Automated discovery of 3D motifs for protein function annotation
Automated discovery of 3D motifs for protein function annotation
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DOI：
10.1093/bioinformatics/btk038
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发表时间：
2006-03-15
期刊：
影响因子：
5.8
通讯作者：
Babbitt, PC
中科院分区：
文献类型：
--
作者：
Polacco, BJ;Babbitt, PC
动机：通过使用残基模式（3D 基序）可以促进从结构中推断功能，这些残基模式通常由专家知识识别，与功能相关。作为通常有限的专业知识的替代方案，我们使用机器学习技术来识别 3-10 个残基的模式，从而最大化功能预测。这种方法使我们能够测试这样的假设：提供功能的残基对于预测功能来说信息量最大。结果：我们将我们的方法 GASPS 应用于卤酸脱卤酶、烯醇化酶、酰胺水解酶和巴豆酶超家族以及丝氨酸蛋白酶。 GASPS 发现的主题在功能预测方面与基于专家知识的 3D 主题一样出色。预测蛋白质功能能力最强的 GASPS 基序主要由已知的功能残基组成。然而，一些功能作用未知的残基同样具有预测作用。对于四组，我们表明我们的 3D 基序的预测能力与使用整个折叠（组合延伸）或序列概况（PSI-BLAST）的方法相当或更好。
Motivation: Function inference from structure is facilitated by the use of patterns of residues (3D motifs), normally identified by expert knowledge, that correlate with function. As an alternative to often limited expert knowledge, we use machine-learning techniques to identify patterns of 3-10 residues that maximize function prediction. This approach allows us to test the assumption that residues that provide function are the most informative for predicting function.Results: We apply our method, GASPS, to the haloacid dehalogenase, enolase, amidohydrolase and crotonase superfamilies and to the serine proteases. The motifs found by GASPS are as good at function prediction as 3D motifs based on expert knowledge. The GASPS motifs with the greatest ability to predict protein function consist mainly of known functional residues. However, several residues with no known functional role are equally predictive. For four groups, we show that the predictive power of our 3D motifs is comparable with or better than approaches that use the entire fold (Combinatorial-Extension) or sequence profiles (PSI-BLAST).