Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species.
Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species.
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DOI：
10.1093/nar/gkn619
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发表时间：
2008-11
影响因子：
14.9
通讯作者：
Ideker T
中科院分区：
文献类型：
--
作者：
Lee K;Chuang HY;Beyer A;Sung MK;Huh WK;Lee B;Ideker T
蛋白质的功能与其亚细胞定位密切相关。尽管已经通过系统的 GFP 融合测量了许多酵母蛋白的定位，但在生命的其他分支中的类似研究仍在进行中。在此期间，人们提出了各种机器学习方法来利用蛋白质的物理特征（例如氨基酸含量、疏水性、侧链质量和结构域组成）来预测定位。然而，利用蛋白质网络预测定位的工作相对较少。在这里，我们使用称为“分而治之 k-最近邻”(DC-kNN) 的分类框架，通过在蛋白质扩展的蛋白质-蛋白质相互作用邻域上整合广泛的蛋白质物理特征来预测蛋白质定位。这些预测的准确度明显高于两种众所周知的预测酵母中蛋白质定位的方法。使用新的 GFP 成像实验，我们表明基于网络的方法可以扩展和修改以前从高通量研究中做出的注释。最后，我们表明，我们的方法在果蝇和人类等高等真核生物中仍然具有高度预测性，其中大多数定位都是未知的，并且蛋白质网络覆盖范围较小。
The function of a protein is intimately tied to its subcellular localization. Although localizations have been measured for many yeast proteins through systematic GFP fusions, similar studies in other branches of life are still forthcoming. In the interim, various machine-learning methods have been proposed to predict localization using physical characteristics of a protein, such as amino acid content, hydrophobicity, side-chain mass and domain composition. However, there has been comparatively little work on predicting localization using protein networks. Here, we predict protein localizations by integrating an extensive set of protein physical characteristics over a protein's extended protein–protein interaction neighborhood, using a classification framework called ‘Divide and Conquer k-Nearest Neighbors’ (DC-kNN). These predictions achieve significantly higher accuracy than two well-known methods for predicting protein localization in yeast. Using new GFP imaging experiments, we show that the network-based approach can extend and revise previous annotations made from high-throughput studies. Finally, we show that our approach remains highly predictive in higher eukaryotes such as fly and human, in which most localizations are unknown and the protein network coverage is less substantial.
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影响因子：
14.9
作者：
Lee, KiYoung;Kim, Dae-Won;Na, DoKyun;Lee, Kwang H.;Lee, Doheon
通讯作者：
Lee, Doheon
影响因子：
14.9
作者：
Kawashima, S;Ogata, H;Kanehisa, M
通讯作者：
Kanehisa, M
影响因子：
64.8
作者：
Gavin, AC;Bösche, M;Superti-Furga, G
通讯作者：
Superti-Furga, G
影响因子：
64.8
作者：
Gavin, AC;Aloy, P;Superti-Furga, G
通讯作者：
Superti-Furga, G
DOI：
10.1093/protein/12.2.107
发表时间：
1999-02-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
Chou, KC;Elrod, DW
通讯作者：
Elrod, DW