Geometric parameters that affect the behavior of logic-gated CAR T cells.

Clinical applications of CAR-T cells are limited by the scarcity of tumor-specific targets and are often afflicted with the same on-target/off-tumor toxicities that plague other cancer treatments. A new promising strategy to enforce tumor selectivity is the use of logic-gated, two-receptor systems. One well-described application is termed Tmod™, which originally utilized a blocking inhibitory receptor directed towards HLA-I target antigens to create a protective NOT gate. Here we show that the function of Tmod blockers targeting non-HLA-I antigens is dependent on the height of the blocker antigen and is generally compatible with small, membrane-proximal targets. We compensate for this apparent limitation by incorporating modular hinge units to artificially extend or retract the ligand-binding domains relative to the effector cell surface, thereby modulating Tmod activator and blocker function. By accounting for structural differences between activator and blocker targets, we developed a set of simple geometric parameters for Tmod receptor design that enables targeting of blocker antigens beyond HLA-I, thereby broadening the applications of logic-gated cell therapies.

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features.

Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ, of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ= 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.

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.

Protocol for high-throughput cloning, expression, purification, and evaluation of bispecific antibodies.

Bispecific antibodies are a powerful new class of therapeutics, but their development often requires enormous amounts of time and resources. Here, we describe a high-throughput protocol for cloning, expressing, purifying, and evaluating bispecific antibodies. This protocol enables the rapid screening of large panels of bispecific molecules to identify top candidates for further development. For complete details on the use and execution of this protocol, please refer to Estes et al. (2021).

Next generation Fc scaffold for multispecific antibodies.

Bispecific antibodies (Bispecifics) demonstrate exceptional clinical potential to address some of the most complex diseases. However, Bispecific production in a single cell often requires the correct pairing of multiple polypeptide chains for desired assembly. This is a considerable hurdle that hinders the development of many immunoglobulin G (IgG)-like bispecific formats. Our approach focuses on the rational engineering of charged residues to facilitate the chain pairing of distinct heavy chains (HC). Here, we deploy structure-guided protein design to engineer charge pair mutations (CPMs) placed in the CH3-CH3' interface of the fragment crystallizable (Fc) region of an antibody (Ab) to correctly steer heavy chain pairing. When used in combination with our stable effector functionless 2 (SEFL2.2) technology, we observed high pairing efficiency without significant losses in expression yields. Furthermore, we investigate the relationship between CPMs and the sequence diversity in the parental antibodies, proposing a rational strategy to deploy these engineering technologies.

Enhancing the Prefusion Conformational Stability of SARS-CoV-2 Spike Protein Through Structure-Guided Design.

The worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented and the impact on public health and the global economy continues to be devastating. Although early therapies such as prophylactic antibodies and vaccines show great promise, there are concerns about the long-term efficacy and universal applicability of these therapies as the virus continues to mutate. Thus, protein-based immunogens that can quickly respond to viral changes remain of continued interest. The Spike protein, the main immunogen of this virus, displays a highly dynamic trimeric structure that presents a challenge for therapeutic development. Here, guided by the structure of the Spike trimer, we rationally design new Spike constructs that show a uniquely high stability profile while simultaneously remaining locked into the immunogen-desirable prefusion state. Furthermore, our approach emphasizes the relationship between the highly conserved S2 region and structurally dynamic Receptor Binding Domains (RBD) to enable vaccine development as well as the generation of antibodies able to resist viral mutation.

Rational selection of building blocks for the assembly of bispecific antibodies.

Bispecific antibodies, engineered to recognize two targets simultaneously, demonstrate exceptional clinical potential for the therapeutic intervention of complex diseases. However, these molecules are often composed of multiple polypeptide chains of differing sequences. To meet industrial scale productivity, enforcing the correct quaternary assembly of these chains is critical. Here, we describe Chain Selectivity Assessment (CSA), a high-throughput method to rationally select parental monoclonal antibodies (mAbs) to make bispecific antibodies requiring correct heavy/light chain pairing. By deploying CSA, we have successfully identified mAbs that exhibit a native preference toward cognate chain pairing that enables the production of hetero-IgGs without additional engineering. Furthermore, CSA also identified rare light chains (LCs) that permit positive binding of the non-cognate arm in the common LC hetero-IgGs, also without engineering. This rational selection of parental mAbs with favorable developability characteristics is critical to the successful development of bispecific molecules with optimal manufacturability properties.

TRPM3 expression and control of glutamate release from primary vagal afferent neurons.

