Heterogeneity in Disulfide Bond Reduction in IgG1 Antibodies Is Governed by Solvent Accessibility of the Cysteines.

We studied unpaired cysteine levels and disulfide bond susceptibility in four different γ-immunoglobulin antibodies using liquid chromatography-mass spectrometry. Our choice of differential alkylating agents ensures that the differential peaks are non-overlapping, thus allowing us to accurately quantify free cysteine levels. For each cysteine residue, we observed no more than 5% to be unpaired, and the free cysteine levels across antibodies were slightly higher in those containing lambda light chains. Interchain and hinge residues were highly susceptible to reducing stresses and showed a 100-1000-fold higher rate of reduction compared to intrachain cysteines. Estimations of the solvent-accessible surface for individual cysteines in IgG1, using an implicit all-atom molecular dynamics simulation, show that interchain and hinge cysteines have >1000-fold higher solvent accessibility compared to intrachain cysteines. Further analyses show that solvent accessibility and the rate of reduction are linearly correlated. Our work clearly establishes the fact that a cysteine's accessibility to the surrounding solvent is one of the primary determinants of its disulfide bond stability.

IgG1 and IgG4 antibodies sample initial structure dependent local conformational states and exhibit non-identical Fab dynamics.

We have investigated the dynamics of two [Formula: see text]-immunoglobulin molecules, IgG1 and IgG4, using long all atom molecular dynamics simulations. We first show that the de novo structures of IgG1 and IgG4 predicted using AlphaFold, with no interactions between the fragment crystallizable (Fc) domain and the antigen fragment binding domain (Fab), eventually relaxes to a state with persistent Fc-Fab interactions that mirrors experimentally resolved structures. We quantified the conformational space sampled by antibody trajectories spawned from six different initial structures and show that the individual trajectories only sample states bound by a local minimum and display very little mixing in their conformational states. Furthermore, the dynamics of the individual Fab domains are strongly dependent on the initial crystal structure and isotype. In all conditions, we observe non-identical dynamics between the Fab arms in an antibody. For a six-bead coarse grained model, we show that non-covalent Fc-Fab interactions can modulate the stiffnesses associated with Fc-Fab distances, angles, and dihedral angles by up to three orders of magnitude. Our results clearly illustrate the inherent complexities in studying antibody dynamics and highlight the need to include non-identical Fab dynamics as an inherent feature in computational models of therapeutic antibodies.

Non-covalent Fc-Fab interactions significantly alter internal dynamics of an IgG1 antibody.

The fragment-antigen-binding arms (Fab1 and Fab2) in a canonical immunoglobulin G (IgG) molecule have identical sequences and hence are always expected to exhibit symmetric conformations and dynamics. Using long all atom molecular simulations of a human IgG1 crystal structure 1HZH, we demonstrate that the translational and rotational dynamics of Fab1 and Fab2 also strongly depend on their interactions with each other and with the fragment-crystallizable (Fc) region. We show that the Fab2 arm in the 1HZH structure is non-covalently bound to the Fc region via long-lived hydrogen bonds, involving its light chain and both heavy chains of the Fc region. These highly stable interactions stabilize non-trivial conformer states with constrained fluctuations. We observe subtle modifications in Fab1 dynamics in response to Fab2-Fc interactions that points to novel allosteric interactions between the Fab arms. These results yield novel insights into the inter- and intra-fragment motions of immunoglobulins which could help us better understand the relation between their structure and function.

Tryptophan Oxidation of a Monoclonal Antibody Under Diverse Oxidative Stress Conditions: Distinct Oxidative Pathways Favor Specific Tryptophan Residues.

Tryptophan oxidation can play an important role in selecting therapeutic monoclonal antibodies for commercialization. Monoclonal antibodies that harbor particularly sensitive tryptophan residues are typically discarded in favor of oxidation resistant antibodies. The susceptibility of any individual tryptophan residue to oxidation is typically evaluated through forced degradation studies during the molecule development process. We compared the results of several common forced degradation "stress tests" for each tryptophan residue in a monoclonal antibody and found that high-stress oxidation conditions consistently provide a different ranking of oxidative sensitivity across the individual tryptophan residues compared to long-term thermal stability or low-stress conditions. We subsequently determined that this difference in ranking is largely due to an overabundance of double oxidation (i.e. detected as +32 Da) of specific tryptophan residues under high stress conditions compared to single oxidation (i.e. +16 Da). We posit that this double oxidation is in fact mechanistically distinct from the observed single oxidation and that high stress conditions favor the double oxidation mechanism (and double oxidation sensitive tryptophan residues) while single oxidation appears to be the primary mode of oxidation under H2O2 stress and long-term thermal stability and favors different tryptophan residues which are more susceptible to the single oxidation mechanism.

Engineered IL-21 Cytokine Muteins Fused to Anti-PD-1 Antibodies Can Improve CD8+ T Cell Function and Anti-tumor Immunity.

Inhibitors that block the programmed cell death-1 (PD-1) pathway can potentiate endogenous antitumor immunity and have markedly improved cancer survival rates across a broad range of indications. However, these treatments work for only a minority of patients. The efficacy of anti-PD-1 inhibitors may be extended by cytokines, however, the incorporation of cytokines into therapeutic regimens has significant challenges. In their natural form when administered as recombinant proteins, cytokine treatments are often associated with low response rates. Most cytokines have a short half-life which limits their exposure and efficacy. In addition, cytokines can activate counterregulatory pathways, in the case of immune-potentiating cytokines this can lead to immune suppression and thereby diminish their potential efficacy. Improving the drug-like properties of natural cytokines using protein engineering can yield synthetic cytokines with improved bioavailability and tissue targeting, allowing for enhanced efficacy and reduced off-target effects. Using structure guided engineering we have designed a novel class of antibody-cytokine fusion proteins consisting of a PD-1 targeting antibody fused together with an interleukin-21 (IL-21) cytokine mutein. Our bifunctional fusion proteins can block PD-1/programmed death-ligand 1 (PD-L1) interaction whilst simultaneously delivering IL-21 cytokine to PD-1 expressing T cells. Targeted delivery of IL-21 can improve T cell function in a manner that is superior to anti-PD-1 monotherapy. Fusion of engineered IL-21 variants to anti-PD1 antibodies can improve the drug-like properties of IL-21 cytokine leading to improved cytokine serum half-life allowing for less frequent dosing. In addition, we show that targeted delivery of IL-21 can minimize any potential detrimental effect on local antigen-presenting cells. A highly attenuated IL-21 mutein variant (R9E:R76A) fused to a PD-1 antibody provides protection in a humanized mouse model of cancer that is refractory to anti-PD-1 monotherapy. Collectively, our preclinical data demonstrate that this approach may improve upon and extend the utility of anti-PD-1 therapeutics currently in the clinic.

Effects of Buffer Composition on Site-Specific Glycation of Lysine Residues in Monoclonal Antibodies.

Candidate antibodies under consideration for development as pharmaceuticals must be screened for potential liabilities. Glycation of lysine side chains is one liability which can significantly alter the efficacy of a therapeutic antibody. Antibody candidates are often subjected to stress-testing after purification to assess liabilities that may arise from variability in the manufacturing process and gauge the manufacturability of the molecule. Because previous publications have shown significant site-specific effects of certain buffer components on the glycation rate of individual lysines, we sought to understand the effects of common buffering agents to find suitable buffers for glycation stress-testing (forced glycation). Therapeutic antibodies are typically only exposed to reducing sugars in cell culture media during production, so we sought to identify buffers that could be used as surrogates for media in forced glycation reactions. Our results indicate that common buffering agents can drastically alter the rate of glycation for specific lysines in an antibody. Forced glycation reactions performed in HEPES and citrate buffers both produce site-specific lysine glycation rates that correlate well with cell culture media, whereas bicarbonate buffer has a highly stimulatory effect on most lysines leading to higher total glycation levels and a poor correlation to glycation rates in media.

Culture temperature modulates half antibody and aggregate formation in a Chinese hamster ovary cell line expressing a bispecific antibody.

Therapeutic bispecific antibodies are formed by assembly of multichain polypeptides. In general, a bispecific antibody has two different light chains and two different heavy chains that fold and correctly pair via engineered interchain interactions. Because of some incorrect assembly, product-related impurities can be prevalent (e.g., half molecules, mispaired light chains, homodimers), requiring its removal during subsequent purification. In this study, we investigated the modulation of impurity levels in a stable Chinese hamster ovary cell line X expressing a bispecific antibody A formed by two light chains (LC1 and LC2) and two heavy chains (HC1 and HC2) that assembled intracellularly into a heterodimer (LC1-HC1 + LC2-HC2) via engineered charged residues. Cell line X exhibited the best volumetric productivity, growth, and viability in culture compared with other clones but also showed higher levels of half antibody species (>10%); therefore, to minimize process yield loss, better understanding, and control of impurity formation was pursued. We found this cell line decreased half antibody levels from 16% to 1% when temperature changed from 36°C to 32.5°C or 31.5°C. However, lower temperature also increased high-molecular-weight (HMW) species from 4% to 12%. To determine the impurity species composition, we characterized enriched fractions with half antibody or HMW. Intact mass spectrometry analysis revealed half antibody was LC2-HC2, whereas HMW was a mixture with ~50% as LC1-HC1 homodimer. Results suggested LC2-HC2 was easily folded and could be secreted as half antibody, especially at 36°C. On the contrary, LC1-HC1 was more susceptible to misfold or aggregate, a phenomenon more acute for cell line X at lower culture temperature because of 60% increased LC1 and HC1 messenger RNA levels. Although temperature modulation was cell line X-specific, the propensity of LC2-HC2 to form half antibodies and LC1-HC1 to aggregate appeared in other cell lines also expressing bispecific antibody A, suggesting an amino-acid sequence-dependent mechanism. In summary, impurity formation in cell line X was temperature-dependent and was influenced by different molecule characteristics between the LC1-HC1 and LC2-HC2 parts. Ultimately, we selected a biphasic cell culture process with a growth phase followed by a lower temperature phase to improve product quality and purification yield.

Prediction of the Hydrogen Peroxide-Induced Methionine Oxidation Propensity in Monoclonal Antibodies.

Methionine oxidation in therapeutic antibodies can impact the product's stability, clinical efficacy, and safety and hence it is desirable to address the methionine oxidation liability during antibody discovery and development phase. Although the current experimental approaches can identify the oxidation-labile methionine residues, their application is limited mostly to the development phase. We demonstrate an in silico method that can be used to predict oxidation-labile residues based solely on the antibody sequence and structure information. Since antibody sequence information is available in the discovery phase, the in silico method can be applied very early on to identify the oxidation-labile methionine residues and subsequently address the oxidation liability. We believe that the in silico method for methionine oxidation liability assessment can aid in antibody discovery and development phase to address the liability in a more rational way.

Rational design of therapeutic mAbs against aggregation through protein engineering and incorporation of glycosylation motifs applied to bevacizumab.

The aggregation of biotherapeutics is a major hindrance to the development of successful drug candidates; however, the propensity to aggregate is often identified too late in the development phase to permit modification to the protein's sequence. Incorporating rational design for the stability of proteins in early discovery has numerous benefits. We engineered out aggregation-prone regions on the Fab domain of a therapeutic monoclonal antibody, bevacizumab, to rationally design a biobetter drug candidate. With the purpose of stabilizing bevacizumab with respect to aggregation, 2 strategies were undertaken: single point mutations of aggregation-prone residues and engineering a glycosylation site near aggregation-prone residues to mask these residues with a carbohydrate moiety. Both of these approaches lead to comparable decreases in aggregation, with an up to 4-fold reduction in monomer loss. These single mutations and the new glycosylation pattern of the Fab domain do not modify binding to the target. Biobetters with increased stability against aggregation can therefore be generated in a rational manner, by either removing or masking the aggregation-prone region or crowding out protein-protein interactions.

Computational tool for the early screening of monoclonal antibodies for their viscosities.

Highly concentrated antibody solutions often exhibit high viscosities, which present a number of challenges for antibody-drug development, manufacturing and administration. The antibody sequence is a key determinant for high viscosity of highly concentrated solutions; therefore, a sequence- or structure-based tool that can identify highly viscous antibodies from their sequence would be effective in ensuring that only antibodies with low viscosity progress to the development phase. Here, we present a spatial charge map (SCM) tool that can accurately identify highly viscous antibodies from their sequence alone (using homology modeling to determine the 3-dimensional structures). The SCM tool has been extensively validated at 3 different organizations, and has proved successful in correctly identifying highly viscous antibodies. As a quantitative tool, SCM is amenable to high-throughput automated analysis, and can be effectively implemented during the antibody screening or engineering phase for the selection of low-viscosity antibodies.

Rational Design of Biobetters with Enhanced Stability.

Biotherapeutics are the fastest growing class of pharmaceutical with a rapidly evolving market facing the rise of biosimilar and biobetter products. In contrast to a biosimilar, which is derived from the same gene sequence as the innovator product, a biobetter has enhanced properties, such as enhanced efficacy or reduced immunogenicity. Little work has been carried out so far to increase the intrinsic stability of biotherapeutics via sequence changes, even though, aggregation, the primary degradation pathway of proteins, leads to issues ranging from manufacturing failure to immunological response and to loss of therapeutic activity. Using our spatial aggregation propensity tool as a first step to a rational design approach to identify aggregation-prone regions, biobetters of rituximab have been produced with enhanced stability by introducing site-specific mutations. Significant stabilization against aggregation was achieved for rituximab with no decrease in its binding affinity to the antigen.

A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins.

Fast and scalable purification of a therapeutic full-length antibody based on process crystallization.

The potential of process crystallization for purification of a therapeutic monoclonal IgG1 antibody was studied. The purified antibody was crystallized in non-agitated micro-batch experiments for the first time. A direct crystallization from clarified CHO cell culture harvest was inhibited by high salt concentrations. The salt concentration of the harvest was reduced by a simple pretreatment step. The crystallization process from pretreated harvest was successfully transferred to stirred tanks and scaled-up from the mL-scale to the 1 L-scale for the first time. The crystallization yield after 24 h was 88-90%. A high purity of 98.5% was reached after a single recrystallization step. A 17-fold host cell protein reduction was achieved and DNA content was reduced below the detection limit. High biological activity of the therapeutic antibody was maintained during the crystallization, dissolving, and recrystallization steps. Crystallization was also performed with impure solutions from intermediate steps of a standard monoclonal antibody purification process. It was shown that process crystallization has a strong potential to replace Protein A chromatography. Fast dissolution of the crystals was possible. Furthermore, it was shown that crystallization can be used as a concentrating step and can replace several ultra-/diafiltration steps. Molecular modeling suggested that a negative electrostatic region with interspersed exposed hydrophobic residues on the Fv domain of this antibody is responsible for the high crystallization propensity. As a result, process crystallization, following the identification of highly crystallizable antibodies using molecular modeling tools, can be recognized as an efficient, scalable, fast, and inexpensive alternative to key steps of a standard purification process for therapeutic antibodies.

A flexible-protein molecular docking study of the binding of ruthenium complex compounds to PIM1, GSK-3ß, and CDK2/Cyclin A protein kinases.

We employ ensemble docking simulations to characterize the interactions of two enantiomeric forms of a Ru-complex compound (1-R and 1-S) with three protein kinases, namely PIM1, GSK-3ß, and CDK2/cyclin A. We show that our ensemble docking computational protocol adequately models the structural features of these interactions and discriminates between competing conformational clusters of ligand-bound protein structures. Using the determined X-ray crystal structure of PIM1 complexed to the compound 1-R as a control, we discuss the importance of including the protein flexibility inherent in the ensemble docking protocol, for the accuracy of the structure prediction of the bound state. A comparison of our ensemble docking results suggests that PIM1 and GSK-3ß bind the two enantiomers in similar fashion, through two primary binding modes: conformation I, which is very similar to the conformation presented in the existing PIM1/compound 1-R crystal structure; conformation II, which represents a 180° flip about an axis through the NH group of the pyridocarbazole moiety, relative to conformation I. In contrast, the binding of the enantiomers to CDK2 is found to have a different structural profile including a suggested bound conformation, which lacks the conserved hydrogen bond between the kinase and the ligand (i.e., ATP, staurosporine, Ru-complex compound). The top scoring conformation of the inhibitor bound to CDK2 is not present among the top-scoring conformations of the inhibitor bound to either PIM1 or GSK-3ß and vice-versa. Collectively, our results help provide atomic-level insights into inhibitor selectivity among the three kinases.

Computational methods to predict therapeutic protein aggregation.

Protein based biotherapeutics have emerged as a successful class of pharmaceuticals. However, these macromolecules endure a variety of physicochemical degradations during manufacturing, shipping, and storage, which may adversely impact the drug product quality. Of these degradations, the irreversible self-association of therapeutic proteins to form aggregates is a major challenge in the formulation of these molecules. Tools to predict and mitigate protein aggregation are, therefore, of great interest to biopharmaceutical research and development. In this chapter, a number of such computational tools developed to understand and predict the various steps involved in protein aggregation are described. These tools can be grouped into three general classes: unfolding kinetics and native state thermal stability, colloidal stability, and sequence/structure based aggregation liabilities. Chapter sections introduce each class by discussing how these predictive tools provide insight into the molecular events leading to protein aggregation. The computational methods are then explained in detail along with their advantages and limitations.

Developability index: a rapid in silico tool for the screening of antibody aggregation propensity.

Determining the aggregation propensity of protein-based biotherapeutics is an important step in the drug development process. Typically, a great deal of data collected over a large period of time is needed to estimate the aggregation propensity of biotherapeutics. Thus, candidates cannot be screened early on for aggregation propensity, but early screening is desirable to help streamline drug development. Here, we present a simple molecular computational method to predict the aggregation propensity via hydrophobic interactions, thought to be the most common mechanism of aggregation, and electrostatic interactions. This method uses a new quantity termed Developability Index. It is a function of an antibody's net charge, calculated on the full-length antibody structure, and the spatial aggregation propensity, calculated on the complementarity-determining region structure. Its accuracy is due to the molecular level details and the incorporation of the tertiary structure of the antibody. It is particularly applicable to antibodies or other proteins for which structures are available or could be determined accurately using homology modeling. Applications include the selection of molecules in the discovery or early development process, selection of mutants for stability, and estimation of resources needed for development of a given biomolecule.

Mesoscale simulations of curvature-inducing protein partitioning on lipid bilayer membranes in the presence of mean curvature fields.

The membrane-surface migration of curvature-inducing proteins in response to membrane curvature gradients has been investigated using Monte Carlo simulations of a curvilinear membrane model based on the Helfrich Hamiltonian. Consistent with theoretical and experimental data, we find the proteins that generate curvature can also sense the background membrane curvature, wherein they preferentially partition to the high curvature regions. The partitioning strength depends linearly on local membrane curvature and the slope (or the coupling constant) of the partitioning probability versus mean curvature depends on the membrane bending rigidity and instantaneous curvature field caused by different proteins. Our simulation study allows us to quantitatively characterize and identify the important factors affecting the coupling constant (slope), which may be difficult to determine in experiments. Furthermore, the membrane model is used to study budding of vesicles where it is found that in order to stabilize a mature vesicle with a stable 'neck-region' (or stable membrane overhangs), the area (extent) of the intrinsic curvature region needs to exceed a threshold-critical value. The migration and partitioning of curvature-inducing proteins in a budding vesicle with a stable neck (with a characteristic negative value of the Gaussian curvature) is investigated.

Systems biology and physical biology of clathrin-mediated endocytosis.

In this review, we describe the application of experimental data and modeling of intracellular endocytic trafficking mechanisms with a focus on the process of clathrin-mediated endocytosis. A detailed parts-list for the protein-protein interactions in clathrin-mediated endocytosis has been available for some time. However, recent experimental, theoretical, and computational tools have proved to be critical in establishing a sequence of events, cooperative dynamics, and energetics of the intracellular process. On the experimental front, total internal reflection fluorescence microscopy, photo-activated localization microscopy, and spinning-disk confocal microscopy have focused on assembly and patterning of endocytic proteins at the membrane, while on the theory front, minimal theoretical models for clathrin nucleation, biophysical models for membrane curvature and bending elasticity, as well as methods from computational structural and systems biology, have proved insightful in describing membrane topologies, curvature mechanisms, and energetics.

Multivalent binding of nanocarrier to endothelial cells under shear flow.

We investigate the effects of particle size, shear flow, and resistance due to the glycocalyx on the multivalent binding of functionalized nanocarriers (NC) to endothelial cells (ECs). We address the much- debated issue of shear-enhanced binding by computing the binding free-energy landscapes of NC binding to the EC surface when the system is subjected to shear, using a model and simulation methodology based on the Metropolis Monte Carlo approach. The binding affinities calculated based on the free-energy profiles are found to be in excellent agreement with experimental measurements for different-sized NCs. The model suggests that increasing the size of NCs significantly increases the multivalency but only moderately enhances the binding affinities due to the entropy loss associated with bound receptors on the EC surface. A significant prediction of our model is that under flow conditions, the binding free energies of NCs are a nonmonotonic function of the shear force. They show a well-defined minimum at a critical shear value, and thus quantitatively mimic the shear-enhanced binding behavior observed in various experiments. More significantly, our results indicate that the interplay between multivalent binding and shear force can reproduce the shear-enhanced binding phenomenon, which suggests that under certain conditions, this phenomenon can also occur in systems that do not show a catch-bond behavior. In addition, the model also suggests that the impact of the glycocalyx thickness on NC binding affinity is exponential, implying a highly nonlinear effect of the glycocalyx on binding.

Aggregation in protein-based biotherapeutics: computational studies and tools to identify aggregation-prone regions.

Because of their large, complex, and conformationally heterogeneous structures, biotherapeutics are vulnerable to several physicochemical stresses faced during the various processing steps from production to administration. In particular, formation of protein aggregates is a major concern. The greatest risk with aggregates arises from their potential to give rise to immunogenic reactions. Hence, it is desirable to bring forward biotherapeutic drug candidates that show low propensity for aggregation and, thus, improved developability. Here, we present a comprehensive review of computational studies into the sequence and structural factors that underpin protein and peptide aggregation. A number of computational approaches have been applied including coarse grain models, atomistic molecular simulations, and bioinformatic approaches. These studies have focused on both the mechanism of aggregation and the identification of potential aggregation-prone sequence and structural motifs. We also survey the computational tools available to predict aggregation in therapeutic proteins. The findings communicated here provide insights that could be potentially useful in the rational design of therapeutic candidates with not only high potency and specificity but also improved stability and solubility. These sequence-structure-based approaches can be applied to both novel as well as follow-on biotherapeutics.