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首页> 外文期刊>Genomics >Simple alignment-free methods for protein classification: a case study from G-protein-coupled receptors.
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Simple alignment-free methods for protein classification: a case study from G-protein-coupled receptors.

机译：简单的无比对方法进行蛋白质分类：以G蛋白偶联受体为例的研究。

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摘要

Computational methods of predicting protein functions rely on detecting similarities among proteins. However, sufficient sequence information is not always available for some protein families. For example, proteins of interest may be new members of a divergent protein family. The performance of protein classification methods could vary in such challenging situations. Using the G-protein-coupled receptor superfamily as an example, we investigated the performance of several protein classifiers. Alignment-free classifiers based on support vector machines using simple amino acid compositions were effective in remote-similarity detection even from short fragmented sequences. Although it is computationally expensive, a support vector machine classifier using local pairwise alignment scores showed very good balanced performance. More commonly used profile hidden Markov models were generally highly specific and well suited to classifying well-established protein family members. It is suggested that different types of protein classifiers should be applied to gain the optimal mining power.
机译：预测蛋白质功能的计算方法依赖于检测蛋白质之间的相似性。但是，对于某些蛋白质家族，并非总是可获得足够的序列信息。例如，目的蛋白质可能是趋异蛋白质家族的新成员。在这种具有挑战性的情况下，蛋白质分类方法的性能可能会有所不同。以G蛋白偶联受体超家族为例，我们研究了几种蛋白质分类器的性能。基于支持向量机的使用简单氨基酸组成的无比对分类器即使在短片段序列中也能有效地进行远程相似性检测。尽管计算量大，但是使用局部成对对齐分数的支持向量机分类器显示出非常好的平衡性能。较常用的配置文件隐藏马尔可夫模型通常具有很高的特异性，非常适合对成熟的蛋白质家族成员进行分类。建议应使用不同类型的蛋白质分类器以获得最佳挖掘能力。

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