Comparative Analysis of the CDR Loops of Antigen Receptors

Unlocking the Gates: Therapeutic Agents for Noninvasive Drug Delivery Across the Blood-Brain Barrier

The blood-brain barrier (BBB) is a highly selective network of various cell types that acts as a filter between the blood and the brain parenchyma. Because of this, the BBB remains a major obstacle for drug delivery to the central nervous system (CNS). In recent years, there has been a focus on developing various modifiable platforms, such as monoclonal antibodies (mAbs), nanobodies (Nbs), peptides, and nanoparticles, as both therapeutic agents and carriers for targeted drug delivery to treat brain cancers and diseases. Methods for bypassing the BBB can be invasive or noninvasive. Invasive techniques, such as transient disruption of the BBB using low pulse electrical fields and intracerebroventricular infusion, lack specificity and have numerous safety concerns. In this review, we will focus on noninvasive transport mechanisms that offer high levels of biocompatibility, personalization, specificity and are regarded as generally safer than their invasive counterparts. Modifiable platforms can be designed to noninvasively traverse the BBB through one or more of the following pathways: passive diffusion through a physio-pathologically disrupted BBB, adsorptive-mediated transcytosis, receptor-mediated transcytosis, shuttle-mediated transcytosis, and somatic gene transfer. Through understanding the noninvasive pathways, new applications, including Chimeric Antigen Receptors T-cell (CAR-T) therapy, and approaches for drug delivery across the BBB are emerging.

T cell receptor-directed antibody-drug conjugates for the treatment of T cell-derived cancers

T cell-derived cancers are hallmarked by heterogeneity, aggressiveness, and poor clinical outcomes. Available targeted therapies are severely limited due to a lack of target antigens that allow discrimination of malignant from healthy T cells. Here, we report a novel approach for the treatment of T cell diseases based on targeting the clonally rearranged T cell receptor displayed by the cancerous T cell population. As a proof of concept, we identified an antibody with unique specificity toward a distinct T cell receptor (TCR) and developed antibody-drug conjugates, precisely recognizing and eliminating target T cells while preserving overall T cell repertoire integrity and cellular immunity. Our anti-TCR antibody-drug conjugates demonstrated effective receptor-mediated cell internalization, associated with induction of cancer cell death with strong signs of apoptosis. Furthermore, cell proliferation-inhibiting bystander effects observed on target-negative cells may contribute to the molecules' anti-tumor properties precluding potential tumor escape mechanisms. To our knowledge, this represents the first anti-TCR antibody-drug conjugate designed as custom-tailored immunotherapy for T cell-driven pathologies.

De Novo Synthesis and Structural Elucidation of CDR-H3 Loop Mimics

The binding affinity of antibodies to specific antigens stems from a remarkably broad repertoire of hypervariable loops known as complementarity-determining regions (CDRs). While recognizing the pivotal role of the heavy-chain 3 CDRs (CDR-H3s) in maximizing antibody-antigen affinity and specificity, the key structural determinants responsible for their adaptability to diverse loop sequences, lengths, and noncanonical structures are hitherto unknown. To address this question, we achieved a de novo synthesis of bulged CDR-H3 mimics excised from their full antibody context. CD and NMR data revealed that these stable standalone β-hairpin scaffolds are well-folded and retain many of the native bulge CDR-H3 features in water. In particular, the tryptophan residue, highly conserved across CDR-H3 sequences, was found to extend the kinked base of these β-bulges through a combination of stabilizing intramolecular hydrogen bond and CH/π interaction. The structural ensemble consistent with our NMR observations exposed the dynamic nature of residues at the base of the loop, suggesting that β-bulges act as molecular hinges connecting the rigid stem to the more flexible loops of CDR-H3s. We anticipate that this deeper structural understanding of CDR-H3s will lay the foundation to inform the design of antibody drugs broadly and engineer novel CDR-H3 peptide scaffolds as therapeutics.

