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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >CLUSTERING OF AMINO ACIDS FOR PROTEIN SECONDARY STRUCTURE PREDICTION
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CLUSTERING OF AMINO ACIDS FOR PROTEIN SECONDARY STRUCTURE PREDICTION

机译:氨基酸的蛋白质二级结构预测

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摘要

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.
机译:提出了简单的隐马尔可夫模型,用于从蛋白质的氨基酸序列预测蛋白质的二级结构。由于蛋白质构象片段的长度在一个狭窄的范围内变化,因此我们忽略了长度分布的持续时间效应,而将重点放在模型中残基和构象状态的短程相关性上。通过适当的互信息为模型设计了构象无关和依赖性氨基酸粗粒度方案。我们比较了复杂程度不同的模型,并建立了具有较高预测精度的实用模型。

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