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arXiv physics/0307076physics.bio-phphysics.data-anq-bio.BM
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基于五联体的蛋白质二级结构预测

Protein secondary structure prediction based on quintuplets

Wei-Mou Zheng

摘要:

提出了一种简单的隐马尔可夫模型,用于从蛋白质的氨基酸序列预测其二级结构。由于蛋白质构象片段的长度在一个狭窄的范围内变化,我们忽略了长度分布的持续时间效应,并专注于包括在模型中的残基和构象状态的短程相关性。通过适当的互信息,设计了构象独立和依赖的氨基酸粗粒化方案。我们比较了不同复杂程度的模型,并建立了一个具有较高预测精度的实用模型。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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