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

Enhanced T cell receptor specificity through framework engineering

Development of T cell receptors (TCRs) as immunotherapeutics is hindered by inherent TCR cross-reactivity. Engineering more specific TCRs has proven challenging, as unlike antibodies, improving TCR affinity does not usually improve specificity. Although various protein design approaches have been explored to surmount this, mutations in TCR binding interfaces risk broadening specificity or introducing new reactivities. Here we explored if TCR specificity could alternatively be tuned through framework mutations distant from the interface. Studying the 868 TCR specific for the HIV SL9 epitope presented by HLA-A2, we used deep mutational scanning to identify a framework mutation above the mobile CDR3β loop. This glycine to proline mutation had no discernable impact on binding affinity or functional avidity towards the SL9 epitope but weakened recognition of SL9 escape variants and led to fewer responses in a SL9-derived positional scanning library. In contrast, an interfacial mutation near the tip of CDR3α that also did not impact affinity or functional avidity towards SL9 weakened specificity. Simulations indicated that the specificity-enhancing mutation functions by reducing the range of loop motions, limiting the ability of the TCR to adjust to different ligands. Although our results are likely to be TCR dependent, using framework engineering to control TCR loop motions may be a viable strategy for improving the specificity of TCR-based immunotherapies.

Strategies and rules for tuning TCR-derived therapy

Manipulation of T cells has revolutionized cancer immunotherapy. Notably, the use of T cells carrying engineered T cell receptors (TCR-T) offers a favourable therapeutic pathway, particularly in the treatment of solid tumours. However, major challenges such as limited clinical response efficacy, off-target effects and tumour immunosuppressive microenvironment have hindered the clinical translation of this approach. In this review, we mainly want to guide TCR-T investigators on several major issues they face in the treatment of solid tumours after obtaining specific TCR sequences: (1) whether we have to undergo affinity maturation or not, and what parameter we should use as a criterion for being more effective. (2) What modifications can be added to counteract the tumour inhibitory microenvironment to make our specific T cells to be more effective and what is the safety profile of such modifications? (3) What are the new forms and possibilities for TCR-T cell therapy in the future?

Rapid screening of TCR-pMHC interactions by the YAMTAD system

Personalized immunotherapy, such as cancer vaccine and TCR-T methods, demands rapid screening of TCR-pMHC interactions. While several screening approaches have been developed, their throughput is limited. Here, the Yeast Agglutination Mediated TCR antigen Discovery system (YAMTAD) was designed and demonstrated to allow fast and unbiased library-on-library screening of TCR-pMHC interactions. Our proof-of-principle study achieved high sensitivity and specificity in identifying antigens for a given TCR and identifying TCRs recognizing a given pMHC for modest library sizes. Finally, the enrichment of high-affinity TCR-pMHC interactions by YAMTAD in library-on-library screening was demonstrated. Given the high throughput (10⁶–10⁸ × 10⁶–10⁸ in theory) and simplicity (identifying TCR-pMHC interactions without purification of TCR and pMHC) of YAMTAD, this study provides a rapid but effective platform for TCR-pMHC interaction screening, with valuable applications in future personalized immunotherapy.

Computational and experimental methods for classifying variants of unknown clinical significance

The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.

Engineering the T cell receptor for fun and profit: Uncovering complex biology, interrogating the immune system, and targeting disease

T cell receptors (TCRs) orchestrate cellular immunity by recognizing peptide antigens bound and presented by major histocompatibility complex (MHC) proteins. Due to the TCR's central role in immunity and tight connection with human health, there has been significant interest in modulating TCR properties through protein engineering methods. Complicating these efforts is the complexity and vast diversity of TCR-peptide/MHC interfaces, the interdependency between TCR affinity, specificity, and cross-reactivity, and the sophisticated relationships between TCR binding properties and T cell function, many aspects of which are not well understood. Here we review TCR engineering, starting with a brief historical overview followed by discussions of more recent developments, including new efforts and opportunities to engineer TCR affinity, modulate specificity, and develop novel TCR-based constructs.

