Investigation of protein retention and selectivity in HIC systems using quantitative structure retention relationship models

Modeling the behavior of monoclonal antibodies on hydrophobic interaction chromatography resins

Monoclonal antibodies (mAbs) require a high level of purity for regulatory approval and safe administration. High-molecular weight (HMW) species are a common impurity associated with mAb therapies. Hydrophobic interaction chromatography (HIC) resins are often used to remove these HMW impurities. Determination of a suitable HIC resin can be a time and resource-intensive process. In this study, we modeled the chromatographic behavior of seven mAbs across 13 HIC resins using measurements of surface hydrophobicity, surface charge, and thermal stability for mAbs, and hydrophobicity and zeta-potential for HIC resins with high fit quality (adjusted R2 > 0.80). We identified zeta-potential as a novel key modeling parameter. When using these models to select a HIC resin for HMW clearance of a test mAb, we were able to achieve 60% HMW clearance and 89% recovery. These models can be used to expedite the downstream process development for mAbs in an industry setting.

Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.

Systematic conformation-to-phenotype mapping via limited deep sequencing of proteins

Non-native conformations drive protein-misfolding diseases, complicate bioengineering efforts, and fuel molecular evolution. No current experimental technique is well suited for elucidating them and their phenotypic effects. Especially intractable are the transient conformations populated by intrinsically disordered proteins. We describe an approach to systematically discover, stabilize, and purify native and non-native conformations, generated in vitro or in vivo, and directly link conformations to molecular, organismal, or evolutionary phenotypes. This approach involves high-throughput disulfide scanning (HTDS) of the entire protein. To reveal which disulfides trap which chromatographically resolvable conformers, we devised a deep-sequencing method for double-Cys variant libraries of proteins that precisely and simultaneously locates both Cys residues within each polypeptide. HTDS of the abundant E. coli periplasmic chaperone HdeA revealed distinct classes of disordered hydrophobic conformers with variable cytotoxicity depending on where the backbone was cross-linked. HTDS can bridge conformational and phenotypic landscapes for many proteins that function in disulfide-permissive environments.

Effect of salt modulators on the elution behavior of insulin and the separation of product-related impurities in reversed-phase chromatography

In this work, the effect of the salt modulators potassium chloride, ammonium chloride, ammonium sulfate, and potassium sulfate on the elution behavior of insulin in reversed-phase chromatography with ethanol as the organic modifier was investigated. Without the addition of salt modulators, insulin shows the formation of multiple peaks under non-linear loading conditions, presumably due to an aggregate formation equilibrium. Flow rate and temperature did not influence the appearance of multiple peaks. The addition of chloride and sulfate salt modulators changed the monomer-multimer equilibrium, and multi-peak formation no longer occurred. Chloride salts induce a Langmuirian elution behavior, whereas sulfate salts induce additional insulin-insulin interactions resulting in an anti-Langmuirian elution behavior. The elution behavior can be influenced by the combination of both chloride and sulfate salts and by varying the concentration ratio. The separation with respect to two product-related impurities also showed significant differences under Langmuirian and anti-Langmuirian elution conditions and the purification of insulin could be optimized. Induced anti-Langmuirian elution by lowering the chloride/sulfate ratio suppresses an observed tag-along effect of one variant resulting in a slightly smaller pool volume with increased insulin concentration and a significantly increased insulin recovery.

A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics

In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.

Development of QSAR models for in silico screening of antibody solubility

Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R² of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.

QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural Representations

Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by high failure rates originating from failed clinical trials and developability issues in process development. There is, therefore, a growing need for better in silico tools to aid in risk assessment of mAb candidates to promote early-stage screening of potentially problematic mAb candidates. In this study, a quantitative structure–activity relationship (QSAR) modelling workflow was designed for the prediction of hydrophobic interaction chromatography (HIC) retention times of mAbs. Three novel descriptor sets derived from primary sequence, homology modelling, and atomistic molecular dynamics (MD) simulations were developed and assessed to determine the necessary level of structural resolution needed to accurately capture the relationship between mAb structures and HIC retention times. The results showed that descriptors derived from 3D structures obtained after MD simulations were the most suitable for HIC retention time prediction with a R² = 0.63 in an external test set. It was found that when using homology modelling, the resulting 3D structures became biased towards the used structural template. Performing an MD simulation therefore proved to be a necessary post-processing step for the mAb structures in order to relax the structures and allow them to attain a more natural conformation. Based on the results, the proposed workflow in this paper could therefore potentially contribute to aid in risk assessment of mAb candidates in early development.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Comparative analysis of the methods used for finding surface energy to investigate protein interaction behavior on chromatographic supports

Hydrophobic interaction chromatography, an important and effective purification strategy, is generally used for the purification of variety of biomolecules. A basic understanding of the protein interaction behavior is required to effectively separate these biomolecules. A colloidal type extended Derjaguin, Landau, Verwey, and Overbeek calculations were utilized to study the interactions behavior of model proteins to commercially available hydrophobic chromatographic materials that is, Toyopearl Phenyl 650C and Toyopearl Butyl 650C. Physicochemical properties of selected model proteins were achieved by contact angle and zeta potential measurements. The contact angle of chromatographic materials used was achieved through sessile drop method on disrupted beads and capillary penetration method (CPM) on intact beads. The surface properties were further used to calculate the interactions of the proteins to chromatographic supports. The calculated secondary energy minimum of the proteins with the chromatographic materials (from the contact angle values determined through both methods can be correlated with the retention volumes from the real chromatography. The secondary energy minimum values are higher for each protein to the chromatographic materials calculated from the inputs derived through sessile drop method compared to CPM. For instance, immunoglobulin G has secondary energy minimum value of 0.17 kT compared to 0.11 kT, obtained through sessile drop method and CPM, respectively. Average relative values of the energy minimum calculated for all proteins are as 1.51 kT and 1.29 kT for Toyopearl Butyl 650C and Toyopearl Phenyl 650C, respectively, as a conversion factor for estimation of secondary energy minimum for both methods.

Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients

Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc.

Investigation and prediction of protein precipitation by polyethylene glycol using quantitative structure-activity relationship models

Precipitation of proteins is considered to be an effective purification method for proteins and has proven its potential to replace costly chromatography processes. Besides salts and polyelectrolytes, polymers, such as polyethylene glycol (PEG), are commonly used for precipitation applications under mild conditions. Process development, however, for protein precipitation steps still is based mainly on heuristic approaches and high-throughput experimentation due to a lack of understanding of the underlying mechanisms. In this work we apply quantitative structure-activity relationships (QSARs) to model two parameters, the discontinuity point m* and the β-value, that describe the complete precipitation curve of a protein under defined conditions. The generated QSAR models are sensitive to the protein type, pH, and ionic strength. It was found that the discontinuity point m* is mainly dependent on protein molecular structure properties and electrostatic surface properties, whereas the β-value is influenced by the variance in electrostatics and hydrophobicity on the protein surface. The models for m* and the β-value exhibit a good correlation between observed and predicted data with a coefficient of determination of R²≥0.90 and, hence, are able to accurately predict precipitation curves for proteins. The predictive capabilities were demonstrated for a set of combinations of protein type, pH, and ionic strength not included in the generation of the models and good agreement between predicted and experimental data was achieved.

Combined effects of potassium chloride and ethanol as mobile phase modulators on hydrophobic interaction and reversed-phase chromatography of three insulin variants

The two main chromatographic modes based on hydrophobicity, hydrophobic interaction chromatography (HIC) and reversed-phase chromatography (RPC), are widely used for both analytical and preparative chromatography of proteins in the pharmaceutical industry. Despite the extensive application of these separation methods, and the vast amount of studies performed on HIC and RPC over the decades, the underlying phenomena remain elusive. As part of a systematic study of the influence of mobile phase modulators in hydrophobicity-based chromatography, we have investigated the effects of both KCl and ethanol on the retention of three insulin variants on two HIC adsorbents and two RPC adsorbents. The focus was on the linear adsorption range, separating the modulator effects from the capacity effects, but some complementary experiments at higher load were included to further investigate observed phenomena. The results show that the modulators have the same effect on the two RPC adsorbents in the linear range, indicating that the modulator concentration only affects the activity of the solute in the mobile phase, and not that of the solute-ligand complex, or that of the ligand. Unfortunately, the HIC adsorbents did not show the same behavior. However, the insulin variants displayed a strong tendency toward self-association on both HIC adsorbents; on one in particular. Since this causes peak fronting, the retention is affected, and this could probably explain the lack of congruity. This conclusion was supported by the results from the non-linear range experiments which were indicative of double-layer adsorption on the HIC adsorbents, while the RPC adsorbents gave the anticipated increased tailing at higher load.

Thermodynamics of coupled protein adsorption and stability using hybrid Monte Carlo simulations

A better understanding of changes in protein stability upon adsorption can improve the design of protein separation processes. In this study, we examine the coupling of the folding and the adsorption of a model protein, the B1 domain of streptococcal protein G, as a function of surface attraction using a hybrid Monte Carlo (HMC) approach with temperature replica exchange and umbrella sampling. In our HMC implementation, we are able to use a molecular dynamics (MD) time step that is an order of magnitude larger than in a traditional MD simulation protocol and observe a factor of 2 enhancement in the folding and unfolding rate. To demonstrate the convergence of our systems, we measure the travel of our order parameter the fraction of native contacts between folded and unfolded states throughout the length of our simulations. Thermodynamic quantities are extracted with minimum statistical variance using multistate reweighting between simulations at different temperatures and harmonic distance restraints from the surface. The resultant free energies, enthalpies, and entropies of the coupled unfolding and absorption processes are in qualitative agreement with previous experimental and computational observations, including entropic stabilization of the adsorbed, folded state relative to the bulk on surfaces with low attraction.

