Enhanced automated function prediction using distantly related sequences and contextual association by PFP
Enhanced automated function prediction using distantly related sequences and contextual association by PFP
复制标题
DOI：
10.1110/ps.062153506
复制
发表时间：
2006-06-01
期刊：
影响因子：
8
通讯作者：
Kihara, Daisuke
中科院分区：
文献类型：
--
作者：
Hawkins, Troy;Luban, Stanislav;Kihara, Daisuke
最近的发展和出现的自动功能预测方法的动力是一个指数增长的洪水新的实验数据，其中的解释是阻碍了缺乏可靠的注释蛋白质缺乏实验表征或显着的同源物在当前的数据库。在这里，我们介绍PFP，一个自动化的功能预测服务器，提供最有可能的注释查询序列中的每一个基因本体的三个分支：生物过程，分子功能和细胞成分。而不是利用精确的模式匹配来识别这些蛋白质的序列和结构中的功能基序，我们设计了PFP，以增加功能注释的覆盖率，当一个详细的功能是不可预测的降低预测的分辨率。要做到这一点，我们扩展了传统的PSI-BLAST搜索提取和评分注释（GO条款）单独，包括注释从远亲序列，并应用一种新的数据挖掘工具，功能关联矩阵，得分强相关的注释对。我们表明，PFP可以正确分配功能，仅使用弱相似序列，具有比标准PSI-BLAST搜索更好的准确性和覆盖率，提高了五倍以上。PFP预测的最具描述性的注释（GO深度>= 8）可以识别GO中的重要子图，其准确率> 60%，并且与我们的基准集的100%覆盖率相似。我们还提供了PFP在自动功能预测特别兴趣小组会议在ISMB 2005（AFP-SIG '05）的自动功能预测服务器的评估中的出色性能的例子。
The impetus for the recent development and emergence of automated function prediction methods is an exponentially growing flood of new experimental data, the interpretation of which is hindered by a shortage of reliable annotations for proteins that lack experimental characterization or significant homologs in current databases. Here we introduce PFP, an automated function prediction server that provides the most probable annotations for a query sequence in each of the three branches of the Gene Ontology: biological process, molecular function, and cellular component. Rather than utilizing precise pattern matching to identify functional motifs in the sequences and structures of these proteins, we designed PFP to increase the coverage of function annotation by lowering resolution of predictions when a detailed function is not predictable. To do this we extend a traditional PSI-BLAST search by extracting and scoring annotations ( GO terms) individually, including annotations from distantly related sequences, and applying a novel data mining tool, the Function Association Matrix, to score strongly associated pairs of annotations. We show that PFP can correctly assign function using only weakly similar sequences with a significantly better accuracy and coverage than a standard PSI-BLAST search, improving it more than fivefold. The most descriptive annotations predicted by PFP (GO depth >= 8) can identify a significant subgraph in the GO with > 60% accuracy and similar to 100% coverage for our benchmark set. We also provide examples of the superb performance of PFP in an assessment of automated function prediction servers at the Automated Function Prediction Special Interest Group meeting at ISMB 2005 (AFP-SIG '05).