CDR Loops Research Articles

Ab initio prediction and characterization of membrane binding of HIV-1 broadly neutralizing antibodies by multi-scale simulations.

Rare but potent broadly neutralizing antibodies (bnAbs) engage the HIV virion membrane and the membrane proximal exposed region (MPER) peptide during neutralization. A preliminary lipid mediated binding step is suggested to increase antigen avidity but it remains unclear how initial membrane association occurs and thus what features are essential to mature bnAbs against a lipid-antigen epitope. In this study, we derived computational methods to investigate biophysical features of MPER bnAbs driving membrane association. We performed microsecond atomistic simulations of Fabs docked in a lipid bilayer. Phospholipid binding in CDR loops is predicted in all 4 replicates of PGZL1, 4E10 and 10E8 where a phospholipid headgroup position closely matches the phosphate position in lipid-soaked x-ray crystallography structures (mean RMSD 0.6, 0.7, 2.7 Å respectively). Interaction profiles also highlight how framework regions FRH3 (PGZL1 and 4E10) or FRL3 (10E8) significantly associate with lipids. We then conducted fixed velocity pulling simulations for 4E10 and mutants with reduced affinity for lipid vesicles in vitro. Max force calculates differences in energy required to dissociate Fabs, establishing a bona fide tool to approximate membrane affinities. We expanded this platform to track evolution of binding affinity with inferred germline, intermediate, and mature PGZL1 variants that show increasing binding strengths. To reduce bias from pre-docked membrane conformations, we generated coarse grained models with a Fab initiated above a bilayer. Spontaneous membrane binding events (>1 µs) are detected in over half of the 18 independent 14 µs bnAb trajectories but only in 2 of 18 replicates for negative control BSA. Altogether we present a computational toolkit to biophysically evaluate residue-specific and surface level features of membrane binding antibodies and differentiate membrane affinities, facilitating future studies of complete MPER or other auto-reactive bnAb maturation pathways.

The CDR3 region as the major driver of TREM-1 interaction with its ligands, an in silico characterization

The triggering receptor expressed on myeloid cells-1 (TREM-1) is a pattern recognition receptor heavily investigated in infectious and non-infectious diseases. Because of its role in amplifying inflammation, TREM-1 has been explored as a diagnostic/prognostic biomarker. Further, as the receptor has been implicated in the pathophysiology of several diseases, therapies aiming at modulating its activity represent a promising strategy to constrain uncontrolled inflammatory or infectious diseases. Despite this, several aspects concerning its interaction with ligands and activation process, remain unclear. Although many molecules have been suggested as TREM-1 ligands, only five have been confirmed to interact with the receptor: actin, eCIRP, HMGB1, Hsp70 and PGLYRP1. However, the domains involved in the interaction between the receptor and these proteins are not clarified yet. Therefore, here we used in silico approaches to investigate the putative binding domains in the receptor, using hot spots analysis, molecular docking and molecular dynamics simulations between TREM-1 and its five known ligands. Our results indicated the complementarity-determining regions (CDRs) of the receptor as the main mediators of antigen recognition, especially the CDR3 loop. We believe that our study could be used as structural basis for the elucidation of TREM-1′s recognition process, and may be useful for prospective in silico and biological investigations exploring the receptor in different contexts.

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.

NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions.

T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BackgroundMonoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.ResultsHere, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance (mathrm{AUC}=0.840) with an ensemble learning of the top 3 best models.ConclusionThe prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

Biophysical analysis of TREM2 interactions reveals a shared binding site for Alzheimer’s disease ligands apoE and oligomeric amyloid beta

