EventsMOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Event submissions
Published
with-doi10.3390/mol2net-03-04613
This submission belongs to the session 02. CHEMBIOMOL-03: Chem. Biol. & Med. Chem. Workshop, Rostock, Germany-Bilbao, Spain-Galveston, Texas, USA, 2017 of the event MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Published date
26 Sep, 2017
Citation
Saúl G. Martínez-Arzate, Esvieta Tenorio-Borroto, Alberto Barbabosa Pliego, Héctor M. Díaz-Albiter, Juan C. Vazquez-Chagoyan, Alignment-Free Model for Prediction of B-cell Epitopes  , in Proceedings of MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed., 15 January–15 December 2017, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-03-04613
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Alignment-Free Model for Prediction of B-cell Epitopes  

Saúl G. Martínez-Arzate 1
Esvieta Tenorio-Borroto 1
Alberto Barbabosa Pliego 1
Héctor M. Díaz-Albiter 2
Juan C. Vazquez-Chagoyan 1
1. Molecular Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), 50200 Mexico State, Mexico.
2. Wellcome Trust Centre for Molecular Parasitology, University of Glasgow, University Place, Glasgow G12 8TA, United Kingdom.
Abstract

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. 

Keywords
Proteome mining
Epitope prediction
B-cell epitope
PCR
Bm86 protein
Machine Learning
Perturbation Theory.
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