1 Sitaatiot (Scopus)
23 Lataukset (Pure)

Abstrakti

Motivation
T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging.

Results
We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models.
AlkuperäiskieliEnglanti
Artikkelibtad743
JulkaisuBioinformatics
Vuosikerta39
Numero12
DOI - pysyväislinkit
TilaJulkaistu - 9 jouluk. 2023
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Sormenjälki

Sukella tutkimusaiheisiin 'EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä