Defining T cell receptor repertoires using nanovial-based binding and functional screening.

The ability to selectively bind to antigenic peptides and secrete effector molecules can define rare and low-affinity populations of cells with therapeutic potential in emerging T cell receptor (TCR) immunotherapies. We leverage cavity-containing hydrogel microparticles, called nanovials, each coated with peptide-major histocompatibility complex (pMHC) monomers to isolate antigen-reactive T cells. T cells are captured and activated by pMHCs inducing the secretion of effector molecules including IFN-γ and granzyme B that are accumulated on nanovials, allowing sorting based on both binding and function. The TCRs of sorted cells on nanovials are sequenced, recovering paired αß-chains using microfluidic emulsion-based single-cell sequencing. By labeling nanovials having different pMHCs with unique oligonucleotide-barcodes and secretions with oligo-barcoded detection antibodies, we could accurately link TCR sequences to specific targets and rank each TCR based on the corresponding cell's secretion level. Using the technique, we identified an expanded repertoire of functional TCRs targeting viral antigens with high specificity and found rare TCRs with activity against cancer-specific splicing-enhanced epitopes.

Prediction of Peptide and TCR CDR3 Loops in Formation of Class I MHC-Peptide-TCR Complexes Using Molecular Models with Solvation.

Formation of major histocompatibility (MHC)-peptide-T cell receptor (TCR) complexes is central to initiation of an adaptive immune response. These complexes form through initial stabilization of the MHC fold via binding of a short peptide, and subsequent interaction of the TCR to form a ternary complex, with contacts made predominantly through the complementarity-determining region (CDR) loops of the TCR. Stimulation of an immune response is central to cancer immunotherapy. This approach depends on identification of the appropriate combinations of MHC molecules, peptides, and TCRs to elicit an antitumor immune response. This prediction is a current challenge in computational biochemistry. In this chapter, we introduce a predictive method that involves generation of multiple peptides and TCR CDR 3 loop conformations, solvation of these conformers in the context of the MHC-peptide-TCR ternary complex, extraction of parameters from the generated complexes, and use of an AI model to evaluate the potential for the assembled ternary complex to support an immune response.

NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions.

T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes.

Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.

The interplay between membrane topology and mechanical forces in regulating T cell receptor activity.

T cells are critically important for host defense against infections. T cell activation is specific because signal initiation requires T cell receptor (TCR) recognition of foreign antigen peptides presented by major histocompatibility complexes (pMHC) on antigen presenting cells (APCs). Recent advances reveal that the TCR acts as a mechanoreceptor, but it remains unclear how pMHC/TCR engagement generates mechanical forces that are converted to intracellular signals. Here we propose a TCR Bending Mechanosignal (TBM) model, in which local bending of the T cell membrane on the nanometer scale allows sustained contact of relatively small pMHC/TCR complexes interspersed among large surface receptors and adhesion molecules on the opposing surfaces of T cells and APCs. Localized T cell membrane bending is suggested to increase accessibility of TCR signaling domains to phosphorylation, facilitate selective recognition of agonists that form catch bonds, and reduce noise signals associated with slip bonds.

Discovery of an ancient MHC category with both class I and class II features.

Two classes of major histocompatibility complex (MHC) molecules, MHC class I and class II, play important roles in our immune system, presenting antigens to functionally distinct T lymphocyte populations. However, the origin of this essential MHC class divergence is poorly understood. Here, we discovered a category of MHC molecules (W-category) in the most primitive jawed vertebrates, cartilaginous fish, and also in bony fish and tetrapods. W-category, surprisingly, possesses class II-type α- and ß-chain organization together with class I-specific sequence motifs for interdomain binding, and the W-category α2 domain shows unprecedented, phylogenetic similarity with ß2-microglobulin of class I. Based on the results, we propose a model in which the ancestral MHC class I molecule evolved from class II-type W-category. The discovery of the ancient MHC group, W-category, sheds a light on the long-standing critical question of the MHC class divergence and suggests that class II type came first.

Heterodimerization of H3K9 histone methyltransferases G9a and GLP activates methyl reading and writing capabilities.

