Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.
Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.
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通过整合蛋白质序列衍生和功能特征，在计算机上准确识别物种特异性乙酰化位点
DOI：
10.1038/srep05765
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发表时间：
2014-07-21
影响因子：
4.6
通讯作者：
Song J
中科院分区：
文献类型：
--
作者：
Li Y;Wang M;Wang H;Tan H;Zhang Z;Webb GI;Song J
赖氨酸乙酰化是一种可逆的翻译后修饰，在细胞因子信号转导、转录调控和细胞凋亡中发挥重要作用。为了充分了解乙酰化机理，识别底物和特定的乙酰化位点是至关重要的。实验鉴定往往既耗时又昂贵。替代的生物信息学方法具有成本效益，可以以高通量的方式使用，以产生相对准确的预测。在这里，我们开发了一种称为SSPKA的物种特定赖氨酸乙酰化预测方法，使用随机森林分类器，将序列派生和功能特征与两步特征选择相结合。特征重要度分析表明，功能特征首次应用于赖氨酸乙酰化位点预测，显著提高了预测性能。我们应用SSPKA模型来筛选整个人类蛋白质组，并确定许多以前没有确定的高置信度假定底物。这些结果以及实现的Java工具，可以作为有用的资源来阐明赖氨酸乙酰化的机制，并促进假设驱动的实验设计和验证。
Lysine acetylation is a reversible post-translational modification, playing an important role in cytokine signaling, transcriptional regulation and apoptosis. To fully understand acetylation mechanisms, identification of substrates and specific acetylation sites is crucial. Experimental identification is often time-consuming and expensive. Alternative bioinformatics methods are cost-effective and can be used in a high-throughput manner to generate relatively precise predictions. Here we develop a method termed as SSPKA for species-specific lysine acetylation prediction, using random forest classifiers that combine sequence-derived and functional features with two-step feature selection. Feature importance analysis indicates functional features, applied for lysine acetylation site prediction for the first time, significantly improve the predictive performance. We apply the SSPKA model to screen the entire human proteome and identify many high-confidence putative substrates that are not previously identified. The results along with the implemented Java tool, serve as useful resources to elucidate the mechanism of lysine acetylation and facilitate hypothesis-driven experimental design and validation.
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影响因子：
14.9
作者：
Jensen LJ;Kuhn M;Stark M;Chaffron S;Creevey C;Muller J;Doerks T;Julien P;Roth A;Simonovic M;Bork P;von Mering C
通讯作者：
von Mering C
影响因子：
14.9
作者：
Hunter S;Jones P;Mitchell A;Apweiler R;Attwood TK;Bateman A;Bernard T;Binns D;Bork P;Burge S;de Castro E;Coggill P;Corbett M;Das U;Daugherty L;Duquenne L;Finn RD;Fraser M;Gough J;Haft D;Hulo N;Kahn D;Kelly E;Letunic I;Lonsdale D;Lopez R;Madera M;Maslen J;McAnulla C;McDowall J;McMenamin C;Mi H;Mutowo-Muellenet P;Mulder N;Natale D;Orengo C;Pesseat S;Punta M;Quinn AF;Rivoire C;Sangrador-Vegas A;Selengut JD;Sigrist CJ;Scheremetjew M;Tate J;Thimmajanarthanan M;Thomas PD;Wu CH;Yeats C;Yong SY
通讯作者：
Yong SY
影响因子：
3
作者：
Faraggi, Eshel;Zhang, Tuo;Yang, Yuedong;Kurgan, Lukasz;Zhou, Yaoqi
通讯作者：
Zhou, Yaoqi
影响因子：
14.9
作者：
Hornbeck PV;Kornhauser JM;Tkachev S;Zhang B;Skrzypek E;Murray B;Latham V;Sullivan M
通讯作者：
Sullivan M
影响因子：
56.9
作者：
Choudhary, Chunaram;Kumar, Chanchal;Mann, Matthias
通讯作者：
Mann, Matthias