Computational Approaches for Automated Classification of Enzyme Sequences.
Computational Approaches for Automated Classification of Enzyme Sequences.
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DOI：
10.4172/jpb.1000183
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发表时间：
2011-08-23
期刊：
影响因子：
--
通讯作者：
Guda C
中科院分区：
文献类型：
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作者：
Mohammed A;Guda C
确定酶的功能作用(S)对于构建生物体的代谢蓝图以及确定酶在代谢和疾病途径中可能发挥的潜在作用非常重要。随着基因和蛋白质序列数据的指数增长，对所有酶的功能进行实验表征(S)是不可行的。或者，可以使用计算方法来注释大量未注释的酶序列。对于酶的功能预测和分类，基于氨基酸组成、序列和结构特性、结构域组成和特定多肽信息的特征已被不同的计算方法广泛使用。每种特征空间在整体预测精度上都有各自的优点和局限性。当使用机器学习方法对酶进行分类时，预测精度会得到提高。考虑到生物数据库中注释的不完全性和不平衡性，集成方法或基于正交特征组合的方法更适合于在酶分类中实现更高的准确率和覆盖率。在这篇综述文章中，我们系统地描述了迄今为止用于酶类预测的所有特征和方法。据作者所知，这篇综述对用于酶类计算预测的方法进行了最详尽的描述。
Determining the functional role(s) of enzymes is very important to build the metabolic blueprint of an organism and to identify the potential roles enzymes may play in metabolic and disease pathways. With exponential growth in gene and protein sequence data, it is not feasible to experimentally characterize the function(s) of all enzymes. Alternatively, computational methods can be used to annotate the enormous amount of unannotated enzyme sequences. For function prediction and classification of enzymes, features based on amino acid composition, sequence and structural properties, domain composition and specific peptide information have been widely used by different computational approaches. Each feature space has its own merits and limitations on the overall prediction accuracy. Prediction accuracy improves when machine-learning methods are used to classify enzymes. Given the incomplete and unbalanced nature of annotations in biological databases, ensemble methods or methods that bank on a combination of orthogonal feature are more desirable for achieving higher accuracy and coverage in enzyme classification. In this review article, we systematically describe all the features and methods used thus far for enzyme class prediction. To the authors’ knowledge, this review represents the most exhaustive description of methods used for computational prediction of enzyme classes.