Affiliation:
1. 1Institute of Microbiology and Biotechnology, University of Latvia, LV-1010, Riga, Latvia
Abstract
AbstractC- and N-terminal sequences (64 amino acid residues each) of 89 non-classically secreted type I, type III and type IV proteins (Swiss-Prot/TrEMBL) from proteobacteria were transformed into predicted secondary structures. Multivariate analysis of variance (MANOVA) confirmed the significance of location (C- or N-termini) and secretion type as essential factors in respect of quantitative representations of structured (a-helices, b-strands) and unstructured (coils) elements. The profiles of secondary structures were transcripted using unequal property values for helices, strands and coils and corresponding numerical vectors (independent variables) were subjected to multiple discriminant analysis with the types of secreted proteins as the dependent variables. The set of strong predictor variables (21 property values located at the region of 2–49 residues from the C-termini) was capable to classify all three types of non-classically secreted proteins with an accuracy of 93.3% for originally and 89.9% for cross-validated (leave-one-out procedure) grouped cases. The average error rate (0.137 ± 0.015) of k-fold (k = 3; 4; 6; 8; 10; 89) cross validation affirmed an acceptable prediction accuracy of defined discriminant functions with regard to the types of non-classically secreted proteins. The proposed prediction tool could be used to specify the secretome proteins from genomic sequences as well as to assess the compatibility between secretion pathways and secretion substrates of proteobacteria.
Subject
General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Neuroscience
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献