Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning
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Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine LearningAutor(es)
Data
2019Cita bibliográfica
Munteanu CR, Gestal M, Martínez-Acevedo YG, et al. Improvement of epitope prediction using peptide sequence descriptors and machine learning. Int J Mol Sci. 2019; 20(18):4362
Resumo
[Abstract] In this work, we improved a previous model used for the prediction of proteomes as new
B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its
sequence, a known reference epitope sequence under specific experimental conditions. The peptide
sequences were transformed into molecular descriptors of sequence recurrence networks and were
mixed under experimental conditions. The new models were generated using 709,100 instances of pair
descriptors for query and reference peptide sequences. Using perturbations of the initial descriptors
under sequence or assay conditions, 10 transformed features were used as inputs for seven Machine
Learning methods. The best model was obtained with random forest classifiers with an Area Under
the Receiver Operating Characteristics (AUROC) of 0.981 ± 0.0005 for the external validation series
(five-fold cross-validation). The database included information about 83,683 peptides sequences,
1448 epitope organisms, 323 host organisms, 15 types of in vivo processes, 28 experimental techniques,
and 505 adjuvant additives. The current model could improve the in silico predictions of epitopes for
vaccine design. The script and results are available as a free repository
Palabras chave
Epitopes
Machine learning
Protein sequences
Qualitative structure–activity relationships
Machine learning
Protein sequences
Qualitative structure–activity relationships
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
1422-0067