Structure-Based, Rational Design of T Cell Receptors

TCR-T cell therapy: current development approaches, preclinical evaluation, and perspectives on regulatory challenges

TCR-T cell therapy represents a promising advancement in adoptive immunotherapy for cancer treatment. Despite its potential, the development and preclinical testing of TCR-T cells face significant challenges. This review provides a structured overview of the key stages in preclinical testing, including in silico, in vitro, and in vivo methods, within the context of the sequential development of novel therapies. This review aimed to systematically outline the processes for evaluating TCR-T cells at each stage: from in silico approaches used to predict target antigens, assess cross-reactivity, and minimize off-target effects, to in vitro assays designed to measure cell functionality, cytotoxicity, and activation. Additionally, the review discusses the limitations of in vivo testing in animal models, particularly in accurately reflecting the human tumor microenvironment and immune responses. Performed analysis emphasizes the importance of these preclinical stages in the safe and effective development of TCR-T cell therapies. While current models provide valuable insights, we identify critical gaps, particularly in in vivo biodistribution and toxicity assessments, and propose the need for enhanced standardization and the development of more representative models. This structured approach aims to improve the predictability and safety of TCR-T cell therapy as it advances towards clinical application.

In silico advancements in Peptide-MHC interaction: A molecular dynamics study of predicted glypican-3 peptides and HLA-A*11:01

Our study employed molecular dynamics (MD) simulations to assess the binding affinity between short peptides derived from the tumor-associated antigen glypican 3 (GPC3) and the major histocompatibility complex (MHC) molecule HLA-A*11:01 in hepatocellular carcinoma. We aimed to improve the reliability of in silico predictions of peptide-MHC interactions, which are crucial for developing targeted cancer therapies. We used five algorithms to discover four peptides (TTDHLKFSK, VINTTDHLK, KLIMTQVSK, and STIHDSIQY), demonstrating the substantial potential for HLA-A11:01 presentation. The Anchored Peptide-MHC Ensemble Generator (APE-Gen) was used to create the initial structure of the peptide-MHC complex. This was followed by a 200 ns molecular dynamics (MD) simulation using AMBER22, which verified the precise positioning of the peptides in the binding groove of HLA-A*11:01, specifically at the A and F pockets. Notably, the 2nd residue, which serves as a critical anchor within the 2nd pocket, played a pivotal role in stabilising the binding interactions.VINTTDHLK (ΔG SIE = -14.46 ± 0.53 kcal/mol and ΔG MM/GBSA = -30.79 ± 0.49 kcal/mol) and STIHDSIQY (ΔG SIE and ΔG MM/GBSA = -14.55 ± 0.16 and -23.21 ± 2.23 kcal/mol) exhibited the most effective binding potential among the examined peptides, as indicated by both their binding free energies and its binding affinity on the T2 cell line (VINTTDHLK: IC50 = 0.45 nM; STIHDSIQY: IC50 = 0.35 nM). The remarkable concordance between in silico and in vitro binding affinity results was of particular significance, indicating that MD simulation is a potent instrument capable of bolstering confidence in in silico peptide predictions. By employing MD simulation as a method, our study provides a promising avenue for improving the prediction of potential peptide-MHC interactions, thereby facilitating the development of more effective and targeted cancer therapies.

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 underexplored. 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-IF1, 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.

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.

Combining SiRPα decoy-coengineered T cells and antibodies augments macrophage-mediated phagocytosis of tumor cells

The adoptive transfer of T cell receptor-engineered (TCR-engineered) T cells (ACT) targeting the HLA-A2-restricted cancer-testis epitope NY-ESO-1157-165 (A2/NY) has yielded favorable clinical responses against several cancers. Two approaches to improve ACT are TCR affinity optimization and T cell coengineering to express immunomodulatory molecules that can exploit endogenous immunity. By computational design we previously developed a panel of binding-enhanced A2/NY-TCRs including A97L, which augmented the in vitro function of gene-modified T cells as compared with WT. Here, we demonstrated higher persistence and improved tumor control by A97L-T cells. In order to harness macrophages in tumors, we further coengineered A97L-T cells to secrete a high-affinity signal regulatory protein α (SiRPα) decoy (CV1) that blocks CD47. While CV1-Fc-coengineered A97L-T cells mediated significantly better control of tumor outgrowth and survival in Winn assays, in subcutaneous xenograft models the T cells, coated by CV1-Fc, were depleted. Importantly, there was no phagocytosis of CV1 monomer-coengineered T cells by human macrophages. Moreover, avelumab and cetuximab enhanced macrophage-mediated phagocytosis of tumor cells in vitro in the presence of CV1 and improved tumor control upon coadministration with A97L-T cells. Taken together, our study indicates important clinical promise for harnessing macrophages by combining CV1-coengineered TCR-T cells with targeted antibodies to direct phagocytosis against tumor cells.