Unique among metazoan repressive histone methyltransferases, G9a and GLP, which chiefly target histone 3 lysine 9 (H3K9), require dimerization for productive H3K9 mono (me1)- and dimethylation (me2) in vivo. Intriguingly, even though each enzyme can independently methylate H3K9, the predominant active form in vivo is a heterodimer of G9a and GLP. How dimerization influences the central H3K9 methyl binding ("reading") and deposition ("writing") activity of G9a and GLP and why heterodimerization is essential in vivo remains opaque. Here, we examine the H3K9me "reading" and "writing" activities of defined, recombinantly produced homo- and heterodimers of G9a and GLP. We find that both reading and writing are significantly enhanced in the heterodimer. Compared with the homodimers, the heterodimer has higher recognition of H3K9me2, and a striking ∼10-fold increased turnover rate for nucleosomal substrates under multiple turnover conditions, which is not evident on histone tail peptide substrates. Cross-linking Mass Spectrometry suggests that differences between the homodimers and the unique activity of the heterodimer may be encoded in altered ground state conformations, as each dimer displays different domain contacts. Our results indicate that heterodimerization may be required to relieve autoinhibition of H3K9me reading and chromatin methylation evident in G9a and GLP homodimers. Relieving this inhibition may be particularly important in early differentiation when large tracts of H3K9me2 are typically deposited by G9a-GLP, which may require a more active form of the enzyme.

Information-Driven Docking for TCR-pMHC Complex Prediction.

T cell receptor (TCR) recognition of peptides presented by major histocompatibility complex (MHC) molecules is a fundamental process in the adaptive immune system. An understanding of this recognition process at the molecular level is crucial for TCR based therapeutics and vaccine design. The broad nature of TCR diversity and cross-reactivity presents a challenge for traditional structural resolution. Computational modelling of TCR-pMHC complexes offers an efficient alternative. This study compares the ability of four general-purpose docking platforms (ClusPro, LightDock, ZDOCK and HADDOCK) to make use of varying levels of binding interface information for accurate TCR-pMHC modelling. Each platform was tested on an expanded benchmark set of 44 TCR-pMHC docking cases. In general, HADDOCK is shown to be the best performer. Docking strategy guidance is provided to obtain the best models for each platform for future research. The TCR-pMHC docking cases used in this study can be downloaded from https://github.com/innate2adaptive/ExpandedBenchmark.

Temporal analysis of T-cell receptor-imposed forces via quantitative single molecule FRET measurements.

Mechanical forces acting on ligand-engaged T-cell receptors (TCRs) have previously been implicated in T-cell antigen recognition, yet their magnitude, spread, and temporal behavior are still poorly defined. We here report a FRET-based sensor equipped either with a TCR-reactive single chain antibody fragment or peptide-loaded MHC, the physiological TCR-ligand. The sensor was tethered to planar glass-supported lipid bilayers (SLBs) and informed most directly on the magnitude and kinetics of TCR-imposed forces at the single molecule level. When confronting T-cells with gel-phase SLBs we observed both prior and upon T-cell activation a single, well-resolvable force-peak of approximately 5 pN and force loading rates on the TCR of 1.5 pN per second. When facing fluid-phase SLBs instead, T-cells still exerted tensile forces yet of threefold reduced magnitude and only prior to but not upon activation.

MHCBI: a pipeline for calculating peptide-MHC binding energy using semi-empirical quantum mechanical methods with explicit/implicit solvent models.

Experimentally estimating peptide-major histocompatibility complex (pMHC) binding affinity has been quite challenging due to the many receptors and the many potential ligands implicated in it. We have thus proposed a straightforward computational methodology considering the different mechanisms involved in pMHC binding to facilitate studying such receptor-ligand interactions. We have developed a pipeline using semi-empirical quantum mechanical methods for calculating pMHC class I and II molecules' binding energy (BE). This pipeline can systematize the methodology for calculating pMHC system BE, enabling the rational design of T-cell epitopes to be used as pharmaceuticals and vaccines.

Factors that influence the thymic selection of CD8αα intraepithelial lymphocytes.

Thymocytes bearing αß T cell receptors (TCRαß) with high affinity for self-peptide-MHC complexes undergo negative selection or are diverted to alternate T cell lineages, a process termed agonist selection. Among thymocytes bearing TCRs restricted to MHC class I, agonist selection can lead to the development of precursors that can home to the gut and give rise to CD8αα-expressing intraepithelial lymphocytes (CD8αα IELs). The factors that influence the choice between negative selection versus CD8αα IEL development remain largely unknown. Using a synchronized thymic tissue slice model that supports both negative selection and CD8αα IEL development, we show that the affinity threshold for CD8αα IEL development is higher than for negative selection. We also investigate the impact of peptide presenting cells and cytokines, and the migration patterns associated with these alternative cell fates. Our data highlight the roles of TCR affinity and the thymic microenvironments on T cell fate.

Immunoinformatics approach for predicting epitopes in HN and F proteins of Porcine rubulavirus.

