Complementarity-determining Region Loops Research Articles

IgLM: Infilling language modeling for antibody sequence design

Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries. Compared with prior methods that leverage unidirectional context for sequence generation, IgLM formulates antibody design based on text-infilling in natural language, allowing it to re-design variable-length spans within antibody sequences using bidirectional context. We trained IgLM on 558 million (M) antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species of origin. We demonstrate that IgLM can generate full-length antibody sequences from a variety of species and its infilling formulation allows it to generate infilled complementarity-determining region (CDR) loop libraries with improved in silico developability profiles. A record of this paper's transparent peer review process is included in the supplemental information.

Molecular interaction analysis of anti-IL-8 scFv-10F8-6His against IL-8 monomer through molecular docking and molecular dynamic simulations

Elevated interleukin 8 (IL-8) expression has been linked to unfavorable outcomes in a range of inflammatory conditions, such as rheumatoid arthritis, psoriasis, and cancer. The human monoclonal antibody (HuMab) 10F8 and the hybridoma 35B11-B bind to an epitope on human IL-8, respectively. 10F8 inhibited interaction between IL-8 and neutrophils in eczema and pustulosis palmoplantaris patients while 35B11-B decreased size lesion in rat model. The binding interaction of monoclonal antibodies and IL-8, especially how complementarity-determining region (CDR) loops could bind the N-terminal of IL-8, has not been fully deliberated at molecular-level. Here, we used a combination of molecular docking, heated and long coarse-grained molecular dynamics simulations to identify key residues of established interaction. Based on heated MD simulation, docked pose of complexes generated by ClusPro showed good binding stability throughout of 70 ns simulation. Based on long molecular dynamic simulations, key residues for the binding were identified throughout of 1000 ns simulation. TYR-53, ASP-99, and ARG-100 of heavy chain CDR together with TYR-33 of light chain CDR are among the highest contributing energy residues within the binding interaction. Meanwhile, LYS11 and TYR13 of IL-8 are important for the determination of overall binding energy. Furthermore, the result of decomposition residues analysis is in good agreement with the interaction analysis data. Current study provides a list of important interacting residues and further scrutiny on these residues is essential for future development and design of a new and stable recombinant antibody against IL-8. Communicated by Ramaswamy H. Sarma

Arginine cluster introduction on framework region in anti-lysozyme antibody improved association rate constant by changing conformational diversity of CDR loops.

Antibodies are used for many therapeutic and biotechnological purposes. Because the affinity of an antibody to the antigen is critical for clinical efficacy of pharmaceuticals, many affinity maturation strategies have been developed. Although we previously reported an affinity maturation strategy in which the association rate of the antibody toward its antigen is improved by introducing a cluster of arginine residues into the framework region of the antibody, the detailed molecular mechanism responsible for this improvement has been unknown. In this study, we introduced five arginine residues into an anti-hen egg white lysozyme antibody (HyHEL10) Fab fragment to create the R5-mutant and comprehensively characterized the interaction between antibody and antigen using thermodynamic analysis, X-ray crystallography, and molecular dynamics (MD) simulations. Our results indicate that introduction of charged residues strongly enhanced the association rate, as previously reported, and the antibody-antigen complex structure was almost the same for the R5-mutant and wild-type Fabs. The MD simulations indicate that the mutation increased conformational diversity in complementarity-determining region loops and thereby enhanced the association rate. These observations provide the molecular basis of affinity maturation by R5 mutation.

Complementarity-determining region clustering may cause CAR-T cell dysfunction

Chimeric antigen receptor (CAR)-T cell therapy is rapidly advancing as cancer treatment, however, designing an optimal CAR remains challenging. A single-chain variable fragment (scFv) is generally used as CAR targeting moiety, wherein the complementarity-determining regions (CDRs) define its specificity. We report here that the CDR loops can cause CAR clustering, leading to antigen-independent tonic signalling and subsequent CAR-T cell dysfunction. We show via CARs incorporating scFvs with identical framework and varying CDR sequences that CARs may cluster on the T cell surface, which leads to antigen-independent CAR-T cell activation, characterized by increased cell size and interferon (IFN)-γ secretion. This results in CAR-T cell exhaustion, activation-induced cell death and reduced responsiveness to target-antigen-expressing tumour cells. CDR mutagenesis confirms that the CAR-clustering is mediated by CDR-loops. In summary, antigen-independent tonic signalling can be induced by CDR-mediated CAR clustering, which could not be predicted from the scFv sequences, but could be tested for by evaluating the activity of unstimulated CAR-T cells.

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.

