TCRep 3D: an automated in silico approach to study the structural properties of TCR repertoires

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

T-Cell Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop Similarities

T-cell receptors (TCR) mediate immune responses recognizing peptides in complex with major histocompatibility complexes (pMHC) displayed on the surface of cells. Resolving the challenge of predicting the cognate pMHC target of a TCR would benefit many applications in the field of immunology, including vaccine design/discovery and the development of immunotherapies. Here, we developed a model for prediction of TCR targets based on similarity to a database of TCRs with known targets. Benchmarking the model on a large set of TCRs with known target, we demonstrated how the predictive performance is increased (i) by focusing on CDRs rather than the full length TCR protein sequences, (ii) by incorporating information from paired α and β chains, and (iii) integrating information for all 6 CDR loops rather than just CDR3. Finally, we show how integration of the structure of CDR loops, as obtained through homology modeling, boosts the predictive power of the model, in particular in situations where no high-similarity TCRs are available for the query. These findings demonstrate that TCRs that bind to the same target also share, to a very high degree, sequence, and structural features. This observation has profound impact for future development of prediction models for TCR-pMHC interactions and for the use of such models for the rational design of T cell based therapies.

Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity

Background

Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with major histocompatibility complex (MHC)-presented epitopes from putative antigens, and thus encode the determinants of recognition.

Results

We here systematically characterize the predictive power that can be obtained from CDR3 sequences, using representative, readily interpretable methods for evaluating CDR sequence similarity and then clustering and classifying sequences based on similarity. An initial analysis of CDR3s of known structure, clustered by structural similarity, helps calibrate the limits of sequence diversity among CDRs that might have a common mode of interaction with presented epitopes. Subsequent analyses demonstrate that this same range of sequence similarity strikes a favorable specificity/sensitivity balance in distinguishing twins from non-twins based on overall CDR3 repertoires, classifying CDR3 repertoires by antigen specificity, and distinguishing general pathologies.

Conclusion

We conclude that within a fairly broad range of sequence similarity, matching CDR3 sequences are likely to share specificities.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2864-8) contains supplementary material, which is available to authorized users.

Sensitive and frequent identification of high avidity neo-epitope specific CD8 + T cells in immunotherapy-naive ovarian cancer

Immunotherapy directed against private tumor neo-antigens derived from non-synonymous somatic mutations is a promising strategy of personalized cancer immunotherapy. However, feasibility in low mutational load tumor types remains unknown. Comprehensive and deep analysis of circulating and tumor-infiltrating lymphocytes (TILs) for neo-epitope specific CD8⁺ T cells has allowed prompt identification of oligoclonal and polyfunctional such cells from most immunotherapy-naive patients with advanced epithelial ovarian cancer studied. Neo-epitope recognition is discordant between circulating T cells and TILs, and is more likely to be found among TILs, which display higher functional avidity and unique TCRs with higher predicted affinity than their blood counterparts. Our results imply that identification of neo-epitope specific CD8⁺ T cells is achievable even in tumors with relatively low number of somatic mutations, and neo-epitope validation in TILs extends opportunities for mutanome-based personalized immunotherapies to such tumors.

DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles

Background

T cell receptor (TCR) molecules are involved in the adaptive immune response as they distinguish between self- and foreign-peptides, presented in major histocompatibility complex molecules (pMHC). Former studies showed that the association angles of the TCR variable domains (Vα/Vβ) can differ significantly and change upon binding to the pMHC complex. These changes can be described as a rotation of the domains around a general Center of Rotation, characterized by the interaction of two highly conserved glutamine residues.

Methods

We developed a computational method, DynaDom, for the prediction of TCR Vα/Vβ inter-domain and TCR/pMHC orientations in TCRpMHC complexes, which allows predicting the orientation of multiple protein-domains. In addition, we implemented a new approach to predict the correct orientation of the carboxamide endgroups in glutamine and asparagine residues, which can also be used as an external, independent tool.

Results

The approach was evaluated for the remodeling of 75 and 53 experimental structures of TCR and TCRpMHC (class I) complexes, respectively. We show that the DynaDom method predicts the correct orientation of the TCR Vα/Vβ angles in 96 and 89% of the cases, for the poses with the best RMSD and best interaction energy, respectively. For the concurrent prediction of the TCR Vα/Vβ and pMHC orientations, the respective rates reached 74 and 72%. Through an exhaustive analysis, we could show that the pMHC placement can be further improved by a straightforward, yet very time intensive extension of the current approach.