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?

Coengineering specificity, safety, and function into T cells for cancer immunotherapy

Adoptive T-cell transfer (ACT) therapies, including of tumor infiltrating lymphocytes (TILs) and T cells gene-modified to express either a T cell receptor (TCR) or a chimeric antigen receptor (CAR), have demonstrated clinical efficacy for a proportion of patients and cancer-types. The field of ACT has been driven forward by the clinical success of CD19-CAR therapy against various advanced B-cell malignancies, including curative responses for some leukemia patients. However, relapse remains problematic, in particular for lymphoma. Moreover, for a variety of reasons, relative limited efficacy has been demonstrated for ACT of non-hematological solid tumors. Indeed, in addition to pre-infusion challenges including lymphocyte collection and manufacturing, ACT failure can be attributed to several biological processes post-transfer including, (i) inefficient tumor trafficking, infiltration, expansion and retention, (ii) chronic antigen exposure coupled with insufficient costimulation resulting in T-cell exhaustion, (iii) a range of barriers in the tumor microenvironment (TME) mediated by both tumor cells and suppressive immune infiltrate, (iv) tumor antigen heterogeneity and loss, or down-regulation of antigen presentation machinery, (v) gain of tumor intrinsic mechanisms of resistance such as to apoptosis, and (vi) various forms of toxicity and other adverse events in patients. Affinity-optimized TCRs can improve T-cell function and innovative CAR designs as well as gene-modification strategies can be used to coengineer specificity, safety, and function into T cells. Coengineering strategies can be designed not only to directly support the transferred T cells, but also to block suppressive barriers in the TME and harness endogenous innate and adaptive immunity. Here, we review a selection of the remarkable T-cell coengineering strategies, including of tools, receptors, and gene-cargo, that have been developed in recent years to augment tumor control by ACT, more and more of which are advancing to the clinic.

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

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

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

Current Trends and Changes in Use of Membrane Molecular Dynamics Simulations within Academia and the Pharmaceutical Industry

There has been an almost exponential increase in the use of molecular dynamics simulations in basic research and industry over the last 5 years, with almost a doubling in the number of publications each year. Many of these are focused on neurological membranes, and biological membranes in general, applied to the medical industry. A smaller portion have utilized membrane simulations to answer more basic questions related to the function of specific proteins, chemicals or biological processes. This review covers some newer studies, alongside studies from the last two decades, to determine changes in the field. Some of these are basic, while others are more profound, such as multi-component embedded membrane machinery. It is clear that many facets of the discipline remain the same, while the focus on and uses of the technology are broadening in scope and utilization as a general research tool. Analysis of recent literature provides an overview of the current methodologies, covers some of the recent trends or advances and tries to make predictions of the overall path membrane molecular dynamics will follow in the coming years. In general, the overview presented is geared towards the general scientific community, who may wish to introduce the use of these methodologies in light of these changes, making molecular dynamic simulations more feasible for general scientific or medical research.

Quantitative immunopeptidomics reveals a tumor stroma-specific target for T cell therapy