Leveraging the biotechnological promise of the hagfish variable lymphocyte receptors: tools for aquatic microbial diseases

The jawless vertebrates (agnathans/cyclostomes) are ancestral animals comprising lampreys and hagfishes as the only extant representatives. They possess an alternative adaptive immune system (AIS) that uses leucine-rich repeats (LRR)-based variable lymphocyte receptors (VLRs) instead of the immunoglobulin (Ig)-based antigen receptors of jawed vertebrates (gnathostomes). The different VLR types are expressed on agnathan lymphocytes and functionally resemble gnathostome antigen receptors. In particular, VLRB is functionally similar to the B cell receptor and is expressed and secreted by B-like lymphocytes as VLRB antibodies that bind antigens with high affinity and specificity. The potential repertoire scale of VLR-based antigen receptors is believed to be at least comparable to that of Ig-based systems. VLR proteins inherently possess characteristics that render them excellent candidates for biotechnological development, including tractability to recombinant approaches. In recent years, scientists have explored the biotechnological development and utility of VLRB proteins as alternatives to conventional mammalian antibodies. The VLRB antibody platform represents a non-traditional approach to generating a highly diverse repertoire of unique antibodies. In this review, we first describe some aspects of the biology of the AIS of the jawless vertebrates, which recognizes antigens by means of unique receptors. We then summarize reports on the development of VLRB-based antibodies and their applications, particularly those from the inshore hagfish (Eptatretus burgeri) and their potential uses to address microbial diseases in aquaculture. Hagfish VLRB antibodies (we call Ccombodies) are being developed and improved, while obstacles to the advancement of the VLRB platform are being addressed to utilize VLRBs effectively as tools in immunology. VLRB antibodies for novel antigen targets are expected to emerge to provide new opportunities to tackle various scientific questions. We anticipate a greater interest in the agnathan AIS in general and particularly in the hagfish AIS for greater elucidation of the evolution of adaptive immunity and its applications to address microbial pathogens in farmed aquatic animals and beyond.

Designing meaningful continuous representations of T cell receptor sequences with deep generative models

T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.

Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

Crystal structure of the long Rib domain of the LPXTG-anchored surface protein from Limosilactobacillus reuteri

The Rib domain, which is often found as tandem-repeat structural modules in surface proteins of Gram-positive bacteria, plays important roles in mediating interactions of bacteria with their environments and hosts. A comprehensive structural analysis of various Rib domains is essential to fully understand their impact on the structure and functionality of these bacterial adhesins. To date, structural information has been limited for this expansive group of domains. In this study, the high-resolution crystal structure of the second member of the long Rib domain, a unique subclass within the Rib-domain family, derived from Limosilactobacillus reuteri is presented. The data not only demonstrate a highly conserved structure within the long Rib domain, but also highlight an evolutionary convergence in structural architecture with other modular domains found in cell-adhesion molecules.

Enhanced T cell receptor specificity through framework engineering

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

PseAAC2Vec protein encoding for TCR protein sequence classification

The classification and prediction of T-cell receptors (TCRs) protein sequences are of significant interest in understanding the immune system and developing personalized immunotherapies. In this study, we propose a novel approach using Pseudo Amino Acid Composition (PseAAC) protein encoding for accurate TCR protein sequence classification. The PseAAC2Vec encoding method captures the physicochemical properties of amino acids and their local sequence information, enabling the representation of protein sequences as fixed-length feature vectors. By incorporating physicochemical properties such as hydrophobicity, polarity, charge, molecular weight, and solvent accessibility, PseAAC2Vec provides a comprehensive and informative characterization of TCR protein sequences. To evaluate the effectiveness of the proposed PseAAC2Vec encoding approach, we assembled a large dataset of TCR protein sequences with annotated classes. We applied the PseAAC2Vec encoding scheme to each sequence and generated feature vectors based on a specified window size. Subsequently, we employed state-of-the-art machine learning algorithms, such as support vector machines (SVM) and random forests (RF), to classify the TCR protein sequences. Experimental results on the benchmark dataset demonstrated the superior performance of the PseAAC2Vec-based approach compared to existing methods. The PseAAC2Vec encoding effectively captures the discriminative patterns in TCR protein sequences, leading to improved classification accuracy and robustness. Furthermore, the encoding scheme showed promising results across different window sizes, indicating its adaptability to varying sequence contexts.

Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms

Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether "novel" features, such as distinct loop conformations can arise from these predictions despite not being present in the training data. Here we have used ABodyBuilder2, a deep learning antibody structure predictor, to predict the structures of ~1.5M paired antibody sequences. We examined the predicted structures of the canonical CDR loops and found that most of these predictions fall into the already described CDR canonical form structural space. We also found a small number of "new" canonical clusters composed of heterogeneous sequences united by a common sequence motif and loop conformation. Analysis of these novel clusters showed their origins to be either shapes seen in the training data at very low frequency or shapes seen at high frequency but at a shorter sequence length. To evaluate explicitly the ability of ABodyBuilder2 to extrapolate, we retrained several models whilst withholding all antibody structures of a specific CDR loop length or canonical form. These "starved" models showed evidence of generalisation across CDRs of different lengths, but they did not extrapolate to loop conformations which were highly distinct from those present in the training data. However, the models were able to accurately predict a canonical form even if only a very small number of examples of that shape were in the training data. Our results suggest that deep learning protein structure prediction methods are unable to make completely out-of-domain predictions for CDR loops. However, in our analysis we also found that even minimal amounts of data of a structural shape allow the method to recover its original predictive abilities. We have made the ~1.5 M predicted structures used in this study available to download at https://doi.org/10.5281/zenodo.10280181.

Elucidation of bezlotoxumab binding specificity to toxin B in Clostridioides difficile

C. difficile or Clostridioides difficile infection (CDI) is currently one of the major causes of epidemics worldwide. Toxin B from Clostridioides difficile toxin B (TcdB) infection is the main target protein inhibiting CDI recurrence. Clinical research suggested that bezlotoxumab's (Bez) efficiency is significantly reduced in neutralizing the B2 strain compared to the B1 strain. The monoclonal antibody (mAb) functions by binding to the epitope 1 and 2 regions in the combined repetitive oligopeptide (CROP) domain. Some binding residues are distinctively different between B1 and B2 strains. In this work, we aimed to elucidate and compare insights into the interaction of toxins B1 and B2 in complex with Bez by using all-atom molecular dynamics (MD) simulations and binding free energy calculations. The predicted ΔGbinding values suggested that the antibody (Ab) could bind to toxin B1 significantly better than B2, supported by higher salt bridge and hydrogen bonding (H-bonding) interactions, as well as the number of contact residues between the two focused proteins. The toxin B1 residues important for binding with Bez were E1878, T1901, E1902, F1905, N1941, V1946, N2031, T2032, E2033, V2076, V2077, and E2092. The lower susceptibility of Bez towards toxin B2 was primarily due to a change of residue E2033 from glutamate to alanine (A2033) and the loss of E1878 and E1902 contributions, as determined by the intermolecular interaction changes from the dynamic residue interaction network (dRIN) analysis. The obtained data strengthen our understanding of Bez/toxin B binding.

Review of phage display: A jack-of-all-trades and master of most biomolecule display

Phage display was first described by George P. Smith when it was shown that virus particles were capable of presenting foreign proteins on their surface. The technology has paved the way for the evolution of various biomolecules presentation and diverse selection strategies. This unique feature has been applied as a versatile platform for numerous applications in drug discovery, protein engineering, diagnostics, and vaccine development. Over the decades, the limits of biomolecules displayed on phage particles have expanded from peptides to proteomes and even alternative scaffolds. This has allowed phage display to be viewed as a versatile display platform to accommodate various biomolecules ranging from small peptides to larger proteomes which has significantly impacted advancements in the biomedical industry. This review will explore the vast array of biomolecules that have been successfully employed in phage display technology in biomedical research.

Polyreactivity of antibodies from different B-cell subpopulations is determined by distinct sequence patterns of variable region

An antibody molecule that can bind to multiple distinct antigens is defined as polyreactive. In the present study, we performed statistical analyses to assess sequence correlates of polyreactivity of >600 antibodies cloned from different B-cell types of healthy humans. The data revealed several sequence patterns of variable regions of heavy and light immunoglobulin chains that determine polyreactivity. The most prominent identified patterns were increased number of basic amino acid residues, reduced frequency of acidic residues, increased number of aromatic and hydrophobic residues, and longer length of CDR L1. Importantly, our study revealed that antibodies isolated from different B-cell populations used distinct sequence patterns (or combinations of them) for polyreactive antigen binding. Furthermore, we combined the data from sequence analyses with molecular modeling of selected polyreactive antibodies and demonstrated that human antibodies can use multiple pathways for achieving antigen-binding promiscuity. These data reconcile some contradictions in the literature regarding the determinants of antibody polyreactivity. Moreover, our study demonstrates that the mechanism of polyreactivity of antibodies evolves during immune response and might be tailored to specific functional properties of different B-cell compartments. Finally, these data can be of use for efforts in the development and engineering of therapeutic antibodies.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

