SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

By Bo Yao, Lin Zhang, Shide Liang, Chi Zhang. PLoS ONE, 2012

357 citations recorded by the source corpus.

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.

Subfields: Immunology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0a220ebb7827ba99fec059c87a4a0428daa64515

How researchers see this paper

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In Immunology

1. Marta Gomez-Perosanz
2. J. L. Sánchez-Trincado
3. Pedro A. Reche

In Molecular Biology

1. Marta Gomez-Perosanz
2. J. L. Sánchez-Trincado
3. Pedro A. Reche

In Radiology, Nuclear Medicine and Imaging

1. Marta Gomez-Perosanz
2. J. L. Sánchez-Trincado
3. Pedro A. Reche

Citations in the corpus

• Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “Both BCPREDS [113] and SVMtrip [114] are based on support vector machines (SVM) but while BCPREDS was trained using various string kernels that eliminate the need for representing the sequence into length-fixed feature vectors, SMVtrip was trained on length-fixed tripeptide composition vectors.”