Computational identification of protein methylation sites through bi-profile Bayes feature extraction.
Computational identification of protein methylation sites through bi-profile Bayes feature extraction.
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DOI：
10.1371/journal.pone.0004920
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发表时间：
2009
期刊：
影响因子：
3.7
通讯作者：
Ngai SM
中科院分区：
文献类型：
--
作者：
Shao J;Xu D;Tsai SN;Wang Y;Ngai SM
蛋白质甲基化是一种可逆翻译后修饰 (PTM)，在转录活性、DNA 修复等许多细胞过程中发挥着至关重要的作用。在没有先验知识的情况下对蛋白质上的甲基化位点进行实验鉴定既昂贵又耗时。甲基化位点的计算机预测不仅可以为研究提供有关候选位点的信息以供进一步确定，而且还有助于进行下游表征和位点特异性研究。在本研究中，采用基于双轮廓贝叶斯特征提取与支持向量机（SVM）相结合的新方法来开发从一级序列预测蛋白质甲基化位点（BPB-PPMS）的模型。甲基化可发生在许多残基上，包括精氨酸、赖氨酸、组氨酸、谷氨酰胺和脯氨酸。目前，BPB-PPMS仅用于预测多肽上赖氨酸和精氨酸残基的甲基化状态，因为缺乏足够的实验验证数据来建立和训练其他残基的预测模型。在5倍交叉验证实验中，BPB-PPMS对精氨酸的灵敏度为74.71%，特异性为94.32%，准确度为87.98%，对赖氨酸的灵敏度为70.05%，特异性为77.08%，准确度为75.51%。从交叉验证实验和独立数据集测试获得的结果表明，此处提出的 BPB-PPMS 可能有助于蛋白质甲基化的识别和注释。此外，BPB-PPMS 可以扩展为轻松构建其他类型 PTM 位点的预测器。对于公众访问，BPB-PPMS 可从 http://www.bioinfo.bio.cuhk.edu.hk/bpbppms 获取。
Protein methylation is one type of reversible post-translational modifications (PTMs), which plays vital roles in many cellular processes such as transcription activity, DNA repair. Experimental identification of methylation sites on proteins without prior knowledge is costly and time-consuming. In silico prediction of methylation sites might not only provide researches with information on the candidate sites for further determination, but also facilitate to perform downstream characterizations and site-specific investigations. In the present study, a novel approach based on Bi-profile Bayes feature extraction combined with support vector machines (SVMs) was employed to develop the model for Prediction of Protein Methylation Sites (BPB-PPMS) from primary sequence. Methylation can occur at many residues including arginine, lysine, histidine, glutamine, and proline. For the present, BPB-PPMS is only designed to predict the methylation status for lysine and arginine residues on polypeptides due to the absence of enough experimentally verified data to build and train prediction models for other residues. The performance of BPB-PPMS is measured with a sensitivity of 74.71%, a specificity of 94.32% and an accuracy of 87.98% for arginine as well as a sensitivity of 70.05%, a specificity of 77.08% and an accuracy of 75.51% for lysine in 5-fold cross validation experiments. Results obtained from cross-validation experiments and test on independent data sets suggest that BPB-PPMS presented here might facilitate the identification and annotation of protein methylation. Besides, BPB-PPMS can be extended to build predictors for other types of PTM sites with ease. For public access, BPB-PPMS is available at http://www.bioinfo.bio.cuhk.edu.hk/bpbppms.
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组蛋白精氨酸甲基化调节早期小鼠胚胎中的多能性。
影响因子：
64.8
作者：
Torres-Padilla, Maria-Elena;Parfitt, David-Emlyn;Kouzarides, Tony;Zernicka-Goetz, Magdalena
通讯作者：
Zernicka-Goetz, Magdalena
影响因子：
5.8
作者：
Wang, Chunlin;Ding, Chris;Holbrook, Stephen R.
通讯作者：
Holbrook, Stephen R.
影响因子：
5.4
作者：
Xie, BD;Wainberg, MA;Frankel, AD
通讯作者：
Frankel, AD
影响因子：
3
作者：
Caragea C;Sinapov J;Silvescu A;Dobbs D;Honavar V
通讯作者：
Honavar V
影响因子：
21.3
作者：
Jansson, Martin;Durant, Stephen T.;La Thangue, Nicholas B.
通讯作者：
La Thangue, Nicholas B.