Thymus-derived lymphocytes protect mammalian hosts against virus- or cancer-related cellular alterations through immune surveillance, eliminating diseased cells. In this process, T cell receptors (TCRs) mediate both recognition and T cell activation via their dimeric alphabeta, CD3 epsilon gamma, CD3 epsilon delta, and CD3 zeta zeta subunits using an unknown structural mechanism. Here, site-specific binding topology of anti-CD3 monoclonal antibodies (mAbs) and dynamic TCR quaternary change provide key clues. Agonist mAbs footprint to the membrane distal CD3 epsilon lobe that they approach diagonally, adjacent to the lever-like C beta FG loop that facilitates antigen (pMHC)-triggered activation. In contrast, a non-agonist mAb binds to the cleft between CD3 epsilon and CD3 gamma in a perpendicular mode and is stimulatory only subsequent to an external tangential but not a normal force ( approximately 50 piconewtons) applied via optical tweezers. Specific pMHC but not irrelevant pMHC activates a T cell upon application of a similar force. These findings suggest that the TCR is an anisotropic mechanosensor, converting mechanical energy into a biochemical signal upon specific pMHC ligation during immune surveillance. Activating anti-CD3 mAbs mimic this force via their intrinsic binding mode. A common TCR quaternary change rather than conformational alterations can better facilitate structural signal initiation, given the vast array of TCRs and their specific pMHC ligands.

Significance: The αβ T-cell receptor (TCR) on mammalian T lymphocytes recognizes intracellular pathogens to afford protective immunity. Detection of various foreign peptides bound to MHC molecules as TCR ligands occurs during immune surveillance where mechanical forces are generated through cell movement. Using single-molecule optical tweezer assays, we show with isolated and complete receptors on single T cells that both sensitivity and specificity of the biological T-lymphocyte response is dependent upon force-based interactions. Our work demonstrates a catch-and-release αβTCR structural conversion correlating with ligand potency wherein a strongly binding/compact state transitions to a weakly binding/extended state. An allosteric mechanism controls bond strength and lifetime, supporting a model in which quaternary αβTCR subunit associations regulate TCR recognition under load.

Small cell lung carcinoma (SCLC) is highly mutated, yet durable response to immune checkpoint blockade (ICB) is rare. SCLC also exhibits cellular plasticity, which could influence its immunobiology. Here we discover that a distinct subset of SCLC uniquely upregulates MHC I, enriching for durable ICB benefit. In vitro modeling confirms epigenetic recovery of MHC I in SCLC following loss of neuroendocrine differentiation, which tracks with derepression of STING. Transient EZH2 inhibition expands these nonneuroendocrine cells, which display intrinsic innate immune signaling and basally restored antigen presentation. Consistent with these findings, murine nonneuroendocrine SCLC tumors are rejected in a syngeneic model, with clonal expansion of immunodominant effector CD8 T cells. Therapeutically, EZH2 inhibition followed by STING agonism enhances T-cell recognition and rejection of SCLC in mice. Together, these data identify MHC I as a novel biomarker of SCLC immune responsiveness and suggest novel immunotherapeutic approaches to co-opt SCLC's intrinsic immunogenicity. Significance: SCLC is poorly immunogenic, displaying modest ICB responsiveness with rare durable activity. In profiling its plasticity, we uncover intrinsically immunogenic MHC Ihi subpopulations of nonneuroendocrine SCLC associated with durable ICB benefit. We also find that combined EZH2 inhibition and STING agonism uncovers this cell state, priming cells for immune rejection. This article is highlighted in the In This Issue feature, p. 1861

Significance: T-lymphocyte activation during immune surveillance begins with recognition by its α β T-cell receptor (TCR) of a peptide ligand bound to an MHC molecule (pMHC) on infected or otherwise altered cells. Using optical traps, we show that activation is fostered at the single-molecule level through synergy between external load on the TCR–pMHC bond and internal, sustained stepping via motor-dependent transport. Chemical thresholds in the absence of load require much higher pMHC density than observed physiologically. With load, however, T lymphocytes can be reliably activated with ∼ 10 pN per TCR molecule, mimicking native shear motions involving a mere two pMHCs at the interaction surface. Initial TCR triggering sensitivity results from synergistic mechanosensing rather than previously postulated serial engagement.

Background: Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.Results: Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201).Conclusion: Non-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction – a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects.

Background: Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW. Results: Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies – the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (A_ROC), 17 predictors showed good (A_ROC > 0.8), 41 showed marginal (A_ROC > 0.7), and 55 showed poor performance (A_ROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds. Conclusion: The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.