Immortalization and functional screening of natively paired human T cell receptor repertoires

Functional analyses of the T cell receptor (TCR) landscape can reveal critical information about protection from disease and molecular responses to vaccines. However, it has proven difficult to combine advanced next-generation sequencing technologies with methods to decode the peptide-major histocompatibility complex (pMHC) specificity of individual TCRs. We developed a new high-throughput approach to enable repertoire-scale functional evaluations of natively paired TCRs. In particular, we leveraged the immortalized nature of physically linked TCRα:β amplicon libraries to analyze binding against multiple recombinant pMHCs on a repertoire scale, and to exemplify the utility of this approach, we also performed affinity-based functional mapping in conjunction with quantitative next-generation sequencing to track antigen-specific TCRs. These data successfully validated a new immortalization and screening platform to facilitate detailed molecular analyses of disease-relevant antigen interactions with human TCRs.

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

Yeast Surface Display: New Opportunities for a Time-Tested Protein Engineering System

Yeast surface display has proven to be a powerful tool for the discovery of antibodies and other novel binding proteins and for engineering the affinity and selectivity of existing proteins for their targets. In the decades since the first demonstrations of the approach, the range of yeast display applications has greatly expanded to include many different protein targets and has grown to encompass methods for rapid protein characterization. Here, we briefly summarize the development of yeast display methodologies and highlight several selected examples of recent applications to timely and challenging protein engineering and characterization problems.

Engineering Proteins by Combining Deep Mutational Scanning and Yeast Display

Protein engineering using display platforms such as yeast display and phage display has allowed discovery of proteins with therapeutic and industrial applications. Antibodies and T cell receptors developed for therapeutic applications are often engineered by constructing libraries of mutations in loops of five to ten residues called complementarity determining regions that are in proximity to the antigen. In the past decade, deep mutational scanning has become a powerful tool in a protein engineer's toolbox, as it allows one to compare the impact of all 20 amino acids at each position, across the length of the protein. Thus, a single experiment can provide a sequence-activity landscape with information about hotspots or suboptimal binding sites in the original proteins. These residues or regions may be overlooked by engineering methods that are driven solely by structures or directed evolution of error-prone PCR libraries. Here, we describe experimental methods to engineer proteins by combining yeast display and deep mutational scanning mutagenesis, using T cell receptors as an example.

High throughput and quantitative enzymology in the genomic era

Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.

Engineering soluble T-cell receptors for therapy

Immunotherapy approaches that target peptide-human leukocyte antigen (pHLA) complexes are becoming highly attractive because of their potential to access virtually all foreign and cellular proteins. For this reason, there has been considerable interest in the development of the natural ligand for pHLA, the T-cell receptor (TCR), as a soluble drug to target disease-associated pHLA presented at the cell surface. However, native TCR stability is suboptimal for soluble drug development, and natural TCRs generally have weak affinities for pHLAs, limiting their potential to reach efficacious receptor occupancy levels as soluble drugs. To overcome these limitations and make full use of the TCR as a soluble drug platform, several protein engineering solutions have been applied to TCRs to enhance both their stability and affinity, with a focus on retaining target specificity and selectivity. Here, we review these advances and look to the future for the next generation of soluble TCR-based therapies that can target monomorphic HLA-like proteins presenting both peptide and nonpeptide antigens.

Linking genome variants to disease: scalable approaches to test the functional impact of human mutations

The application of genomics to medicine has accelerated the discovery of mutations underlying disease and has enhanced our knowledge of the molecular underpinnings of diverse pathologies. As the amount of human genetic material queried via sequencing has grown exponentially in recent years, so too has the number of rare variants observed. Despite progress, our ability to distinguish which rare variants have clinical significance remains limited. Over the last decade, however, powerful experimental approaches have emerged to characterize variant effects orders of magnitude faster than before. Fueled by improved DNA synthesis and sequencing and, more recently, by CRISPR/Cas9 genome editing, multiplex functional assays provide a means of generating variant effect data in wide-ranging experimental systems. Here, I review recent applications of multiplex assays that link human variants to disease phenotypes and I describe emerging strategies that will enhance their clinical utility in coming years.

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.

