A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces

Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

TCR-engineered T cell therapy in solid tumors: State of the art and perspectives

T cell engineering has changed the landscape of cancer immunotherapy. Chimeric antigen receptor T cells have demonstrated a remarkable efficacy in the treatment of B cell malignancies in hematology. However, their clinical impact on solid tumors has been modest so far. T cells expressing an engineered T cell receptor (TCR-T cells) represent a promising therapeutic alternative. The target repertoire is not limited to membrane proteins, and intrinsic features of TCRs such as high antigen sensitivity and near-to-physiological signaling may improve tumor cell detection and killing while improving T cell persistence. In this review, we present the clinical results obtained with TCR-T cells targeting different tumor antigen families. We detail the different methods that have been developed to identify and optimize a TCR candidate. We also discuss the challenges of TCR-T cell therapies, including toxicity assessment and resistance mechanisms. Last, we share some perspectives and highlight future directions in the field.

HLA-A∗02-gated safety switch for cancer therapy has exquisite specificity for its allelic target antigen

Innovative cell-based therapies are important new weapons in the fight against difficult-to-treat cancers. One promising strategy involves cell therapies equipped with multiple receptors to integrate signals from more than one antigen. We developed a specific embodiment of this approach called Tmod, a two-receptor system that combines activating and inhibitory inputs to distinguish between tumor and normal cells. The selectivity of Tmod is enforced by the inhibitory receptor (blocker) that recognizes an antigen, such as an HLA allele, whose expression is absent from tumors because of loss of heterozygosity. Although unwanted cross-reactivity of the blocker likely reduces efficacy rather than safety, it is important to verify the blocker's specificity. We have tested an A∗02-directed blocker derived from the PA2.1 mouse antibody as a safety mechanism paired with a mesothelin-specific activating CAR in our Tmod construct. We solved the crystal structure of humanized PA2.1 Fab in complex with HLA-A∗02 to determine its binding epitope, which was used to bioinformatically select specific class I HLA alleles to test the blocker's functional specificity in vitro. We found that this A∗02-directed blocker is highly specific for its cognate antigen, with only one cross-reactive allele (A∗69) capable of triggering comparable function.

Structural insights into engineering a T-cell receptor targeting MAGE-A10 with higher affinity and specificity for cancer immunotherapy

BACKGROUND

T-cell receptor (TCR) immunotherapy is becoming a viable modality in cancer treatment with efficacy in clinical trials. The safety of patients is paramount, so innovative cell engineering methods are being employed to exploit adaptive immunity while controlling the factors governing antigen receptor (ie, TCR) specificity and cross-reactivity. We recently reported a TCR engineering campaign and selectivity profiling assay (X-scan) targeting a melanoma antigen gene (MAGE)-A10 peptide. This helped to distinguish between two well-performing TCRs based on cross-reactivity potential during preclinical drug evaluation, allowing one to be advanced to T-cell immunotherapeutic clinical trials. Here, we present three-dimensional structural information on those TCRs, highlighting engineering improvements and molecular mechanisms likely underpinning differential selectivity.

METHODS

Parental and engineered TCRs were purified and crystallized either alone or complexed to human leucocyte antigen (HLA)-A*02:01 presenting the MAGE-A10 9-mer peptide, GLYDGMEHL (pHLA/MAGE-A10-9). Using X-ray diffraction, we solved four high-resolution crystal structures and evaluated them relative to previously reported functional results.

RESULTS

The unligated parental TCR displayed similar complementarity-determining region (CDR) loop conformations when bound to pHLA/MAGE-A10-9; a rigid-body movement of TCR beta chain variable domain (TRBV) relative to TCR alpha chain variable domain helped optimal pHLA engagement. This first view of an HLA-bound MAGE-A10 peptide revealed an intrachain non-covalent 'staple' between peptide Tyr3 and Glu7. A subtle Glu31-Asp mutation in βCDR1 of the parental TCR generated a high-affinity derivative. Its pHLA-complexed structure shows that the shorter Asp leans toward the pHLA with resulting rigid-body TRBV shift, creating localized changes around the peptide's C-terminus. Structural comparison with a less selective TCR indicated that differential cross-reactivity to MAGE-A10 peptide variants is most readily explained by alterations in surface electrostatics, and the size and geometry of TCR-peptide interfacial cavities.

CONCLUSIONS

Modest changes in engineered TCRs targeting MAGE-A10 produced significantly different properties. Conformational invariance of TCR and antigen peptide plus more space-filling CDR loop sequences may be desirable properties for clinically relevant TCR-pHLA systems to reduce the likelihood of structurally similar peptide mimics being tolerated by a TCR. Such properties may partially explain why the affinity-enhanced, in vitro-selected TCR has been generally well tolerated in patients.

Adoptive T cell therapy: Boosting the immune system to fight cancer

Cellular therapies have shown increasing promise as a cancer treatment. Encouraging results against hematologic malignancies are paving the way to move into solid tumors. In this review, we will focus on T-cell therapies starting from tumor infiltrating lymphocytes (TILs) to optimized T-cell receptor-modified (TCR) cells and chimeric antigen receptor-modified T cells (CAR-Ts). We will discuss the positive preclinical and clinical findings of these approaches, along with some of the persisting barriers that need to be overcome to improve outcomes.

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.

Understanding TCR affinity, antigen specificity, and cross-reactivity to improve TCR gene-modified T cells for cancer immunotherapy

Adoptive cell transfer (ACT) using T cell receptor (TCR) gene-modified T cells is an exciting and rapidly evolving field. Numerous preclinical and clinical studies have demonstrated various levels of feasibility, safety, and efficacy using TCR-engineered T cells to treat cancer and viral infections. Although evidence suggests their use can be effective, to what extent and how to improve these therapeutics are still matters of investigation. As TCR affinity has been generally accepted as the central role in defining T cell specificity and sensitivity, selection for and generation of high affinity TCRs has remained a fundamental approach to design more potent T cells. However, traditional methods for affinity-enhancement by random mutagenesis can induce undesirable cross-reactivity causing on- and off-target adverse events, generate exhausted effectors by overstimulation, and ignore other kinetic and cellular parameters that have been shown to impact antigen specificity. In this Focussed Research Review, we comment on the preclinical and clinical potential of TCR gene-modified T cells, summarize our contributions challenging the role TCR affinity plays in antigen recognition, and explore how structure-guided design can be used to manipulate antigen specificity and TCR cross-reactivity to improve the safety and efficacy of TCR gene-modified T cells used in ACT.

STCRDab: the structural T-cell receptor database

The Structural T–cell Receptor Database (STCRDab; http://opig.stats.ox.ac.uk/webapps/stcrdab) is an online resource that automatically collects and curates TCR structural data from the Protein Data Bank. For each entry, the database provides annotations, such as the α/β or γ/δ chain pairings, major histocompatibility complex details, and where available, antigen binding affinities. In addition, the orientation between the variable domains and the canonical forms of the complementarity-determining region loops are also provided. Users can select, view, and download individual or bulk sets of structures based on these criteria. Where available, STCRDab also finds antibody structures that are similar to TCRs, helping users explore the relationship between TCRs and antibodies.

Improving T Cell Receptor On-Target Specificity via Structure-Guided Design

T cell receptors (TCRs) have emerged as a new class of immunological therapeutics. However, though antigen specificity is a hallmark of adaptive immunity, TCRs themselves do not possess the high specificity of monoclonal antibodies. Although a necessary function of T cell biology, the resulting cross-reactivity presents a significant challenge for TCR-based therapeutic development, as it creates the potential for off-target recognition and immune toxicity. Efforts to enhance TCR specificity by mimicking the antibody maturation process and enhancing affinity can inadvertently exacerbate TCR cross-reactivity. Here we demonstrate this concern by showing that even peptide-targeted mutations in the TCR can introduce new reactivities against peptides that bear similarity to the original target. To counteract this, we explored a novel structure-guided approach for enhancing TCR specificity independent of affinity. Tested with the MART-1-specific TCR DMF5, our approach had a small but discernible impact on cross-reactivity toward MART-1 homologs yet was able to eliminate DMF5 cross-recognition of more divergent, unrelated epitopes. Our study provides a proof of principle for the use of advanced structure-guided design techniques for improving TCR specificity, and it suggests new ways forward for enhancing TCRs for therapeutic use.

