Protein secondary structure prediction based on quintuplets

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dc.creatorZheng, Wei-Mou
dc.date2003-07-16
dc.date.accessioned2026-07-07T05:49:41Z
dc.date.available2026-07-07T05:49:41Z
dc.descriptionSimple 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.
dc.description10 pages with 6 tables
dc.identifierhttps://arxiv.org/abs/physics/0307076
dc.identifierhttp://arxiv.org/abs/physics/0307076
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/85477
dc.subjectBiological Physics
dc.subjectData Analysis, Statistics and Probability
dc.subjectBiomolecules
dc.titleProtein secondary structure prediction based on quintuplets
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