In this work, we developed a general Perturbation Theory model for prediction of B-cell epitopes in vaccine design. The method predicts the epitope activity εq(cqj) of one query peptide (q-peptide) in a set of experimental query conditions (cqj). The model proposed here is able to classify 1,048,190 pairs of query and reference peptide sequences reported on IEDB database with perturbations in sequence or assay conditions. The model has accuracy, sensitivity, and specificity between 71% and 80% for training and external validation series. The model may become a useful tool for epitope selection towards vaccine design. The theoretic-experimental results on Bm86 protein may help on the future design of a new vaccine based on this protein. Ref: J Proteome Res. 2017 Sep 18. doi: 10.1021/acs.jproteome.7b00477.
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Alignment-Free Model for Prediction of B-cell Epitopes
Published:
26 September 2017
by MDPI
in MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
congress CHEMBIOMOL-03: Chem. Biol. & Med. Chem. Workshop, Rostock, Germany-Bilbao, Spain-Galveston, Texas, USA, 2017
Abstract:
Keywords: Proteome mining; Epitope prediction; B-cell epitope; PCR; Bm86 protein; Machine Learning; Perturbation Theory.
