Computational Design To Reduce Conformational Flexibility and Aggregation Rates of an Antibody Fab Fragment

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

Molecular Dynamics Simulations Reveal How Competing Protein-Surface Interactions for Glycine, Citrate, and Water Modulate Stability in Antibody Fragment Formulations

The design of stable formulations remains a major challenge for protein therapeutics, particularly the need to minimize aggregation. Experimental formulation screens are typically based on thermal transition midpoints (Tm), and forced degradation studies at elevated temperatures. Both approaches give limited predictions of long-term storage stability, particularly at low temperatures. Better understanding of the mechanisms of action for formulation of excipients and buffers could lead to improved strategies for formulation design. Here, we identified a complex impact of glycine concentration on the experimentally determined stability of an antibody Fab fragment and then used molecular dynamics simulations to reveal mechanisms that underpin these complex behaviors. Tm values increased monotonically with glycine concentration, but associated ΔSvh measurements revealed more complex changes in the native ensemble dynamics, which reached a maximum at 30 mg/mL. The aggregation kinetics at 65 °C were similar at 0 and 20 mg/mL glycine, but then significantly slower at 50 mg/mL. These complex behaviors indicated changes in the dominant stabilizing mechanisms as the glycine concentration was increased. MD revealed a complex balance of glycine self-interaction, and differentially preferred interactions of glycine with the Fab as it displaced hydration-shell water, and surface-bound water and citrate buffer molecules. As a result, glycine binding to the Fab surface had different effects at different concentrations, and led from preferential interactions at low concentrations to preferential exclusion at higher concentrations. During preferential interaction, glycine displaced water from the Fab hydration shell, and a small number of water and citrate molecules from the Fab surface, which reduced the protein dynamics as measured by root-mean-square fluctuation (RMSF) on the short time scales of MD. By contrast, the native ensemble dynamics increased according to ΔSvh, suggesting increased conformational changes on longer time scales. The aggregation kinetics did not change at low glycine concentrations, and so the opposing dynamics effects either canceled out or were not directly relevant to aggregation. During preferential exclusion at higher glycine concentrations, glycine could only bind to the Fab surface through the displacement of citrate buffer molecules already favorably bound on the Fab surface. Displacement of citrate increased the flexibility (RMSF) of the Fab, as glycine formed fewer bridging hydrogen bonds to the Fab surface. Overall, the slowing of aggregation kinetics coincided with reduced flexibility in the Fab ensemble at the very highest glycine concentrations, as determined by both RMSF and ΔSvh, and occurred at a point where glycine binding displaced neither water nor citrate. These final interactions with the Fab surface were driven by mass action and were the least favorable, leading to a macromolecular crowding effect under the regime of preferential exclusion that stabilized the dynamics of Fab.

Effects of molecule hydrophobicity and structural flexibility of appended bispecific antibody on Protein A chromatography

Appended bispecific antibody (aBsAb) with two single chain variable fragments (scFv) linked at the c-terminus of its heavy chains is one of the promising formats in bispecific therapeutics. The presence of hydrophobic and flexible scFv fragments render aBsAb molecules higher molecule hydrophobicity and structural flexibility compared to monoclonal antibody (mAb), thus making its purification more challenging. We set out to investigate how the unique molecular properties of aBsAb affect its performance on Protein A chromatography. We showed that aBsAb has a high propensity for chromatography-induced aggregation due to its high molecule hydrophobicity, and this couldn't be improved by the addition of common chaotropic salts. Moreover, the presence of chaotropic salts, such as arginine hydrochloride (Arg-HCl), retarded aBsAb elution during Protein A chromatography rather than facilitating which was widely observed in mAb Protein A elution. Nevertheless, we were able to overcome the aggregation issue by optimizing elution condition and improved aBsAb purity from 29 % to 93 % in Protein A eluate with a high molecular weight (HMW) species of less than 5 %. We also showed that the high molecular flexibility of aBsAb leads to different hydrodynamic sizes of the aBsAb molecule post Protein A elution, neutralization, and re-acidification, which are pH dependent. This is different from mAbs where their sizes do not change post neutralization even with re-exposure to acid. The above unique observations of aBsAb in Protein A chromatography were clearly explained from the perspectives of its high molecular hydrophobicity and structural flexibility.

Residue Effect-Guided Design: Engineering of S. Solfataricus β-Glycosidase to Enhance Its Thermostability and Bioproduction of Ginsenoside Compound K

β-Glycosidase from Sulfolobus solfataricus (SS-BGL) is a highly effective biocatalyst for the synthesis of compound K (CK) from glycosylated protopanaxadiol ginsenosides. In order to improve the thermal stability of SS-BGL, molecular dynamics simulations were used to determine the residue-level binding energetics of ginsenoside Rd in the SS-BGL-Rd docked complex and to identify the top ten critical contributors. Target sites for mutations were determined using dynamic cross-correlation mapping of residues via the Ohm server to identify networks of distal residues that interact with the key binding residues. Target mutations were determined rationally based on site characteristics. Single mutants and then recombination of top hits led to the two most promising variants SS-BGL-Q96E/N97D/N302D and SS-BGL-Q96E/N97D/N128D/N302D with 2.5-fold and 3.3-fold increased half-lives at 95 °C, respectively. The enzyme activities relative to those of wild-type for ginsenoside conversion were 161 and 116%, respectively..

Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations

The computational prediction of the viscosity of dense protein solutions is highly desirable, for example, in the early development phase of high-concentration biopharmaceutical formulations where the material needed for experimental determination is typically limited. Here, we use large-scale atomistic molecular dynamics (MD) simulations with explicit solvation to de novo predict the dynamic viscosities of solutions of a monoclonal IgG1 antibody (mAb) from the pressure fluctuations using a Green-Kubo approach. The viscosities at simulated mAb concentrations of 200 and 250 mg/mL are compared to the experimental values, which we measured with rotational rheometry. The computational viscosity of 24 mPa·s at the mAb concentration of 250 mg/mL matches the experimental value of 23 mPa·s obtained at a concentration of 213 mg/mL, indicating slightly different effective concentrations (or activities) in the MD simulations and in the experiments. This difference is assigned to a slight underestimation of the effective mAb-mAb interactions in the simulations, leading to a too loose dynamic mAb network that governs the viscosity. Taken together, this study demonstrates the feasibility of all-atom MD simulations for predicting the properties of dense mAb solutions and provides detailed microscopic insights into the underlying molecular interactions. At the same time, it also shows that there is room for further improvements and highlights challenges, such as the massive sampling required for computing collective properties of dense biomolecular solutions in the high-viscosity regime with reasonable statistical precision.

Crystal structures and molecular dynamics simulations of a humanised antibody fragment at acidic to basic pH

Antibody-fragment (Fab) therapy development has the potential to be accelerated by computational modelling and simulations that predict their target binding, stability, formulation, manufacturability, and the impact of further protein engineering. Such approaches are currently predicated on starting with good crystal structures that closely represent those found under the solution conditions to be simulated. A33 Fab, is an undeveloped immunotherapeutic antibody candidate that was targeted to the human A33 antigen homogeneously expressed in 95% cases of primary and metastatic colorectal cancers. It is now used as a very well characterised testing ground for developing analytics, formulation and protein engineering strategies, and to gain a deeper understanding of mechanisms of destabilisation, representative of the wider therapeutic Fab platform. In this article, we report the structure of A33 Fab in two different crystal forms obtained at acidic and basic pH. The structures overlapped with RMSD of 1.33 Å overall, yet only 0.5 Å and 0.76 Å for the variable- and constant regions alone. While most of the differences were within experimental error, the switch linker between the variable and the constant regions showed some small differences between the two pHs. The two structures then enabled a direct evaluation of the impact of initial crystal structure selection on the outcomes of molecular dynamics simulations under different conditions, and their subsequent use for determining best fit solution structures using previously obtained small-angle x-ray scattering (SAXS) data. The differences in the two structures did not have a major impact on MD simulations regardless of the pH, other than a slight persistence of structure affecting the solvent accessibility of one of the predicted APR regions of A33 Fab. Interestingly, despite being obtained at pH 4 and pH 9, the two crystal structures were more similar to the SAXS solution structures obtained at pH 7, than to those at pH 4 or pH 9. Furthermore, the P65 crystal structure from pH 4 was also a better representation of the solution structures at any other pH, than was the P1 structure obtained at pH 9. Thus, while obtained at different pH, the two crystal structures may represent highly (P65) and lesser (P1) populated species that both exist at pH 7 in solution. These results now lay the foundation for confident MD simulations of A33 Fab that rationalise or predict behaviours in a range of conditions.

Experimentally Observed Conformational Changes in Antibodies Due to Binding and Paratope-epitope Asymmetries

Understanding binding related changes in antibody conformations is important for epitope prediction and antibody refinement. The increase of available data in the PDB allowed a more detailed investigation of the conformational landscape for free and bound antibodies. A dataset containing a total of 835 unique PDB entries of antibodies that were crystallized in complex with their antigen and in a free state was constructed. It was examined for binding related conformation changes. We present further evidence supporting the theory of a pre-existing-equilibrium in experimental data. Multiple sequence alignments did not show binding induced tendencies in the solvent accessibility of residues in any specific position. Evaluating the changes in solvent accessibility per residue revealed a certain binding induced increase for several amino acids. Antibody-antigen interaction statistics were established and quantify a significant directional asymmetry between many interacting antibody and antigen residue pairs, especially a richness in tyrosine in the antibody epitope compared to its paratope. This asymmetry could potentially facilitate an increase in the success rate of computationally guided antibody refinement.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Enhanced Thermal Stability and Reduced Aggregation in an Antibody Fab Fragment at Elevated Concentrations

The aggregation of protein therapeutics such as antibodies remains a major challenge in the biopharmaceutical industry. The present study aimed to characterize the impact of the protein concentration on the mechanisms and potential pathways for aggregation, using the antibody Fab fragment A33 as the model protein. Aggregation kinetics were determined for 0.05 to 100 mg/mL Fab A33, at 65 °C. A surprising trend was observed whereby increasing the concentration decreased the relative aggregation rate, ln(v) (% day^-1), from 8.5 at 0.05 mg/mL to 4.4 at 100 mg/mL. The absolute aggregation rate (mol L^-1 h^-1) increased with the concentration following a rate order of approximately 1 up to a concentration of 25 mg/mL. Above this concentration, there was a transition to an apparently negative rate order of -1.1 up to 100 mg/mL. Several potential mechanisms were examined as possible explanations. A greater apparent conformational stability at 100 mg/mL was observed from an increase in the thermal transition midpoint (T_m) by 7-9 °C, relative to those at 1-4 mg/mL. The associated change in unfolding entropy (△S_vh) also increased by 14-18% at 25-100 mg/mL, relative to those at 1-4 mg/mL, indicating reduced conformational flexibility in the native ensemble. Addition of Tween or the crowding agents Ficoll and dextran, showed that neither surface adsorption, diffusion limitations nor simple volume crowding affected the aggregation rate. Fitting of kinetic data to a wide range of mechanistic models implied a reversible two-state conformational switch mechanism from aggregation-prone monomers (N*) into non-aggregating native forms (N) at higher concentrations. k_D measurements from DLS data also suggested a weak self-attraction while remaining colloidally stable, consistent with macromolecular self-crowding within weakly associated reversible oligomers. Such a model is also consistent with compaction of the native ensemble observed through changes in T_m and △S_vh.

Assessing and Engineering Antibody Stability Using Experimental and Computational Methods

Engineering increased stability into antibodies can improve their developability. While a range of properties need to be optimized, thermal stability and aggregation are two key factors that affect the antibody yield, purity, and specificity throughout the development and manufacturing pipeline. Therefore, an ideal goal would be to apply protein engineering methods early-on, such as in parallel to affinity maturation, to screen out potential drug molecules with the desired conformational and colloidal stability. This chapter introduces our methods to computationally characterize an antibody Fab fragment, propose stabilizing variants, and then experimentally verify these predictions.

Stability Convergence in Antibody Coformulations

Combined administration of antibody therapeutics has proven to be beneficial for patients with cancer or infectious diseases. As a result, there is a growing trend toward multiple antibodies premixed into a single product form and delivered to patients as a fixed-dose coformulation. However, combining antibodies into a single coformulation could be challenging as proteins have the potential to interact and alter their stability and degradation profiles in the mixture, compared to that in isolation. We show that in two specific antibody-antibody coformulations, the more stable antibody component increased the stability of the less stable component, which in return destabilized the more stable component, hence exhibiting an overall convergence of stability in the coformulation.

Protein Engineering and HDX Identify Structural Regions of G-CSF Critical to Its Stability and Aggregation

The protein engineering and formulation of therapeutic proteins for prolonged shelf-life remain a major challenge in the biopharmaceutical industry. Understanding the influence of mutations and formulations on the protein structure and dynamics could lead to more predictive approaches to their improvement. Previous intrinsic fluorescence analysis of the chemically denatured granulocyte colony-stimulating factor (G-CSF) suggested that loop AB could subtly reorganize to form an aggregation-prone intermediate state. Hydrogen deuterium exchange mass spectrometry (HDX-MS) has also revealed that excipient binding increased the thermal unfolding transition midpoint (T_m) by stabilizing loop AB. Here, we have combined protein engineering with biophysical analyses and HDX-MS to reveal that increased exchange in a core region of the G-CSF comprising loop AB (ABI, a small helix, ABII) and loop CD packed onto helix B and the beginning of loop BC leads to a decrease in T_m and higher aggregation rates. Furthermore, some mutations can increase the population of the aggregation-prone conformation within the native ensemble, as measured by the greater local exchange within this core region.

Self-Interactions of Two Monoclonal Antibodies: Small-Angle X-ray Scattering, Light Scattering, and Coarse-Grained Modeling

Using light scattering (LS), small-angle X-ray scattering (SAXS), and coarse-grained Monte Carlo (MC) simulations, we studied the self-interactions of two monoclonal antibodies (mAbs), PPI03 and PPI13. With LS measurements, we obtained the osmotic second virial coefficient, B₂₂, and the molecular weight, M_w, of the two mAbs, while with SAXS measurements, we studied the mAbs' self-interaction behavior in the high protein concentration regime up to 125 g/L. Through SAXS-derived coarse-grained representations of the mAbs, we performed MC simulations with either a one-protein or a two-protein model to predict B₂₂. By comparing simulation and experimental results, we validated our models and obtained insights into the mAbs' self-interaction properties, highlighting the role of both ion binding and charged patches on the mAb surfaces. Our models provide useful information about mAbs' self-interaction properties and can assist the screening of conditions driving to colloidal stability.

Comparison of the pH- and thermally-induced fluctuations of a therapeutic antibody Fab fragment by molecular dynamics simulation

Successful development of protein therapeutics depends critically on achieving stability under a range of conditions. A deeper understanding of the drivers of instability across different stress conditions, will enable the engineering of more robust protein scaffolds. We compared the impacts of low pH and high temperature stresses on the structure of a humanized antibody fragment (Fab) A33, using atomistic molecular dynamics simulations, using a recent 2.5 Å crystal structure. This revealed that low-pH induced the loss of native contacts in the domain C_L. By contrast, thermal stress led to 5-7% loss of native contacts in all four domains, and simultaneous loss of >30% of native contacts in the V_L-V_H and C_L-C_H interfaces. This revealed divergent destabilising pathways under the two different stresses. The underlying cause of instability was probed using FoldX and Rosetta mutation analysis, and packing density calculations. These agreed that mutations in the C_L domain, and C_L-C_H1 interface have the greatest potential for stabilisation of Fab A33. Several key salt bridge losses underpinned the conformational change in C_L at low pH, whereas at high temperature, salt bridges became more dynamic, thus contributing to an overall destabilization. Lastly, the unfolding events at the two stress conditions exposed different predicted aggregation-prone regions (APR) to solvent, which would potentially lead to different aggregation mechanisms. Overall, our results identified the early stages of unfolding and stability-limiting regions of Fab A33, and the V_H and C_L domains as interesting future targets for engineering stability to both pH- and thermal-stresses simultaneously.