Synthesis of epitope-targeting nanobody based on native protein-protein interactions for FtsZ filamentation suppressor.

Phage display and biopanning are powerful tools for generating binding molecules for a specific target. However, the selection process based only on binding affinity provides no assurance for the antibody's affinity to the target epitope. In this study, we propose a molecular-evolution approach guided by native protein-protein interactions to generate epitope-targeting antibodies. The binding-site sequence in a native protein was grafted into a complementarity-determining region (CDR) in the nanobody, and a nonrelated CDR loop (in the grafted nanobody) was randomized to create a phage display library. In this construction of nanobodies by integrating graft and evolution technology (CAnIGET method), suitable grafting of the functional sequence added functionality to the nanobody, and the molecular-evolution approach enhanced the binding function to inhibit the native protein-protein interactions. To apply for biological tool with growth screening, model nanobodies with an affinity for filamenting temperature-sensitive mutant Z (FtsZ) from Staphylococcus aureus were constructed and completely inhibited the polymerization of FtsZ as a function. Consequently, the expression of these nanobodies drastically decreased the cell division rate. We demonstrate the potential of the CAnIGET method with the use of native protein-protein interactions for steady epitope-specific evolutionary engineering.

Identifying Epitope Sites on the Receptor Binding Domain (RBD) of SARS-CoV-2

Abstract COVID-19 and SARS-CoV-2 variants continue to threaten human health and life worldwide. Thousands of structures related to SARS-CoV-2 have been rapidly determined, either by X-ray crystallography or CryoEM and deposited in the Protein Data Bank (PDB) since 2020. Here, we systematically investigated the structures of 302 antibodies and 78 nanobodies in complex with spike protein or the receptor binding domain (RBD) of SARS-CoV-2. We identified 23 common epitopic sites (ES) on the RBD surface and revealed the vital role of the complementarity-determining Region (CDR) loops in recognizing epitopes. The 23 ES are characterized according to the secondary structure feature and accessible surface area. About 75% of the total surface of RBD areas could access by the elicited antibodies, while the CDR3 loops occupied 50% of the contact surface. The analysis of paratope-epitope (antibody-antigen) interaction based on these epitope sites revealed the features of potent neutralizing the virus and the unique usage of amino acids of antibody. The clustering analysis of the antibody biophysical properties and surface area availabilities on the RBD found many binding motifs. Remarkably, most variants of concern (VOC) escape mutations, including Omicron occur within these 23 ES. This analysis not only explains the differential ability of antibodies to recognize epitope sites on the RBD surface but also offers predictive guidelines for understanding the role that accumulated SARS mutations might play in escape from antibodies elicited by immunogens. This structural characterization of the epitope-paratope interactions provides insights for structure-based vaccine design and therapeutic strategies and drugs against the future virus.

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.

Facile repurposing of peptide–MHC-restricted antibodies for cancer immunotherapy

Monoclonal antibodies (Abs) that recognize major histocompatability complex (MHC)-presented tumor antigens in a manner similar to T cell receptors (TCRs) have great potential as cancer immunotherapeutics. However, isolation of ‘TCR-mimic’ (TCRm) Abs is laborious because Abs have not evolved the structurally nuanced peptide–MHC restriction of αβ-TCRs. Here, we present a strategy for rapid isolation of highly peptide-specific and ‘MHC-restricted’ Abs by re-engineering preselected Abs that engage peptide–MHC in a manner structurally similar to that of conventional αβ-TCRs. We created structure-based libraries focused on the peptide-interacting residues of TCRm Ab complementarity-determining region (CDR) loops, and rapidly generated MHC-restricted Abs to both mouse and human tumor antigens that specifically killed target cells when formatted as IgG, bispecific T cell engager (BiTE) and chimeric antigen receptor-T (CAR-T). Crystallographic analysis of one selected pMHC-restricted Ab revealed highly peptide-specific recognition, validating the engineering strategy. This approach can yield tumor antigen-specific antibodies in several weeks, potentially enabling rapid clinical translation.

Prediction of Peptide and TCR CDR3 Loops in Formation of Class I MHC-Peptide-TCR Complexes Using Molecular Models with Solvation.

Formation of major histocompatibility (MHC)-peptide-T cell receptor (TCR) complexes is central to initiation of an adaptive immune response. These complexes form through initial stabilization of the MHC fold via binding of a short peptide, and subsequent interaction of the TCR to form a ternary complex, with contacts made predominantly through the complementarity-determining region (CDR) loops of the TCR. Stimulation of an immune response is central to cancer immunotherapy. This approach depends on identification of the appropriate combinations of MHC molecules, peptides, and TCRs to elicit an antitumor immune response. This prediction is a current challenge in computational biochemistry. In this chapter, we introduce a predictive method that involves generation of multiple peptides and TCR CDR 3 loop conformations, solvation of these conformers in the context of the MHC-peptide-TCR ternary complex, extraction of parameters from the generated complexes, and use of an AI model to evaluate the potential for the assembled ternary complex to support an immune response.

