AIMC Topic: Receptors, Antigen, T-Cell

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T Cell Epitope Prediction and Its Application to Immunotherapy.

Frontiers in immunology Sep 15, 2021
T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for ep...
T-Cell Antigen Receptor Specificity Humans Neural Networks, Computer Neoplasms Sequence Analysis, DNA Mass Spectrometry Models, Molecular Histocompatibility Antigens Class I Base Sequence Antigen Presentation Computational Biology Epitopes, T-Lymphocyte Immunotherapy Peptides Antigens, Neoplasm Receptors, Antigen, T-Cell
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NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data.

Communications biology Sep 10, 2021
Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that "shallow...
Neural Networks, Computer Receptors, Antigen, T-Cell Protein Binding
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A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes.

Molecular immunology Aug 26, 2021
The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can on...
Humans Machine Learning Lymphocyte Activation Receptors, Antigen, T-Cell Protein Binding Major Histocompatibility Complex
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Applications of Machine and Deep Learning in Adaptive Immunity.

Annual review of chemical and biomolecular engineering Apr 14, 2021
Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs)...
Deep Learning Receptors, Antigen, T-Cell Adaptive Immunity T-Lymphocytes
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DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nature communications Mar 11, 2021
Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diver...
Neural Networks, Computer Algorithms Animals Humans Receptors, Antigen, T-Cell Mice Databases, Genetic T-Lymphocytes Deep Learning RNA-Seq Amino Acid Sequence High-Throughput Nucleotide Sequencing
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Use of machine learning to identify a T cell response to SARS-CoV-2.

Cell reports. Medicine Jan 16, 2021
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease ...
Complementarity Determining Regions Receptors, Antigen, B-Cell Receptors, Antigen, T-Cell Sequence Analysis, DNA Cluster Analysis Principal Component Analysis Amino Acid Sequence High-Throughput Nucleotide Sequencing COVID-19 SARS-CoV-2 T-Lymphocytes Humans Machine Learning
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Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.

Frontiers in immunology Aug 25, 2020
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...
Antigens Binding Sites Protein Interaction Domains and Motifs T-Lymphocytes Signal Transduction Epitopes, T-Lymphocyte Databases, Protein Humans Ligands Peptides Software Deep Learning Receptors, Antigen, T-Cell Protein Binding
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Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.

Genome biology Aug 3, 2020
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 ...
Receptors, Antigen, T-Cell Sequence Analysis, RNA Signal Transduction Humans Deep Learning Single-Cell Analysis
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Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Frontiers in immunology Apr 16, 2019
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...
Immunodominant Epitopes Computational Biology Datasets as Topic T-Lymphocytes Thermodynamics Epitopes, T-Lymphocyte Adaptive Immunity Receptors, Antigen, T-Cell Humans Major Histocompatibility Complex Machine Learning Antigen Presentation Computer Simulation
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Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.

Computer methods and programs in biomedicine Aug 1, 2025
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...
Humans Machine Learning Algorithms Protein Binding Computational Biology T-Lymphocytes Major Histocompatibility Complex Peptides Receptors, Antigen, T-Cell
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