T cell receptor (TCR)-based immunotherapy has emerged as a promising therapeutic approach for the treatment of patients with solid cancers. Identifying peptide-human leukocyte antigen (pHLA) complexes highly presented on tumors and rarely expressed on healthy tissue in combination with high-affinity TCRs that when introduced into T cells can redirect T cells to eliminate tumor but not healthy tissue is a key requirement for safe and efficacious TCR-based therapies. To discover promising shared tumor antigens that could be targeted via TCR-based adoptive T cell therapy, we employed population-scale immunopeptidomics using quantitative mass spectrometry across ~1500 tumor and normal tissue samples. We identified an HLA-A*02:01-restricted pan-cancer epitope within the collagen type VI α-3 (COL6A3) gene that is highly presented on tumor stroma across multiple solid cancers due to a tumor-specific alternative splicing event that rarely occurs outside the tumor microenvironment. T cells expressing natural COL6A3-specific TCRs demonstrated only modest activity against cells presenting high copy numbers of COL6A3 pHLAs. One of these TCRs was affinity-enhanced, enabling transduced T cells to specifically eliminate tumors in vivo that expressed similar copy numbers of pHLAs as primary tumor specimens. The enhanced TCR variants exhibited a favorable safety profile with no detectable off-target reactivity, paving the way to initiate clinical trials using COL6A3-specific TCRs to target an array of solid tumors.

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.

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.

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.

Turning tumors from cold to inflamed to improve immunotherapy response

Immune checkpoint inhibitors have revolutionized the treatment landscape for a number of cancers over the last few decades. Nevertheless, a majority of patients still do not benefit from these treatments. Such patient-specific lack of response can be predicted, in part, from the immune phenotypes present in the tumor microenvironment. We provide a perspective on options to reprogram the tumors and their microenvironment to increase the therapeutic efficacy of immunotherapies and expand their efficacy against cold tumors. Additionally, we review data from current preclinical and clinical trials aimed at testing the different therapeutic options in monotherapy or preferably in combination with checkpoint inhibitors.

A T-Cell Epitope-Based Multi-Epitope Vaccine Designed Using Human HLA Specific T Cell Epitopes Induces a Near-Sterile Immunity against Experimental Visceral Leishmaniasis in Hamsters

Visceral leishmaniasis is a neglected tropical disease affecting 12 million people annually. Even in the second decade of the 21st century, it has remained without an effective vaccine for human use. In the current study, we designed three multiepitope vaccine candidates by the selection of multiple IFN-γ inducing MHC-I and MHC-II binder T-cell specific epitopes from three previously identified antigen genes of Leishmania donovani from our lab by an immuno-informatic approach using IFNepitope, the Immune Epitope Database (IEDB) T cell epitope identification tools, NET-MHC-1, and NET MHC-2 webservers. We tested the protective potential of these three multiepitope proteins as a vaccine in a hamster model of visceral leishmaniasis. The immunization data revealed that the vaccine candidates induced a very high level of Th1 biased protective immune response in-vivo in a hamster model of experimental visceral leishmaniasis, with one of the candidates inducing a near-sterile immunity. The vaccinated animals displayed highly activated monocyte macrophages with the capability of clearing intracellular parasites due to increased respiratory burst. Additionally, these proteins induced activation of polyfunctional T cells secreting INF-γ, TNF-α, and IL-2 in an ex-vivo stimulation of human peripheral blood mononuclear cells, further supporting the protective nature of the designed candidates.

Arming Immune Cells for Battle: A Brief Journey through the Advancements of T and NK Cell Immunotherapy

Simple Summary

This review is intended to provide an overview on the history and recent advances of T cell and natural killer (NK) cell-based immunotherapy. While the thymus was discovered as the origin of T cells in the 1960s, and NK cells were first described in 1975, the clinical application of adoptive cell therapies (ACT) only began in the early 1980s with the first lymphokine activated killer (LAK) cell product for the treatment of cancer patients. Over the past decades, further immunotherapies have been developed, including ACT using cytokine-induced killer (CIK) cells, products based on the NK cell line NK-92 as well as specific T and NK cell preparations. Recent advances have successfully improved the effectiveness of T, NK, CIK or NK-92 cells towards tumor-targeting antigens generated by genetic engineering of the immune cells. Herein, we summarize the promising development of ACT over the past decades in the fight against cancer.

Abstract

The promising development of adoptive immunotherapy over the last four decades has revealed numerous therapeutic approaches in which dedicated immune cells are modified and administered to eliminate malignant cells. Starting in the early 1980s, lymphokine activated killer (LAK) cells were the first ex vivo generated NK cell-enriched products utilized for adoptive immunotherapy. Over the past decades, various immunotherapies have been developed, including cytokine-induced killer (CIK) cells, as a peripheral blood mononuclear cells (PBMCs)-based therapeutic product, the adoptive transfer of specific T and NK cell products, and the NK cell line NK-92. In addition to allogeneic NK cells, NK-92 cell products represent a possible “off-the-shelf” therapeutic concept. Recent approaches have successfully enhanced the specificity and cytotoxicity of T, NK, CIK or NK-92 cells towards tumor-specific or associated target antigens generated by genetic engineering of the immune cells, e.g., to express a chimeric antigen receptor (CAR). Here, we will look into the history and recent developments of T and NK cell-based immunotherapy.