Integrating Experiment and Theory to Understand TCR-pMHC Dynamics

The conformational dynamism of proteins is well established. Rather than having a single structure, proteins are more accurately described as a conformational ensemble that exists across a rugged energy landscape, where different conformational sub-states interconvert. The interaction between αβ T cell receptors (TCR) and cognate peptide-MHC (pMHC) is no exception, and is a dynamic process that involves substantial conformational change. This review focuses on technological advances that have begun to establish the role of conformational dynamics and dynamic allostery in TCR recognition of the pMHC and the early stages of signaling. We discuss how the marriage of molecular dynamics (MD) simulations with experimental techniques provides us with new ways to dissect and interpret the process of TCR ligation. Notably, application of simulation techniques lags behind other fields, but is predicted to make substantial contributions. Finally, we highlight integrated approaches that are being used to shed light on some of the key outstanding questions in the early events leading to TCR signaling.

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.

Molecular properties of gp100-reactive T-cell receptors drive the cytokine profile and antitumor efficacy of transgenic host T cells

To study the contribution of T-cell receptors (TCR) to resulting T-cell responses, we studied three different human αβ TCRs, reactive to the same gp100-derived peptide presented in the context of HLA-A*0201. When expressed in primary CD8 T cells, all receptors elicited classic antigen-induced IFN-γ responses, which correlated with TCR affinity for peptide-MHC in the order T4H2 > R6C12 > SILv44. However, SILv44 elicited superior IL-17A release. Importantly, in vivo, SILv44-transgenic T cells mediated superior antitumor responses to 888-A2 + human melanoma tumor cells upon adoptive transfer into tumor-challenged mice while maintaining IL-17 expression. Modeling of the TCR ternary complexes suggested architectural differences between SILv44 and the other complexes, providing a potential structural basis for the observed differences. Overall, the data reveal a more prominent role for the T-cell receptor in defining host T-cell physiology than traditionally assumed, while parameters beyond IFN-γ secretion and TCR affinity ultimately determine the reactivity of tumor-reactive T cells.

ATLAS: A database linking binding affinities with structures for wild-type and mutant TCR-pMHC complexes

The ATLAS (Altered TCR Ligand Affinities and Structures) database (https://zlab.umassmed.edu/atlas/web/) is a manually curated repository containing the binding affinities for wild-type and mutant T cell receptors (TCRs) and their antigens, peptides presented by the major histocompatibility complex (pMHC). The database links experimentally measured binding affinities with the corresponding three dimensional (3D) structures for TCR-pMHC complexes. The user can browse and search affinities, structures, and experimental details for TCRs, peptides, and MHCs of interest. We expect this database to facilitate the development of next-generation protein design algorithms targeting TCR-pMHC interactions. ATLAS can be easily parsed using modeling software that builds protein structures for training and testing. As an example, we provide structural models for all mutant TCRs in ATLAS, built using the Rosetta program. Utilizing these structures, we report a correlation of 0.63 between experimentally measured changes in binding energies and our predicted changes. Proteins 2017; 85:908-916. © 2016 Wiley Periodicals, Inc.

Using Global Analysis to Extend the Accuracy and Precision of Binding Measurements with T cell Receptors and Their Peptide/MHC Ligands

In cellular immunity, clonally distributed T cell receptors (TCRs) engage complexes of peptides bound to major histocompatibility complex proteins (pMHCs). In the interactions of TCRs with pMHCs, regions of restricted and variable diversity align in a structurally complex fashion. Many studies have used mutagenesis to attempt to understand the "roles" played by various interface components in determining TCR recognition properties such as specificity and cross-reactivity. However, these measurements are often complicated or even compromised by the weak affinities TCRs maintain toward pMHC. Here, we demonstrate how global analysis of multiple datasets can be used to significantly extend the accuracy and precision of such TCR binding experiments. Application of this approach should positively impact efforts to understand TCR recognition and facilitate the creation of mutational databases to help engineer TCRs with tuned molecular recognition properties. We also show how global analysis can be used to analyze double mutant cycles in TCR-pMHC interfaces, which can lead to new insights into immune recognition.

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.