[HTML][HTML] Can we predict T cell specificity with digital biology and machine learning?

D Hudson, RA Fernandes, M Basham, G Ogg… - Nature Reviews …, 2023 - nature.com
Recent advances in machine learning and experimental biology have offered breakthrough
solutions to problems such as protein structure prediction that were long thought to be …

Bystander T cells in cancer immunology and therapy

SL Meier, AT Satpathy, DK Wells - Nature Cancer, 2022 - nature.com
Cancer-specific T cells are required for effective anti-cancer immunity and have a central
role in cancer immunotherapy. However, emerging evidence suggests that only a small …

[HTML][HTML] Pan-peptide meta learning for T-cell receptor–antigen binding recognition

Y Gao, Y Gao, Y Fan, C Zhu, Z Wei, C Zhou… - Nature Machine …, 2023 - nature.com
The identification of the mechanisms by which T-cell receptors (TCRs) interact with human
antigens provides a crucial opportunity to develop new vaccines, diagnostics and …

Challenges in neoantigen-directed therapeutics

L Lybaert, S Lefever, B Fant, E Smits, B De Geest… - Cancer Cell, 2023 - cell.com
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of
functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a …

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

P Bradley - Elife, 2023 - elifesciences.org
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 …

Explainable deep hypergraph learning modeling the peptide secondary structure prediction

Y Jiang, R Wang, J Feng, J Jin, S Liang, Z Li… - Advanced …, 2023 - Wiley Online Library
Accurately predicting peptide secondary structures remains a challenging task due to the
lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep …

TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes

B Meynard-Piganeau, C Feinauer… - Proceedings of the …, 2024 - National Acad Sciences
The accurate prediction of binding between T cell receptors (TCR) and their cognate
epitopes is key to understanding the adaptive immune response and developing …

[HTML][HTML] Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

P Meysman, J Barton, B Bravi, L Cohen-Lavi… - ImmunoInformatics, 2023 - Elsevier
Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR)
have been proposed. However several questions on the advantages and disadvantages of …

[HTML][HTML] On TCR binding predictors failing to generalize to unseen peptides

F Grazioli, A Mösch, P Machart, K Li… - Frontiers in …, 2022 - frontiersin.org
Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine
learning or deep learning approaches. Many of these methods achieve impressive results …

epiTCR: a highly sensitive predictor for TCR–peptide binding

MDN Pham, TN Nguyen, LS Tran, QTB Nguyen… - …, 2023 - academic.oup.com
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 …