Homology Modeling of Antibody Variable Regions: Methods and Applications.

Adaptive immunity specifically protects us from antigenic challenges. Antibodies are key effector proteins of adaptive immunity, and they are remarkable in their ability to recognize a virtually limitless number of antigens. Fragment variable (FV), the antigen-binding region of antibodies, can be split into two main components, namely, framework and complementarity determining regions. The framework (FR) consists of light-chain framework (FRL) and heavy-chain framework (FRH). Similarly, the complementarity determining regions (CDRs) comprises of light-chain CDRs 1-3 (CDRs L1-3) and heavy-chain CDRs 1-3 (CDRs H1-3). While FRs are relatively constant in sequence and structure across diverse antibodies, sequence variation in CDRs leading to differential conformations of CDR loops accounts for the distinct antigenic specificities of diverse antibodies. The conserved structural features in FRs and conformity of CDRs to a limited set of standard conformations allow for the accurate prediction of FV models using homology modeling techniques. Antibody structure prediction from its amino acid sequence has numerous important applications including prediction of antibody-antigen interaction interfaces and redesign of therapeutically and biotechnologically useful antibodies with improved affinity. This chapter summarizes the current practices employed in the successful homology modeling of antibody variable regions and the potential applications of the generated homology models.

Structural insights into engineering a T-cell receptor targeting MAGE-A10 with higher affinity and specificity for cancer immunotherapy

BackgroundT-cell receptor (TCR) immunotherapy is becoming a viable modality in cancer treatment with efficacy in clinical trials. The safety of patients is paramount, so innovative cell engineering methods are being...

Coevolved Canonical Loops Conformations of Single-Domain Antibodies: A Tale of Three Pockets Playing Musical Chairs.

Single-domain antibodies (sdAbs) are a promising class of biotherapeutics with unique structural traits within their paratope region. The distribution of canonical conformations explored by their complementarity determining region (CDR) loops differs to some extent from conventional two-chain Fv fragments of monoclonal antibodies (mAbs). In this study, we explored in detail the canonical structures of sdAb CDR-H1 and CDR-H2 loops and compared those with mAbs from the IGHV3 and IGHV1 gene families. We surveyed the antibody structures catalogued in SAbDab and clustered the CDR canonical loops in Cartesian space. While most of the sdAb clusters were sub-populations of previously defined canonical Fv conformations of CDR-H1 and CDR-H2, our stricter clustering approach defined narrower clusters in sequence-space. Meticulous visual inspection of sub-populations allowed a clearer understanding of sequence-structure relationships. The packing densities within structural pockets contacted by CDR-H1 and CDR-H2 canonical conformations were analyzed on the premise that these pockets cannot be left vacant as they would leave exposed supportive hydrophobic residues. The fine resolution of the canonical clusters defined here revealed unique signatures within these pockets, including distinct structural complementarities between CDR-H1 and CDR-H2 canonical clusters, which could not be perceived with the previous coarser clusters. We highlight examples where a single residue change in CDR-H1 sequence is sufficient to induce a dramatic population shift in CDR-H2 conformation. This suggests that preferences in combining CDR-H1 and CDR-H2 emerged naturally during antibody evolution, leading to preferred sets of conserved amino acids at key positions in the framework as well as within the CDR loops. We outline a game of musical chairs that is necessary to maintain the integrity of the antibody structures that arose during evolution. Our study also provides refined CDR-H1 and CDR-H2 structural templates for sdAb homology modeling that could be leveraged for improved antibody design.

ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation.

MotivationAntibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen-binding function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures.ResultsIn this work, we present ABlooper, an end-to-end equivariant deep learning-based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49 Å, which drops to 2.05 Å when considering only its 75% most confident predictions.Availability and implementation https://github.com/oxpig/ABlooper.Supplementary information Supplementary data are available at Bioinformatics online.

