例和2621个阴性实例,所提出的模型可以产生有希望的性能,灵敏度为84.40%,特异性为86.99%,准确度为86.79%,MCC值为0.489。最后,所提出的方法已实现为基于Web的预测工具(CNN-SuccSite),现在可从http://csb.cse.yzu.edu.tw/CNN-SuccSite/免费访问。

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