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   Articles with "tcr peptide" as a keyword



Attentive Variational Information Bottleneck for TCR–peptide interaction prediction

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recommendations! Published in 2022 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btac820

Abstract: Abstract Motivation We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent… read more here.

Keywords: information; tcr peptide; information bottleneck; variational information ... See more keywords
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epiTCR: a highly sensitive predictor for TCR–peptide binding

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recommendations! Published in 2023 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btad284

Abstract: Abstract Motivation Predicting the binding between T-cell receptor (TCR) and peptide presented by human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite… read more here.

Keywords: epitcr highly; tcr peptide; prediction; tcr ... See more keywords

Structure-based prediction of T cell receptor:peptide-MHC interactions

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recommendations! Published in 2022 at "eLife"

DOI: 10.1101/2022.08.05.503004

Abstract: The regulatory and effector functions of T cells are initiated by the binding of their cell-surface T cell receptor (TCR) to peptides presented by major histocompatibility complex (MHC) proteins on other cells. The specificity of… read more here.

Keywords: tcr peptide; prediction; mhc interactions; cell receptor ... See more keywords

TCRcost: a deep learning model utilizing TCR 3D structure for enhanced of TCR–peptide binding

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recommendations! Published in 2024 at "Frontiers in Genetics"

DOI: 10.3389/fgene.2024.1346784

Abstract: Introduction Predicting TCR–peptide binding is a complex and significant computational problem in systems immunology. During the past decade, a series of computational methods have been developed for better predicting TCR–peptide binding from amino acid sequences.… read more here.

Keywords: deep learning; structure; peptide binding; tcrcost deep ... See more keywords