Prediction of RNA binding proteins comes of age from low resolution to high resolution.
Prediction of RNA binding proteins comes of age from low resolution to high resolution.
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DOI：
10.1039/c3mb70167k
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发表时间：
2013-10
影响因子：
--
通讯作者：
Zhou Y
中科院分区：
文献类型：
--
作者：
Zhao H;Yang Y;Zhou Y
蛋白质-RNA相互作用的网络可能比蛋白质-蛋白质和蛋白质-DNA相互作用的网络更大，因为RNA转录物的编码是蛋白质的数十倍（例如，只有3%的人类基因组编码蛋白质），具有不同的功能和定位，并且从出生（转录）到死亡（降解）都受到蛋白质的控制。这个庞大的网络被最近几个实验发现的大量以前未知的RNA结合蛋白（RBP）所证明。与此同时，超过400个非冗余的蛋白质-RNA复合物结构（序列同一性为25%或更低）已存入蛋白质数据库。RBP的这些序列和结构资源为开发专用于RBP预测的计算技术提供了充足的数据，因为实验确定RNA结合功能是耗时且昂贵的。这篇综述比较了传统的基于机器学习的方法与新兴的基于模板的方法在几个层次的预测分辨率，从两个状态的结合/非结合预测，结合残基预测和蛋白质-RNA复合物结构预测。分析表明，这两种方法是互补的，它们的结合可能导致进一步的改进。
Networks of protein–RNA interactions is likely to be larger than protein–protein and protein–DNA interaction networks because RNA transcripts are encoded tens of times more than proteins (e.g. only 3% of human genome coded for proteins), have diverse function and localization, and are controlled by proteins from birth (transcription) to death (degradation). This massive network is evidenced by several recent experimental discoveries of large numbers of previously unknown RNA-binding proteins (RBPs). Meanwhile, more than 400 non-redundant protein–RNA complex structures (at 25% sequence identity or less) have been deposited into the protein databank. These sequences and structural resources for RBPs provide ample data for the development of computational techniques dedicated to RBP prediction, as experimentally determining RNA-binding functions is time-consuming and expensive. This review compares traditional machine-learning based approaches with emerging template-based methods at several levels of prediction resolution ranging from two-state binding/non-binding prediction, to binding residue prediction and protein–RNA complex structure prediction. The analysis indicates that the two approaches are complementary and their combinations may lead to further improvements.
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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：
5.6
作者：
Gabb, HA;Jackson, RM;Sternberg, MJE
通讯作者：
Sternberg, MJE
影响因子：
1.6
作者：
Li, Quan;Cao, Zanxia;Liu, Haiyan
通讯作者：
Liu, Haiyan
影响因子：
3
作者：
Cheng CW;Su EC;Hwang JK;Sung TY;Hsu WL
通讯作者：
Hsu WL
DOI：
10.1093/dnares/dsm011
发表时间：
2007-06-30
期刊：
DNA research : an international journal for rapid publication of reports on genes and genomes
影响因子：
--
作者：
Fujishima K;Komasa M;Kitamura S;Suzuki H;Tomita M;Kanai A
通讯作者：
Kanai A