Accurate protein structure annotation through competitive diffusion of enzymatic functions over a network of local evolutionary similarities.
Accurate protein structure annotation through competitive diffusion of enzymatic functions over a network of local evolutionary similarities.
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DOI：
10.1371/journal.pone.0014286
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发表时间：
2010-12-13
期刊：
影响因子：
3.7
通讯作者：
Lichtarge O
中科院分区：
文献类型：
--
作者：
Venner E;Lisewski AM;Erdin S;Ward RM;Amin SR;Lichtarge O
高通量结构基因组学产生了许多分子功能未知的新蛋白质结构。本研究旨在通过全局比较结构蛋白质组中的选定功能残基来揭示这些缺失的注释。首先，进化追踪注释（ETA）识别哪些蛋白质具有共同的局部进化和结构特征；接下来，这些蛋白质被连接在一起形成一个具有 ETA 相似性的蛋白质组网络；然后，从具有已知功能的蛋白质开始，竞争的功能标签在整个网络上逐个链接地扩散。因此，每个节点都会为每个函数分配一个似然 z 分数，并且每个节点上最重要的一个获胜并定义其注释。 In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively.这对应的假阳性率比最近邻注释低 4 倍，比基于序列的注释低 5 倍。 In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. Web 服务器位于 http://mammoth.bcm.tmc.edu/networks。
High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks.
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