An Engineered T Cell Receptor Variant Realizes the Limits of Functional Binding Modes

T cell receptors (TCRs) orchestrate cellular immunity by recognizing peptides presented by a range of major histocompatibility complex (MHC) proteins. Naturally occurring TCRs bind the composite peptide/MHC surface, recognizing peptides that are structurally and chemically compatible with the TCR binding site. Here we describe a molecularly evolved TCR variant that binds the human class I MHC protein HLA-A2 independent of the bound peptide, achieved by a drastic perturbation of the TCR binding geometry that places the molecule far from the peptide binding groove. This unique geometry is unsupportive of normal T cell signaling. A substantial divergence between affinity measurements in solution and in two dimensions between proximal cell membranes leads us to attribute the lack of signaling to steric hindrance that limits binding in the confines of a cell-cell interface. Our results provide an example of how receptor binding geometry can impact T cell function and provide further support for the view that germline-encoded residues in TCR binding loops evolved to drive productive TCR recognition and signaling.

Molecular Rules Underpinning Enhanced Affinity Binding of Human T Cell Receptors Engineered for Immunotherapy

Immuno-oncology approaches that utilize T cell receptors (TCRs) are becoming highly attractive because of their potential to target virtually all cellular proteins, including cancer-specific epitopes, via the recognition of peptide-human leukocyte antigen (pHLA) complexes presented at the cell surface. However, because natural TCRs generally recognize cancer-derived pHLAs with very weak affinities, efforts have been made to enhance their binding strength, in some cases by several million-fold. In this study, we investigated the mechanisms underpinning human TCR affinity enhancement by comparing the crystal structures of engineered enhanced affinity TCRs with those of their wild-type progenitors. Additionally, we performed molecular dynamics simulations to better understand the energetic mechanisms driving the affinity enhancements. These data demonstrate that supra-physiological binding affinities can be achieved without altering native TCR-pHLA binding modes via relatively subtle modifications to the interface contacts, often driven through the addition of buried hydrophobic residues. Individual energetic components of the TCR-pHLA interaction governing affinity enhancements were distinct and highly variable for each TCR, often resulting from additive, or knock-on, effects beyond the mutated residues. This comprehensive analysis of affinity-enhanced TCRs has important implications for the future rational design of engineered TCRs as efficacious and safe drugs for cancer treatment.

Predicting antigen specificity of single T cells based on TCR CDR3 regions

It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining.

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations

To deal with the huge number of novel protein-coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top-ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO, SuSPect and REVEL based upon their performance in these analyses.

Structure-guided engineering of the affinity and specificity of CARs against Tn-glycopeptides

The potency of adoptive T cell therapies targeting the cell surface antigen CD19 has been demonstrated in hematopoietic cancers. It has been difficult to identify appropriate targets in nonhematopoietic tumors, but one class of antigens that have shown promise is aberrant O-glycoprotein epitopes. It has long been known that dysregulated synthesis of O-linked (threonine or serine) sugars occurs in many cancers, and that this can lead to the expression of cell surface proteins containing O-glycans comprised of a single N-acetylgalactosamine (GalNAc, known as Tn antigen) rather than the normally extended carbohydrate. Previously, we used the scFv fragment of antibody 237 as a chimeric antigen receptor (CAR) to mediate recognition of mouse tumor cells that bear its cognate Tn-glycopeptide epitope in podoplanin, also called OTS8. Guided by the structure of the 237 Fab:Tn-OTS8-glycopeptide complex, here we conducted a deep mutational scan showing that residues flanking the Tn-glycan contributed significant binding energy to the interaction. Design of 237-scFv libraries in the yeast display system allowed us to isolate scFv variants with higher affinity for Tn-OTS8. Selection with a noncognate human antigen, Tn-MUC1, yielded scFv variants that were broadly reactive with multiple Tn-glycoproteins. When configured as CARs, engineered T cells expressing these scFv variants showed improved activity against mouse and human cancer cell lines defective in O-linked glycosylation. This strategy provides CARs with Tn-peptide specificities, all based on a single scFv scaffold, that allows the same CAR to be tested for toxicity in mice and efficacy against mouse and human tumors.

