Advances and Challenges in Predicting TCR Specificity: From Clustering to Protein Language Models
Practical Solutions and Value
Recent advances in immune sequencing and experimental methods have enabled the development of models to predict T cell receptor (TCR) binding specificity, crucial for targeted immune responses to pathogens and diseased cells.
Researchers have emphasized the importance of improving model interpretability and extracting biological insights from large, complex models to enhance TCR-pMHC binding predictions and revolutionize immunotherapy development.
Despite challenges such as limited and biased data, advances in machine learning, including Protein Language Models (PLMs), have significantly enhanced TCR prediction models, offering potential solutions for improving immunotherapies and understanding autoimmune diseases.
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