Timothy P. Riley

How structural adaptability exists alongside HLA-A2 bias in the human αβ TCR repertoire

Significance T-cell receptor (TCR) recognition of antigenic peptides presented by major histocompatibility complex (MHC) proteins defines specificity in cellular immunity. Evidence suggests that TCRs are intrinsically biased toward MHC proteins, yet how this bias coexists alongside the considerable structural variability that is necessary for TCRs to engage different ligands has been a longstanding puzzle. By examining structural and sequence data, we found evidence that human αβ TCRs have an inherent compatibility with structural and chemical properties of MHC proteins. This compatibility leads TCRs to an intrinsic MHC bias but does not compel the formation of particular modes of binding, providing a solution to how TCRs can be MHC-biased but still structurally adaptable. How T-cell receptors (TCRs) can be intrinsically biased toward MHC proteins while simultaneously display the structural adaptability required to engage diverse ligands remains a controversial puzzle. We addressed this by examining αβ TCR sequences and structures for evidence of physicochemical compatibility with MHC proteins. We found that human TCRs are enriched in the capacity to engage a polymorphic, positively charged “hot-spot” region that is almost exclusive to the α1-helix of the common human class I MHC protein, HLA-A*0201 (HLA-A2). TCR binding necessitates hot-spot burial, yielding high energetic penalties that must be offset via complementary electrostatic interactions. Enrichment of negative charges in TCR binding loops, particularly the germ-line loops encoded by the TCR Vα and Vβ genes, provides this capacity and is correlated with restricted positioning of TCRs over HLA-A2. Notably, this enrichment is absent from antibody genes. The data suggest a built-in TCR compatibility with HLA-A2 that biases receptors toward, but does not compel, particular binding modes. Our findings provide an instructional example for how structurally pliant MHC biases can be encoded within TCRs.

Hepatitis C virus‐cross‐reactive TCR gene‐modified T cells: a model for immunotherapy against diseases with genomic instability

A major obstacle hindering the development of effective immunity against viral infections, their associated disease, and certain cancers is their inherent genomic instability. Accumulation of mutations can alter processing and presentation of antigens recognized by antibodies and T cells that can lead to immune escape variants. Use of an agent that can intrinsically combat rapidly mutating viral or cancer‐associated antigens would be quite advantageous in developing effective immunity against such disease. We propose that T cells harboring cross‐reactive TCRs could serve as a therapeutic agent in these instances. With the use of hepatitis C virus, known for its genomic instability as a model for mutated antigen recognition, we demonstrate cross‐reactivity against immunogenic and mutagenic nonstructural protein 3:1406‐1415 and nonstructural protein 3:1073‐1081 epitopes in PBL‐derived, TCR‐gene‐modified T cells. These single TCR‐engineered T cells can CD8‐independently recognize naturally occurring and epidemiologically relevant mutant variants. TCR‐peptide MHC modeling data allow us to rationalize how TCR structural properties accommodate recognition of certain mutated epitopes and how these substitutions impact the requirement of CD8 affinity enhancement for recognition. A better understanding of such TCRs’ promiscuous behavior may allow for exploitation of these properties to develop novel, adoptive T cell‐based therapies for viral infections and cancers exhibiting similar genomic instability.

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.

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.

Differential scanning fluorimetry based assessments of the thermal and kinetic stability of peptide-MHC complexes.

Measurements of thermal stability by circular dichroism (CD) spectroscopy have been widely used to assess the binding of peptides to MHC proteins, particularly within the structural immunology community. Although thermal stability assays offer advantages over other approaches such as IC50 measurements, CD-based stability measurements are hindered by large sample requirements and low throughput. Here we demonstrate that an alternative approach based on differential scanning fluorimetry (DSF) yields results comparable to those based on CD for both class I and class II complexes. As they require much less sample, DSF-based measurements reduce demands on protein production strategies and are amenable for high throughput studies. DSF can thus not only replace CD as a means to assess peptide/MHC thermal stability, but can complement other peptide-MHC binding assays used in screening, epitope discovery, and vaccine design. Due to the physical process probed, DSF can also uncover complexities not observed with other techniques. Lastly, we show that DSF can also be used to assess peptide/MHC kinetic stability, allowing for a single experimental setup to probe both binding equilibria and kinetics.

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.

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

AbstractT 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.Structural analysis shows that cross-reactivity of the T cell receptor DMF5 is governed by adaptability of the peptide antigen, which can undergo TCR-binding-induced frameshifting forcing the peptide C terminus to extend from the MHC-binding groove.

Computational Reprogramming of T Cell Antigen Receptor Binding Properties.

T-cell receptor (TCR) binding to peptide/MHC is key to antigen-specific cellular immunity, and there has been considerable interest in modulating TCR affinity and specificity for the development of therapeutics and imaging reagents. While in vitro engineering efforts using molecular evolution have yielded remarkable improvements in TCR affinity, such approaches do not offer structural control and can adversely affect receptor specificity, particularly if the attraction towards the MHC is enhanced independently of the peptide. Here we describe an approach to computational design that begins with structural information and offers the potential for more controlled manipulation of binding properties. Our design process models point mutations in selected regions of the TCR and ranks the resulting change in binding energy. Consideration is given to designing optimized scoring functions tuned to particular TCR-peptide/MHC interfaces. Validation of highly ranked predictions can be used to refine the modeling methodology and scoring functions, improving the design process. Our approach results in a strong correlation between predicted and measured changes in binding energy, as well as good agreement between modeled and experimental structures.

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.

Modeling Sequence-Dependent Peptide Fluctuations in Immunologic Recognition

In cellular immunity, T cells recognize peptide antigens bound and presented by major histocompatibility complex (MHC) proteins. The motions of peptides bound to MHC proteins play a significant role in determining immunogenicity. However, existing approaches for investigating peptide/MHC motional dynamics are challenging or of low throughput, hindering the development of algorithms for predicting immunogenicity from large databases, such as those of tumor or genetically unstable viral genomes. We addressed this by performing extensive molecular dynamics simulations on a large structural database of peptides bound to the most commonly expressed human class-I MHC protein, HLA-A*0201. The simulations reproduced experimental indicators of motion and were used to generate simple models for predicting site-specific, rapid motions of bound peptides through differences in their sequence and chemical composition alone. The models can easily be applied on their own or incorporated into immunogenicity prediction algorithms. Beyond their predictive power, the models provide insight into how amino acid substitutions can influence peptide and protein motions and how dynamic information is communicated across peptides. They also indicate a link between peptide rigidity and hydrophobicity, two features known to be important in influencing cellular immune responses.