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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >EnzDP: Improved enzyme annotation for metabolic network reconstruction based on domain composition profiles
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EnzDP: Improved enzyme annotation for metabolic network reconstruction based on domain composition profiles

机译：EnzDP：改进的酶注释，用于基于域组成谱的代谢网络重建

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摘要

Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction.
机译：确定酶的完整补体及其酶功能是重建细胞代谢网络的基本步骤。高质量的酶注释有助于增强从基因组重建的代谢网络，特别是通过减少缺口和增加酶的覆盖范围。当前，基于结构和基于网络的方法只能覆盖有限数量的酶家族，并且可以进一步提高基于同源性的方法的准确性。自下而上的基于同源性的方法通过为所有已知酶重建隐马尔可夫模型（HMM）配置文件来提高覆盖率。但是，其聚类过程完全依赖BLAST相似度评分，而忽略了蛋白结构域/模式，并且对截止阈值的变化敏感。在这里，我们使用功能域结构来对域家族和酶家族之间的关联进行评分（域-酶关联评分，DEAS）。 DEAS得分用于计算蛋白质之间的相似性，然后将其用于聚类过程，而不是使用序列相似性得分。我们使用严格的分类程序，通过选择最佳阈值设置并检查活性位点，改进了酶注释协议。我们的分析表明，我们严格的协议EnzDP可以覆盖Swiss-Prot中多达90％的酶家族。基于五重交叉验证，它可达到94.5％的高精度。在多个测试方案中，EnzDP的性能均优于现有方法。因此，EnzDP可作为可靠的自动化工具用于酶注释和代谢网络重建。

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