Your browser doesn't support javascript.
loading
MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.
Wongklaew, Patiphan; Sriswasdi, Sira; Chuangsuwanich, Ekapol.
Afiliação
  • Wongklaew P; Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.
  • Sriswasdi S; Center of Excellence in Computational Molecular Biology, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
  • Chuangsuwanich E; Center for Artificial Intelligence in Medicine, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
Bioinformatics ; 40(1)2024 01 02.
Article em En | MEDLINE | ID: mdl-38152987
ABSTRACT
MOTIVATION The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models.

RESULTS:

In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. AVAILABILITY AND IMPLEMENTATION The source code and installation instruction for MHCSeqNet2 are available at https//github.com/cmb-chula/MHCSeqNet2.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Peptídeos / Antígenos de Histocompatibilidade Classe I Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Peptídeos / Antígenos de Histocompatibilidade Classe I Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article