Probing the Conformational Dynamics of Affinity-Enhanced T Cell Receptor Variants upon Binding the Peptide-Bound Major Histocompatibility Complex by Hydrogen/Deuterium Exchange Mass Spectrometry

Binding of the T cell receptor (TCR) to its cognate, peptide antigen-loaded major histocompatibility complex (pMHC) is a key interaction for triggering T cell activation and ultimately elimination of the target cell. Despite the importance of this interaction for cellular immunity, a comprehensive molecular understanding of TCR specificity and affinity is lacking. We conducted hydrogen/deuterium exchange mass spectrometry (HDX-MS) analyses of individual affinity-enhanced TCR variants and clinically relevant pMHC class I molecules (HLA-A*0201/NY-ESO-1157-165) to investigate the causality between increased binding affinity and conformational dynamics in TCR-pMHC complexes. Differential HDX-MS analyses of TCR variants revealed that mutations for affinity enhancement in TCR CDRs altered the conformational response of TCR to pMHC ligation. Improved pMHC binding affinity was in general observed to correlate with greater differences in HDX upon pMHC binding in modified TCR CDR loops, thereby providing new insights into the TCR-pMHC interaction. Furthermore, a specific point mutation in the β-CDR3 loop of the NY-ESO-1 TCR associated with a substantial increase in binding affinity resulted in a substantial change in pMHC binding kinetics (i.e., very slow kon, revealed by the detection of EX1 HDX kinetics), thus providing experimental evidence for a slow induced-fit binding mode. We also examined the conformational impact of pMHC binding on an unrelated TRAV12-2 gene-encoded TCR directed against the immunodominant MART-126-35 cancer antigen restricted by HLA-A*0201. Our findings provide a molecular basis for the observed TRAV12-2 gene bias in natural CD8+ T cell-based immune responses against the MART-1 antigen, with potential implications for general ligand discrimination and TCR cross-reactivity processes.

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

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

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

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

Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.

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.

Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors

In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.

Chronic TCR-MHC (self)-interactions limit the functional potential of TCR affinity-increased CD8 T lymphocytes

Background

Affinity-optimized T cell receptor (TCR)-engineered lymphocytes targeting tumor antigens can mediate potent antitumor responses in cancer patients, but also bear substantial risks for off-target toxicities. Most preclinical studies have focused on T cell responses to antigen-specific stimulation. In contrast, little is known on the regulation of T cell responsiveness through continuous TCR triggering and consequent tonic signaling. Here, we addressed the question whether increasing the TCR affinity can lead to chronic interactions occurring directly between TCRs and MHC-(self) molecules, which may modulate the overall functional potency of tumor-redirected CD8 T cells. For this purpose, we developed two complementary human CD8 T cell models (i.e. HLA-A2 knock-in and knock-out) engineered with incremental-affinity TCRs to the HLA-A2/NY-ESO-1 tumor antigen.

Methods

The impact of HLA-A2 recognition, depending on TCR affinity, was assessed at the levels of the TCR/CD3 complex, regulatory receptors, and signaling, under steady-state conditions and in kinetic studies. The quality of CD8 T cell responses was further evaluated by gene expression and multiplex cytokine profiling, as well as real-time quantitative cell killing, combined with co-culture assays.

Results

We found that HLA-A2 per se (in absence of cognate peptide) can trigger chronic activation followed by a tolerance-like state of tumor-redirected CD8 T cells with increased-affinity TCRs. HLA-A2^pos but not HLA-A2^neg T cells displayed an activation phenotype, associated with enhanced upregulation of c-CBL and multiple inhibitory receptors. T cell activation preceded TCR/CD3 downmodulation, impaired TCR signaling and functional hyporesponsiveness. This stepwise activation-to-hyporesponsive state was dependent on TCR affinity and already detectable at the upper end of the physiological affinity range (K_D ≤ 1 μM). Similar findings were made when affinity-increased HLA-A2^neg CD8 T cells were chronically exposed to HLA-A2^pos-expressing target cells.