Molecular correlates of vaccine-induced protection against typhoid fever

BACKGROUNDTyphoid fever is caused by the Gram-negative bacterium Salmonella enterica serovar Typhi and poses a substantial public health burden worldwide. Vaccines have been developed based on the surface Vi-capsular polysaccharide of S. Typhi; these include a plain-polysaccharide-based vaccine, ViPS, and a glycoconjugate vaccine, ViTT. To understand immune responses to these vaccines and their vaccine-induced immunological protection, molecular signatures were analyzed using bioinformatic approaches.METHODSBulk RNA-Seq data were generated from blood samples obtained from adult human volunteers enrolled in a vaccine trial, who were then challenged with S. Typhi in a controlled human infection model (CHIM). These data were used to conduct differential gene expression analyses, gene set and modular analyses, B cell repertoire analyses, and time-course analyses at various post-vaccination and post-challenge time points between participants receiving ViTT, ViPS, or a control meningococcal vaccine.RESULTSTranscriptomic responses revealed strong differential molecular signatures between the 2 typhoid vaccines, mostly driven by the upregulation in humoral immune signatures, including selective usage of immunoglobulin heavy chain variable region (IGHV) genes and more polarized clonal expansions. We describe several molecular correlates of protection against S. Typhi infection, including clusters of B cell receptor (BCR) clonotypes associated with protection, with known binders of Vi-polysaccharide among these.CONCLUSIONThe study reports a series of contemporary analyses that reveal the transcriptomic signatures after vaccination and infectious challenge, while identifying molecular correlates of protection that may inform future vaccine design and assessment.TRIAL REGISTRATIONClinicalTrials.gov NCT02324751.

Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigen-binding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as subtypes, CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our results indicate that the rigid minor loops fine-tune the structure of CDR-H3, thereby contributing to the generation of surfaces complementary to the vast number of possible epitope topologies, and provide insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process.

A current and future perspective on T cell receptor repertoire profiling

T cell receptors (TCR) play a vital role in the immune system's ability to recognize and respond to foreign antigens, relying on the highly polymorphic rearrangement of TCR genes. The recognition of autologous peptides by adaptive immunity may lead to the development and progression of autoimmune diseases. Understanding the specific TCR involved in this process can provide insights into the autoimmune process. RNA-seq (RNA sequencing) is a valuable tool for studying TCR repertoires by providing a comprehensive and quantitative analysis of the RNA transcripts. With the development of RNA technology, transcriptomic data must provide valuable information to model and predict TCR and antigen interaction and, more importantly, identify or predict neoantigens. This review provides an overview of the application and development of bulk RNA-seq and single-cell (SC) RNA-seq to examine the TCR repertoires. Furthermore, discussed here are bioinformatic tools that can be applied to study the structural biology of peptide/TCR/MHC (major histocompatibility complex) and predict antigenic epitopes using advanced artificial intelligence tools.

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins

Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of deep learning models trained to accurately predict the structure of antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) and T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state of the art accuracy while being far faster than AlphaFold2. For example, on a benchmark of 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.81Å, a 0.09Å improvement over AlphaFold-Multimer, while being over a hundred times faster. Similar results are also achieved for nanobodies, (NanoBodyBuilder2 predicts CDR-H3 loops with an average RMSD of 2.89Å, a 0.55Å improvement over AlphaFold2) and TCRs. By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction. ImmuneBuilder is made freely available, both to download ( https://github.com/oxpig/ImmuneBuilder ) and to use via our webserver ( http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred ). We also make available structural models for ~150 thousand non-redundant paired antibody sequences ( https://doi.org/10.5281/zenodo.7258553 ).

Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies

T-cell receptor-mimetic antibodies (TCRms) targeting disease-associated peptides presented by Major Histocompatibility Complexes (pMHCs) are set to become a major new drug modality. However, we lack a general understanding of how TCRms engage pMHC targets, which is crucial for predicting their specificity and safety. Several new structures of TCRm:pMHC complexes have become available in the past year, providing sufficient initial data for a holistic analysis of TCRms as a class of pMHC binding agents. Here, we profile the complete set of TCRm:pMHC complexes against representative TCR:pMHC complexes to quantify the TCR-likeness of their pMHC engagement. We find that intrinsic molecular differences between antibodies and TCRs lead to fundamentally different roles for their heavy/light chains and Complementarity-Determining Region loops during antigen recognition. The idiotypic properties of antibodies may increase the likelihood of TCRms engaging pMHCs with less peptide selectivity than TCRs. However, the pMHC recognition features of some TCRms, including the two TCRms currently in clinical trials, can be remarkably TCR-like. The insights gained from this study will aid in the rational design and optimisation of next-generation TCRms.

Chickens, more than humans, focus the diversity of their immunoglobulin genes on the complementarity-determining region but utilise amino acids, indicative of a more cross-reactive antibody repertoire

The mechanisms of B-cell diversification differ greatly between aves and mammals, but both produce B cells and antibodies capable of supporting an effective immune response. To see how differences in the generation of diversity might affect overall repertoire diversity, we have compared the diversity characteristics of immunoglobulin genes from domestic chickens to those from humans. Both use V(D)J gene rearrangement and somatic hypermutation, but only chickens use somatic gene conversion. A range of diversity analysis tools were used to investigate multiple aspects of amino acid diversity at both the germline and repertoire levels. The effect of differing amino acid usages on antibody characteristics was assessed. At both the germline and repertoire levels, chickens exhibited lower amino acid diversity in comparison to the human immunoglobulin genes, especially outside of the complementarity-determining region (CDR). Chickens were also found to possess much larger and more hydrophilic CDR3s with a higher predicted protein binding potential, suggesting that the antigen-binding site in chicken antibodies is more flexible and more polyreactive than that seen in human antibodies.

Joining Forces for Cancer Treatment: From "TCR versus CAR" to "TCR and CAR"

T cell-based immunotherapy has demonstrated great therapeutic potential in recent decades, on the one hand, by using tumor-infiltrating lymphocytes (TILs) and, on the other hand, by engineering T cells to obtain anti-tumor specificities through the introduction of either engineered T cell receptors (TCRs) or chimeric antigen receptors (CARs). Given the distinct design of both receptors and the type of antigen that is encountered, the requirements for proper antigen engagement and downstream signal transduction by TCRs and CARs differ. Synapse formation and signal transduction of CAR T cells, despite further refinement of CAR T cell designs, still do not fully recapitulate that of TCR T cells and might limit CAR T cell persistence and functionality. Thus, deep knowledge about the molecular differences in CAR and TCR T cell signaling would greatly advance the further optimization of CAR designs and elucidate under which circumstances a combination of both receptors would improve the functionality of T cells for cancer treatment. Herein, we provide a comprehensive review about similarities and differences by directly comparing the architecture, synapse formation and signaling of TCRs and CARs, highlighting the knowns and unknowns. In the second part of the review, we discuss the current status of combining CAR and TCR technologies, encouraging a change in perspective from "TCR versus CAR" to "TCR and CAR".

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein-Protein Complexes

Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein-protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3-5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody-antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

The Full Model of the pMHC-TCR-CD3 Complex: A Structural and Dynamical Characterization of Bound and Unbound States

The machinery involved in cytotoxic T-cell activation requires three main characters: the major histocompatibility complex class I (MHC I) bound to the peptide (p), the T-cell receptor (TCR), and the CD3 complex, a multidimer interfaced with the intracellular side. The pMHC:TCR interaction has been largely studied by means of both experimental and computational models, giving a contribution in understanding the complexity of the TCR triggering. Nevertheless, a detailed study of the structural and dynamical characterization of the full complex (pMHC:TCR:CD3 complex) is still missing due to a lack of structural information of the CD3-chains arrangement around the TCR. Very recently, the determination of the TCR:CD3 complex structure by means of Cryo-EM technique has given a chance to build the entire system essential in the activation of T-cells, a fundamental mechanism in the adaptive immune response. Here, we present the first complete model of the pMHC interacting with the TCR:CD3 complex, built in a lipid environment. To describe the conformational behavior associated with the unbound and the bound states, all-atom Molecular Dynamics simulations were performed for the TCR:CD3 complex and for two pMHC:TCR:CD3 complex systems, bound to two different peptides. Our data point out that a conformational change affecting the TCR Constant β (Cβ) region occurs after the binding to the pMHC, revealing a key role of this region in the propagation of the signal. Moreover, we found that TCR reduces the flexibility of the MHC I binding groove, confirming our previous results.