Engineering Strategies to Enhance TCR-Based Adoptive T Cell Therapy

T cell receptor (TCR)-based adoptive T cell therapies (ACT) hold great promise for the treatment of cancer, as TCRs can cover a broad range of target antigens. Here we summarize basic, translational and clinical results that provide insight into the challenges and opportunities of TCR-based ACT. We review the characteristics of target antigens and conventional αβ-TCRs, and provide a summary of published clinical trials with TCR-transgenic T cell therapies. We discuss how synthetic biology and innovative engineering strategies are poised to provide solutions for overcoming current limitations, that include functional avidity, MHC restriction, and most importantly, the tumor microenvironment. We also highlight the impact of precision genome editing on the next iteration of TCR-transgenic T cell therapies, and the discovery of novel immune engineering targets. We are convinced that some of these innovations will enable the field to move TCR gene therapy to the next level.

T-cell receptor affinity in the age of cancer immunotherapy

The strength of the interaction between T-cell receptors (TCRs) and their ligands, peptide/major histocompatibility complex complexes (pMHCs), is one of the most frequently discussed and investigated features of T cells in immuno-oncology today. Although there are many molecules on the surface of T cells that interact with ligands on other cells, the TCR/pMHC is the only receptor-ligand pair that offers antigen specificity and dictates the functional response of the T cell. The strength of the TCR/pMHC interaction, along with the environment in which this interaction takes place, is key to how the T cell will respond. The TCR repertoire of T cells that interact with tumor-associated antigens is vast, although typically of low affinity. Here, we focus on the low-affinity interactions between TCRs from CD8+ T cells and different models used in immuno-oncology.

TLmutation: Predicting the Effects of Mutations Using Transfer Learning

A reccurring challenge in bioinformatics is predicting the phenotypic consequence of amino acid variation in proteins. With the recent advancements in sequencing techniques, sufficient genomic data has become available to train models that predict the evolutionary statistical energies, but there is still inadequate experimental data to directly predict functional effects. One approach to overcome this data scarcity is to apply transfer learning and train more models with available data sets. In this study, we propose a set of transfer learning algorithms we call TLmutation, which implements a supervised transfer learning algorithm that transfers knowledge from survival data of a protein to a particular function of that protein. This is followed by an unsupervised transfer learning algorithm that extends the knowledge to a homologous protein. We explore the application of our algorithms in three cases. First, we test the supervised transfer on 17 previously published deep mutagenesis data sets to complete and refine missing data points. We further investigate these data sets to identify which mutations build better predictors of variant functions. In the second case, we apply the algorithm to predict higher-order mutations solely from single point mutagenesis data. Finally, we perform the unsupervised transfer learning algorithm to predict mutational effects of homologous proteins from experimental data sets. These algorithms are generalized to transfer knowledge between Markov random field models. We show the benefit of our transfer learning algorithms to utilize informative deep mutational data and provide new insights into protein variant functions. As these algorithms are generalized to transfer knowledge between Markov random field models, we expect these algorithms to be applicable to other disciplines.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

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.

T-cell Receptors Engineered De Novo for Peptide Specificity Can Mediate Optimal T-cell Activity without Self Cross-Reactivity

Despite progress in adoptive T-cell therapies, the identification of targets remains a challenge. Although chimeric antigen receptors recognize cell-surface antigens, T-cell receptors (TCR) have the advantage that they can target the array of intracellular proteins by binding to peptides associated with major histocompatibility complex (MHC) products (pepMHC). Although hundreds of cancer-associated peptides have been reported, it remains difficult to identify effective TCRs against each pepMHC complex. Conventional approaches require isolation of antigen-specific CD8⁺ T cells, followed by TCRαβ gene isolation and validation. To bypass this process, we used directed evolution to engineer TCRs with desired peptide specificity. Here, we compared the activity and cross-reactivity of two affinity-matured TCRs (T1 and RD1) with distinct origins. T1-TCR was isolated from a melanoma-reactive T-cell line specific for MART-1/HLA-A2, whereas RD1-TCR was derived de novo against MART-1/HLA-A2 by in vitro engineering. Despite their distinct origins, both TCRs exhibited similar peptide fine specificities, focused on the center of the MART-1 peptide. In CD4⁺ T cells, both TCRs mediated activity against MART-1 presented by HLA-A2. However, in CD8⁺ T cells, T1, but not RD1, demonstrated cross-reactivity with endogenous peptide/HLA-A2 complexes. Based on the fine specificity of these and other MART-1 binding TCRs, we conducted bioinformatics scans to identify structurally similar self-peptides in the human proteome. We showed that the T1-TCR cross-reacted with many of these self-peptides, whereas the RD1-TCR was rarely cross-reactive. Thus, TCRs such as RD1, generated de novo against cancer antigens, can serve as an alternative to TCRs generated from T-cell clones.

