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Categoria WoS
  • Biochemistry & Molecular Biology - Q1 - T1 - 74/297
  • Chemistry, Multidisciplinary - Q2 - T1 - 48/177
Titulo: Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning
Tipo: revista nacional
Fecha: 9,2019
Revista: International Journal of Molecular Science
JCR Journal; Impact Factor: 4.556
Volumen: 20(18)
Paginas: 1-10
ISSN: 1422-0067
Editorial: MDPI
Pubmed ID: 31491969


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 at

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