Computational Construction of a Single-Chain Bi-Paratopic Antibody Allosterically Inhibiting TCR-Staphylococcal Enterotoxin B Binding

Staphylococcal enterotoxin B (SEB) simultaneously crosslinks MHC class II antigen and TCR, promoting proliferation of T cells and releasing a large number of toxic cytokines. In this report, we computationally examined the possibility of using a single-chain biparatopic bispecific antibody to target SEB and prevent TCR binding. The design was inspired by the observation that mixing two anti-SEB antibodies 14G8 and 6D3 can block SEB-TCR activation, and we used 14G8-6D3-SEB tertiary crystal structure as a template. Twelve simulation systems were constructed to systematically examine the effects of the designed bispecific scFV MB102a, including isolated SEB, MB102a with different linkers, MB102a-SEB complex, MB102a-SEB-TCRβ complex, MB102a-SEB-TCR-MHC II complex, and MB102a-SEB-MHC II. Our all atom molecular dynamics simulations (total 18,900 ns) confirmed that the designed single-chain bispecific antibody may allosterically prevent SEB-TCRβ chain binding and inhibit SEB-TCR-MHC II formation. Subsequent analysis indicated that the binding of scFV to SEB correlates with SEB-TCR binding site motion and weakens SEB-TCR interactions.

ssDNA recombineering boosts in vivo evolution of nanobodies displayed on bacterial surfaces

ssDNA recombineering has been exploited to hyperdiversify genomically-encoded nanobodies displayed on the surface of Escherichia coli for originating new binding properties. As a proof-of-principle a nanobody recognizing the antigen TirM from enterohaemorrhagic E. coli (EHEC) was evolved towards the otherwise not recognized TirM antigen from enteropathogenic E. coli (EPEC). To this end, E. coli cells displaying this nanobody fused to the intimin outer membrane-bound domain were subjected to multiple rounds of mutagenic oligonucleotide recombineering targeting the complementarity determining regions (CDRs) of the cognate VHH gene sequence. Binders to the EPEC-TirM were selected upon immunomagnetic capture of bacteria bearing active variants and nanobodies identified with a new ability to strongly bind the new antigen. The results highlight the power of combining evolutionary properties of bacteria in vivo with oligonucleotide synthesis in vitro for the sake of focusing diversification to specific segments of a gene (or protein thereof) of interest.

DLAB: deep learning methods for structure-based virtual screening of antibodies

MOTIVATION

Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders.

RESULTS

We demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.

AVAILABILITY AND IMPLEMENTATION

The DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular Insights of Nickel Binding to Therapeutic Antibodies as a Possible New Antibody Superantigen

The binding of nickel by immune proteins can manifest as Type IV contact dermatitis (Ni-specific T cells mediated) and less frequently as Type I hypersensitivity with both mechanisms remaining unknown to date. Since there are reports of patients co-manifesting the two hypersensitivities, a common mechanism may underlie both the TCR and IgE nickel binding. Focusing on Trastuzumab and Pertuzumab IgE variants as serendipitous investigation models, we found Ni-NTA interactions independent of Her2 binding to be due to glutamine stretches. These stretches are both Ni-inducible and in fixed pockets at the antibody complementarity-determining regions (CDRs) and framework regions (FWRs) of both the antibody heavy and light chains with influence from the heavy chain constant region. Comparisons with TCRs structures revealed similar interactions, demonstrating the possible underlying mechanism in selecting for Ni-binding IgEs and TCRs respectively. With the elucidation of the interaction, future therapeutic antibodies could also be sagaciously engineered to utilize such nickel binding for biotechnological purposes.

