Molecular Basis of TCR Selectivity, Cross-Reactivity, and Allelic Discrimination by a Bacterial Superantigen: Integrative Functional and Energetic Mapping of the SpeC-Vβ2.1 Molecular Interface
Bibliographic record
Abstract
Superantigens activate large fractions of T cells through unconventional interactions with both TCR beta-chain V domains (Vbetas) and MHC class II molecules. The bacterial superantigen streptococcal pyrogenic exotoxin C (SpeC) primarily stimulates human Vbeta2(+) T cells. Herein, we have analyzed the SpeC-Vbeta2.1 interaction by mutating all SpeC residues that make contact with Vbeta2.1 and have determined the energetic and functional consequences of these mutations. Our comprehensive approach, including mutagenesis, functional readouts from both bulk T cell populations, and an engineered Vbeta2.1(+) Jurkat T cell, as well as surface plasmon resonance binding analysis, has defined the SpeC "functional epitope" for TCR engagement. Although only two SpeC residues (Tyr(15) and Arg(181)) are critical for activation of virtually all human CD3(+) T cells, a larger cluster of four hot spot residues are required for interaction with Vbeta2.1. Three of these residues (Tyr(15), Phe(75), and Arg(181)) concentrate their binding energy on the CDR2 loop residue Ser(52a), a noncanonical residue insertion found only in Vbeta2 and Vbeta4 chains. Plasticity of this loop is important for recognition by SpeC. Although SpeC interacts with the Vbeta2.1 hypervariable CDR3 loop, our data indicate these contacts have little to no influence on the functional interaction with Vbeta2.1. These studies also provide a molecular basis for selectivity and cross-reactivity of SpeC-TCR recognition and reveal a degree of fine specificity in these interactions, whereby certain SpeC mutants are capable of distinguishing between different alleles of the same Vbeta domain subfamily.
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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 | 0.000 |
| 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".