Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.
Binding affinity
MHC-peptide
Machine learning
Journal
Biochimica et biophysica acta. General subjects
ISSN: 1872-8006
Titre abrégé: Biochim Biophys Acta Gen Subj
Pays: Netherlands
ID NLM: 101731726
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
29
10
2019
revised:
09
01
2020
accepted:
14
01
2020
pubmed:
20
1
2020
medline:
15
9
2020
entrez:
20
1
2020
Statut:
ppublish
Résumé
Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding predictions. Here, we train support vector machine classifier (SVMC) models on physicochemical sequence-based and structure-based descriptor sets to predict peptide binding to a well-studied model mouse MHC I allele, H-2D
Identifiants
pubmed: 31954798
pii: S0304-4165(20)30025-8
doi: 10.1016/j.bbagen.2020.129535
pii:
doi:
Substances chimiques
Histocompatibility Antigens Class I
0
Peptides
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
129535Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.