Development of a structure-based computational simulation to optimize the blocking efficacy of pro-antibodies

The on-target toxicity of monoclonal antibodies (Abs) is mainly due to the fact that Abs cannot distinguish target antigens (Ags) expressed in disease regions from those in normal tissues during systemic administration. In order to overcome this issue, we “copied” an autologous Ab hinge as an “Ab lock” and “pasted” it on the binding site of the Ab by connecting a protease substrate and linker in between to generate a pro-Ab, which can be specifically activated in the disease region to enhance Ab selectivity and reduce side effects. Previously, we reported that 70% of pro-Abs can achieve more than 100-fold blocking ability compared to the parental Abs. However, 30% of pro-Abs do not have such efficient blocking ability. This is because the same Ab lock linker cannot be applied to every Ab due to the differences in the complementarity-determining region (CDR) loops. Here we designed a method which uses structure-based computational simulation (MSCS) to optimize the blocking ability of the Ab lock for all Ab drugs. MSCS can precisely adjust the amino acid composition of the linker between the Ab lock and Ab drug with the assistance of molecular simulation. We selected αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab as models and attached the Ab lock with various linkers (L1 to L7) to form pro-Abs by MSCS, respectively. The resulting cover rates of the Ab lock with various linkers compared to the Ab drug were in the range 28.33–42.33%. The recombinant pro-Abs were generated by MSCS prediction in order to verify the application of molecular simulation for pro-Ab development. The binding kinetics effective concentrations (EC-50) for αPD-1 (200-250-fold), αIL-1β (152-186-fold), αCTLA-4 (68-150-fold) and αTNFα Ab (20-123-fold) were presented as the blocking ability of pro-Ab compared to the Ab drug. Further, there was a positive correlation between cover rate and blocking ability of all pro-Ab candidates. The results suggested that MSCS was able to predict the Ab lock linker most suitable for application to αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab to form pro-Abs efficiently. The success of MSCS in optimizing the pro-Ab can aid the development of next-generation pro-Ab drugs to significantly improve Ab-based therapies and thus patients' quality of life.

The pro-Ab blocks the Ag binding site using an Ab lock. We designed a method which uses structure-based computational simulation (MSCS) to predict the cover rate of Ab locks with various linkers and select the suitable linker for each Ab.

Characterization of amino acid residues of T-cell receptors interacting with HLA-A*02-restricted antigen peptides

Background

The present study aimed to explore residues’ properties interacting with HLA-A*02-restricted peptides on T-cell receptors (TCRs) and their effects on bond types of interaction and binding free energy.

Methods

We searched the crystal structures of HLA-A*02-restricted peptide-TCR complexes from the Protein Data Bank (PDB) database and subsequently collected relevant parameters. We then employed Schrodinger to analyze the bond types of interaction and Gromacs 2019 to evaluate the TCR-antigen peptide complex’s molecular dynamics simulation. Finally, we compared the changes of bond types of interaction and binding free energy before and after residue substitution to ensure consistency of the conditions before and after residue substitution.

Results

The main sites on the antigen peptides that formed the intermolecular interaction [hydrogen bond (HB) and pi stack] with TCRs were P4, P8, P2, and P6. The hydrophobicity of the amino acids inside or outside the disulfide bond of TCRs may be related to the intermolecular interaction and binding free energy between TCRs and peptides. Residues located outside the disulfide bond of TCR α or β chains and forming pi stack force played favorable roles in the complex intermolecular interaction and binding free energy. The residues of the TCR α or β chains that interacted with peptides were replaced by alanine (Ala) or glycine (Gly), and their intermolecular binding free energy of the complex had been improved. However, it had nothing to do with the formation of HB.

Conclusions

The findings of this study suggest that the hydrophobic nature of the amino acids inside or outside the disulfide bonds on the TCR may be associated with the intermolecular interaction and binding between the TCR and polypeptide. The residues located outside the TCR α or β single-chain disulfide bond and forming the pi-stack force showed a beneficial effect on the intermolecular interaction and binding of the complex. In addition, the part of the residues on the TCR α or β single chain that produced bond types of interaction with the polypeptide after being replaced by Ala or Gly, the intermolecular binding free energy of the complex was increased, regardless of whether HB was formed.