Conclusions

Our observations indicate that sustained interactions between affinity-increased TCR and self-MHC can directly adjust the functional potential of T cells, even in the absence of antigen-specific stimulation. The observed tolerance-like state depends on TCR affinity and has therefore potential implications for the design of affinity-improved TCRs for adoptive T cell therapy, as several engineered TCRs currently used in clinical trials share similar affinity properties.

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 Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop Similarities

T-cell receptors (TCR) mediate immune responses recognizing peptides in complex with major histocompatibility complexes (pMHC) displayed on the surface of cells. Resolving the challenge of predicting the cognate pMHC target of a TCR would benefit many applications in the field of immunology, including vaccine design/discovery and the development of immunotherapies. Here, we developed a model for prediction of TCR targets based on similarity to a database of TCRs with known targets. Benchmarking the model on a large set of TCRs with known target, we demonstrated how the predictive performance is increased (i) by focusing on CDRs rather than the full length TCR protein sequences, (ii) by incorporating information from paired α and β chains, and (iii) integrating information for all 6 CDR loops rather than just CDR3. Finally, we show how integration of the structure of CDR loops, as obtained through homology modeling, boosts the predictive power of the model, in particular in situations where no high-similarity TCRs are available for the query. These findings demonstrate that TCRs that bind to the same target also share, to a very high degree, sequence, and structural features. This observation has profound impact for future development of prediction models for TCR-pMHC interactions and for the use of such models for the rational design of T cell based therapies.

STCRDab: the structural T-cell receptor database

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

Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition

Adaptive immune recognition is mediated by antigen receptors on B and T cells generated by somatic recombination during lineage development. The high level of diversity resulting from this process posed technical limitations that previously limited the comprehensive analysis of adaptive immune recognition. Advances over the last ten years have produced data and approaches allowing insights into how T cells develop, evolutionary signatures of recombination and selection, and the features of T cell receptors that mediate epitope-specific binding and T cell activation. The size and complexity of these data have necessitated the generation of novel computational and analytical approaches, which are transforming how T cell immunology is conducted. Here we review the development and application of novel biological, theoretical, and computational methods for understanding T cell recognition and discuss the potential for improved models of receptor:antigen interactions.

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.

Sensitive and frequent identification of high avidity neo-epitope specific CD8 + T cells in immunotherapy-naive ovarian cancer

Immunotherapy directed against private tumor neo-antigens derived from non-synonymous somatic mutations is a promising strategy of personalized cancer immunotherapy. However, feasibility in low mutational load tumor types remains unknown. Comprehensive and deep analysis of circulating and tumor-infiltrating lymphocytes (TILs) for neo-epitope specific CD8+ T cells has allowed prompt identification of oligoclonal and polyfunctional such cells from most immunotherapy-naive patients with advanced epithelial ovarian cancer studied. Neo-epitope recognition is discordant between circulating T cells and TILs, and is more likely to be found among TILs, which display higher functional avidity and unique TCRs with higher predicted affinity than their blood counterparts. Our results imply that identification of neo-epitope specific CD8+ T cells is achievable even in tumors with relatively low number of somatic mutations, and neo-epitope validation in TILs extends opportunities for mutanome-based personalized immunotherapies to such tumors.

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient’s own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide–ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide–MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC “hot-spots” for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences

Diversity of T cell receptor (TCR) repertoires, generated by somatic DNA rearrangements, is central to immune system function. However, the level of sequence similarity of TCR repertoires within and between species has not been characterized. Using network analysis of high-throughput TCR sequencing data, we found that abundant CDR3-TCRβ sequences were clustered within networks generated by sequence similarity. We discovered a substantial number of public CDR3-TCRβ segments that were identical in mice and humans. These conserved public sequences were central within TCR sequence-similarity networks. Annotated TCR sequences, previously associated with self-specificities such as autoimmunity and cancer, were linked to network clusters. Mechanistically, CDR3 networks were promoted by MHC-mediated selection, and were reduced following immunization, immune checkpoint blockade or aging. Our findings provide a new view of T cell repertoire organization and physiology, and suggest that the immune system distributes its TCR sequences unevenly, attending to specific foci of reactivity.