Conclusions

The results obtained in the present remodeling study prove the suitability of our approach for interdomain-angle optimization. In addition, the high prediction rate obtained specifically for the energetically highest ranked poses further demonstrates that our method is a powerful candidate for blind prediction. Therefore it should be well suited as part of any accurate atomistic modeling pipeline for TCRpMHC complexes and potentially other large molecular assemblies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-016-0071-7) contains supplementary material, which is available to authorized users.

High-Throughput Analysis of the T Cell Receptor Beta Chain Repertoire in PBMCs from Chronic Hepatitis B Patients with HBeAg Seroconversion

T lymphocytes are the most important immune cells that affect both the development and treatment of hepatitis B. We used high-throughput sequencing to determine the diversity in the V and J regions of the TCRβ chain in 4 chronic hepatitis B patients before and after HBeAg seroconversion. Here, we demonstrate that the 4 patients expressed Vβ12-4 at the highest frequencies of 10.6%, 9.2%, 17.5%, and 7.5%, and Vβ28 was the second most common, with frequencies of 7.8%, 6.7%, 5.3%, and 10.9%, respectively. No significant changes were observed following seroconversion. With regard to the Jβ gene, Jβ2-1 was the most commonly expressed in the 4 patients at frequencies of 5.8%, 6.5%, 11.3%, and 7.3%, respectively. Analysis of the V-J region genes revealed several differences, including significant increases in the expression levels of V7-2-01-J2-1, V12-4-J1-1, and V28-1-J1-5 and a decrease in that of V19-01-J2-3. These results illustrate the presence of biased TCRVβ and Jβ gene expression in the chronic hepatitis B patients. TRBVβ12-4, Vβ28, Jβ2-1, V7-2-01-J2-1, V12-4-J1-1, and V28-1-J1-5 may be associated with the development and treatment of CHB.

Computational Modeling of T Cell Receptor Complexes

T-cell receptor (TCR) binding to peptide/MHC determines specificity and initiates signaling in antigen-specific cellular immune responses. Structures of TCR-pMHC complexes have provided enormous insight to cellular immune functions, permitted a rational understanding of processes such as pathogen escape, and led to the development of novel approaches for the design of vaccines and other therapeutics. As production, crystallization, and structure determination of TCR-pMHC complexes can be challenging, there is considerable interest in modeling new complexes. Here we describe a rapid approach to TCR-pMHC modeling that takes advantage of structural features conserved in known complexes, such as the restricted TCR binding site and the generally conserved diagonal docking mode. The approach relies on the powerful Rosetta suite and is implemented using the PyRosetta scripting environment. We show how the approach can recapitulate changes in TCR binding angles and other structural details, and highlight areas where careful evaluation of parameters is needed and alternative choices might be made. As TCRs are highly sensitive to subtle structural perturbations, there is room for improvement. Our method nonetheless generates high-quality models that can be foundational for structure-based hypotheses regarding TCR recognition.

Quantitative Analysis of the Association Angle between T-cell Receptor Vα/Vβ Domains Reveals Important Features for Epitope Recognition

T-cell receptors (TCR) play an important role in the adaptive immune system as they recognize pathogen- or cancer-based epitopes and thus initiate the cell-mediated immune response. Therefore there exists a growing interest in the optimization of TCRs for medical purposes like adoptive T-cell therapy. However, the molecular mechanisms behind T-cell signaling are still predominantly unknown. For small sets of TCRs it was observed that the angle between their Vα- and Vβ-domains, which bind the epitope, can vary and might be important for epitope recognition. Here we present a comprehensive, quantitative study of the variation in the Vα/Vβ interdomain-angle and its influence on epitope recognition, performing a systematic bioinformatics analysis based on a representative set of experimental TCR structures. For this purpose we developed a new, cuboid-based superpositioning method, which allows a unique, quantitative analysis of the Vα/Vβ-angles. Angle-based clustering led to six significantly different clusters. Analysis of these clusters revealed the unexpected result that the angle is predominantly influenced by the TCR-clonotype, whereas the bound epitope has only a minor influence. Furthermore we could identify a previously unknown center of rotation (CoR), which is shared by all TCRs. All TCR geometries can be obtained by rotation around this center, rendering it a new, common TCR feature with the potential of improving the accuracy of TCR structure prediction considerably. The importance of Vα/Vβ rotation for signaling was confirmed as we observed larger variances in the Vα/Vβ-angles in unbound TCRs compared to epitope-bound TCRs. Our results strongly support a two-step mechanism for TCR-epitope: First, preformation of a flexible TCR geometry in the unbound state and second, locking of the Vα/Vβ-angle in a TCR-type specific geometry upon epitope-MHC association, the latter being driven by rotation around the unique center of rotation.

