AIMC Topic: Receptors, Antigen, T-Cell

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Feature selection enhances peptide binding predictions for TCR-specific interactions.

Frontiers in immunology Jan 23, 2025
INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for a...
Protein Binding Peptides Peptide Library Mice Receptors, Antigen, T-Cell Machine Learning Animals
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Meta-Learning Enables Complex Cluster-Specific Few-Shot Binding Affinity Prediction for Protein-Protein Interactions.

Journal of chemical information and modeling Jan 7, 2025
Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein interactions and for the targeted screening of peptide- or protein-based drugs. We introduce MCGLPPI++, a ...
Protein Binding Proteins Machine Learning Protein Interaction Mapping Receptors, Antigen, T-Cell
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Exploring the potential of structure-based deep learning approaches for T cell receptor design.

PLoS computational biology Sep 30, 2024
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optim...
Models, Molecular Protein Conformation Protein Engineering Protein Binding Computational Biology Deep Learning Receptors, Antigen, T-Cell Humans
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TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.

Frontiers in immunology Aug 16, 2024
Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to gener...
Computational Biology Support Vector Machine Epitopes, T-Lymphocyte Receptors, Antigen, T-Cell Protein Binding Humans Machine Learning
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Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens.

Advanced biology Jul 6, 2024
Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoe...
Antigens, Neoplasm HLA Antigens Neoplasms Receptors, Antigen, T-Cell Immunotherapy Humans Machine Learning
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A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Scientific reports Jun 13, 2024
Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some concerns remain that they may cause serious cardiovascular adverse events (AEs), for which data are scar...
Immunotherapy, Adoptive Receptors, Antigen, T-Cell Pharmacovigilance Machine Learning Female Humans Cardiovascular Diseases Middle Aged Child, Preschool Child Adolescent Adult Hematologic Neoplasms Male Aged Young Adult
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Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing.

British journal of cancer Jun 7, 2024
BACKGROUND: It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for ident...
CD8-Positive T-Lymphocytes Gastrointestinal Neoplasms Antigens, Neoplasm Lymphocytes, Tumor-Infiltrating Machine Learning Tumor Microenvironment Single-Cell Analysis Receptors, Antigen, T-Cell Transcriptome Humans
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HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.

Communications biology Jun 4, 2024
Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, w...
Humans Receptors, Antigen, T-Cell Protein Binding Computational Biology Neural Networks, Computer Peptides
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Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T cells.

Methods in cell biology Sep 15, 2023
Discovery of epitope-specific T-cell receptors (TCRs) for cancer therapies is a time consuming and expensive procedure that usually requires a large amount of patient cells. To maximize information from and minimize the need of precious samples in ca...
CD8-Positive T-Lymphocytes Epitopes Humans Leukocytes, Mononuclear Neoplasms Receptors, Antigen, T-Cell
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DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis.

Science advances Aug 9, 2023
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for pr...
Humans Antigens Adaptive Immunity Deep Learning Receptors, Antigen, B-Cell Receptors, Antigen, T-Cell
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