Ju Zhe, Cao Jun-Zhe
College of Science, Shenyang Aerospace University, 110136, People's Republic of China.
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China.
Anal Biochem. 2017 Oct 1;534:40-45. doi: 10.1016/j.ab.2017.07.011. Epub 2017 Jul 11.
As one of important protein post-translational modifications, N-formylation has been reported to be involved in various biological processes. The accurate identification of N-formylation sites is crucial for understanding the underlying mechanisms of N-formylation. Since the traditional experimental methods are generally labor-intensive and expensive, it is important to develop computational methods to predict N-formylation sites. In this paper, a predictor named NformPred is proposed to improve the prediction of N-formylation sites by using composition of k-spaced amino acid pairs encoding scheme and support vector machine algorithm. As illustrated by 10-fold cross-validation, NformPred achieves a promising performance with a Sensitivity of 86.00%, a Specificity of 96.25%, an Accuracy of 94.48% and a Matthew's correlation coefficient of 0.8099, which are much better than those of current computational method. Feature analysis shows that some k-spaced amino acid pairs such as 'IxxL', 'LV' and 'IxxxI' play the most important roles in the prediction of N-formylation sites. These predictive and analytical results suggest that NformPred might facilitate the identification of protein N-formylation. A free online service for NformPred is accessible at http://123.206.31.171/NformPred/.
作为重要的蛋白质翻译后修饰之一，N-甲酰化已被报道参与多种生物过程。准确识别N-甲酰化位点对于理解N-甲酰化的潜在机制至关重要。由于传统实验方法通常 labor-intensive 且昂贵，因此开发计算方法来预测N-甲酰化位点很重要。本文提出了一种名为NformPred的预测器，通过使用k间隔氨基酸对编码方案和支持向量机算法来改进N-甲酰化位点的预测。如10折交叉验证所示，NformPred具有良好的性能，灵敏度为86.00%，特异性为96.25%，准确率为94.48%，马修斯相关系数为0.8099，远优于当前的计算方法。特征分析表明，一些k间隔氨基酸对，如“IxxL”、“LV”和“IxxxI”在N-甲酰化位点的预测中起最重要作用。这些预测和分析结果表明，NformPred可能有助于蛋白质N-甲酰化的识别。可通过http://123.206.31.171/NformPred/访问NformPred的免费在线服务。
