In silico identification and functional annotation of yeast E3 ubiquitin ligase Rsp5 substrates

IF 0.2 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY International Journal of Data Mining and Bioinformatics Pub Date : 2015-10-01 DOI:10.1504/IJDMB.2015.072754
Xiaofeng Song, Lizhen Hu, P. Han, Xuejiang Guo, J. Sha
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Abstract

Rsp5, E3 ligases conserved from yeast to mammals, plays a key role in diverse processes in yeast. However, many of Rsp5 substrates are still unclear. Therefore we proposed an in silico method to recognise new substrates of Rsp5. To investigate the molecular determinants that affect the interaction between Rsp5 and its substrate, we have systematically analysed many features that perhaps correlated with the Rsp5 substrate recognition. It is found that PPxY motif, transmembrane region, disorder region and N-linked glycosylation modification are the most important features for substrate recognition. We have constructed an SVM-based classifier to recognise Rsp5 substrates, obtaining 81.5% sensitivity and 74.1% specificity averagely on ten independent testing dataset. We also applied the model on the whole yeast proteome, and identified -66 new Rsp5 substrates. Functional annotation reveals that half of these novel substrates function in the Rsp5 involved cell processes as Rsp5-interacting proteins.
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酵母E3泛素连接酶Rsp5底物的硅基鉴定和功能注释
Rsp5是一种从酵母到哺乳动物保守的E3连接酶,在酵母的多种过程中起着关键作用。然而,许多Rsp5底物仍不清楚。因此,我们提出了一种识别Rsp5新底物的计算机方法。为了研究影响Rsp5与其底物之间相互作用的分子决定因素,我们系统地分析了可能与Rsp5底物识别相关的许多特征。发现PPxY基序、跨膜区、紊乱区和n -链糖基化修饰是底物识别的最重要特征。我们构建了基于svm的Rsp5底物识别分类器,在10个独立测试数据集上平均获得81.5%的灵敏度和74.1%的特异性。我们还将该模型应用于整个酵母蛋白质组,鉴定出-66个新的Rsp5底物。功能注释显示,这些新底物中有一半作为Rsp5相互作用蛋白在Rsp5参与的细胞过程中起作用。
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CiteScore
1.00
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0.00%
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0
审稿时长
>12 weeks
期刊介绍: Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.
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