AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
PRISM embeds receptors into a peptide-organized latent space, returns top-k peptides by nearest-neighbor retrieval, and abstains on out-of-distribution receptors by modeling an intrinsic uncertainty that tracks annotation correctness.
Evaluation across multiple settings indicates that TCR-IFNet achieves competitive performance compared with existing methods, with higher AUPRC observed on both antigen-specific and healthy-sourced datasets, as well as improved results on independent test sets.
TCRspec, an interpretable multimodal framework combining sequence embeddings, gene-usage features, and complex-level structural representations, providing a structure-informed framework for TCR specificity prediction, is developed.
This review takes public TCR sequences as its protagonist and traces the family of computational methods that exploit public sequences as features: frequency vectors, sequence‐similarity networks, self‐supervised transformer embeddings, and the own anchor‐based graph neural networks (GraffiTee).
OmniTCR bridges heterogeneous immune sequence data, providing a foundation for computational immunology and receptor design, and achieved the highest sequence recovery on internal and external generation benchmarks.
It is shown that combining structural modeling with label denoising substantially improves TCR-pMHC specificity prediction, making such approaches an attractive complement to current sequence-based approaches for refining TCR target selection.
This work introduces TCRTSdesign, a framework that concurrently generates novel TCR sequences with specific binding capabilities to target pMHC molecules and predicts the full-atom structures of the TCR-pMHC complex, while optimizing their binding affinity.
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