MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.
MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.
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Metago：通过低分辨率蛋白质结构预测和蛋白质 - 蛋白质网络映射预测非同源蛋白的基因本体论。
DOI：
10.1016/j.jmb.2018.03.004
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发表时间：
2018-07-20
影响因子：
5.6
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Zhang C;Zheng W;Freddolino PL;Zhang Y
基于同源性的迁移仍然是计算蛋白质功能注释的主要方法，但当查询和模板之间的序列一致性降低到30%以下时，它变得越来越不可靠。我们提出了一种新的管道MetaGO，该管道将基于序列同源的注释与低分辨率结构预测和比较相结合，并基于伙伴同源的蛋白质-蛋白质网络映射来推断蛋白质的基因本体属性。该管道在来自CAFA3实验的1000个非冗余蛋白的大规模集合上进行了测试。在排除与查询序列同一性为>30%的模板的严格基准测试条件下，MetaGO在Molecular Function、Biological Process和Cellular Component上的平均f值分别达到0.487、0.408和0.598，显著高于其他最先进的函数注释方法。详细的数据分析表明，MetaGO的主要优势在于基于伙伴同源性的网络映射和基于结构的局部和全局结构比对的新功能同源检测，并可以通过逻辑回归将其置信度得分进行最优组合。这些数据证明了使用结合蛋白质结构和相互作用网络的混合模型的力量，可以推断出超越传统基于序列同源性的新功能见解，特别是对于缺乏同源功能模板的蛋白质。MetaGO管道可在http://zhanglab.ccmb.med.umich.edu/MetaGO/上获得。
Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner’s-homology based protein-protein network mapping. The pipeline was tested on a large-scale set of 1,000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner’s-homology based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence-homology based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/.
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博洛尼亚注释资源（BAR 3.0）：改善蛋白质功能注释。
影响因子：
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影响因子：
14.9
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