AIMC Topic: Receptors, Antigen, T-Cell

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Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.

Frontiers in immunology
Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents too...

Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.

Genome biology
BACKGROUND: Deep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the ...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Frontiers in immunology
Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this "convergence" of adaptive immunity among different...

Large-Scale T-cell Receptor Repertoire Profiling Unveils Tumor-Specific Signals for Diagnosing Indeterminate Pulmonary Nodules.

Cancer research
UNLABELLED: Indeterminate pulmonary nodules (IPN) are increasingly detected due to increasing health awareness and widespread lung cancer screening, yet distinguishing benign from malignant nodules remains a critical challenge. Emerging evidence sugg...

T-cell receptor insights: Determinants of Major Histocompatibility Complex class I versus class II recognition.

Protein science : a publication of the Protein Society
In this study, we analyzed large-scale T-cell receptor (TCR) sequence data to determine whether TCRs preferentially bind to major histocompatibility complex (MHC) class I (CD8+) or class II (CD4+) epitopes. Using the International ImMunoGeneTics info...

Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: T cells play a vital role in the immune system by recognizing and eliminating infected or cancerous cells, thus driving adaptive immune responses. Their activation is triggered by the binding of T cell receptors (TCRs) to ep...

Leveraging Artificial Intelligence for Neoantigen Prediction.

Cancer research
Neoantigens represent a class of antigens within tumor microenvironments that arise from diverse somatic mutations and aberrations specific to tumorigenesis, holding substantial promise for advancing tumor immunotherapy. However, only a subset of neo...

Phage display enables machine learning discovery of cancer antigen-specific TCRs.

Science advances
T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. Epitope recognition is mediated by the binding of the T cell receptor (TCR), and TCRs recognizing clinically relevant...

LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt.

Briefings in bioinformatics
Identification of T cell receptor (TCR) specificities for antigens from large-scale single-cell or bulk TCR repertoire data plays a vital role in disease diagnosis and immunotherapy. In silico prediction models have emerged in recent years. However, ...

Understanding TCR T cell knockout behavior using interpretable machine learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer immunotherapies, but understanding the changes to T cell behavior induced by genetic perturbations rema...