Prediction of B-cell epitopes using evolutionary information and propensity scales.

ABSTRACT:

Background

Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.

Methods

We propose a method, BEEPro (B-cell epitope prediction by evolutionary information and propensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process.

SUBMITTER: Lin SY 

PROVIDER: S-EPMC3549808 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature