Antibody Complementarity-determining Regions Research Articles

An Integrated Strategy to Identify Tyrosine Sulfation from the Therapeutic Proteins.

Posttranslational modifications (PTMs) are potential critical quality attributes in biotherapeutic development, as they can affect drug efficacy and safety. Tyrosine sulfation plays a critical role in protein-protein interactions and has been found on many surface receptors as well as antibody complementarity-determining regions (CDR). However, the presence and function of tyrosine sulfation in therapeutic proteins have not been broadly investigated due to difficulties in detecting the modification. Here, we establish an integrated strategy to identify tyrosine sulfation in biotherapeutic proteins. In silico prediction was used to estimate possible modification sites, followed by the elucidation with intact LCMS and native SCX-MS. The combination of these three steps takes less than 1 h, which provides quick and confident preliminary detection of potential CQAs. Taking NB1 as an example, three +80 Da mass shifts were observed from intact mass analysis and three acidic peaks were monitored by SCX, allowing confirmation of modification as either phosphorylation or sulfation. Peptide mapping, Fe3+-IMAC enrichment, and dephosphorylation were further conducted to provide improved signal intensity and differentiation of modification such as sulfation or phosphorylation. With this integrated strategy, we were able to identify for the first time both tyrosine sulfation and serine phosphorylation in one therapeutic protein.

Humanization of Pan-HLA-DR mAb 44H10 Hinges on Critical Residues in the Antibody Framework.

Reducing the immunogenicity of animal-derived monoclonal antibodies (mAbs) for use in humans is critical to maximize therapeutic effectiveness and preclude potential adverse events. While traditional humanization methods have primarily focused on grafting antibody Complementarity-Determining Regions (CDRs) on homologous human antibody scaffolds, framework regions can also play essential roles in antigen binding. Here, we describe the humanization of the pan-HLA-DR mAb 44H10, a murine antibody displaying significant involvement of the framework region in antigen binding. Using a structure-guided approach, we identify and restore framework residues that directly interact with the antigen or indirectly modulate antigen binding by shaping the antibody paratope and engineer a humanized antibody with affinity, biophysical profile, and molecular binding basis comparable to that of the parental 44H10 mAb. As a humanized molecule, this antibody holds promise as a scaffold for the development of MHC class II-targeting therapeutics and vaccines.

Cross-Gate MLP with Protein Complex Invariant Embedding Is a One-Shot Antibody Designer

Antibodies are crucial proteins produced by the immune system in response to foreign substances or antigens. The specificity of an antibody is determined by its complementarity-determining regions (CDRs), which are located in the variable domains of the antibody chains and form the antigen-binding site. Previous studies have utilized complex techniques to generate CDRs, but they suffer from inadequate geometric modeling. Moreover, the common iterative refinement strategies lead to an inefficient inference. In this paper, we propose a simple yet effective model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner. To achieve this, we decouple the antibody CDR design problem into two stages: (i) geometric modeling of protein complex structures and (ii) sequence-structure co-learning. We develop a novel macromolecular structure invariant embedding, typically for protein complexes, that captures both intra- and inter-component interactions among the backbone atoms, including Calpha, N, C, and O atoms, to achieve comprehensive geometric modeling. Then, we introduce a simple cross-gate MLP for sequence-structure co-learning, allowing sequence and structure representations to implicitly refine each other. This enables our model to design desired sequences and structures in a one-shot manner. Extensive experiments are conducted to evaluate our results at both the sequence and structure level, which demonstrate that our model achieves superior performance compared to the state-of-the-art antibody CDR design methods.

Overcoming Incomplete Peptide Mapping of Antibody Complementarity-Determining Regions with Alternate Digestion Workflows

Peptide mapping of antibodies is an essential method to monitor peptide modifications in antibody lots that could affect the safety and efficacy of the product. Conventional protocols rely on protein digestion using proteases, such as trypsin, before mapping with mass spectrometry (MS). However, trypsin digestion may cause incomplete mapping of peptides, especially those that include highly hydrophobic peptides. Here, we show how pepsin can be used as an alternative and complementary protease for digestion that allows for improved sequence coverage, especially in proteins with highly hydrophobic regions. We also show that using guanidine hydrochloride (GuHCl) post-digestion improves peptide mapping results. Overall, these two methods—pepsin digestion and GuHCl post-digestion—can be used to provide more comprehensive antibody peptide maps, thereby enabling more thorough quality checking of biopharmaceutical products.

Antibody CDR amino acids underlying the functionality of antibody repertoires in recognizing diverse protein antigens

Antibodies recognize protein antigens with exquisite specificity in a complex aqueous environment, where interfacial waters are an integral part of the antibody–protein complex interfaces. In this work, we elucidate, with computational analyses, the principles governing the antibodies’ specificity and affinity towards their cognate protein antigens in the presence of explicit interfacial waters. Experimentally, in four model antibody–protein complexes, we compared the contributions of the interaction types in antibody–protein antigen complex interfaces with the antibody variants selected from phage-displayed synthetic antibody libraries. Evidently, the specific interactions involving a subset of aromatic CDR (complementarity determining region) residues largely form the predominant determinant underlying the specificity of the antibody–protein complexes in nature. The interfacial direct/water-mediated hydrogen bonds accompanying the CDR aromatic interactions are optimized locally but contribute little in determining the epitope location. The results provide insights into the phenomenon that natural antibodies with limited sequence and structural variations in an antibody repertoire can recognize seemingly unlimited protein antigens. Our work suggests guidelines in designing functional artificial antibody repertoires with practical applications in developing novel antibody-based therapeutics and diagnostics for treating and preventing human diseases.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Low pKa of Lys promotes glycation at one complementarity-determining region of a bispecific antibody

Protein glycation is a common, normally innocuous, post-translational modification in therapeutic monoclonal antibodies. However, when glycation occurs on complementarity-determining regions (CDRs) of a therapeutic monoclonal antibody, its biological activities (e.g., potency) may be impacted. Here, we present a comprehensive approach to understanding the mechanism of protein glycation using a bispecific antibody. Cation exchange chromatography and liquid chromatography-mass spectrometry were used to characterize glycation at a lysine residue within a heavy chain (HC) CDR (HC-CDR3-Lys98) of a bispecific antibody. Thermodynamic analysis revealed that this reaction is reversible and can occur under physiological conditions with an apparent affinity of 8–10 mM for a glucose binding to HC-CDR3-Lys98. Results from kinetic analysis demonstrated that this reaction follows Arrhenius behavior in the temperature range of 5°C–45°C and can be well predicted in vitro and in a non-human primate. In addition, this glycation reaction was found to be driven by an unusually low pKa on the ε-amino group of HC-CDR3-Lys98. Van't Hoff analysis and homology modeling suggested that this reaction is enthalpically driven, with this lysine residue surrounded by a microenvironment with low polarity. This study provides, to our knowledge, new insights toward a mechanistic understanding of protein glycation and strategies to mitigate the impact of protein glycation during pharmaceutical development.

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.

A stepwise mutagenesis approach using histidine and acidic amino acid to engineer highly pH-dependent protein switches.

Antibody-based drugs can be highly toxic, because they target normal tissue as well as tumor tissue. The pH value of the extracellular microenvironments around tumor tissues is lower than that of normal tissues. Therefore, antibodies that engage in pH-dependent binding at slightly acidic pH are crucial for improving the safety of antibody-based drugs. Thus, we implemented a stepwise mutagenesis approach to engineering pH-dependent antibodies capable of selective binding in the acidic microenvironment in this study. The first step involved single-residue histidine scanning mutagenesis of the antibody's complementarity-determining regions to prescreen for pH-dependent mutants and identify ionizable sensitive hot-spot residues that could be substituted by acidic amino acids to obtain pH-dependent antibodies. The second step involved single-acidic amino acid residue substitutions of the identified residues and the assessment of pH-dependent binding. We identified six ionizable sensitive hot-spot residues using single-histidine scanning mutagenesis. Nine pH-dependent antibodies were isolated using single-acidic amino acid residue mutagenesis at the six hot-spot residue positions. Relative to wild-type anti-CEA chimera antibody, the binding selectivity of the best performing mutant was improved by approximately 32-fold according to ELISA and by tenfold according to FACS assay. The mutant had a high affinity in the pH range of 5.5–6.0. This study supports the development of pH-dependent protein switches and increases our understanding of the role of ionizable residues in protein interfaces. The stepwise mutagenesis approach is rapid, general, and robust and is expected to produce pH-sensitive protein affinity reagents for various applications.

