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首页> 外文期刊>Journal of Theoretical Biology >iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou's general PseAAC
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iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou's general PseAAC

机译：iLM-2L：通过将K间隙氨基酸对纳入Chou的一般PseAAC中来识别蛋白质赖氨酸甲基化位点及其甲基化程度的二级预测器

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摘要

As one of the most critical post-translational modifications, lysine methylation plays a key role in regulating various protein functions. In order to understand the molecular mechanism of lysine methylation, it is important to identify lysine methylation sites and their methylation degrees accurately. As the traditional experimental methods are time-consuming and labor-intensive, several computational methods have been developed for the identification of methylation sites. However, the prediction accuracy of existing computational methods is still unsatisfactory. Moreover, they are only focused on predicting whether a query lysine residue is a methylation site, without considering its methylation degrees. In this paper, a novel two-level predictor named iLM-2L is proposed to predict lysine methylation sites and their methylation degrees using composition of k-spaced amino acid pairs feature coding scheme and support vector machine algorithm. The 1st level is to identify whether a query lysine residue is a methylation site, and the 2nd level is to identify which methylation degree(s) the query lysine residue belongs to if it has been predicted as a methyllysine site in the 1st level identification. The iLM-2L achieves a promising performance with a Sensitivity of 76.46%, a Specificity of 91.90%, an Accuracy of 85.31% and a Matthew's correlation coefficient of 69.94% for the 1st level as well as a Precision of 84.81%, an accuracy of 79.35%, a recall of 80.83%, an Absolute_Ture of 73.89% and a Hamming_loss of 15.63% for the 2nd level in jackknife test. As illustrated by independent test, the performance of iLM-2L outperforms other existing lysine methylation site predictors significantly. A matlab software package for iLM-2L can be freely downloaded from https://github.com/juzhe1120/Matlab_Software/blob/master/iLM-2L_Matlab_Softwaresar. (C) 2015 Published by Elsevier Ltd.
机译：作为最关键的翻译后修饰之一，赖氨酸甲基化在调节各种蛋白质功能中起关键作用。为了了解赖氨酸甲基化的分子机制，准确识别赖氨酸甲基化位点及其甲基化程度非常重要。由于传统的实验方法既费时又费力，因此已经开发出几种计算方法来鉴定甲基化位点。但是，现有计算方法的预测精度仍然不能令人满意。此外，他们仅关注预测赖氨酸残基是否为甲基化位点，而不考虑其甲基化程度。本文提出了一种新型的两级预测因子iLM-2L，该预测因子利用k间隔氨基酸对特征编码方案和支持向量机算法的组成来预测赖氨酸甲基化位点及其甲基化程度。第一级是识别赖氨酸残基是否为甲基化位点，第二级是如果赖氨酸残基在第一级识别中已被预测为甲基赖氨酸位点，则确定该赖氨酸残基属于哪个甲基化程度。 iLM-2L的灵敏度为76.46％，特异性为91.90％，准确度为85.31％，第一级的Matthew相关系数为69.94％，精确度为84.81％，准确度达到了令人满意的水平。折刀测试中第二级的召回率为79.35％，召回率为80.83％，Absolute_Ture为73.89％，Hamming_loss为15.63％。如独立测试所示，iLM-2L的性能明显优于其他现有的赖氨酸甲基化位点预测因子。可以从https://github.com/juzhe1120/Matlab_Software/blob/master/iLM-2L_Matlab_Softwaresar免费下载iLM-2L的matlab软件包。 （C）2015由Elsevier Ltd.出版

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