DMN Activity Pattern Under General Anesthesia Induced by Isoflurane is Dose-Dependent in Rats Brain

For the rational design of antibody, it is important to clarify the characteristics of the interaction between antigen and antibody. In this study, we evaluated a contribution of the respective complementarity determining region (CDR) loops on the antibody recognition of antigen by performing molecular dynamics simulations for 20 kinds of antigen-antibody complexes. Ser and Tyr showed high appearance rates at CDR loops and the sum of averaged appearance rates of Ser and Tyr was about 20⿿30% at all the loops. For example, Ser and Tyr occupied 23.9% at the light chain first loop (L1) and 23.6% at the heavy chain third loop (H3). The direct hydrogen bonds between antigen and antibody were not equally distributed over heavy and light chains. That is, about 70% of the hydrogen bonds were observed at CDRs of the heavy chain and also the direct hydrogen bond with the shortest distance mainly existed at the loops of the heavy chain for all the complexes. It was revealed from the comparison in contribution to the binding free energy among CDR loops that the heavy chain (especially at H2 and H3) had significant influence on the binding between antigen and antibody because three CDR loops of the heavy chain showed the lowest binding free energy (οGbind) in 19 complexes out of 20. Tyr in heavy chain (especially in H2 and H3) largely contributed to οGbind whereas Ser hardly contributed to οGbind even if the number of the direct hydrogen bond with Ser was the fourth largest and also the appearance rate at CDR was the highest among 20 kinds of amino acid residues. The contributions ofTrp and Phe, which bear aromatic ring in the side chain, were often observed in the heavy chain although the energetic contribution of these residues was not so high as Tyr. The present computational analysis suggests that Tyr plays an outstanding role for the antigen-antibody interaction and the CDR loops of the heavy chain is critically important for antibody recognition of antigen.

Insertion of atypical glycans into the tumor antigen-binding site identifies DLBCLs with distinct origin and behavior

AbstractGlycosylation of the surface immunoglobulin (Ig) variable region is a remarkable follicular lymphoma–associated feature rarely seen in normal B cells. Here, we define a subset of diffuse large B-cell lymphomas (DLBCLs) that acquire N-glycosylation sites selectively in the Ig complementarity-determining regions (CDRs) of the antigen-binding sites. Mass spectrometry and X-ray crystallography demonstrate how the inserted glycans are stalled at oligomannose-type structures because they are buried in the CDR loops. Acquisition of sites occurs in ∼50% of germinal-center B-cell–like DLBCL (GCB-DLBCL), mainly of the genetic EZB subtype, irrespective of IGHV-D-J use. This markedly contrasts with the activated B-cell–like DLBCL Ig, which rarely has sites in the CDR and does not seem to acquire oligomannose-type structures. Acquisition of CDR-located acceptor sites associates with mutations of epigenetic regulators and BCL2 translocations, indicating an origin shared with follicular lymphoma. Within the EZB subtype, these sites are associated with more rapid disease progression and with significant gene set enrichment of the B-cell receptor, PI3K/AKT/MTORC1 pathway, glucose metabolism, and MYC signaling pathways, particularly in the fraction devoid of MYC translocations. The oligomannose-type glycans on the lymphoma cells interact with the candidate lectin dendritic cell–specific intercellular adhesion molecule 3 grabbing non-integrin (DC-SIGN), mediating low-level signals, and lectin-expressing cells form clusters with lymphoma cells. Both clustering and signaling are inhibited by antibodies specifically targeting the DC-SIGN carbohydrate recognition domain. Oligomannosylation of the tumor Ig is a posttranslational modification that readily identifies a distinct GCB-DLBCL category with more aggressive clinical behavior, and it could be a potential precise therapeutic target via antibody-mediated inhibition of the tumor Ig interaction with DC-SIGN–expressing M2-polarized macrophages.

Germline-Dependent Antibody Paratope States and Pairing Specific VH-VL Interface Dynamics.

Antibodies have emerged as one of the fastest growing classes of biotherapeutic proteins. To improve the rational design of antibodies, we investigate the conformational diversity of 16 different germline combinations, which are composed of 4 different kappa light chains paired with 4 different heavy chains. In this study, we systematically show that different heavy and light chain pairings strongly influence the paratope, interdomain interaction patterns and the relative VH-VL interface orientations. We observe changes in conformational diversity and substantial population shifts of the complementarity determining region (CDR) loops, resulting in distinct dominant solution structures and differently favored canonical structures. Additionally, we identify conformational changes in the structural diversity of the CDR-H3 loop upon different heavy and light chain pairings, as well as upon changes in sequence and structure of the neighboring CDR loops, despite having an identical CDR-H3 loop amino acid sequence. These results can also be transferred to all CDR loops and to the relative VH-VL orientation, as certain paratope states favor distinct interface angle distributions. Furthermore, we directly compare the timescales of sidechain rearrangements with the well-described transition kinetics of conformational changes in the backbone of the CDR loops. We show that sidechain flexibilities are strongly affected by distinct heavy and light chain pairings and decipher germline-specific structural features co-determining stability. These findings reveal that all CDR loops are strongly correlated and that distinct heavy and light chain pairings can result in different paratope states in solution, defined by a characteristic combination of CDR loop conformations and VH-VL interface orientations. Thus, these results have broad implications in the field of antibody engineering, as they clearly show the importance of considering paired heavy and light chains to understand the antibody binding site, which is one of the key aspects in the design of therapeutics.