AbstractBackgroundThe development of new innovative treatments to prevent and ameliorate AD requires knowledge of molecular mechanisms that are critical to neuronal health. The triggering receptor expressed on myeloid cells 2 (TREM2) receptor is part of a signaling complex that modulates inflammatory responses, phagocytosis and cell survival in microglia, resident immune cells in the brain that play a critical role in clearing misfolded aggregates. Examples include neurotoxic aggregates consisting largely of amyloid beta (Ab) and apoliproteinE (apoE). Both molecules have emerged as important signaling ligands for TREM2. Although TREM2 signaling in microglia is generally associated with beneficial outcomes, intense or prolonged signaling may produce overactivated microglia leading to neuronal damage. Such events may also contribute to other diseases such as Parkinson’s or cancers. Furthermore, rare TREM2 variants, most notably R47H and R62H, have been identified that are associated with a significantly increased risk of developing AD. Given these significant roles, TREM2 has emerged as an important yet challenging therapeutic target for AD. Although a number of ligands for TREM2 with relevance to AD have been identified, little is known regarding the molecular details of how TREM2 engages them. Detailed knowledge of the molecular mechanisms underlying TREM2 signaling in microglia is urgently needed to facilitate the development of specific, potent, safe and efficacious therapies for AD that target the TREM2 signaling pathway. The objective of this study was to identify the critical surfaces on TREM2 that mediate interactions with both apolipoproteinE (apoE) and oligomeric amyloid b1‐42 (oAb42) using rigorous structural and biophysical methods.MethodWe used structure‐based mutations and comprehensive biolayer interferometry (BLI) analysis to investigate TREM2 interactions with apoE and oAb42.ResultWe found that TREM2 utilizes a distal hydrophobic surface, consisting of the CDR1, CDR2, and CDR3 loops, to engage apoE. Surprisingly, we found that this same surface is utilized to engage oAb42.ConclusionOur results indicate that the hydrophobic site on TREM2 (consisting of the CDR1, CDR2, and CDR3 loops) is the critical ligand‐engaging surface on TREM2. They also indicate that therapeutics that either directly bind or allosterically alter the hydrophobic site on TREM2 should modulate TREM2 signaling as potential AD treatments.

Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops).

Antibodies and T-cell receptors have been a subject of much interest due to their central role in the immune system and their potential applications in several biotechnological and medical applications from cancer therapy to vaccine development. A unique feature of these two lymphocyte receptors is their ability to bind a huge variety of different (pathogen) targets. This ability stems from six short loops in the binding domain that have hypervariable sequence due to genetic recombination mechanism. Particularly one of these loops, the third complementarity determining region (CDR3), has the highest sequence variability and a dominant role in binding the target. However, it has also been proven the most difficult to be modeled structurally, which is vitally important for downstream tasks such as binding prediction. This difficulty stems from its variability in sequence that both reduces the possibility of finding homologues and introduces unique structural features in the loop. We present here a general protocol for modeling such loops in antibodies and T-cell receptors. We also discuss the difficulties in loop modeling and the advantages and limitations of different modeling methods.

Hallucinating structure-conditioned antibody libraries for target-specific binders.

Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the FvHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.

A potent synthetic nanobody with broad-spectrum activity neutralizes SARS-CoV-2 virus and the Omicron variant BA.1 through a unique binding mode

The major challenge to controlling the COVID pandemic is the rapid mutation rate of the SARS-CoV-2 virus, leading to the escape of the protection of vaccines and most of the neutralizing antibodies to date. Thus, it is essential to develop neutralizing antibodies with broad-spectrum activity targeting multiple SARS-CoV-2 variants. Here, we report a synthetic nanobody (named C5G2) obtained by phage display and subsequent antibody engineering. C5G2 has a single-digit nanomolar binding affinity to the RBD domain and inhibits its binding to ACE2 with an IC50 of 3.7 nM. Pseudovirus assays indicated that monovalent C5G2 could protect the cells from infection with SARS-CoV-2 wild-type virus and most of the viruses of concern, i.e., Alpha, Beta, Gamma and Omicron variants. Strikingly, C5G2 has the highest potency against Omicron BA.1 among all the variants, with an IC50 of 4.9 ng/mL. The cryo-EM structure of C5G2 in complex with the spike trimer showed that C5G2 binds to RBD mainly through its CDR3 at a conserved region that does not overlap with the ACE2 binding surface. Additionally, C5G2 binds simultaneously to the neighboring NTD domain of the spike trimer through the same CDR3 loop, which may further increase its potency against viral infection. Third, the steric hindrance caused by FR2 of C5G2 could inhibit the binding of ACE2 to RBD as well. Thus, this triple-function nanobody may serve as an effective drug for prophylaxis and therapy against Omicron as well as future variants.

Engagement with the TCR induces plasticity in antigenic ligands bound to MHC class I and CD1 molecules.

Complementarity-determining regions (CDRs) of αβ T-cell receptors (TCRs) sense peptide-bound MHC (pMHC) complexes via chemical interactions, thereby mediating antigen specificity and MHC restriction. Flexible finger-like movement of CDR loops contributes to the establishment of optimal interactions with pMHCs. In contrast, peptide ligands captured in MHC molecules are considered more static because of the rigid hydrogen-bond network that stabilizes peptide ligands in the antigen-binding groove of MHC molecules. An array of crystal structures delineating pMHC complexes in TCR-docked and TCR-undocked forms is now available, which enables us to assess TCR engagement-induced conformational changes in peptide ligands. In this short review, we overview conformational changes in MHC class I-bound peptide ligands upon TCR docking, followed by those for CD1-bound glycolipid ligands. Finally, we analyze the co-crystal structure of the TCR:lipopeptide-bound MHC class I complex that we recently reported. We argue that TCR engagement-induced conformational changes markedly occur in lipopeptide ligands, which are essential for exposure of a primary T-cell epitope to TCRs. These conformational changes are affected by amino acid residues, such as glycine, that do not interact directly with TCRs. Thus, ligand recognition by specific TCRs involves not only T-cell epitopes but also non-epitopic amino acid residues. In light of their critical function, we propose to refer to these residues as non-epitopic residues affecting ligand plasticity and antigenicity (NR-PA).

NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.

Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing need for high-throughput epitope mapping based on accurate structural modeling of the variable domains that share a common fold and differ in the Complementarity Determining Regions (CDRs). We develop a deep learning end-to-end model, NanoNet, that given a sequence directly produces the 3D coordinates of the backbone and Cβ atoms of the entire VH domain. For the Nb test set, NanoNet achieves 3.16Å average RMSD for the most variable CDR3 loops and 2.65Å, 1.73Å for the CDR1, CDR2 loops, respectively. The accuracy for antibody VH domains is even higher: 2.38Å RMSD for CDR3 and 0.89Å, 0.96Å for the CDR1, CDR2 loops, respectively. NanoNet run times allow generation of ∼1M nanobody structures in less than 4 hours on a standard CPU computer enabling high-throughput structure modeling. NanoNet is available at GitHub: https://github.com/dina-lab3D/NanoNet

Peptide Centric Vβ Specific Germline Contacts Shape a Specialist T Cell Response.

Certain CD8 T cell responses are particularly effective at controlling infection, as exemplified by elite control of HIV in individuals harboring HLA-B57. To understand the structural features that contribute to CD8 T cell elite control, we focused on a strongly protective CD8 T cell response directed against a parasite-derived peptide (HF10) presented by an atypical MHC-I molecule, H-2Ld. This response exhibits a focused TCR repertoire dominated by Vβ2, and a representative TCR (TG6) in complex with Ld-HF10 reveals an unusual structure in which both MHC and TCR contribute extensively to peptide specificity, along with a parallel footprint of TCR on its pMHC ligand. The parallel footprint is a common feature of Vβ2-containing TCRs and correlates with an unusual Vα-Vβ interface, CDR loop conformations, and Vβ2-specific germline contacts with peptides. Vβ2 and Ld may represent “specialist” components for antigen recognition that allows for particularly strong and focused T cell responses.

Structures of antibodies and nanobodies in complex with spike/RBD: the vital role of CDR loops in capturing epitopes

Structures of antibodies and nanobodies in complex with spike/RBD: the vital role of CDR loops in capturing epitopes

Structural insights into the binding of nanobody Rh57 to active RhoA-GTP

RhoA protein is a small GTPase that acts as a molecular switch. When bound to guanosine triphosphate (GTP), RhoA can activate several key signal pathways. Recently, nanobody Rh57 specific binding with GTP bound active RhoA was discovered and developed as a BRET biosensor without cytotoxicity. To further clarify the nanobody Rh57's mechanism of action, we co-expressed, purified, and crystallized the RhoA-Rh57 nanobody complex and solved the structure by X-ray diffraction with a resolution of 2.76 Å. The structure showed that the interaction is mainly through hydrogen bonds, salt bridges, aromatic-aromatic interactions, and hydrophobic interactions. The involved regions include CDR3 and non-hypervariable loop of Rh57, and the SWI switch loops of RhoA, respectively. The different SWI conformation of inactivated RhoA-GDP prevented the Rh57's binding. The possible explanation of Rh57 as a non-cytotoxic BRET intracellular tracer is that Rh57's binding did not overlap with downstream PRK1 and thus did not interfere with the downstream signaling pathway. Our research provides an in-depth understanding of how nanobodies recognize activated RhoA-GTP while not binding inactivated RhoA-GDP. This structural information may also provide critical information for further optimization of relevant nanobodies.

Differential performance of RoseTTAFold in antibody modeling.

Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody-antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling.

Nanobody Paratope Ensembles in Solution Characterized by MD Simulations and NMR.

Variable domains of camelid antibodies (so-called nanobodies or VHH) are the smallest antibody fragments that retain complete functionality and therapeutic potential. Understanding of the nanobody-binding interface has become a pre-requisite for rational antibody design and engineering. The nanobody-binding interface consists of up to three hypervariable loops, known as the CDR loops. Here, we structurally and dynamically characterize the conformational diversity of an anti-GFP-binding nanobody by using molecular dynamics simulations in combination with experimentally derived data from nuclear magnetic resonance (NMR) spectroscopy. The NMR data contain both structural and dynamic information resolved at various timescales, which allows an assessment of the quality of protein MD simulations. Thus, in this study, we compared the ensembles for the anti-GFP-binding nanobody obtained from MD simulations with results from NMR. We find excellent agreement of the NOE-derived distance maps obtained from NMR and MD simulations and observe similar conformational spaces for the simulations with and without NOE time-averaged restraints. We also compare the measured and calculated order parameters and find generally good agreement for the motions observed in the ps–ns timescale, in particular for the CDR3 loop. Understanding of the CDR3 loop dynamics is especially critical for nanobodies, as this loop is typically critical for antigen recognition.

Dromedary camel nanobodies broadly neutralize SARS-CoV-2 variants

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike is a trimer of S1/S2 heterodimers with three receptor-binding domains (RBDs) at the S1 subunit for human angiotensin-converting enzyme 2 (hACE2). Due to their small size, nanobodies can recognize protein cavities that are not accessible to conventional antibodies. To isolate high-affinity nanobodies, large libraries with great diversity are highly desirable. Dromedary camels (Camelus dromedarius) are natural reservoirs of coronaviruses like Middle East respiratory syndrome CoV (MERS-CoV) that are transmitted to humans. Here, we built large dromedary camel VHH phage libraries to isolate nanobodies that broadly neutralize SARS-CoV-2 variants. We isolated two VHH nanobodies, NCI-CoV-7A3 (7A3) and NCI-CoV-8A2 (8A2), which have a high affinity for the RBD via targeting nonoverlapping epitopes and show broad neutralization activity against SARS-CoV-2 and its emerging variants of concern. Cryoelectron microscopy (cryo-EM) complex structures revealed that 8A2 binds the RBD in its up mode with a long CDR3 loop directly involved in the ACE2 binding residues and that 7A3 targets a deeply buried region that uniquely extends from the S1 subunit to the apex of the S2 subunit regardless of the conformational state of the RBD. At a dose of ≥5 mg/kg, 7A3 efficiently protected transgenic mice expressing hACE2 from the lethal challenge of variants B.1.351 or B.1.617.2, suggesting its therapeutic use against COVID-19 variants. The dromedary camel VHH phage libraries could be helpful as a unique platform ready for quickly isolating potent nanobodies against future emerging viruses.

Structural arrangement of the VH and VL domains in the COBRA™ T-cell engaging single-chain diabody.

COBRA™ (COnditional Bispecific Redirected Activation) T-cell engagers are designed to target solid tumors as a single polypeptide chain prodrug that becomes activated by proteolysis in the tumor microenvironment. One COBRA molecule comprises seven Ig domains: three single-domain antibodies (sdAbs) recognizing a tumor target or human serum albumin (HSA), and CD3ε-binding variable fragment heavy chain (VH) and variable fragment light chain (VL) and their inactivated counterparts, VHi and VLi. Pairing of VH and VL, and VLi and VHi into single-chain variable fragments (Fv) is prevented by shortened inter-domain linkers. Instead, VH and VL are expected to interact with VLi and VHi, respectively, thus making a diabody whose binding to CD3ε on the T-cells is impaired. We analyzed the structure of an epidermal growth factor receptor (EGFR) COBRA in solution using negative stain electron microscopy (EM) and small-angle X-ray scattering (SAXS). We found that this EGFR COBRA forms stable monomers with a very dynamic interdomain arrangement. At most, only five domains at a time appeared ordered, and only one VH-VL pair was found in the Fv orientation. Nonenzymatic posttranslational modifications suggest that the CDR3 loops in the VL-VHi pair are exposed but are buried in the VH-VLi pair. The MMP9 cleavage rate of the prodrug when bound to recombinant EGFR or HSA is not affected, indicating positioning of the MMP9-cleavable linker away from the EGFR and HSA binding sites. Here, we propose a model for EGFR COBRA where VH and VLi form an Fv, and VL and VHi do not, possibly interacting with other Ig domains. SAXS and MMP9 cleavage analyses suggest that all COBRA molecules tested have a similar structural architecture.

The eptinezumab:CGRP complex structure – the role of conformational changes in binding stabilization

ABSTRACT To further elucidate the mechanism of action and binding properties of eptinezumab to calcitonin gene-related peptide (CGRP), X-ray crystallography, computational alanine scanning, and molecular dynamics were used. X-ray diffraction data were collected to determine the three-dimensional structures of the unbound eptinezumab antigen-binding fragment (Fab) and the Fab:CGRP complex. Molecular dynamics simulations were performed to analyze the transition between uncomplexed and complex states. The amidated C-terminus of CGRP was shown to bind in a pocket formed by the Fab heavy and light chains. There was extensive contact between all six complementarity-determining regions (CDRs; composed of light-chain [L1, L2, and L3] and heavy-chain [H1, H2, H3]) of eptinezumab and CGRP. The complex demonstrated a high ligand-binding surface area dominated by aromatic residues. CDR L3 contains a disulfide bond that stabilizes the loop, contributes surface area to the binding pocket, and provides van der Waals contacts. Comparison of the uncomplexed and complex structures revealed motion near the binding cleft. The CDR loops H2 and H3 were displaced ~1.4–2.0 Å and residue H-Tyr33 changed conformation, creating a ‘latch-and-lock’ mechanism for binding CGRP and preventing dissociation. Computational alanine scanning of CGRP identified energetic ‘hot spots’ that contribute to binding energy; mutating these positions to residues in homologous neuropeptides resulted in unfavorable binding energies. The attributes of the Fab region and the conformational changes that occur in eptinezumab during binding to CGRP contribute to the specificity, durability, and strength of the interaction, and likely underlie the rapid and sustained migraine preventive effect observed in clinical studies.

Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, https://doi.org/10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.