Learning immune receptor representations with protein language models
arxiv(2024)
摘要
Protein language models (PLMs) learn contextual representations from protein
sequences and are profoundly impacting various scientific disciplines spanning
protein design, drug discovery, and structural predictions. One particular
research area where PLMs have gained considerable attention is adaptive immune
receptors, whose tremendous sequence diversity dictates the functional
recognition of the adaptive immune system. The self-supervised nature
underlying the training of PLMs has been recently leveraged to implement a
variety of immune receptor-specific PLMs. These models have demonstrated
promise in tasks such as predicting antigen-specificity and structure,
computationally engineering therapeutic antibodies, and diagnostics. However,
challenges including insufficient training data and considerations related to
model architecture, training strategies, and data and model availability must
be addressed before fully unlocking the potential of PLMs in understanding,
translating, and engineering immune receptors.
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