The geometry of pMHC-coated nanoparticles and T cell receptor clusters governs the sensitivity-specificity trade-off in T cell response
Bibliographic record
Abstract
Abstract T cells must reliably discriminate between foreign-derived antigens that require an adaptive immune response from non-specific self-antigens that do not. This discrimination is highly specific to the affinity of the bond between ligand and T cell receptors (TCRs), as well as highly sensitive to the concentration of ligand. In this study, we examined these features of T cell mediated immunity in the context of multivalent ligand-receptor interactions between clusters of TCRs with pMHC-coated nanoparticles (NPs). Using Monte Carlo simulations of NP-T cell surface interactions, we compared the effect of TCR clustering on the dose-response profiles of various NP designs. These simulations revealed a trade-off between sensitivity and specificity, mediated by the spatial clustering of TCRs and the geometry of the NP. In particular, large clusters of TCRs were more sensitive to both NP valence and ligand concentration at the expense of antigen specificity. Conversely, uniformly distributed TCR landscapes were better suited to affinity-based ligand discrimination, while sacrificing sensitivity to ligand concentration. These features of NP-mediated T cell activation depended significantly on NP size and valence rather than on the average ligand concentration. Furthermore, we demonstrated how kinetic proofreading mechanisms may help compensate for the limitations associated with TCR clustering. These findings thus highlight the importance of interacting geometries of NP design and TCR landscape in modulating the specificity and sensitivity of the T cell response. Significance T cells rely on surface T cell receptors (TCRs) to recognize foreign antigens presented as peptide-major histocompatibility complex (pMHC) molecules. TCR clustering is crucial for T cell activation, though its full role remains not entirely clear. Using Monte Carlo simulations, we demonstrate that TCR clustering profoundly influences both surface binding dynamics of multivalent pMHC-coated nanoparticles, used in autoimmune disease therapies, as well as downstream intracellular signals leading to T cell activation. Our findings thus provide important insights into the role of interaction geometries in shaping T cell response, with implications for optimizing nanoparticle design to enhance their therapeutic efficacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".