The development of highly accurate deep learning models for protein structure prediction has transformed the landscape of T‐cell receptor (TCR) structure data, which can now be accessed at repertoire scale. We provide a perspective on the growing field of structural TCR immunoinformatics, summarizing core principles of...
Nele P. Quast, M. Raybould, C. Deane· Immunological Reviews· 0 citations
High-quality experimental datasets that link protein–ligand structures with binding affinity data are essential for developing and evaluating structure-based machine learning methods. To help address this need, we established OpenBind as an open-science initiative to generate large-scale experimental datasets for struc...
Jochem Nelen, Omeir Khan, Etowah Adams et al.· bioRxiv· 1 citation
GenCDR, a family of LLaMa-based autoregressive language models that read all frameworks as a conditioning prompt and generate all CDRs jointly as a variable-length response, makes CDR likelihoods a clean, separable target for reward attribution, achieves the highest CDR recovery among autoregressive models.
Ferran Gonzalez Hernandez, Oliver M. Turnbull, Maliha Sultana et al.· bioRxiv· 0 citations
A new physics-informed representation using Fourier transforms as an inductive bias for the multiscale temporal nature of protein dynamics, DynaMode is presented, achieving strong performance across a set of ensemble-based metrics.
H. Phipps, M. Cagiada, S. Villalba et al.· 0 citations
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