Receptors, Antigen, T-Cell - AI Medical Compendium

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TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

Protein science : a publication of the Protein Society Jan 1, 2024

The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for develo...

T-Lymphocytes Epitopes, T-Lymphocyte Deep Learning Receptors, Antigen, T-Cell Protein Binding

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MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

Briefings in bioinformatics Jul 20, 2023

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensiv...

Neural Networks, Computer Epitopes, T-Lymphocyte Peptides Receptors, Antigen, T-Cell Amino Acid Sequence

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TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

Nucleic acids research Jul 5, 2023

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the...

Humans Computer Simulation Peptides Deep Learning Receptors, Antigen, T-Cell Antigens

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TEINet: a deep learning framework for prediction of TCR-epitope binding specificity.

Briefings in bioinformatics Mar 19, 2023

The adaptive immune response to foreign antigens is initiated by T-cell receptor (TCR) recognition on the antigens. Recent experimental advances have enabled the generation of a large amount of TCR data and their cognate antigenic targets, allowing m...

Epitopes, T-Lymphocyte Deep Learning Receptors, Antigen, T-Cell Protein Binding

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PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2023

Accurate prediction of TCR binding affinity to a target antigen is important for development of immunotherapy strategies. Recent computational methods were built on various deep neural networks and used the evolutionary-based distance matrix BLOSUM t...

Humans Neural Networks, Computer Receptors, Antigen, T-Cell Computational Biology Amino Acids Epitopes

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AIMC Topic: Receptors, Antigen, T-Cell

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity.

PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder.

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AIMC Topic: Receptors, Antigen, T-Cell

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity.

PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder.

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