title: Predicting protein structural class by functional domain composition.
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.
authors.affiliation: Gordon Life Science Institute, San Diego, CA 92130, USA. kchou@san.rr.com
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D030562:Databases, Protein; D008956:Models, Chemical; D015394:Molecular Structure; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification
chemical_list: D011506:Proteins