Secondary Structure and Conformational Stability of the Antigen Residues Making Contact with Antibodies.

Antibodies are crucial biomolecules that bring high therapeutic efficacy in medicine and accurate molecular detection in diagnosis. Many studies have been devoted to analyzing the antigen-antibody interaction from the importance of understanding the antibody recognition mechanism. However, most of the previous studies examined the characteristic of the antibody for interaction. It is also informative to clarify the significant antigen residues contributing to the binding. To characterize the molecular interaction of antigens, we computationally analyzed 350 antigen-antibody complex structures by molecular mechanics (MM) calculations and molecular dynamics (MD) simulations. Based on the MM calculations, the antigen residues contributing to the binding were extracted from all the 350 complexes. The extracted residues are located at the antigen-antibody interface and are responsible for making contact with the antibody. The appearances of the charged polar residues, Asp, Glu, Arg, and Lys, were noticeably large. In contrast, the populations of the hydrophobic residues, Leu, Val, and Ala, were relatively low. The appearance frequencies of the other amino acid residues were almost close to the abundance of general proteins of eukaryotes. The binding score indicated that the hydrophilic interaction was dominant at the antigen-antibody contact instead of the hydrophobic one. The positively charged residues, Arg and Lys, remarkably contributed to the binding compared to the negatively charged ones, Asp and Glu. Considerable contributions were also observed for the noncharged polar residues, Asn and Gln. The analysis of the secondary structures of the extracted antigen residues suggested that there was no marked difference in recognition by antibodies among helix, sheet, turn, and coil. A long helix of the antigen sometimes made contact with antibody complementarity-determining regions, and a large sheet also frequently covered the antibody heavy and light chains. The turn structure was the most popularly observed at the contact with antibody among 350 complexes. Three typical complexes were picked up for each of the four secondary structures. MD simulations were performed to examine the stability of the interfacial structures of the antigens for these 12 complex models. The alterations of secondary structures were monitored through the simulations. The structural fluctuations of the contact residues were low compared with the other domains of antigen molecules. No drastic conversion was observed for every model during the 100 ns simulation. The motions of the interfacial antigen residues were small compared to the other residues on the protein surface. Therefore, diverse molecular conformations are possible for antibody recognition as long as the target areas are polar, nonflexible, and protruding on the protein surface.

Short Read-Length Next Generation DNA Sequencing of Antibody CDR Combinations from Phage Selection Outputs.

Phage display is commonly used to select target-binding antibody fragments from large libraries containing billions of unique antibody clones. In practice, selection outputs are often highly heterogenous, making it desirable to recover sequence information from the selected pool. Next Generation DNA Sequencing (NGS) enables the acquisition of sufficient sequencing reads to cover the pool diversity, however read-lengths are typically too short to capture paired antibody complementarity-determining regions (CDRs), which is needed to reconstruct target-binding antibody fragments. Here, we describe a simple in vitro protocol to bring the DNA encoding the antibody CDRs closer together. The final PCR product referred to as a "CDR strip" is suitable for short read-length NGS. In this method, phagemid ssDNA is recovered from antibody phage display biopanning and used as a template to create a heteroduplex with deletions between CDRs of interest. The shorter strand in the heteroduplex is preferentially PCR amplified to generate a CDR strip that is sequenced using NGS. We have also included a bioinformatics approach to analyze the CDR strip populations so that single antibody clones can be created from paired CDR sequences.

Generation and Characterization of Torudokimab (LY3375880): A Monoclonal Antibody That Neutralizes Interleukin-33.

BackgroundInterleukin-33 (IL-33) is an alarmin that is released following cellular damage, mechanical injury, or necrosis. It is a member of the IL-1 family and binds to a heterodimer receptor consisting of ST2 and IL-1RAP to induce the production of a wide range of cellular mediators, including the type 2 cytokines IL-4, IL-5 and IL-13. This relationship has led to the hypothesis that the IL-33/ST2 pathway is a driver of allergic disease and inhibition of the IL-33 and ST2 association could have therapeutic benefit.MethodsIn this paper, we describe the selection of a phage antibody through the ability to bind human IL-33 and block IL-33/ST2 interaction. This hit antibody was then affinity matured by site-directed mutagenesis of the antibody complementarity-determining regions (CDRs). Further characterization of a fully human monoclonal antibody (mAb), torudokimab (LY3375880) included demonstration of human IL-33 neutralization activity in vitro with an NFκB reporter assay and IL-33 induced mast cell cytokine secretion assay, followed by an in vivo IL-33-induced pharmacodynamic inhibition assay in mice that used IL-5 production as the endpoint.ResultsTorudokimab is highly specific to IL-33 and does not bind any of the other IL-1 family members. Furthermore, torudokimab binds human and cynomolgus monkey IL-33 with higher affinity than the binding affinity of IL-33 to ST2, but does not bind mouse, rat, or rabbit IL-33. Torudokimab’s half-life in cynomolgous monkey projects monthly dosing in the clinic.ConclusionDue to torudokimab’s high affinity, its ability to completely neutralize IL-33 activity in vitro and in vivo, and the observed cynomolgus monkey pharmacokinetic properties, this molecule was selected for clinical development.

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

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

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.

Anticancer Nanometre-Biomissiles Carrying a Leucine Insulator and an Antibody Complementarity-Determining Region

The tumour specificities and potency of miniaturized cancer targeting peptides are among the major issues of current cancer research. Over 100 peptides were evaluated in this study. According to the observations, leucine could sustain the antibacterial and anticancer efficacy of (leucine/lysine)n ((L/K)n) peptides across different assay conditions, whereas (valine/lysine)n ((V/K)n) and (isoleucine/lysine)n ((I/K)n) peptides were less effective. Tumour targeting peptides, consisting of a leucine/lysine peptide and an antibody complementarity-determining region (CDR) fragment against CD47 receptor separated by a leucine 4 insulator, eradicated 100% of the lung cancer A549 cells at 100 μm concentration after 24 h of incubation. It also inhibited tumour growth in a mouse model of colon cancer cells, showing extensive apoptosis at the end of the treatment. Our data suggest that leucine insulators could be effective spacers in the design of targeted or plurispecific peptide drugs given the lack of σ –σ hyperconjugation on β -carbon and the lack of strong van der Waals interactions between the side group and the carbonyl group. Significant reduction of hemolysis by P45-3 with terminal alanine replacement on peptide P129 suggests that biosafety attribute of a peptide can be substantially improved.

Transitions of CDR-L3 Loop Canonical Cluster Conformations on the Micro-to-Millisecond Timescale.

Sequence and structural diversity of antibodies are concentrated on six hypervariable loops, also known as the complementarity determining regions (CDRs). Five of six antibody CDR loops presumably adopt a so-called canonical structure out of a limited number of conformations. However, here we show for four antibody CDR-L3 loops differing in length and sequence, that each loop undergoes conformational transitions between different canonical structures. By extensive sampling in combination with Markov-state models we reconstruct the kinetics and probabilities of the transitions between canonical structures. Additionally, for these four CDR-L3 loops, we identify all relevant conformations in solution. Thereby we extend the model of static canonical structures to a dynamic conformational ensemble as a new paradigm in the field of antibody structure design.

Comparative Analysis of the CDR Loops of Antigen Receptors.

The adaptive immune system uses two main types of antigen receptors: T-cell receptors (TCRs) and antibodies. While both proteins share a globally similar β-sandwich architecture, TCRs are specialized to recognize peptide antigens in the binding groove of the major histocompatibility complex, while antibodies can bind an almost infinite range of molecules. For both proteins, the main determinants of target recognition are the complementarity-determining region (CDR) loops. Five of the six CDRs adopt a limited number of backbone conformations, known as the “canonical classes”; the remaining CDR (β3in TCRs and H3 in antibodies) is more structurally diverse. In this paper, we first update the definition of canonical forms in TCRs, build an auto-updating sequence-based prediction tool (available at http://opig.stats.ox.ac.uk/resources) and demonstrate its application on large scale sequencing studies. Given the global similarity of TCRs and antibodies, we then examine the structural similarity of their CDRs. We find that TCR and antibody CDRs tend to have different length distributions, and where they have similar lengths, they mostly occupy distinct structural spaces. In the rare cases where we found structural similarity, the underlying sequence patterns for the TCR and antibody version are different. Finally, where multiple structures have been solved for the same CDR sequence, the structural variability in TCR loops is higher than that in antibodies, suggesting TCR CDRs are more flexible. These structural differences between TCR and antibody CDRs may be important to their different biological functions.

Back-to-Germline (B2G) Procedure for Antibody Devolution.

Bispecific antibodies (bsAbs) with avidity-enhanced specificity can be used to address target cells with increased specificity, ideally binding efficiently to cells that express two cognate antigens, yet not to cells that express only one of those. Building blocks required to generate such bsAbs are binders that recognize the two antigens with high specificity yet with various (including very low monovalent) affinities. The herein described ‘back-to-germline’ (B2G) procedure defines such derivatives. It converts parent antibodies with high specificity to derivatives that retain specificity but modulate affinity. The approach defines mutations to be introduced into antibody complementarity-determining regions (CDRs) regions without requiring structures of antibody-antigen complexes. Instead, it reverses the B-cell maturation process that increases affinities, with preference on CDR residues with high antigen contact probability. Placing germline residues at those positions generates VH and VL domains and Fv-combinations thereof that retain specificities but are ‘de-matured’ to different degrees. De-maturation influences on-rates and off-rates, and can produce entities with extremely low affinity for which binding can only be detected in bivalent formats. A comparison with alanine replacement in CDRs (so far, the most frequently applied technology) indicates that B2G may be more reliable/predictable without introduction of stickiness or poly-reactivity. The applicability for generating sets of affinity-modulated monospecific variants is exemplarily shown for antibodies that bind CD138, Her2/neu, and EGFR.

Net charge of antibody complementarity-determining regions is a key predictor of specificity.

Specificity is one of the most important and complex properties that is central to both natural antibody function and therapeutic antibody efficacy. However, it has proven extremely challenging to define robust guidelines for predicting antibody specificity. Here we evaluated the physicochemical determinants of antibody specificity for multiple panels of antibodies, including >100 clinical-stage antibodies. Surprisingly, we find that the theoretical net charge of the complementarity-determining regions (CDRs) is a strong predictor of antibody specificity. Antibodies with positively charged CDRs have a much higher risk of low specificity than antibodies with negatively charged CDRs. Moreover, the charge of the entire set of six CDRs is a much better predictor of antibody specificity than the charge of individual CDRs, variable domains (VH or VL) or the entire variable fragment (Fv). The best indicators of antibody specificity in terms of CDR amino acid composition are reduced levels of arginine and lysine and increased levels of aspartic and glutamic acid. Interestingly, clinical-stage antibodies with negatively charged CDRs also have a lower risk for poor biophysical properties in general, including a reduced risk for high levels of self-association. These findings provide powerful guidelines for predicting antibody specificity and for identifying safe and potent antibody therapeutics.

SCALOP: sequence-based antibody canonical loop structure annotation.

MotivationCanonical forms of the antibody complementarity-determining regions (CDRs) were first described in 1987 and have been redefined on multiple occasions since. The canonical forms are often used to approximate the antibody binding site shape as they can be predicted from sequence. A rapid predictor would facilitate the annotation of CDR structures in the large amounts of repertoire data now becoming available from next generation sequencing experiments.ResultsSCALOP annotates CDR canonical forms for antibody sequences, supported by an auto-updating database to capture the latest cluster information. Its accuracy is comparable to that of a standard structural predictor but it is 800 times faster. The auto-updating nature of SCALOP ensures that it always attains the best possible coverage.Availability and implementationSCALOP is available as a web application and for download under a GPLv3 license at opig.stats.ox.ac.uk/webapps/scalop.Supplementary information Supplementary data are available at Bioinformatics online.