DOI:http://dx.doi.org/10.7554/eLife.22057.001

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

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

DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles

Background

T cell receptor (TCR) molecules are involved in the adaptive immune response as they distinguish between self- and foreign-peptides, presented in major histocompatibility complex molecules (pMHC). Former studies showed that the association angles of the TCR variable domains (Vα/Vβ) can differ significantly and change upon binding to the pMHC complex. These changes can be described as a rotation of the domains around a general Center of Rotation, characterized by the interaction of two highly conserved glutamine residues.

Methods

We developed a computational method, DynaDom, for the prediction of TCR Vα/Vβ inter-domain and TCR/pMHC orientations in TCRpMHC complexes, which allows predicting the orientation of multiple protein-domains. In addition, we implemented a new approach to predict the correct orientation of the carboxamide endgroups in glutamine and asparagine residues, which can also be used as an external, independent tool.

Results

The approach was evaluated for the remodeling of 75 and 53 experimental structures of TCR and TCRpMHC (class I) complexes, respectively. We show that the DynaDom method predicts the correct orientation of the TCR Vα/Vβ angles in 96 and 89% of the cases, for the poses with the best RMSD and best interaction energy, respectively. For the concurrent prediction of the TCR Vα/Vβ and pMHC orientations, the respective rates reached 74 and 72%. Through an exhaustive analysis, we could show that the pMHC placement can be further improved by a straightforward, yet very time intensive extension of the current approach.

Conclusions

The results obtained in the present remodeling study prove the suitability of our approach for interdomain-angle optimization. In addition, the high prediction rate obtained specifically for the energetically highest ranked poses further demonstrates that our method is a powerful candidate for blind prediction. Therefore it should be well suited as part of any accurate atomistic modeling pipeline for TCRpMHC complexes and potentially other large molecular assemblies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-016-0071-7) contains supplementary material, which is available to authorized users.

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

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

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.

Identifying Individual T Cell Receptors of Optimal Avidity for Tumor Antigens

Cytotoxic T cells recognize, via their T cell receptors (TCRs), small antigenic peptides presented by the major histocompatibility complex (pMHC) on the surface of professional antigen-presenting cells and infected or malignant cells. The efficiency of T cell triggering critically depends on TCR binding to cognate pMHC, i.e., the TCR–pMHC structural avidity. The binding and kinetic attributes of this interaction are key parameters for protective T cell-mediated immunity, with stronger TCR–pMHC interactions conferring superior T cell activation and responsiveness than weaker ones. However, high-avidity TCRs are not always available, particularly among self/tumor antigen-specific T cells, most of which are eliminated by central and peripheral deletion mechanisms. Consequently, systematic assessment of T cell avidity can greatly help distinguishing protective from non-protective T cells. Here, we review novel strategies to assess TCR–pMHC interaction kinetics, enabling the identification of the functionally most-relevant T cells. We also discuss the significance of these technologies in determining which cells within a naturally occurring polyclonal tumor-specific T cell response would offer the best clinical benefit for use in adoptive therapies, with or without T cell engineering.

Immune response to cancer therapy: mounting an effective antitumor response and mechanisms of resistance

Chemotherapy and radiotherapy have been extensively used to eradicate cancer based on their direct cytocidal effects on rapidly proliferating tumor cells. Accumulating evidence indicates that these therapies also dramatically affect resident and recruited immune cells that actively support tumor growth. We now appreciate that mobilization of effector CD8⁺ T cells enhances efficacy of chemotherapy and radiotherapy; remarkable clinical advances have been achieved by blocking regulatory programs limiting cytotoxic CD8⁺ T cell activity . This review discusses immune-mediated mechanisms underlying efficacy of chemotherapy and radiotherapy, and provides a perspective on how understanding tissue-based immune mechanisms can be used to guide therapeutic approaches combining immune and cytotoxic therapies to improve outcomes for a larger subset of patients than currently achievable.

