EFICAz2: enzyme function inference by a combined approach enhanced by machine learning.
EFICAz2: enzyme function inference by a combined approach enhanced by machine learning.
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DOI：
10.1186/1471-2105-10-107
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发表时间：
2009-04-13
影响因子：
3
通讯作者：
Skolnick J
中科院分区：
文献类型：
--
作者：
Arakaki AK;Huang Y;Skolnick J
我们以前开发了EFICAz，一种酶功能推断方法，结合了非完全重叠组分方法的预测。原始EFICAz的四个组件中有两个是基于功能性鉴别残基（FDR）的检测。FDR区分同功能（根据感兴趣的EC编号分类）或异功能（用另一个EC编号注释或缺乏酶活性）的酶家族成员。两种基于FDR的组分中的每一种都与两种特定类型的酶家族中的一种相关联。EFICAz表现出高精度性能，除了当训练序列同一性的最大检验（MTTSI）低于30%时。为了提高EFICAz在该机制中的性能，我们：i）增加了预测组件的数量，ii）利用来自不同组件的共识信息进行最终EC编号分配。我们已经开发了两个新的EFICAz组件，类似于两个基于FDR的组件，其中同源和异源功能成员之间的区分是基于通过支持向量机模型对查询序列和与酶家族相关的多序列比对之间的所有比对位置进行评估。基准测试结果表明：i）新的基于SVM的组件优于其基于FDR的对应组件，以及ii）基于SVM和基于FDR的组件都生成唯一的预测。我们开发了分类树模型，以最佳方式联合收割机将来自六个EFICAz组件的结果组合到最终的EC数预测中。我们的方法的新实现EFICAz2与原始EFICAz相比，在MTTSI < 30%时表现出高度提高的预测精度，而预测召回率仅略有下降。通过EFICAz2和KEGG对人类蛋白质组的酶功能注释的比较分析表明：i）当两个来源对相同的蛋白质序列进行EC编号分配时，分配趋于一致，并且ii）EFICAz2产生比KEGG多得多的独特分配。性能基准测试和与KEGG的比较表明，EFICAz 2是一种功能强大且精确的酶功能注释工具，在基因组分析和代谢途径重建中具有多种应用。EFICAz 2网络服务可在以下网址获得：
We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz2, exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz2 and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz2 generates considerably more unique assignments than KEGG. Performance benchmarks and the comparison with KEGG demonstrate that EFICAz2 is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz2 web service is available at:
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影响因子：
3
作者：
Espadaler J;Eswar N;Querol E;Avilés FX;Sali A;Marti-Renom MA;Oliva B
通讯作者：
Oliva B
影响因子：
3
作者：
Jones CE;Brown AL;Baumann U
通讯作者：
Baumann U
DOI：
10.1073/pnas.0408677102
发表时间：
2005-05-03
影响因子：
11.1
作者：
Atchley, WR;Zhao, JP;Drüke, T
通讯作者：
Drüke, T
影响因子：
5.8
作者：
Arakaki, AK;Zhang, Y;Skolnick, J
通讯作者：
Skolnick, J
影响因子：
46.9
作者：
Kingsford, Carl;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.