SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity.

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ID: 100981
2012
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Abstract
Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.
Reference Key
yao2012svmtripplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yao, Bo;Zhang, Lin;Liang, Shide;Zhang, Chi;
Journal PloS one
Year 2012
DOI
10.1371/journal.pone.0045152
URL
Keywords
Monoclonal antibody cdr grafting antibody humanization antibody variable domain orientation

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