T-cell-receptor cross-recognition and strategies to select safe T-cell receptors for clinical translation

Adoptive transfer of T-cell-receptor (TCR)-transduced T cells has shown promising results for cancer treatment, but has also produced severe immunotoxicities caused by on-target as well as off-target TCR recognition. Off-target toxicities are related to the ability of a single T cell to cross-recognize and respond to several different peptide–major histocompatibility complex (pMHC) antigens; a property that is essential for providing broad antigenic coverage despite a confined number of unique TCRs in the human body. However, this degeneracy makes it incredibly difficult to account for the range of targets that any TCR might recognize, which represents a major challenge for the clinical development of therapeutic TCRs. The prospect of using affinity-optimized TCRs has been impeded due to observations that affinity enhancement might alter the specificity of a TCR, thereby increasing the risk that it will cross-recognize endogenous tissue. Strategies for selecting safe TCRs for the clinic have included functional assessment after individual incubations with tissue-derived primary cells or with peptides substituted with single amino acids. However, these strategies have not been able to predict cross-recognition sufficiently, leading to fatal cross-reactivity in clinical trials. Novel technologies have emerged that enable extensive characterization of the exact interaction points of a TCR with pMHC, which provides a foundation from which to make predictions of the cross-recognition potential of individual TCRs. This review describes current advances in strategies for dissecting the molecular interaction points of TCRs, focusing on their potential as tools for predicting cross-recognition of TCRs in clinical development.

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T-cell-receptor (TCR) degeneracy plays a fundamental role in the capacity of our immune systems to recognize foreign antigens.

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TCR cross-reactivity provides an inherent risk in TCR–gene transfer cell therapies.

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Advances in description of TCR cross-recognition can guide the selection process for TCRs into clinical use.

Circular permutation profiling by deep sequencing libraries created using transposon mutagenesis

Deep mutational scanning has been used to create high-resolution DNA sequence maps that illustrate the functional consequences of large numbers of point mutations. However, this approach has not yet been applied to libraries of genes created by random circular permutation, an engineering strategy that is used to create open reading frames that express proteins with altered contact order. We describe a new method, termed circular permutation profiling with DNA sequencing (CPP-seq), which combines a one-step transposon mutagenesis protocol for creating libraries with a functional selection, deep sequencing and computational analysis to obtain unbiased insight into a protein's tolerance to circular permutation. Application of this method to an adenylate kinase revealed that CPP-seq creates two types of vectors encoding each circularly permuted gene, which differ in their ability to express proteins. Functional selection of this library revealed that >65% of the sampled vectors that express proteins are enriched relative to those that cannot translate proteins. Mapping enriched sequences onto structure revealed that the mobile AMP binding and rigid core domains display greater tolerance to backbone fragmentation than the mobile lid domain, illustrating how CPP-seq can be used to relate a protein's biophysical characteristics to the retention of activity upon permutation.

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 fingerprinting enables in-depth characterization of the interactions governing recognition of peptide-MHC complexes

The promiscuous nature of T-cell receptors (TCRs) allows T cells to recognize a large variety of pathogens, but makes it challenging to understand and control T-cell recognition. Existing technologies provide limited information about the key requirements for T-cell recognition and the ability of TCRs to cross-recognize structurally related elements. Here we present a 'one-pot' strategy for determining the interactions that govern TCR recognition of peptide-major histocompatibility complex (pMHC). We measured the relative affinities of TCRs to libraries of barcoded peptide-MHC variants and applied this knowledge to understand the recognition motif, here termed the TCR fingerprint. The TCR fingerprints of 16 different TCRs were identified and used to predict and validate cross-recognized peptides from the human proteome. The identified fingerprints differed among TCRs recognizing the same epitope, demonstrating the value of this strategy for understanding T-cell interactions and assessing potential cross-recognition before selection of TCRs for clinical development.

