SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence.

ABSTRACT:

Background

The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.

Results

We developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a naïve Bayesian and a rand

SUBMITTER: Dalkas GA 

PROVIDER: S-EPMC5301386 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature