Complementarity-determining Region Loops Research Articles

A general system for targeting MHC class II-antigen complex via a single adaptable loop.

Major histocompatibility complex class II (MHCII) bound to a peptide antigen mediates interactions between CD4+ T cells and antigen-presenting cells. Targeting peptide-MHCII with T cell antigen receptors (TCRs) and TCR-like antibodies has shown promise for autoimmune diseases and microbiome tolerance. To develop a general targeting approach, we introduce targeted recognition of antigen-MHC complex reporter for MHCII (TRACeR-II) for the rapid development of peptide-specific MHCII binders. TRACeR-II binders have a small helical bundle scaffold and use a single loop to recognize peptide-MHCII, which offers versatility and enables structural modeling of the interactions to target MHCII antigens. We demonstrate rapid generation of TRACeR-II binders to multiple molecules with affinities in the low-nanomolar to low-micromolar range, comparable to best-in-class TCRs and antibodies. Through computational protein design, we created specific binding sequences in silico from only the sequence of a severe acute respiratory syndrome coronavirus 2 peptide. TRACeR-II provides a straightforward approach to target antigen-MHCII without relying on combinatorial selection on complementarity-determining region loops.

AI-driven antibody design with generative diffusion models: current insights and future directions.

Therapeutic antibodies are at the forefront of biotherapeutics, valued for their high target specificity and binding affinity. Despite their potential, optimizing antibodies for superior efficacy presents significant challenges in both monetary and time costs. Recent strides in computational and artificial intelligence (AI), especially generative diffusion models, have begun to address these challenges, offering novel approaches for antibody design. This review delves into specific diffusion-based generative methodologies tailored for antibody design tasks, de novo antibody design, and optimization of complementarity-determining region (CDR) loops, along with their evaluation metrics. We aim to provide an exhaustive overview of this burgeoning field, making it an essential resource for leveraging diffusion-based generative models in antibody design endeavors.

Strengths and limitations of web servers for the modeling of TCRpMHC complexes

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

Identification and biophysical characterization of a novel domain-swapped camelid antibody specific for fentanyl

Opioid use disorders (OUD) and overdoses are ever-evolving public health threats that continue to grow in incidence and prevalence in the United States and abroad. Current treatments consist of opioid receptor agonists and antagonists, which are safe and effective but still suffer from some limitations. Murine and humanized monoclonal antibodies (mAb) have emerged as an alternative and complementary strategy to reverse and prevent opioid-induced respiratory depression. To explore antibody applications beyond traditional heavy-light chain mAbs, we identified and biophysically characterized a novel single-domain antibody specific for fentanyl from a camelid variable-heavy-heavy (VHH) domain phage display library. Structural data suggested that VHH binding to fentanyl was facilitated by a unique domain-swapped dimerization mechanism, which accompanied a rearrangement of complementarity-determining region loops leading to the formation of a fentanyl-binding pocket. Structure-guided mutagenesis further identified an amino acid substitution that improved the affinity and relaxed the requirement for dimerization of the VHH in fentanyl binding. Our studies demonstrate VHH engagement of an opioid and inform on how to further engineer a VHH for enhanced stability and efficacy, laying the groundwork for exploring the invivo applications of VHH-based biologics against OUD and overdose.

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.