Chemometrics in Protein Formulation: Stability Governed by Repulsion and Protein Unfolding.

Therapeutic proteins can be challenging to develop due to their complexity and the requirement of an acceptable formulation to ensure patient safety and efficacy. To date, there is no universal formulation development strategy that can identify optimal formulation conditions for all types of proteins in a fast and reliable manner. In this work, high-throughput characterization, employing a toolbox of five techniques, was performed on 14 structurally different proteins formulated in 6 different buffer conditions and in the presence of 4 different excipients. Multivariate data analysis and chemometrics were used to analyze the data in an unbiased way. First, observed changes in stability were primarily determined by the individual protein. Second, pH and ionic strength are the two most important factors determining the physical stability of proteins, where there exists a significant statistical interaction between protein and pH/ionic strength. Additionally, we developed prediction methods by partial least-squares regression. Colloidal stability indicators are important for prediction of real-time stability, while conformational stability indicators are important for prediction of stability under accelerated stress conditions at 40 °C. In order to predict real-time storage stability, protein-protein repulsion and the initial monomer fraction are the most important properties to monitor.

Dynamics of Human Serum Transferrin in Varying Physicochemical Conditions Explored by Using Molecular Dynamics Simulations.

Conformational stability of human serum transferrin (Tf) at varying pH values and salt and excipient concentrations were investigated using molecular dynamics (MD) simulations, and the results are compared with previously published small-angle X-ray scattering (SAXS) experiments. SAXS study showed that at pH 5, Tf is predominantly present in a partially open (PO) form, and the factions of PO differ based on the physicochemical condition and drift toward the closed form (HO) as the pH increases. Tf is a bilobal glycoprotein that is composed of homologous halves termed the N- and C-lobes. The current study shows that the protonation of Y188 and K206 at pH 5 is the primary conformational drive into PO, which shifts toward the closed (HO) conformer as the pH increases. Furthermore, at pH 6.5, PO is unfavorable due to negative charge-charge repulsion at the N/C-lobe interface linker region causing increased hinge distance when compared to HO, which has favorable attractive electrostatic interactions in this region. Subsequently, the effect of salt concentration was studied at 70 and 140 mM NaCl. At 70 mM NaCl and pH 5, chloride ions bind strongly in the N-lobe iron-binding site, whereas these interactions are weak at pH 6.5. With increasing salt concentration at pH 5, the regions surrounding the N-lobe iron-binding site are saturated, and as a consequence, sodium and chloride ions accumulate into the bulk. Additionally, protein-excipient interactions were investigated. At pH 5, the excipients interact in similar loop regions, E89-T93, and D416-D420, located in the N- and C-lobes of the HO conformer, respectively. It is anticipated that interactions of additives in these two loop regions cause conformational changes that lead to iron-coordinating residues in the N-lobe to drift away from iron and thus drive HO to PO conversion. Furthermore, at pH 6.5 and 140 mM histidine, these interactions are negligible leading to the stabilization of HO.

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations.

Granulocyte-colony stimulating factor (GCSF) is a widely used therapeutic protein to treat neutropenia. GCSF has an increased propensity to aggregate if the pH is increased above 5.0. Although GCSF is very well experimentally characterized, the exact pH-dependent aggregation mechanism of GCSF is still under debate. This study aimed to model the complex pH-dependent aggregation behavior of GCSF using state-of-the-art simulation techniques. The conformational stability of GCSF was investigated by performing metadynamics simulations, while the protein-protein interactions were investigated using coarse-grained (CG) simulations of multiple GCSF monomers. The CG simulations were directly compared with small-angle X-ray (SAXS) data. The metadynamics simulations demonstrated that the orientations of Trp residues in GCSF are dependent on pH. The conformational change of Trp residues is due to the loss of Trp-His interactions at the physiological pH, which in turn may increase protein flexibility. The helical structure of GCSF was not affected by the pH conditions of the simulations. Our CG simulations indicate that at pH 4.0, the colloidal stability may be more important than the conformational stability of GCSF. The electrostatic potential surface and CG simulations suggested that the basic residues are mainly responsible for colloidal stability as deprotonation of these residues causes a reduction of the highly positively charged electrostatic barrier close to the aggregation-prone long loop regions.

Chirality transmission in macromolecular domains.

Chiral communications exist in secondary structures of foldamers and copolymers via a network of noncovalent interactions within effective intermolecular force (IMF) range. It is not known whether long-range chiral communication exists between macromolecular tertiary structures such as peptide coiled-coils beyond the IMF distance. Harnessing the high sensitivity of single-molecule force spectroscopy, we investigate the chiral interaction between covalently linked DNA duplexes and peptide coiled-coils by evaluating the binding of a diastereomeric pair of three DNA-peptide conjugates. We find that right-handed DNA triple helices well accommodate peptide triple coiled-coils of the same handedness, but not with the left-handed coiled-coil stereoisomers. This chiral communication is effective in a range (<4.5 nm) far beyond canonical IMF distance. Small-angle X-ray scattering and molecular dynamics simulation indicate that the interdomain linkers are tightly packed via hydrophobic interactions, which likely sustains the chirality transmission between DNA and peptide domains. Our findings establish that long-range chiral transmission occurs in tertiary macromolecular domains, explaining the presence of homochiral pairing of superhelices in proteins.

Combination of high throughput and structural screening to assess protein stability - A screening perspective.

High throughput screening for measuring the stability of industrially relevant proteins and their variants is necessary for quality assessment in the development process. Advances in automation, measurement time and sample consumption for many techniques allow rapid measurements with minimal amount of protein. However, many methods include automated data analysis, potentially neglecting important aspects of the protein's behavior in certain conditions. In this study we implement small angle X-ray scattering (SAXS), typically not used to assess protein behavior in industrial screening, in a high throughput screening workflow to address problems of contradicting results and reproducibility among different high throughput methods. As a case study we use the lipases of Thermomyces lanuginosus and Rhizomucor miehei, widely used industrial biocatalysts. We show that even the initial analysis of the SAXS data without performing any time-consuming modelling provide valuable information on interparticle interactions. We conclude that recent advances in automation and data processing, have enabled SAXS to be used more widely as a tool to gain in-depth knowledge highly useful for protein formulation development. This is especially relevant in light of increasing accessibility to SAXS due to the commercial availability of benchtop instruments.

A Multi-Method Approach to Assess the Self-Interaction Behavior of Infliximab.

Attractive self-interaction processes in antibody formulations increase the risk of aggregation and extraordinarily elevated viscosity at high protein concentrations. These challenges affect manufacturing and application. This study aimed to understand the self-interaction process of Infliximab as a model system with pronounced attractive self-interaction. The association mechanism was studied by a multi-method approach comprising analytical ultracentrifugation, dynamic light scattering, small angle X-ray scattering, self-interaction bio-layer interferometry and hydrogen-deuterium exchange mass spectrometry. Based on our results, both Fab and Fc regions of Infliximab are involved in self-interaction. We hypothesize a mechanism based on electrostatic interactions of polar and charged residues within the identified areas of the heavy and the light chain of the mAb. The combination of fast and reliable screening methods and low throughput but high resolution methods can contribute to detailed characterization and deeper understanding of specific self-interaction processes.

The extracellular juncture domains in the intimin passenger adopt a constitutively extended conformation inducing restraints to its sphere of action.

Enterohemorrhagic and enteropathogenic Escherichia coli are among the most important food-borne pathogens, posing a global health threat. The virulence factor intimin is essential for the attachment of pathogenic E. coli to the intestinal host cell. Intimin consists of four extracellular bacterial immunoglobulin-like (Big) domains, D00-D2, extending into the fifth lectin subdomain (D3) that binds to the Tir-receptor on the host cell. Here, we present the crystal structures of the elusive D00-D0 domains at 1.5 Å and D0-D1 at 1.8 Å resolution, which confirms that the passenger of intimin has five distinct domains. We describe that D00-D0 exhibits a higher degree of rigidity and D00 likely functions as a juncture domain at the outer membrane-extracellular medium interface. We conclude that D00 is a unique Big domain with a specific topology likely found in a broad range of other inverse autotransporters. The accumulated data allows us to model the complete passenger of intimin and propose functionality to the Big domains, D00-D0-D1, extending directly from the membrane.

Small angle X-ray scattering and molecular dynamic simulations provide molecular insight for stability of recombinant human transferrin.

Transferrin is an attractive candidate for drug delivery due to its ability to cross the blood brain barrier. However, in order to be able to use it for therapeutic purposes, it is important to investigate how its stability depends on different formulation conditions. Combining high-throughput thermal and chemical denaturation studies with small angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations, it was possible to connect the stability of transferrin with its conformational changes. Lowering pH induces opening of the transferrin N-lobe, which results in a negative effect on the stability. Presence of NaCl or arginine at low pH enhances the opening and has a negative impact on the overall protein stability. STATEMENT OF SIGNIFICANCE: Protein-based therapeutics have become an essential part of medical treatment. They are highly specific, have high affinity and fewer off-target effects. However, stabilization of proteins is critical, time-consuming, and expensive, and it is not yet possible to predict the behavior of proteins under different conditions. The current work is focused on a molecular understanding of the stability of human serum transferrin; a protein which is abundant in blood serum, may pass the blood brain barrier and therefore with high potential in drug delivery. Combination of high throughput unfolding techniques and structural studies, using small angle X-ray scattering and molecular dynamic simulations, allows us to understand the behavior of transferrin on a molecular level.

Albumin-neprilysin fusion protein: understanding stability using small angle X-ray scattering and molecular dynamic simulations.

Fusion technology is widely used in protein-drug development to increase activity, stability, and bioavailability of protein therapeutics. Fusion proteins, like any other type of biopharmaceuticals, need to remain stable during production and storage. Due to the high complexity and additional intramolecular interactions, it is not possible to predict the behavior of fusion proteins based on the behavior the individual proteins. Therefore, understanding the stability of fusion proteins on the molecular level is crucial for the development of biopharmaceuticals. The current study on the albumin-neprilysin (HSA-NEP) fusion protein uses a combination of thermal and chemical unfolding with small angle X-ray scattering and molecular dynamics simulations to show a correlation between decreasing stability and increasing repulsive interactions, which is unusual for most biopharmaceuticals. It is also seen that HSA-NEP is not fully flexible: it is present in both compact and extended conformations. Additionally, the volume fraction of each conformation changes with pH. Finally, the presence of NaCl and arginine increases stability at pH 6.5, but decreases stability at pH 5.0.

Isothermal Chemical Denaturation: Data Analysis, Error Detection, and Correction by PARAFAC2.

Characterization of a protein's conformational stability is a key step in the development of biotherapeutics, where protein unfolding leads to adverse properties, such as aggregation and loss of efficacy. Isothermal chemical denaturation (ICD) can be applied to determine chemical stability, aiming to identify the optimal solvent conditions, in terms of pH, salt concentration, and added excipients. For seven monoclonal antibodies, this study investigates the observed intrinsic protein fluorescence emission spectra as a function of denaturant concentration. Protein formulations are screened in two experimental series. We show how the peak shapes of folded and unfolded proteins are preserved under added salt (0-140 mM NaCl) and added excipients concentrations, as typically found in biotherapeutic formulations and that only minor effects in tryptophan fluorescence peak tailing are observed over a large pH range (5.5-9.0). The data of seven mAbs, where GuHCl was a suitable denaturant, are modeled using PARAFAC2. PARAFAC2, a linear decomposition method, is well suited for the data and yields robust, valid, and automated models that allow for the detection of erroneous measurements. Analysis of the errors show correlation with the well-based experimental setup, and differences in observed errors between the two experimental series. We additionally show a correction method for these outliers based on PARAFAC2 model scores, such that full transition curves can be retrieved, increasing the accuracy of any subsequent analysis.

Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization.

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.

Orthogonal Techniques to Study the Effect of pH, Sucrose, and Arginine Salts on Monoclonal Antibody Physical Stability and Aggregation During Long-Term Storage.

Understanding the effects of additives on therapeutic protein stability is of paramount importance for obtaining stable formulations. In this work, we apply several high- and medium-throughput methods to study the physical stability of a model monoclonal antibody at pH 5.0 and 6.5 in the presence of sucrose, arginine hydrochloride, and arginine glutamate. In low ionic strength buffer, the addition of salts reduces the antibody colloidal and thermal stability, attributed to screening of electrostatic interactions. The presence of glutamate ion in the arginine salt partially reduces the damaging effect of ionic strength increase. The addition of 280 mM sucrose shifts the thermal protein unfolding to a higher temperature. Arginine salts in the used concentration reduce the relative monomer yield after refolding from urea, whereas sucrose has a favorable effect on antibody refolding. In addition, we show 12-month long-term stability data and observe correlations between thermal protein stability, relative monomer yield after refolding, and monomer loss during storage. The monomer loss during storage is related to protein aggregation and formation of subvisible particles in some of the formulations. This study shows that the effect of commonly used additives on the long-term antibody physical stability can be predicted using orthogonal biophysical measurements.

Novel non-linear curve fitting to resolve protein unfolding transitions in intrinsic fluorescence differential scanning fluorimetry.

In biotherapeutic protein research, an estimation of the studied protein's thermal stability is one of the important steps that determine developability as a function of solvent conditions. Differential Scanning Fluorimetry (DSF) can be applied to measure thermal stability. Label-free DSF measures amino acid fluorescence as a function of temperature, where conformational changes induce observable peak deformation, yielding apparent melting temperatures. The estimation of the stability parameters can be hindered in the case of multidomain, multimeric or aggregating proteins when multiple transitions partially coincide. These overlapping protein unfolding transitions are hard to evaluate by the conventional methodology, as peak maxima are shifted by convolution. We show how non-linear curve fitting of intrinsic fluorescence DSF can deconvolute highly overlapping transitions in formulation screening in a semi-automated process. The proposed methodology relies on synchronous, constrained fits of the fluorescence intensity, ratio and their derivatives, by combining linear baselines with generalized logistic transition functions. The proposed algorithm is applied to data from three proteins; a single transition, a double separated transition and a double overlapping transition. Extracted thermal stability parameters; apparent melting temperatures Tm,1, Tm,2 and melting onset temperature Tonset are obtained and compared with reference software analysis. The fits show R2 = 0.94 for single and R2 = 0.88 for separated transitions. Obtaining values and trends for Tonset in a well-described and automated way, will aid protein scientist to better evaluate the thermal stability of proteins.

Application of interpretable artificial neural networks to early monoclonal antibodies development.

The development of a new protein drug typically starts with the design, expression and biophysical characterization of many different protein constructs. The initially high number of constructs is radically reduced to a few candidates that exhibit the desired biological and physicochemical properties. This process of protein expression and characterization to find the most promising molecules is both expensive and time-consuming. Consequently, many companies adopt and implement philosophies, e.g. platforms for protein expression and formulation, computational approaches, machine learning, to save resources and facilitate protein drug development. Inspired by this, we propose the use of interpretable artificial neuronal networks (ANNs) to predict biophysical properties of therapeutic monoclonal antibodies i.e. melting temperature Tm, aggregation onset temperature Tagg, interaction parameter kD as a function of pH and salt concentration from the amino acid composition. Our ANNs were trained with typical early-stage screening datasets achieving high prediction accuracy. By only using the amino acid composition, we could keep the ANNs simple which allows for high general applicability, robustness and interpretability. Finally, we propose a novel "knowledge transfer" approach, which can be readily applied due to the simple algorithm design, to understand how our ANNs come to their conclusions.