Analysis of protein function and its prediction from amino acid sequence
Analysis of protein function and its prediction from amino acid sequence
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DOI：
10.1002/prot.23029
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发表时间：
2011-07-01
影响因子：
2.9
通讯作者：
Radivojac, Predrag
中科院分区：
文献类型：
--
作者：
Clark, Wyatt T.;Radivojac, Predrag
了解蛋白质功能是理解分子水平生命的关键之一。在人类疾病的背景下，这也很重要，因为许多疾病是由于蛋白质功能改变而出现的。最新的相对廉价测序技术的可用性导致数千个完全或部分测序的基因组，具有数百万个功能上未表征的蛋白质。如此大量的数据，加上缺乏高通量实验测定来在功能上注释蛋白质，这归因于自动化功能预测的重要性。在这里，我们研究了通过基因本体论（GO）术语注释的蛋白质，并仅估计仅蛋白质序列的功能转移的准确性。我们发现，通过成对序列比对的GO项的传递仅是适度精确的，显示出众多的序列身份（SID）的影响很小（30-100％）。我们从氨基酸序列开发并评估了蛋白质功能，功能性注释者（FANN）的新预测指标。预测变量利用了多输出神经网络框架，该框架非常适合在功能术语之间同时建模依赖关系。实验提供了证据表明，fann-go（GO术语的预测因子；可从http://www.informatics.indiana.edu/predrag获得）优于标准方法，例如全球或本地SID以及GOTCHA的转移，该方法融合了这种方法，该方法融合了这种方法GO的结构。
Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in the context of human disease because many conditions arise as a consequence of alterations of protein function. The recent availability of relatively inexpensive sequencing technology has resulted in thousands of complete or partially sequenced genomes with millions of functionally uncharacterized proteins. Such a large volume of data, combined with the lack of high-throughput experimental assays to functionally annotate proteins, attributes to the growing importance of automated function prediction. Here, we study proteins annotated by Gene Ontology (GO) terms and estimate the accuracy of functional transfer from protein sequence only. We find that the transfer of GO terms by pairwise sequence alignments is only moderately accurate, showing a surprisingly small influence of sequence identity (SID) in a broad range (30-100%). We developed and evaluated a new predictor of protein function, functional annotator (FANN), from amino acid sequence. The predictor exploits a multioutput neural network framework which is well suited to simultaneously modeling dependencies between functional terms. Experiments provide evidence that FANN-GO (predictor of GO terms; available from http://www.informatics.indiana.edu/predrag) outperforms standard methods such as transfer by global or local SID as well as GOtcha, a method that incorporates the structure of GO.