The recognition of antigenic peptides by cytotoxic T-cells is one of the crucial steps during the adaptive immune response. Thus a detailed understanding of this process is not only important for elucidating the mechanism behind T-cell signaling, but also for various emerging new medical applications like T-cell based immunotherapies and designed bio-therapeutics. However, despite the fast growing interest in this field, the mechanistic basis of the immune response is still largely unknown. Previous qualitative studies suggested that the T-cell receptor (TCR) Vα/Vβ-interdomain angle plays a crucial role in epitope recognition as it predetermines the relative position of its antigen-recognizing CDR1-3 loops and thus TCR specificity. In the manuscript we present a systematic bioinformatic analysis of the structural characteristics of bound and unbound TCR molecules focusing on the Vα/Vβ-angle. Our results demonstrate the importance of this angle for signaling, as several distinct Vα/Vβ-angle based structural clusters could be observed and larger angle flexibilities exist for unbound TCRs than for bound TCRs, providing quantitative proof for a two-step locking mechanism upon epitope recognition. In this context, we could identify a unique rotational point, which allows a quantitative, yet intuitive description of all observed angle variations and the structural changes upon epitope binding.

Profiling the repertoire of T-cell receptor beta-chain variable genes in peripheral blood lymphocytes from subjects who have recovered from acute hepatitis B virus infection

The profile of T-cell receptor beta-chain variable (TRBV) genes usually skews in subjects with virus infection or cancer. The gene melting spectral pattern (GMSP) can be used to determine the profile of the TRBV gene family. To explore the portrait of the TRBV family in peripheral blood lymphocytes from subjects who have recovered from acute hepatitis B virus infection (AHI), peripheral blood mononuclear cells (PBMCs) were separated and further sorted into CD4+ and CD8+ T-cell subsets. The molecular features of the TRBV complementary determining region 3 (CDR3) motifs were determined using GMSP analysis. When aGMSP profile showed a single peak, the monoclonally expanded TRBV gene was cloned and sequenced. Skewed expansions of multiple TRBV genes were observed among the CD4+ and CD8+ T-cell subsets and the PBMCs. The frequency of monoclonally expanded TRBV genes in the CD8+ T-cell subset was significantly higher than that of the CD4+ T-cell subset and the PBMCs. Compared to other members of the TRBV gene family, TRBV11, BV15 and BV20 were predominantly expressed in the repertoire of peripheral blood lymphocytes in recovered AHI subjects. The relatively conserved amino acid motifs of TRBV5.1 and BV20 CDR3 were also detected in the CD4+ and CD8+ T-cell subsets. These results demonstrate the presence of multiple biased TRBV families in recovered AHI subjects. TRBV11, BV15 and BV20, especially from the CD8+ T-cell subset, may be relevant to the pathogenesis of subjects with AHI. The preferentially selected TRBV5.1 and BV20 with the relatively conserved CDR3 motif may be potential targets for personalized treatments of chronic HBV infection.

Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes

Background

T cell receptors (TCRs) can recognize diverse lipid and metabolite antigens presented by MHC-like molecules CD1 and MR1, and the molecular basis of many of these interactions has not been determined. Here we applied our protein docking algorithm TCRFlexDock, previously developed to perform docking of TCRs to peptide-MHC (pMHC) molecules, to predict the binding of αβ and γδ TCRs to CD1 and MR1, starting with the structures of the unbound molecules.

Results

Evaluating against TCR-CD1d complexes with crystal structures, we achieved near-native structures in the top 20 models for two out of four cases, and an acceptable-rated prediction for a third case. We also predicted the structure of an interaction between a MAIT TCR and MR1-antigen that has not been structurally characterized, yielding a top-ranked model that agreed remarkably with a characterized TCR-MR1-antigen structure that has a nearly identical TCR α chain but a different β chain, highlighting the likely dominance of the conserved α chain in MR1-antigen recognition. Docking performance was improved by re-training our scoring function with a set of TCR-pMHC complexes, and for a case with an outlier binding mode, we found that alternative docking start positions improved predictive accuracy. We then performed unbound docking with two mycolyl-lipid specific TCRs that recognize lipid-bound CD1b, which represent a class of interactions that is not structurally characterized. Highly-ranked models of these complexes showed remarkable agreement between their binding topologies, as expected based on their shared germline sequences, while differences in residue-level interactions with their respective antigens point to possible mechanisms underlying their distinct specificities.

