T cells are defined by a heterodimeric surface receptor (the T cell receptor or TCR) that mediates recognition of pathogen-associated epitopes via interactions with peptide-major histocompatibility complexes (pMHC). TCRs are generated by genomic rearrangements of the germline TCR locus, a process termed V(D)J recombination that has the potential to generate a staggering diversity of TCRs (estimated to range from 1015 1 to as high as 1061 2 possible receptors). Despite this potential diversity, TCRs from T cells that recognize the same pMHC epitope often share conserved sequence features, suggesting that it may be possible to predictively model epitope specificity. Here we report the in-depth characterization of ten epitope-specific CD8+ TCR repertoires from mice and humans representing 4600+ in-frame, single cell-derived TCRαβ sequence pairs from 110 subjects. We developed novel analytical tools to characterize these epitope-specific repertoires: a distance measure on the space of TCRs that permits clustering and visualization (TCRdist), a robust repertoire diversity metric (TCRdiv) that accommodates the low number of paired public receptors observed when compared to single chain analyses, and a distance-based classifier capable of assigning previously unobserved TCRs to characterized repertoires with robust sensitivity and specificity. Our analysis demonstrates that each epitope-specific repertoire contains a clustered group of receptors that share core sequence similarities, together with a dispersed set of diverse “outlier” sequences. By identifying shared motifs in core sequences, we were able to highlight key conserved residues driving essential elements of TCR recognition. These analyses provide insights into the generalizable, underlying features of epitope-specific repertoires and adaptive immune recognition.

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta’s success is the energy function: a model parameterized from small molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta Energy Function, REF15. Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membrane proteins, peptides containing non-canonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.