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Structural class tendency of polypeptide: A new conception in predicting protein structural class

机译:多肽的结构分类趋势:预测蛋白质结构分类的新概念

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摘要

Prediction of protein domain structural classes is an important topic in protein science. In this paper, we proposed a new conception: structural class tendency of polypeptides (SCTP), which is based on the fact that a given amino acid fragment tends to be presented in certain type of proteins. The SCTP is obtained from an available training data set PDB40-B. When using the SCTP to predict protein structural classes by Intimate Sorting predictive method, we got the predictive accuracy (jack knife test) with 93.7%, 96.5%, and 78.6% for the testing data set PDB40-j, Chou&Maggiora and CHOU. These results indicate that the SCTP approach is quite encouraging and promising. This new conception provides an effective tool to extract valuable information from protein sequences. (c) 2007 Elsevier B.V. All rights reserved.
机译:蛋白质结构域结构类别的预测是蛋白质科学中的重要课题。在本文中,我们提出了一个新的概念:多肽的结构分类趋势(SCTP),其基于这样一个事实,即给定的氨基酸片段倾向于在某些类型的蛋白质中呈现。 SCTP是从可用的训练数据集PDB40-B中获得的。当使用SCTP通过亲密排序预测方法预测蛋白质结构分类时,对于测试数据集PDB40-j,Chou&Maggiora和CHOU,我们的预测准确度(千刀试验)为93.7%,96.5%和78.6%。这些结果表明,SCTP方法是令人鼓舞和有希望的。这一新概念为从蛋白质序列中提取有价值的信息提供了有效的工具。 (c)2007 Elsevier B.V.保留所有权利。

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