Conclusions

Together these results indicate that flexible docking simulations can provide accurate models and atomic-level insights into TCR recognition of MHC-like molecules presenting lipid and other small molecule antigens.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-319) contains supplementary material, which is available to authorized users.

A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes

T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein-protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near-native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes.

Structure-Based, Rational Design of T Cell Receptors

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1_157–165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8⁺ T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K_D = ∼1 − 5 μM. Beyond the affinity threshold at K_D < 1 μM we observed an attenuation in cellular function, in line with the “half-life” model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.

Molecular characterization of T cell receptor beta variable in the peripheral blood T cell repertoire in subjects with active tuberculosis or latent tuberculosis infection

Background

T cells are closely linked to the clinical manifestations of subjects with Mycobacterium tuberculosis (MTB) infection. T cell receptor beta variable (TCRBV) is a signal and indicative molecule on the membrane of T lymphocytes, reflecting the composition and specificity of T cells. The molecular profiles of TCRBV in peripheral blood mononuclear cells (PBMCs) and their subpopulations (CD4⁺ and CD8⁺ T cells) from subjects with active tuberculosis (TB) or latent TB infection (LTBI) have not been well described.

Methods

In 42 subjects with active TB or LTBI, PMBCs and their subsets were separated and sorted. The molecular profiles of the TCRBV complementarity determining region 3 (CDR3) in the three cell populations were investigated using our recently developed gene melting spectral pattern (GMSP) assay. The TCRBV members were then cloned and sequenced when their GMSP image profiles showed a single-peak.

Results

The average number of skewed TCRBV molecules in the CD4⁺ cell subset was significantly higher than that in PBMCs and CD8⁺ T cells. TCRBV12, BV13.1, BV13.2, and BV24 were expressed more prevalently than other TCRBV gene families in the three cell populations. In addition, relatively conserved amino acid motifs were identified in TCRBV5.1 and BV20 CDR3 in PBMCs and its subsets. The monoclonal TCRBV14 and BV23 expressed were different between active TB and LTBI subjects.

Conclusions

These results indicate that the T cell immune response is complex and multi-specific in active TB and LTBI subjects. Analysis of TCRBV expression in CD4⁺ T cells suggest that it could be useful in assessing the composition and status of circulating T cells. Furthermore, the expression of TCRBV14, BV23 and the sequencing of CDR3 amino acid motifs of TCRBV5.1, BV20 could be used in the differential diagnosis and treatment of subjects with active TB or LTBI.

T-cell receptors binding orientation over peptide/MHC class I is driven by long-range interactions

Crystallographic data about T-Cell Receptor - peptide - major histocompatibility complex class I (TCRpMHC) interaction have revealed extremely diverse TCR binding modes triggering antigen recognition. Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge. We present a simplified rigid approach applied on all non-redundant TCRpMHC crystal structures available. The CHARMM force field in combination with the FACTS implicit solvation model is used to study the role of long-distance interactions between the TCR and pMHC. We demonstrate that the sum of the coulomb interactions and the electrostatic solvation energies is sufficient to identify two orientations corresponding to energetic minima at 0° and 180° from the native orientation. Interestingly, these results are shown to be robust upon small structural variations of the TCR such as changes induced by Molecular Dynamics simulations, suggesting that shape complementarity is not required to obtain a reliable signal. Accurate energy minima are also identified by confronting unbound TCR crystal structures to pMHC. Furthermore, we decompose the electrostatic energy into residue contributions to estimate their role in the overall orientation. Results show that most of the driving force leading to the formation of the complex is defined by CDR1,2/MHC interactions. This long-distance contribution appears to be independent from the binding process itself, since it is reliably identified without considering neither short-range energy terms nor CDR induced fit upon binding. Ultimately, we present an attempt to predict the TCR/pMHC binding mode for a TCR structure obtained by homology modeling. The simplicity of the approach and the absence of any fitted parameters make it also easily applicable to other types of macromolecular protein complexes.