Artículo
QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for Predicting “In Silico” New Optimal Peptide Sequences and/or Boundary Conditions for Vaccine Development
Vázquez Prieto, Severo
; Paniagua Crespo, María Esperanza; Ubeira, Florencio M.; González Díaz, Humberto
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Fecha de publicación:
12/2016
Editorial:
Springer
Revista:
International Journal Of Peptide Research And Therapeutics
ISSN:
1573-3149
e-ISSN:
1573-3904
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
In the present study, three different physicochemical molecular properties for peptides were calculated using the program MARCH-INSIDE: atomic polarizability, partition coefficient, and polarity. These measures were used as input parameters of a linear discriminant analysis (LDA) in order to develop three different quantitative structure–property relationship (QSPR)-perturbation models for the prediction of B-epitopes reported in the immune epitope database (IEDB) given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms. The accuracy, sensitivity and specificity of the models were >90 % for both training and cross-validation series. The statistical parameters of the models were compared to the results achieved with the electronegativity QSPR-perturbation model previously reported by González-Díaz et al. (J Immunol Res. doi:10.1155/2014/768515, 2014). The results indicate that this type of approach may constitute a potentially valuable route for predicting “in silico” new optimal peptide sequences and/or boundary conditions for vaccine development.
Palabras clave:
Epitopes
,
Markov Chains
,
Perturbation Theory
,
Qsar/Qspr Models
,
Vaccine Design
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Articulos(CIVETAN)
Articulos de CENTRO DE INVESTIGACION VETERINARIA DE TANDIL
Articulos de CENTRO DE INVESTIGACION VETERINARIA DE TANDIL
Citación
Vázquez Prieto, Severo; Paniagua Crespo, María Esperanza; Ubeira, Florencio M.; González Díaz, Humberto; QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for Predicting “In Silico” New Optimal Peptide Sequences and/or Boundary Conditions for Vaccine Development; Springer; International Journal Of Peptide Research And Therapeutics; 22; 4; 12-2016; 445-450
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