Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens.

T cell receptors (TCRs) that recognize cancer neoantigens are important for anti-cancer immune responses and immunotherapy. Understanding the structural basis of TCR recognition of neoantigens provides insights into their exquisite specificity and can enable design of optimized TCRs. We determined crystal structures of a human TCR in complex with NRAS Q61K and Q61R neoantigen peptides and HLA-A1 MHC, revealing the molecular underpinnings for dual recognition and specificity versus wild-type NRAS peptide. We then used multiple versions of AlphaFold to model the corresponding complex structures, given the challenge of immune recognition for such methods. Interestingly, one implementation of AlphaFold2 (TCRmodel2) was able to generate accurate models of the complexes, while AlphaFold3 also showed strong performance, although success was lower for other complexes. This study provides insights into TCR recognition of a shared cancer neoantigen, as well as the utility and practical considerations for using AlphaFold to model TCR-peptide-MHC complexes.

Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively unexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.

Structural analysis of TrkA mutations in patients with congenital insensitivity to pain reveals PLCγ as an analgesic drug target.

Chronic pain is a major health issue, and the search for new analgesics has become increasingly important because of the addictive properties and unwanted side effects of opioids. To explore potentially new drug targets, we investigated mutations in the NTRK1 gene found in individuals with congenital insensitivity to pain with anhidrosis (CIPA). NTRK1 encodes tropomyosin receptor kinase A (TrkA), the receptor for nerve growth factor (NGF) and that contributes to nociception. Molecular modeling and biochemical analysis identified mutations that decreased the interaction between TrkA and one of its substrates and signaling effectors, phospholipase Cγ (PLCγ). We developed a cell-permeable phosphopeptide derived from TrkA (TAT-pQYP) that bound the Src homology domain 2 (SH2) of PLCγ. In HEK-293T cells, TAT-pQYP inhibited the binding of heterologously expressed TrkA to PLCγ and decreased NGF-induced, TrkA-mediated PLCγ activation and signaling. In mice, intraplantar administration of TAT-pQYP decreased mechanical sensitivity in an inflammatory pain model, suggesting that targeting this interaction may be analgesic. The findings demonstrate a strategy to identify new targets for pain relief by analyzing the signaling pathways that are perturbed in CIPA.

Protein Disulfide Isomerase Modulates the Activation of Thyroid Hormone Receptors.

Thyroid hormone receptors (TRs) are responsible for mediating thyroid hormone (T3 and T4) actions at a cellular level. They belong to the nuclear receptor (NR) superfamily and execute their main functions inside the cell nuclei as hormone-regulated transcription factors. These receptors also exhibit so-called "non-classic" actions, for which other cellular proteins, apart from coregulators inside nuclei, regulate their activity. Aiming to find alternative pathways of TR modulation, we searched for interacting proteins and found that PDIA1 interacts with TRß in a yeast two-hybrid screening assay. The functional implications of PDIA1-TR interactions are still unclear; however, our co-immunoprecipitation (co-IP) and fluorescence assay results showed that PDI was able to bind both TR isoforms in vitro. Moreover, T3 appears to have no important role in these interactions in cellular assays, where PDIA1 was able to regulate transcription of TRα and TRß-mediated genes in different ways depending on the promoter region and on the TR isoform involved. Although PDIA1 appears to act as a coregulator, it binds to a TR surface that does not interfere with coactivator binding. However, the TR:PDIA1 complex affinity and activation are different depending on the TR isoform. Such differences may reflect the structural organization of the PDIA1:TR complex, as shown by models depicting an interaction interface with exposed cysteines from both proteins, suggesting that PDIA1 might modulate TR by its thiol reductase/isomerase activity.