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PubMed · 15358128

Predicting protein structural class by functional domain composition.

Kuo-Chen Chou·Yu-Dong Cai
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Abstract

The functional domain composition is introduced to predict the structural class of a protein or domain according to the following classification: all-alpha, all-beta, alpha/beta, alpha+beta, micro (multi-domain), sigma (small protein), and rho (peptide). The advantage by doing so is that both the sequence-order-related features and the function-related features are naturally incorporated in the predictor. As a demonstration, the jackknife cross-validation test was performed on a dataset that consists of proteins and domains with only less than 20% sequence identity to each other in order to get rid of any homologous bias. The overall success rate thus obtained was 98%. In contrast to this, the corresponding rates obtained by the simple geometry approaches based on the amino acid composition were only 36-39%. This indicates that using the functional domain composition to represent the sample of a protein for statistical prediction is very promising, and that the functional type of a domain is closely correlated with its structural class.

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Databases, ProteinModels, ChemicalMolecular StructureProtein Structure, TertiaryProteinsJournal Article

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Kuo-Chen Chou, Yu-Dong Cai. 2004-09-03. Predicting protein structural class by functional domain composition.. https://doi.org/10.1016/j.bbrc.2004.07.059

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