Structural Characterization of a Minimal Antibody against Human APOBEC3B.

APOBEC3B (A3B) is one of seven human APOBEC3 DNA cytosine deaminases that restrict viral infections as part of the overall innate immune response, but it also plays a major role in tumor evolution by mutating genomic DNA. Given the importance of A3B as a restriction factor of viral infections and as a driver of multiple human cancers, selective antibodies against A3B are highly desirable for its specific detection in various research and possibly diagnostic applications. Here, we describe a high-affinity minimal antibody, designated 5G7, obtained via a phage display screening against the C-terminal catalytic domain (ctd) of A3B. 5G7 also binds APOBEC3A that is highly homologous to A3Bctd but does not bind the catalytic domain of APOBEC3G, another Z1-type deaminase domain. The crystal structure of 5G7 shows a canonical arrangement of the heavy and light chain variable domains, with their complementarity-determining region (CDR) loops lining an antigen-binding cleft that accommodates a pair of α-helices. To understand the mechanism of A3Bctd recognition by 5G7, we used the crystal structures of A3Bctd and 5G7 as templates and computationally predicted the A3B-5G7 complex structure. Stable binding poses obtained by the simulation were further tested by site-directed mutagenesis and in vitro binding analyses. These studies mapped the epitope for 5G7 to a portion of C-terminal α6 helix of A3Bctd, with Arg374 playing an essential role. The same region of A3Bctd was used previously as a peptide antigen for generating a rabbit monoclonal antibody (mAb 5210-87-13), suggesting that this region is particularly immunogenic and that these antibodies from very different origins may share similar binding modes. Our studies provide a platform for the development of selective antibodies against A3B and other APOBEC3 family enzymes.

Mutation of Framework Residue H71 Results in Different Antibody Paratope States in Solution.

Characterizing and understanding the antibody binding interface have become a pre-requisite for rational antibody design and engineering. The antigen-binding site is formed by six hypervariable loops, known as the complementarity determining regions (CDRs) and by the relative interdomain orientation (VH–VL). Antibody CDR loops with a certain sequence have been thought to be limited to a single static canonical conformation determining their binding properties. However, it has been shown that antibodies exist as ensembles of multiple paratope states, which are defined by a characteristic combination of CDR loop conformations and interdomain orientations. In this study, we thermodynamically and kinetically characterize the prominent role of residue 71H (Chothia nomenclature), which does not only codetermine the canonical conformation of the CDR-H2 loop but also results in changes in conformational diversity and population shifts of the CDR-H1 and CDR-H3 loop. As all CDR loop movements are correlated, conformational rearrangements of the heavy chain CDR loops also induce conformational changes in the CDR-L1, CDR-L2, and CDR-L3 loop. These overall conformational changes of the CDR loops also influence the interface angle distributions, consequentially leading to different paratope states in solution. Thus, the type of residue of 71H, either an alanine or an arginine, not only influences the CDR-H2 loop ensembles, but co-determines the paratope states in solution. Characterization of the functional consequences of mutations of residue 71H on the paratope states and interface orientations has broad implications in the field of antibody engineering.

PreTCRs use horizontal docking geometry

Immunology The T cell receptor (TCR) recognizes peptide-bound major histocompatibility complex molecules (pMHCs) and consists of an α chain in association with a β chain. Both chains have hypervariable complementarity-determining regions (CDRs) that inform whether a particular TCR can recognize a given pMHC. To successfully graduate from the thymus, aspiring αβT cells must generate a functional TCR. During one early checkpoint in this process, the β chain is first paired with a preTβ chain to form the preTCR. Li et al. used x-ray crystallography to visualize how preTCRs recognize pMHCs. They report that the CDR3 loop of the preTCR β chain contacts the pMHC with a distinctive lateral topography. This is in contrast to the established binding modality of mature TCRs, whereby all three CDR loops on both α and β chains bind in a vertical orientation. These complexes help solve the mystery of how only functionally rearranged β chains using competent CDR3 loops can properly engage with pMHC at the preTCR stage. Science , this issue p. [181][1] [1]: /lookup/doi/10.1126/science.abe0918