A pentapeptide-based method for protein secondary structure prediction
A pentapeptide-based method for protein secondary structure prediction
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DOI：
10.1093/proeng/gzg019
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发表时间：
2003-02-01
期刊：
影响因子：
--
通讯作者：
Tohá, J
中科院分区：
文献类型：
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作者：
Figureau, A;Soto, MA;Tohá, J
我们提出了一种新的蛋白质二级结构预测方法，该方法基于对大型数据库中定义明确的五肽的识别。使用635个蛋白质链的数据库，我们获得了68.6%的成功率。我们表明，当数据库扩大时，当20个氨基酸被充分分组为10组时，当更多的五肽被归为定义的构象之一，α -螺旋或β -链时，取得了进展。对模型的分析表明，数据库中结构明确的五肽的数量是模型的基本变量。我们的模型很简单，不依赖于任意参数，并允许对每个选择的假设的结果进行详细的分析。
We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of the defined conformations, alpha-helices or beta-strands. The analysis of the model indicates that the essential variable is the number of pentapeptides of well-defined structure in the database. Our model is simple, does not rely on arbitrary parameters and allows the analysis in detail of the results of each chosen hypothesis.