Designer T-cells and T-cell receptors for customized cancer immunotherapies

Cancer immunotherapy, focused on harnessing and empowering the immune system against tumours, has transformed modern oncology. One of the most promising avenues in development involves using genetically engineered T-cells to target cancer antigens via specific T-cell receptors (TCRs). TCRs have a naturally low affinity towards cancer-associated antigens, and therefore show scope for improvement. Here we describe approaches to procure TCRs with enhanced affinity and specificity towards cancer, using protein engineering or selection of natural TCRs from unadulterated repertoires. In particular, we discuss novel methods facilitating the targeting of tumour-specific mutations. Finally, we provide a prospective outlook on the potential development of novel, off-the-shelf immunotherapies by leveraging recent advances in genome editing.

Subtle changes at the variable domain interface of the T-cell receptor can strongly increase affinity

Most affinity-maturation campaigns for antibodies and T-cell receptors (TCRs) operate on the residues at the binding site, located within the loops known as complementarity-determining regions (CDRs). Accordingly, mutations in contact residues, or so-called "second shell" residues, that increase affinity are typically identified by directed evolution involving combinatorial libraries. To determine the impact of residues located at a distance from the binding site, here we used single-codon libraries of both CDR and non-CDR residues to generate a deep mutational scan of a human TCR against the cancer antigen MART-1·HLA-A2. Non-CDR residues included those at the interface of the TCR variable domains (Vα and Vβ) and surface-exposed framework residues. Mutational analyses showed that both Vα/Vβ interface and CDR residues were important in maintaining binding to MART-1·HLA-A2, probably due to either structural requirements for proper Vα/Vβ association or direct contact with the ligand. More surprisingly, many Vα/Vβ interface substitutions yielded improved binding to MART-1·HLA-A2. To further explore this finding, we constructed interface libraries and selected them for improved stability or affinity. Among the variants identified, one conservative substitution (F45βY) was most prevalent. Further analysis of F45βY showed that it enhanced thermostability and increased affinity by 60-fold. Thus, introducing a single hydroxyl group at the Vα/Vβ interface, at a significant distance from the TCR·peptide·MHC-binding site, remarkably affected ligand binding. The variant retained a high degree of specificity for MART-1·HLA-A2, indicating that our approach provides a general strategy for engineering improvements in either soluble or cell-based TCRs for therapeutic purposes.

Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes

T cell specificity emerges from a myriad of processes, ranging from the biological pathways that control T cell signaling to the structural and physical mechanisms that influence how TCRs bind peptides and MHC proteins. Of these processes, the binding specificity of the TCR is a key component. However, TCR specificity is enigmatic: TCRs are at once specific but also cross-reactive. Although long appreciated, this duality continues to puzzle immunologists and has implications for the development of TCR-based therapeutics. In this review, we discuss TCR specificity, emphasizing results that have emerged from structural and physical studies of TCR binding. We show how the TCR specificity/cross-reactivity duality can be rationalized from structural and biophysical principles. There is excellent agreement between predictions from these principles and classic predictions about the scope of TCR cross-reactivity. We demonstrate how these same principles can also explain amino acid preferences in immunogenic epitopes and highlight opportunities for structural considerations in predictive immunology.

Affinity Maturation of a Cyclic Peptide Handle for Therapeutic Antibodies Using Deep Mutational Scanning

Meditopes are cyclic peptides that bind in a specific pocket in the antigen-binding fragment of a therapeutic antibody such as cetuximab. Provided their moderate affinity can be enhanced, meditope peptides could be used as specific non-covalent and paratope-independent handles in targeted drug delivery, molecular imaging, and therapeutic drug monitoring. Here we show that the affinity of a recently reported meditope for cetuximab can be substantially enhanced using a combination of yeast display and deep mutational scanning. Deep sequencing was used to construct a fitness landscape of this protein-peptide interaction, and four mutations were identified that together improved the affinity for cetuximab 10-fold to 15 nm Importantly, the increased affinity translated into enhanced cetuximab-mediated recruitment to EGF receptor-overexpressing cancer cells. Although in silico Rosetta simulations correctly identified positions that were tolerant to mutation, modeling did not accurately predict the affinity-enhancing mutations. The experimental approach reported here should be generally applicable and could be used to develop meditope peptides with low nanomolar affinity for other therapeutic antibodies.