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Prediction of pattern recognition receptor family using pseudo-amino acid composition.
MedLine Citation:
PMID:  22138239     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Pattern recognition receptors (PRRs) play a key role in the innate immune response by recognizing pathogen associated molecular patterns derived from a diverse collection of microbial pathogens. PRRs form a superfamily of proteins related to host health and disease. Thus, prediction of PRR family might supply biologically significant information for functional annotation of PRRs and development of novel drugs. In this paper, a computational method is proposed for predicting the families of PRRs. The prediction was performed on the basis of amino acid composition and pseudo-amino acid composition (PseAAC) from primary sequences of proteins using support vector machines. A non-redundant dataset consisted of 332 PRRs in seven families was constructed to do training and testing. It was demonstrated that different families of PRRs were quite closely correlated with amino acid composition as well as PseAAC. In the jackknife test, overall accuracies of amino acid composition-based and PseAAC-based classifiers reached 96.1% and 97.9%, respectively. The results indicate that families of PRRs are predictable with high accuracy. It is anticipated that this computational method might be a powerful tool for the automated assignment of families of PRRs.
Authors:
Qing-Bin Gao; Hongyu Zhao; Xiaofei Ye; Jia He
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-11-19
Journal Detail:
Title:  Biochemical and biophysical research communications     Volume:  -     ISSN:  1090-2104     ISO Abbreviation:  -     Publication Date:  2011 Nov 
Date Detail:
Created Date:  2011-12-5     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0372516     Medline TA:  Biochem Biophys Res Commun     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011. Published by Elsevier Inc.
Affiliation:
Department of Health Statistics, Second Military Medical University, Shanghai 200433, China; School of Epidemiology and Public Health, Yale University, New Haven, CT 06511, USA.
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