Vagal afferent fibers contact neurons in the nucleus of the solitary tract (NTS) and release glutamate via three distinct release pathways: synchronous, asynchronous, and spontaneous. The presence of TRPV1 in vagal afferents is predictive of activity-dependent asynchronous glutamate release along with temperature-sensitive spontaneous vesicle fusion. However, pharmacological blockade or genetic deletion of TRPV1 does not eliminate the asynchronous profile and only attenuates the temperature-dependent spontaneous release at high temperatures (>40°C), indicating additional temperature-sensitive calcium conductance(s) contributing to these release pathways. The transient receptor potential cation channel melastatin subtype 3 (TRPM3) is a calcium-selective channel that functions as a thermosensor (30-37°C) in somatic primary afferent neurons. We predict that TRPM3 is expressed in vagal afferent neurons and contributes to asynchronous and spontaneous glutamate release pathways. We investigated these hypotheses via measurements on cultured nodose neurons and in brainstem slice preparations containing vagal afferent to NTS synaptic contacts. We found histological and genetic evidence that TRPM3 is highly expressed in vagal afferent neurons. The TRPM3-selective agonist, pregnenolone sulfate, rapidly and reversibly activated the majority (∼70%) of nodose neurons; most of which also contained TRPV1. We confirmed the role of TRPM3 with pharmacological blockade and genetic deletion. In the brain, TRPM3 signaling strongly controlled both basal and temperature-driven spontaneous glutamate release. Surprisingly, genetic deletion of TRPM3 did not alter synchronous or asynchronous glutamate release. These results provide convergent evidence that vagal afferents express functional TRPM3 that serves as an additional temperature-sensitive calcium conductance involved in controlling spontaneous glutamate release onto neurons in the NTS.NEW & NOTEWORTHY Vagal afferent signaling coordinates autonomic reflex function and informs associated behaviors. Thermosensitive transient receptor potential (TRP) channels detect temperature and nociceptive stimuli in somatosensory afferent neurons, however their role in vagal signaling remains less well understood. We report that the TRPM3 ion channel provides a major thermosensitive point of control over vagal signaling and synaptic transmission. We conclude that TRPM3 translates physiological changes in temperature to neurophysiological outputs and can serve as a cellular integrator in vagal afferent signaling.

Structure Based Prediction of Neoantigen Immunogenicity.

The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8+ T cell responses of HLA-A2 presented nonameric peptides, while also providing insight into the underlying structural and biophysical mechanisms governing immunogenicity. Our proof-of-concept study demonstrates the potential for structure-based immunogenicity predictions in the development of personalized peptide-based vaccines.

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.

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 αß 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.

The intersection of affinity and specificity in the development and optimization of T cell receptor based therapeutics.

The role of the αß T cell receptor (TCR) in identifying immunological targets and signaling appropriate responses provides for exciting translational opportunities. Yet TCRs mediate one of the most complex protein-protein interactions in biology, with intricate signaling and selection mechanisms adding additional layers of sophistication. In this review, we discuss how these complexities influence the development and optimization of TCR-based therapeutics, focusing on the intersection between structure, affinity, and specificity. We highlight similarities between TCRs and germline antibodies in molecular recognition, but emphasize that engineering TCRs by mimicking antibody maturation may not translate into improved biological outcomes. A key point is the need to distinguish TCR biochemical recognition from T cell functional recognition and the complications this distinction has for efforts in TCR engineering. We suggest learning from natural immunity and taking advantage of structural features and state-of-the-art protein design principles as a means to optimize TCRs for therapeutic use.

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.

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.

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.

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.

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

T-cell receptors (TCRs) have emerged as a new class of therapeutics, most prominently for cancer where they are the key components of new cellular therapies as well as soluble biologics. Many studies have generated high affinity TCRs in order to enhance sensitivity. Recent outcomes, however, have suggested that fine manipulation of TCR binding, with an emphasis on specificity may be more valuable than large affinity increments. Structure-guided design is ideally suited for this role, and here we studied the generality of structure-guided design as applied to TCRs. We found that a previous approach, which successfully optimized the binding of a therapeutic TCR, had poor accuracy when applied to a broader set of TCR interfaces. We thus sought to develop a more general purpose TCR design framework. After assembling a large dataset of experimental data spanning multiple interfaces, we trained a new scoring function that accounted for unique features of each interface. Together with other improvements, such as explicit inclusion of molecular flexibility, this permitted the design new affinity-enhancing mutations in multiple TCRs, including those not used in training. Our approach also captured the impacts of mutations and substitutions in the peptide/MHC ligand, and recapitulated recent findings regarding TCR specificity, indicating utility in more general mutational scanning of TCR-pMHC interfaces.

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.

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.

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.