TCRBuilder: multi-state T-cell receptor structure prediction

MOTIVATION

T-cell receptors (TCRs) are immune proteins that primarily target peptide antigens presented by the major histocompatibility complex. They tend to have lower specificity and affinity than their antibody counterparts, and their binding sites have been shown to adopt multiple conformations, which is potentially an important factor for their polyspecificity. None of the current TCR-modelling tools predict this variability which limits our ability to accurately predict TCR binding.

RESULTS

We present TCRBuilder, a multi-state TCR structure prediction tool. Given a paired αβTCR sequence, TCRBuilder returns a model or an ensemble of models covering the potential conformations of the binding site. This enables the analysis of structurally driven polyspecificity in TCRs, which is not possible with existing tools.

AVAILABILITY AND IMPLEMENTATION

http://opig.stats.ox.ac.uk/resources.

CONTACT

deane@stats.ox.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antibodies exhibit multiple paratope states influencing VH-VL domain orientations

In the last decades, antibodies have emerged as one of the most important and successful classes of biopharmaceuticals. The highest variability and diversity of an antibody is concentrated on six hypervariable loops, also known as complementarity determining regions (CDRs) shaping the antigen-binding site, the paratope. Whereas it was assumed that certain sequences can only adopt a limited set of backbone conformations, in this study we present a kinetic classification of several paratope states in solution. Using molecular dynamics simulations in combination with experimental structural information we capture the involved conformational transitions between different canonical clusters and additional dominant solution structures occurring in the micro-to-millisecond timescale. Furthermore, we observe a strong correlation of CDR loop movements. Another important aspect when characterizing different paratope states is the relative VH/VL orientation and the influence of the distinct CDR loop states on the VH/VL interface. Conformational rearrangements of the CDR loops do not only have an effect on the relative VH/VL orientations, but also influence in some cases the elbow-angle dynamics and shift the respective distributions. Thus, our results show that antibodies exist as several interconverting paratope states, each contributing to the antibody's properties.

T-Cell Receptor CDR3 Loop Conformations in Solution Shift the Relative Vα-Vβ Domain Distributions

T-cell receptors are an important part in the adaptive immune system as they are responsible for detecting foreign proteins presented by the major histocompatibility complex (MHC). The affinity is predominantly determined by structure and sequence of the complementarity determining regions (CDRs), of which the CDR3 loops are responsible for peptide recognition. We present a kinetic classification of T-cell receptor CDR3 loops with different loop lengths into canonical and non-canonical solution structures. Using molecular dynamics simulations, we do not only sample available X-ray structures, but we also observe a substantially broader CDR3 loop ensemble with various distinct kinetic minima in solution. Our results strongly imply, that for given CDR3 loop sequences several canonical structures have to be considered to characterize the conformational diversity of these loops. Our suggested dominant solution structures could extend the repertoire of available canonical clusters by including kinetic minimum structures present in solution. Thus, the CDR3 loops need to be characterized as conformational ensembles in solution. Furthermore, the conformational changes of the CDR3 loops follow the paradigm of conformational selection, because the experimentally determined binding competent state is present within this ensemble of pre-existing conformations without the presence of the antigen. We also identify strong correlations between the CDR3 loops and include combined state descriptions. Additionally, we observe a strong dependency of the CDR3 loop conformations on the relative Vα-Vβ interdomain orientations, revealing that certain CDR3 loop states favor specific interface orientations.

Homeostasis and regulation of autoreactive B cells

In contrast to the previous belief that autoreactive B cells are eliminated from the normal repertoire of B cells, many autoreactive B cells actually escape clonal deletion and develop into mature B cells. These autoreactive B cells in healthy individuals perform some beneficial functions in the host and are homeostatically regulated by regulatory T and B cells or other mechanisms to prevent autoimmune diseases. Autoreactive B-1 cells constitutively produce polyreactive natural antibodies for tissue homeostasis. Recently, autoreactive follicular B cells were reported to participate actively in the germinal center reaction. Furthermore, the selection and usefulness of autoreactive marginal zone (MZ) B cells found in autoimmune diseases are not well understood, although the repertoire of MZ B-cell receptors (BCRs) is presumed to be biased to detect bacterial antigens. In this review, we discuss the autoreactive B-cell populations among all three major B-cell subsets and their regulation in immune responses and diseases.