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首页> 外文期刊>Journal of Molecular Biology >PREDICTION OF PROTEIN SECONDARY STRUCTURE BY COMBINING NEAREST-NEIGHBOR ALGORITHMS AND MULTIPLE SEQUENCE ALIGNMENTS
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PREDICTION OF PROTEIN SECONDARY STRUCTURE BY COMBINING NEAREST-NEIGHBOR ALGORITHMS AND MULTIPLE SEQUENCE ALIGNMENTS

机译：结合近邻算法和多序列对数预测蛋白质二级结构

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Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains. [References: 19]
机译：最近，Yi＆Lander将神经网络和最近邻方法与评分系统结合使用，该系统将序列相似性矩阵与Bowie及其同事描述的局部结构环境评分方案相结合，以预测蛋白质的二级结构。我们通过考虑α-螺旋和β-链的N和C末端位置以及β-转角作为二级结构的独特类型，改进了他们的评分系统。通过限制数据库使用与查询序列相似的较小蛋白质子集，可以实现另一项改进，从而也减少了计算时间。使用多个序列比对而不是单个序列，并且采用简单的陪审团判决程序，我们的方法可以达到72.2％的持续三态准确度，这比在同一数据集上测试的最准确的多层神经网络方法所观察到的要好。 126条非同源蛋白质链。 [参考：19]

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