SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes
- 104 Downloads
Identifying protein antigenic epitopes recognizable by antibodies is the key step for new immuno-diagnostic reagent discovery and vaccine design. To facilitate this process and improve its efficiency, computational methods were developed to predict antigenic epitopes. For the linear B-cell epitope prediction, many methods were developed, including BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, and SVMTriP. Among these methods, SVMTriP, a frontrunner, utilized Support Vector Machine by combining the tri-peptide similarity and Propensity scores. Applied on non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value was 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. A webserver based on this method was constructed for public use. The server and all datasets used in the corresponding study are available at http://sysbio.unl.edu/SVMTriP. This chapter describes the webserver of SVMTriP.
Key wordsLinear B-cell epitope prediction Support vector machine
The work is supported by funding under C.Z.’s startup funds from the University of Nebraska, Lincoln, NE. This work was completed utilizing the Holland Computing Center of the University of Nebraska.
- 23.Joachims T (1999) Making large-Scale SVM Learning Practical. In: Schölkopf B, Burges C (eds) Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, MA, pp 169–184Google Scholar