Porcine rubulavirus (PRV), which belongs to the family Paramyxoviridae, causes blue eye disease in pigs, characterized by encephalitis and reproductive failure in newborn and adult pigs, respectively. There is no effective treatment against PRV and no information on the effectiveness of the available vaccines. Continuous outbreaks have occurred in Mexico since the early 1980s, which have caused serious economic losses to pig producers. Vaccination can be used to control this disease. Searching for effective antigen candidates against PRV, we first sequenced the PAC1 F protein, then we used various immunoinformatics tools to predict antigenic determinants of B-cells and T-cells against the two glycoproteins of the virus (HN and F proteins). Finally, we used AutoDock Vina to determine the binding energies. We obtained the F gene sequence of a PRV strain collected in the early 1990s in Mexico and compared its amino acid profile with previous and more recent strains, obtaining an identity similarity of 97.78 to 99.26%. For the F proteins, seven linear B-cell epitopes, six conformational B-cell epitopes and twenty-nine T-cell MHC class I epitopes were predicted. For the HN proteins, sixteen linear B-cell epitopes, seven conformational B-cell epitopes and thirty-four T-cell MHC class I epitopes were predicted. The ATRSETDYY and AAYTTTTCF epitopes of the HN protein might be important for neutralizing the viral infection. We determined the in silico binding energy between the predicted epitopes on the F and HN proteins and swine MHC-I molecules. The binding energy of these epitopes ranged from -5.8 to -7.8 kcal/mol. The present study aimed to assess the use of HN and F proteins as antigens, either as recombinant proteins or as a series of peptides that could activate different responses of the immune system. This may help identify relevant immunogens, saving time and costs in the development of new vaccines or diagnostic tools.

Identification of Immunogenic Epitopes That Permit the Detection of Antigen-Specific T Cell Responses in Multiple Serotypes of Group B Coxsackievirus Infections.

Coxsackievirus group B (CVB) contains six serotypes that can affect various organs. Some of these organ-specific diseases such as myocarditis and pancreatitis can be caused by more than one serotype. Thus, development of immunological tools common to multiple serotypes is desired. This is especially critical for analyzing antigen-specific T cell responses at a single cell level. To this end, we made efforts to identify the immunogenic epitopes of CVB3 leading us to localize three T cell epitopes within the viral protein 1 (VP1) namely, VP1 681-700, VP1 721-740 and VP1 771-790. First, we confirmed their immunogenicity in the immunization settings. Second, we sought to verify the ability of VP1 epitopes to bind major histocompatibility complex (MHC) class II (IAk) molecules. Third, we created MHC class II (IAk) dextramers and tetramers and ascertained the T cell responses to be antigen-specific. Fourth, we analyzed the T cell responses in animals infected with CVB3 and noted the magnitude of antigen-specific T cell responses occurring in the order of VP1 721-740 and VP1 681-700 followed by VP1 771-790 as verified by proliferation assay and IAk tetramer staining. All epitopes induced interferon (IFN)-γ as a major cytokine. Finally, we investigated whether the VP1 tools generated for CVB3 can also be used to verify T cell responses in infections caused by other serotypes. To this end, we established the CVB4 infection model in A/J mice and found that the CVB4 infection led to the induction of IFN-γ-producing T cell responses primarily for VP1 721-740 and VP1 681-700. Thus, the VP1-specific tools, particularly IAk tetramers can be used to monitor anti-viral T cell responses in multiple CVB serotypes.

Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach.

In this work, we analyze the structure-activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape analysis rapidly led to the identification of activity cliffs, scaffolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity cliffs, scaffold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identification of interactions with key residues involved in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this work is expected to further advance the development of G9a inhibitors.

TCR-pMHC bond conformation controls TCR ligand discrimination.

A major unanswered question is how a TCR discriminates between foreign and self-peptides presented on the APC surface. Here, we used in situ fluorescence resonance energy transfer (FRET) to measure the distances of single TCR-pMHC bonds and the conformations of individual TCR-CD3ζ receptors at the membranes of live primary T cells. We found that a TCR discriminates between closely related peptides by forming single TCR-pMHC bonds with different conformations, and the most potent pMHC forms the shortest bond. The bond conformation is an intrinsic property that is independent of the binding affinity and kinetics, TCR microcluster formation, and CD4 binding. The bond conformation dictates the degree of CD3ζ dissociation from the inner leaflet of the plasma membrane via a positive calcium signaling feedback loop to precisely control the accessibility of CD3ζ ITAMs for phosphorylation. Our data revealed the mechanism by which a TCR deciphers the structural differences among peptides via the TCR-pMHC bond conformation.

CD8 Binding of MHC-Peptide Complexes in cis or trans Regulates CD8+ T-cell Responses.

