Computational Modeling of T Cell Receptor Complexes

Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids

Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the twenty canonical amino acids. This omits a large number of peptides containing non-canonical amino acids (NCAA), or residues that undergo varied post-translational modifications such as glycosylation or phosphorylation. These modifications fundamentally alter peptide immunogenicity. Similarly, existing structure-based methods are biased towards canonical peptide backbone structures, which may or may not be preserved when NCAAs are present. Rosetta FlexPepDock ab-initio is a structure-based computational protocol able to evaluate peptide-receptor interaction where no prior information of the peptide backbone is known. We benchmarked FlexPepDock ab-initio for docking canonical peptides to MHC-I, and illustrate for the first time the method's ability to accurately model MHC-I bound epitopes containing NCAAs. FlexPepDock ab-initio protocol was able to recapitulate near-native structures (≤1.5Å) in the top lowest-energy models for 20 out of 25 cases in our initial benchmark. Using known experimental binding affinities of twenty peptides derived from an influenza-derived peptide, we showed that FlexPepDock protocol is able to predict relative binding affinity as Rosetta energies correlate well with experimental values (r = 0.59, p = 0.006). ROC analysis revealed 80% true positive and a 40% false positive rate, with a prediction power of 93%. Finally, we demonstrate the protocol's ability to accurately recapitulate HLA-A*02:01 bound phosphopeptide backbone structures and relative binding affinity changes, the theoretical structure of the lymphocytic choriomeningitis derived glycosylated peptide GP392 bound to MHC-I H-2Db, and isolevuglandin-adducted peptides. The ability to use non-canonical amino acids in the Rosetta FlexPepDock protocol may provide useful insight into critical amino acid positions where the post-translational modification modulates immunologic responses.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Geometrical characterization of T cell receptor binding modes reveals class-specific binding to maximize access to antigen

Recognition of antigenic peptides bound to major histocompatibility complex (MHC) proteins by αβ T cell receptors (TCRs) is a hallmark of T cell mediated immunity. Recent data suggest that variations in TCR binding geometry may influence T cell signaling, which could help explain outliers in relationships between physical parameters such as TCR-pMHC binding affinity and T cell function. Traditionally, TCR binding geometry has been described with simple descriptors such as the crossing angle, which quantifies what has become known as the TCR's diagonal binding mode. However, these descriptors often fail to reveal distinctions in binding geometry that are apparent through visual inspection. To provide a better framework for relating TCR structure to T cell function, we developed a comprehensive system for quantifying the geometries of how TCRs bind peptide/MHC complexes. We show that our system can discern differences not clearly revealed by more common methods. As an example of its potential to impact biology, we used it to reveal differences in how TCRs bind class I and class II peptide/MHC complexes, which we show allow the TCR to maximize access to and "read out" the peptide antigen. We anticipate our system will be of use in not only exploring these and other details of TCR-peptide/MHC binding interactions, but also addressing questions about how TCR binding geometry relates to T cell function, as well as modeling structural properties of class I and class II TCR-peptide/MHC complexes from sequence information. The system is available at https://tcr3d.ibbr.umd.edu/tcr_com or for download as a script.

Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition

Adaptive immune recognition is mediated by antigen receptors on B and T cells generated by somatic recombination during lineage development. The high level of diversity resulting from this process posed technical limitations that previously limited the comprehensive analysis of adaptive immune recognition. Advances over the last ten years have produced data and approaches allowing insights into how T cells develop, evolutionary signatures of recombination and selection, and the features of T cell receptors that mediate epitope-specific binding and T cell activation. The size and complexity of these data have necessitated the generation of novel computational and analytical approaches, which are transforming how T cell immunology is conducted. Here we review the development and application of novel biological, theoretical, and computational methods for understanding T cell recognition and discuss the potential for improved models of receptor:antigen interactions.

T cell receptor cross-reactivity expanded by dramatic peptide-MHC adaptability

T cell receptor cross-reactivity allows a fixed T cell repertoire to respond to a much larger universe of potential antigens. Recent work has emphasized the importance of peptide structural and chemical homology, as opposed to sequence similarity, in T cell receptor cross-reactivity. Surprisingly though, T cell receptors can also cross-react between ligands with little physiochemical commonalities. Studying the clinically relevant receptor DMF5, we demonstrate that cross-recognition of such divergent antigens can occur through mechanisms that involve heretofore unanticipated rearrangements in the peptide and presenting MHC protein, including binding-induced peptide register shifts and extensions from MHC peptide binding grooves. Moreover, cross-reactivity can proceed even when such dramatic rearrangements do not translate into structural or chemical molecular mimicry. Beyond demonstrating new principles of T cell receptor cross-reactivity, our results have implications for efforts to predict and control T cell specificity and cross-reactivity, and highlight challenges associated with predicting T cell reactivities.

Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring

Interactions of T cell receptors (TCR) to peptides in complex with MHC (p:MHC) are key features that mediate cellular immune responses. While MHC binding is required for a peptide to be presented to T cells, not all MHC binders are immunogenic. The interaction of a TCR to the p:MHC complex holds a key, but currently poorly comprehended, component for our understanding of this variation in the immunogenicity of MHC binding peptides. Here, we demonstrate that identification of the cognate target of a TCR from a set of p:MHC complexes to a high degree is achievable using simple force-field energy terms. Building a benchmark of TCR:p:MHC complexes where epitopes and non-epitopes are modelled using state-of-the-art molecular modelling tools, scoring p:MHC to a given TCR using force-fields, optimized in a cross-validation setup to evaluate TCR inter atomic interactions involved with each p:MHC, we demonstrate that this approach can successfully be used to distinguish between epitopes and non-epitopes. A detailed analysis of the performance of this force-field-based approach demonstrate that its predictive performance depend on the ability to both accurately predict the binding of the peptide to the MHC and model the TCR:p:MHC complex structure. In summary, we conclude that it is possible to identify the TCR cognate target among different candidate peptides by using a force-field based model, and believe this works could lay the foundation for future work within prediction of TCR:p:MHC interactions.

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient’s own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide–ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide–MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC “hot-spots” for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

Deep Mutational Scans as a Guide to Engineering High Affinity T Cell Receptor Interactions with Peptide-bound Major Histocompatibility Complex

Proteins are often engineered to have higher affinity for their ligands to achieve therapeutic benefit. For example, many studies have used phage or yeast display libraries of mutants within complementarity-determining regions to affinity mature antibodies and T cell receptors (TCRs). However, these approaches do not allow rapid assessment or evolution across the entire interface. By combining directed evolution with deep sequencing, it is now possible to generate sequence fitness landscapes that survey the impact of every amino acid substitution across the entire protein-protein interface. Here we used the results of deep mutational scans of a TCR-peptide-MHC interaction to guide mutational strategies. The approach yielded stable TCRs with affinity increases of >200-fold. The substitutions with the greatest enrichments based on the deep sequencing were validated to have higher affinity and could be combined to yield additional improvements. We also conducted in silico binding analyses for every substitution to compare them with the fitness landscape. Computational modeling did not effectively predict the impacts of mutations distal to the interface and did not account for yeast display results that depended on combinations of affinity and protein stability. However, computation accurately predicted affinity changes for mutations within or near the interface, highlighting the complementary strengths of computational modeling and yeast surface display coupled with deep mutational scanning for engineering high affinity TCRs.