6533b823fe1ef96bd127f6b8
RESEARCH PRODUCT
Discriminative features of type I and type III secreted proteins from Gram-negative bacteria
Martina BaltkalneInara AndersonePeteris Zikmanissubject
chemistry.chemical_classificationGram-negative bacteriaGeneral Immunology and MicrobiologybiologyQH301-705.5General Neurosciencediscriminant analysisbiology.organism_classificationLinear discriminant analysisgram-negative bacteriaGeneral Biochemistry Genetics and Molecular Biologyamino acid sequenceAmino acidSecretory proteinBiochemistrychemistryprotein secretionSecretionBiology (General)General Agricultural and Biological SciencesPeptide sequenceBacteriaGramdescription
AbstractThe amino acid composition of sequences and structural attributes (α-helices, β-sheets) of C-and N-terminal fragments (50 amino acids) were compared to annotated (SWISS-PROT/ TrEMBL) type I (20 sequences) and type III (22 sequences) secreted proteins of Gram-negative bacteria.The discriminant analysis together with the stepwise forward and backward selection of variables revealed the frequencies of the residues Arg, Glu, Gly, Ile, Met, Pro, Ser, Tyr, Val as a set of strong (1-P < 0.001) predictor variables to discriminate between the sequences of type I and type III secreted proteins with a cross-validated accuracy of 98.6–100 %. The internal and external validity of discriminant analysis was confirmed by multiple (15 repeats) test-retest procedures using a randomly split original set of proteins; this validation method demonstrated an accuracy of 100 % for 191 non-selected (retest) sequences.The discriminant analysis was also applied using selected variables from the propensities for β-sheets and polarity of C-terminal fragments. This approach produced the next highest and comparable cross-validated classification accuracy for randomly selected and retest proteins (85.4–86.0 % and 82.4–84.5 %, respectively).The proposed sets of predictor variables could be used to assess the compatibility between secretion substrates and secretion pathways of Gram-negative bacteria by means of discriminant analysis.
year | journal | country | edition | language |
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2006-03-01 | Open Life Sciences |