Machine learning reveals hidden stability code in protein native fluorescence

Conformational stability of a protein is usually obtained by spectroscopically measuring the unfolding melting temperature. However, optical spectra under native conditions are considered to contain too little resolution to probe protein stability. Here, we have built and trained a neural network model to take the temperature-dependence of intrinsic fluorescence emission under native-only conditions as inputs, and then predict the spectra at the unfolding transition and denatured state. Application to a therapeutic antibody fragment demonstrates that thermal transitions obtained from the predicted spectra correlate highly with those measured experimentally. Crucially, this work reveals that the temperature-dependence of native fluorescence spectra contains a high-degree of previously hidden information relating native ensemble features to stability. This could lead to rapid screening of therapeutic protein variants and formulations based on spectroscopic measurements under non-denaturing temperatures only.

Toward Drug-Like Multispecific Antibodies by Design

The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.

Fine-tuning the activity and stability of an evolved enzyme active-site through noncanonical amino-acids

Site-specific saturation mutagenesis within enzyme active sites can radically alter reaction specificity, though often with a trade-off in stability. Extending saturation mutagenesis with a range of noncanonical amino acids (ncAA) potentially increases the ability to improve activity and stability simultaneously. Previously, an Escherichia coli transketolase variant (S385Y/D469T/R520Q) was evolved to accept aromatic aldehydes not converted by wild-type. The aromatic residue Y385 was critical to the new acceptor substrate binding, and so was explored here beyond the natural aromatic residues, to probe side chain structure and electronics effects on enzyme function and stability. A series of five variants introduced decreasing aromatic ring electron density at position 385 in the order para-aminophenylalanine (pAMF), tyrosine (Y), phenylalanine (F), para-cyanophenylalanine (pCNF) and para-nitrophenylalanine (pNTF), and simultaneously modified the hydrogen-bonding potential of the aromatic substituent from accepting to donating. The fine-tuning of residue 385 yielded variants with a 43-fold increase in specific activity for 50 mm 3-HBA and 100% increased k_cat (pCNF), 290% improvement in K_m (pNTF), 240% improvement in k_cat /K_m (pAMF) and decreased substrate inhibition relative to Y. Structural modelling suggested switching of the ring-substituted functional group, from donating to accepting, stabilised a helix-turn (D259-H261) through an intersubunit H-bond with G262, to give a 7.8 °C increase in the thermal transition mid-point, T_m , and improved packing of pAMF. This is one of the first examples in which both catalytic activity and stability are simultaneously improved via site-specific ncAA incorporation into an enzyme active site, and further demonstrates the benefits of expanding designer libraries to include ncAAs.

EasyAmber: A comprehensive toolbox to automate the molecular dynamics simulation of proteins

Conformational plasticity of the functionally important regions and binding sites in protein/enzyme structures is one of the key factors affecting their function and interaction with substrates/ligands. Molecular dynamics (MD) can address the challenge of accounting for protein flexibility by predicting the time-dependent behavior of a molecular system. It has a potential of becoming a particularly important tool in protein engineering and drug discovery, but requires specialized training and skills, what impedes practical use by many investigators. We have developed the easyAmber - a comprehensive set of programs to automate the molecular dynamics routines implemented in the Amber package. The toolbox can address a wide set of tasks in computational biology struggling to account for protein flexibility. The automated workflow includes a complete set of steps from the initial "static" molecular model to the MD "production run": the full-atom model building, optimization/equilibration of the molecular system, classical/conventional and accelerated molecular dynamics simulations. The easyAmber implements advanced MD protocols, but is highly automated and easy-to-operate to attract a broad audience. The toolbox can be used on a personal desktop station equipped with a compatible gaming GPU-accelerator, as well as help to manage huge workloads on a powerful supercomputer. The software provides an opportunity to operate multiple simulations of different proteins at the same time, thus significantly increasing work efficiency. The easyAmber takes the molecular dynamics to the next level in terms of usability for complex processing of large volumes of data, thus supporting the recent trend away from inefficient "static" approaches in biology toward a deeper understanding of the dynamics in protein structures. The software is freely available for download at https://biokinet.belozersky.msu.ru/easyAmber, no login required.

The uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies

The development of therapeutic monoclonal antibodies (mAbs) can be hindered by their tendency to aggregate throughout their lifetime, which can illicit immunogenic responses and render mAb manufacturing unfeasible. Consequently, there is a need to identify mAbs with desirable thermodynamic stability, solubility, and lack of self-association. These behaviors are assessed using an array of in silico and in vitro assays, as no single assay can predict aggregation and developability. We have developed an extensional and shear flow device (EFD), which subjects proteins to defined hydrodynamic forces which mimic those experienced in bioprocessing. Here, we utilize the EFD to explore the aggregation propensity of 33 IgG1 mAbs, whose variable domains are derived from clinical antibodies. Using submilligram quantities of material per replicate, wide-ranging EFD-induced aggregation (9-81% protein in pellet) was observed for these mAbs, highlighting the EFD as a sensitive method to assess aggregation propensity. By comparing the EFD-induced aggregation data to those obtained previously from 12 other biophysical assays, we show that the EFD provides distinct information compared with current measures of adverse biophysical behavior. Assessing a candidate's liability to hydrodynamic force thus adds novel insight into the rational selection of developable mAbs that complements other assays.

An Expanded Conformation of an Antibody Fab Region by X-Ray Scattering, Molecular Dynamics, and smFRET Identifies an Aggregation Mechanism

Protein aggregation is the underlying cause of many diseases, and also limits the usefulness of many natural and engineered proteins in biotechnology. Better mechanistic understanding and characterization of aggregation-prone states is needed to guide protein engineering, formulation, and drug-targeting strategies that prevent aggregation. While several final aggregated states-notably amyloids-have been characterized structurally, very little is known about the native structural conformers that initiate aggregation. We used a novel combination of small-angle x-ray scattering (SAXS), atomistic molecular dynamic simulations, single-molecule Förster resonance energy transfer, and aggregation-prone region predictions, to characterize structural changes in a native humanized Fab A33 antibody fragment, that correlated with the experimental aggregation kinetics. SAXS revealed increases in the native state radius of gyration, R_g, of 2.2% to 4.1%, at pH 5.5 and below, concomitant with accelerated aggregation. In a cutting-edge approach, we fitted the SAXS data to full MD simulations from the same conditions and located the conformational changes in the native state to the constant domain of the light chain (C_L). This C_L displacement was independently confirmed using single-molecule Förster resonance energy transfer measurements with two dual-labeled Fabs. These conformational changes were also found to increase the solvent exposure of a predicted APR, suggesting a likely mechanism through which they promote aggregation. Our findings provide a means by which aggregation-prone conformational states can be readily determined experimentally, and thus potentially used to guide protein engineering, or ligand binding strategies, with the aim of stabilizing the protein against aggregation.

Selective Stabilization and Destabilization of Protein Domains in Tissue-Type Plasminogen Activator Using Formulation Excipients

Multidomain biotherapeutic proteins present additional behavioral and analytical challenges for the optimization of their kinetic stability by formulation. Tissue-type plasminogen activator (tPA) comprises six protein domains that exhibit a complex and pH-dependent thermal unfolding profile, due to partially independent domain unfolding. Here we have used tPA as a model for evaluating the relationships between various thermal unfolding and aggregation parameters in multidomain proteins. We show that changes in the thermal unfolding profile of tPA were parametrized by the overall thermal midpoint transition temperature, T_m, and the Van't Hoff entropy for unfolding, Δ S_vh, which is a measure of unfolding cooperativity. The kinetics of degradation at 45 °C, leading to aggregation, were measured as rates of monomer and activity loss. These two rates were found to be coincident at all pH. Aggregation accelerated at pH 4 due to the early unfolding of the serine protease N-terminal domain (SP-N), whereas at pH 5-8, the fraction unfolded at 45 °C ( f₄₅) was <1%, resulting in a baseline rate of aggregation from the native ensemble. We used a Design of Experiments (DoE) approach to evaluate how formulation excipients impact and control the thermal unfolding profile for tPA and found that the relative stability of each of the tPA domains was dependent on the formulation. Therefore, the optimization of formulations for complex multidomain proteins such as tPA may need to be multiobjective, with careful selection of the desired attributes that improve stability. As aggregation rates (ln v) correlated well to T_m ( R² = 0.77) and Δ S_vh ( R² = 0.71) but not T_agg ( R² = 0.01), we analyzed how formulation excipients and pH would be able to optimize T_m and Δ S_vh. Formulation excipient behaviors were found to group according to their combined impact on T_m and Δ S_vh. The effects of each excipient were often selectively stabilizing or destabilizing to specific tPA domains and changed the stability of particular domains relative to the others. The types of mechanism by which this could occur might involve specific interactions with the protein surface, or otherwise effects that are mediated via the solvent as a result of the different surface hydrophobicities and polarities of each domain.

Exploiting correlated molecular-dynamics networks to counteract enzyme activity-stability trade-off

Rigidifying flexible sites is a powerful method to improve enzyme stability. However, if the highly flexible regions form the active site, modifying them risks losing activity due to the activity–stability trade-off. We hypothesized here that regions outside the active site whose dynamics were highly correlated to flexible active sites, would provide good targets for stabilizing mutations. To test this hypothesis, six variants were constructed in the 3M variant of Escherichia coli transketolase. The best variant had a 10.8-fold improved half-life at 55 °C, and increased the T_m and T_agg by 3 °C and 4.3 °C, respectively. The variants even increased the activity, by up to threefold. This study highlights how protein engineering strategies could be potentially improved by considering long-range dynamics.

The directed evolution of enzymes for improved activity or substrate specificity commonly leads to a trade-off in stability. We have identified an activity–stability trade-off and a loss in unfolding cooperativity for a variant (3M) of Escherichia coli transketolase (TK) engineered to accept aromatic substrates. Molecular dynamics simulations of 3M revealed increased flexibility in several interconnected active-site regions that also form part of the dimer interface. Mutating the newly flexible active-site residues to regain stability risked losing the new activity. We hypothesized that stabilizing mutations could be targeted to residues outside of the active site, whose dynamics were correlated with the newly flexible active-site residues. We previously stabilized WT TK by targeting mutations to highly flexible regions. These regions were much less flexible in 3M and would not have been selected a priori as targets using the same strategy based on flexibility alone. However, their dynamics were highly correlated with the newly flexible active-site regions of 3M. Introducing the previous mutations into 3M reestablished the WT level of stability and unfolding cooperativity, giving a 10.8-fold improved half-life at 55 °C, and increased midpoint and aggregation onset temperatures by 3 °C and 4.3 °C, respectively. Even the activity toward aromatic aldehydes increased up to threefold. Molecular dynamics simulations confirmed that the mutations rigidified the active-site via the correlated network. This work provides insights into the impact of rigidifying mutations within highly correlated dynamic networks that could also be useful for developing improved computational protein engineering strategies.

An Evaluation of the Potential of NMR Spectroscopy and Computational Modelling Methods to Inform Biopharmaceutical Formulations

Protein-based therapeutics are considered to be one of the most important classes of pharmaceuticals on the market. The growing need to prolong stability of high protein concentrations in liquid form has proven to be challenging. Therefore, significant effort is being made to design formulations which can enable the storage of these highly concentrated protein therapies for up to 2 years. Currently, the excipient selection approach involves empirical high-throughput screening, but does not reveal details on aggregation mechanisms or the molecular-level effects of the formulations under storage conditions. Computational modelling approaches have the potential to elucidate such mechanisms, and rapidly screen in silico prior to experimental testing. Nuclear Magnetic Resonance (NMR) spectroscopy can also provide complementary insights into excipient–protein interactions. This review will highlight the underpinning principles of molecular modelling and NMR spectroscopy. It will also discuss the advancements in the applications of computational and NMR approaches in investigating excipient–protein interactions.