The coreceptor CD8αß can greatly promote activation of T cells by strengthening T-cell receptor (TCR) binding to cognate peptide-MHC complexes (pMHC) on antigen presenting cells and by bringing p56Lck to TCR/CD3. Here, we demonstrate that CD8 can also bind to pMHC on the T cell (in cis) and that this inhibits their activation. Using molecular modeling, fluorescence resonance energy transfer experiments on living cells, biochemical and mutational analysis, we show that CD8 binding to pMHC in cis involves a different docking mode and is regulated by posttranslational modifications including a membrane-distal interchain disulfide bond and negatively charged O-linked glycans near positively charged sequences on the CD8ß stalk. These modifications distort the stalk, thus favoring CD8 binding to pMHC in cis. Differential binding of CD8 to pMHC in cis or trans is a means to regulate CD8+ T-cell responses and provides new translational opportunities.

Nanodiamond-Mediated Delivery of a G9a Inhibitor for Hepatocellular Carcinoma Therapy.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with high mortality but limited therapeutic options. Epigenetic regulations including DNA methylation and histone modification control gene expressions and play a crucial role during tumorigenesis. G9a, also known as EHMT2 (euchromatic histone-lysine N-methyltransferase 2), is a histone methyltransferase predominantly responsible for dimethylation of histone H3 lysine 9 (H3K9). G9a has been shown to play a key role in promoting tumor progression. Recent studies have identified that G9a is a critical mediator of HCC pathogenesis. UNC0646 is a G9a inhibitor that has shown potent in vitro efficacy. However, due to its water insolubility, the in vivo efficacy of UNC0646 is not satisfactory. In this study, nanodiamonds (NDs) were utilized as a drug delivery platform to improve in vivo delivery of this small-molecule inhibitor. Our results showed that ND-UNC0646 complexes could be rapidly synthesized by physical adsorption, meanwhile possessing favorable drug delivery properties and was able to improve the dispersibility of UNC0646 in water, therefore making it amenable for intravenous administration. The release profile of UNC0646 from ND-UNC0646 was demonstrated to be pH-responsive. Moreover, ND-UNC0646 maintained the biological functionality of UNC0646, with higher efficacy in reducing H3K9 methylation as well as enhanced invasion suppressive effects. Most importantly, increased in vivo efficacy was demonstrated using an orthotopic HCC mouse model, which paves the way of translating this small-molecule inhibitor toward HCC treatment. Our work demonstrates the potential of NDs in the clinical application for HCC treatment.

Inherent reactivity of unselected TCR repertoires to peptide-MHC molecules.

The repertoire of αß T cell antigen receptors (TCRs) on mature T cells is selected in the thymus where it is rendered both self-tolerant and restricted to the recognition of major histocompatibility complex molecules presenting peptide antigens (pMHC). It remains unclear whether germline TCR sequences exhibit an inherent bias to interact with pMHC prior to selection. Here, we isolated TCR libraries from unselected thymocytes and upon reexpression of these random TCR repertoires in recipient T cell hybridomas, interrogated their reactivities to antigen-presenting cell lines. While these random TCR combinations could potentially have reacted with any surface molecule on the cell lines, the hybridomas were stimulated most frequently by pMHC ligands. The nature and CDR3 loop composition of the TCRß chain played a dominant role in determining pMHC-reactivity. Replacing the germline regions of mouse TCRß chains with those of other jawed vertebrates preserved reactivity to mouse pMHC. Finally, introducing the CD4 coreceptor into the hybridomas increased the proportion of cells that could respond to pMHC ligands. Thus, αß TCRs display an intrinsic and evolutionary conserved bias for pMHC molecules in the absence of any selective pressure, which is further strengthened in the presence of coreceptors.

Structure Based Prediction of Neoantigen Immunogenicity.

The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8+ T cell responses of HLA-A2 presented nonameric peptides, while also providing insight into the underlying structural and biophysical mechanisms governing immunogenicity. Our proof-of-concept study demonstrates the potential for structure-based immunogenicity predictions in the development of personalized peptide-based vaccines.

MHC binding affects the dynamics of different T-cell receptors in different ways.

T cells use their T-cell receptors (TCRs) to scan other cells for antigenic peptides presented by MHC molecules (pMHC). If a TCR encounters a pMHC, it can trigger a signalling pathway that could lead to the activation of the T cell and the initiation of an immune response. It is currently not clear how the binding of pMHC to the TCR initiates signalling within the T cell. One hypothesis is that conformational changes in the TCR lead to further downstream signalling. Here we investigate four different TCRs in their free state as well as in their pMHC bound state using large scale molecular simulations totalling 26 000 ns. We find that the dynamical features within TCRs differ significantly between unbound TCR and TCR/pMHC simulations. However, apart from expected results such as reduced solvent accessibility and flexibility of the interface residues, these features are not conserved among different TCR types. The presence of a pMHC alone is not sufficient to cause cross-TCR-conserved dynamical features within a TCR. Our results argue against models of TCR triggering involving conserved allosteric conformational changes.