An old Twist in HLA-A: CDR3α Hook up at an R65-joint

T-cell ontogeny optimizes the α/β T-cell receptor (TCR) repertoire for recognition of major histocompatibility complex (MHC) class-I/II genetic polymorphism, and co-evolution of TCR germline V-gene segments and the MHC must entail somatic diversity generated in the third complimentary determining regions (CDR3α/β); however, it is still not clear how. Herein, a conspicuous structural link between the V-Jα used by several different TCR [all in complex with the same MHC molecule (HLA-A2)], and a conserved MHC motif (a.a., R65-X-X-K-A-X-S-Q72) is described. We model this R65-joint in detail, and show that the same TCR’s CDR3α loop maintains its CDR2α loop at a distance of ~4 Å from polymorphic amino acid (a.a.) positions of the α-2 helix in all but one of the analyzed crystal structures. Indeed, the pitch of docked TCRs varies as their twist/tilt/sway maintains the R65-joint and peptide contacts. Thus, the R65-joint appears to have poised the HLA-A lineage toward alloreactivity.

Identification of Rare High-Avidity, Tumor-Reactive CD8+ T Cells by Monomeric TCR-Ligand Off-Rates Measurements on Living Cells

The avidity of the T-cell receptor (TCR) for antigenic peptides presented by the peptide-MHC (pMHC) on cells is a key parameter for cell-mediated immunity. Yet a fundamental feature of most tumor antigen-specific CD8(+) T cells is that this avidity is low. In this study, we addressed the need to identify and select tumor-specific CD8(+) T cells of highest avidity, which are of the greatest interest for adoptive cell therapy in patients with cancer. To identify these rare cells, we developed a peptide-MHC multimer technology, which uses reversible Ni(2+)-nitrilotriacetic acid histidine tags (NTAmers). NTAmers are highly stable but upon imidazole addition, they decay rapidly to pMHC monomers, allowing flow-cytometric-based measurements of monomeric TCR-pMHC dissociation rates of living CD8(+) T cells on a wide avidity spectrum. We documented strong correlations between NTAmer kinetic results and those obtained by surface plasmon resonance. Using NTAmers that were deficient for CD8 binding to pMHC, we found that CD8 itself stabilized the TCR-pMHC complex, prolonging the dissociation half-life several fold. Notably, our NTAmer technology accurately predicted the function of large panels of tumor-specific T cells that were isolated prospectively from patients with cancer. Overall, our results demonstrated that NTAmers are effective tools to isolate rare high-avidity cytotoxic T cells from patients for use in adoptive therapies for cancer treatment.

Changing the peptide specificity of a human T-cell receptor by directed evolution

Binding of a T-cell receptor (TCR) to a peptide/major histocompatibility complex is the key interaction involved in antigen specificity of T cells. The recognition involves up to six complementarity determining regions (CDR) of the TCR. Efforts to examine the structural basis of these interactions and to exploit them in adoptive T-cell therapies has required the isolation of specific T-cell clones and their clonotypic TCRs. Here we describe a strategy using in vitro-directed evolution of a single TCR to change its peptide specificity, thereby avoiding the need to isolate T-cell clones. The human TCR A6, which recognizes the viral peptide Tax/HLA-A2, was converted to TCR variants that recognized the cancer peptide MART1/HLA-A2. Mutational studies and molecular dynamics simulations identified CDR residues that were predicted to be important in the specificity switch. Thus, in vitro engineering strategies alone can be used to discover TCRs with desired specificities.

Large scale characterization of the LC13 TCR and HLA-B8 structural landscape in reaction to 172 altered peptide ligands: a molecular dynamics simulation study

The interplay between T cell receptors (TCRs) and peptides bound by major histocompatibility complexes (MHCs) is one of the most important interactions in the adaptive immune system. Several previous studies have computationally investigated their structural dynamics. On the basis of these simulations several structural and dynamical properties have been proposed as effectors of the immunogenicity. Here we present the results of a large scale Molecular Dynamics simulation study consisting of 100 ns simulations of 172 different complexes. These complexes consisted of all possible point mutations of the Epstein Barr Virus peptide FLRGRAYGL bound by HLA-B*08:01 and presented to the LC13 TCR. We compare the results of these 172 structural simulations with experimental immunogenicity data. We found that simulations with more immunogenic peptides and those with less immunogenic peptides are in fact highly similar and on average only minor differences in the hydrogen binding footprints, interface distances, and the relative orientation between the TCR chains are present. Thus our large scale data analysis shows that many previously suggested dynamical and structural properties of the TCR/peptide/MHC interface are unlikely to be conserved causal factors for peptide immunogenicity.