The role of ligands on protein retention in adsorption chromatography: a surface energetics approach

Protein adsorption onto hydrophobic chromatographic supports has been investigated using a colloid theory surface energetics approach. The surface properties of commercially available chromatographic beads, Toyopearl Phenyl 650-C, and Toyopearl Butyl 650-C, have been experimentally determined by contact angle and zeta potential measurements. The adsorption characteristics of these beads, which bear the same backbone matrix but harbor different ligands, have been studied toward selected model proteins, in the hydrated as well as dehydrated state. There were two prominent groups of proteins observed with respect to the chromatographic supports presented in this work: loosely retained proteins, which were expected to have lower average interaction energies, and the strongly retained proteins, which were expected to have higher average interaction energies. Results were also compared and contrasted with calculations derived from adsorbent surface energies determined by inverse liquid chromatography. These results showed a good qualitative agreement, and the interaction energy minima obtained from these extended Derjaguin, Landau, Verwey and Overbeek calculations were shown to correlate well with the experimentally determined adsorption behavior of each protein.

The use of quantitative structure-activity relationship models to develop optimized processes for the removal of tobacco host cell proteins during biopharmaceutical production

The production of recombinant pharmaceutical proteins in plants benefits from the low cost of upstream production and the greater scalability of plants compared to fermenter-based systems. Now that manufacturing processes that comply with current good manufacturing practices have been developed, plants can compete with established platforms on equal terms. However, the costs of downstream processing remain high, in part because of the dedicated process steps required to remove plant-specific process-related impurities. We therefore investigated whether the ideal strategy for the chromatographic removal of tobacco host cell proteins can be predicted by quantitative structure-activity relationship (QSAR) modeling to reduce the process development time and overall costs. We identified more than 100 tobacco proteins by mass spectrometry and their structures were reconstructed from X-ray crystallography, nuclear magnetic resonance spectroscopy and/or homology modeling data. The resulting three-dimensional models were used to calculate protein descriptors, and significant descriptors were selected based on recently-published retention data for model proteins to develop QSAR models for protein retention on anion, cation and mixed-mode resins. The predicted protein retention profiles were compared with experimental results using crude tobacco protein extracts. Because of the generic nature of the method, it can easily be transferred to other expression systems such as mammalian cells. The quality of the models and potential improvements are discussed.

Mechanisms for covalent immobilization of horseradish peroxidase on ion-beam-treated polyethylene

The surface of polyethylene was modified by plasma immersion ion implantation. Structure changes including carbonization and oxidation were observed. High surface energy of the modified polyethylene was attributed to the presence of free radicals on the surface. The surface energy decay with storage time after treatment was explained by a decay of the free radical concentration while the concentration of oxygen-containing groups increased with storage time. Horseradish peroxidase was covalently attached onto the modified surface by the reaction with free radicals. Appropriate blocking agents can block this reaction. All aminoacid residues can take part in the covalent attachment process, providing a universal mechanism of attachment for all proteins. The native conformation of attached protein is retained due to hydrophilic interactions in the interface region. The enzymatic activity of covalently attached protein remained high. The long-term activity of the modified layer to attach protein is explained by stabilisation of unpaired electrons in sp(2) carbon structures. A high concentration of free radicals can give multiple covalent bonds to the protein molecule and destroy the native conformation and with it the catalytic activity. The universal mechanism of protein attachment to free radicals could be extended to various methods of radiation damage of polymers.

High-throughput protein precipitation and hydrophobic interaction chromatography: salt effects and thermodynamic interrelation

Salt-induced protein precipitation and hydrophobic interaction chromatography (HIC) are two widely used methods for protein purification. In this study, salt effects in protein precipitation and HIC were investigated for a broad combination of proteins, salts and HIC resins. Interrelation between the critical thermodynamic salting out parameters in both techniques was equally investigated. Protein precipitation data were obtained by a high-throughput technique employing 96-well microtitre plates and robotic liquid handling technology. For the same protein-salt combinations, isocratic HIC experiments were performed using two or three different commercially available stationary phases-Phenyl Sepharose low sub, Butyl Sepharose and Resource Phenyl. In general, similar salt effects and deviations from the lyotropic series were observed in both separation methods, for example, the reverse Hofmeister effect reported for lysozyme below its isoelectric point and at low salt concentrations. The salting out constant could be expressed in terms of the preferential interaction parameter in protein precipitation, showing that the former is, in effect, the net result of preferential interaction of a protein with water molecules and salt ions in its vicinity. However, no general quantitative interrelation was found between salting out parameters or the number of released water molecules in protein precipitation and HIC. In other words, protein solubility and HIC retention factor could not be quantitatively interrelated, although for some proteins, regular trends were observed across the different resins and salt types.

Effect of low molecular weight additives on immobilization strength, activity, and conformation of protein immobilized on PVC and UHMWPE

Horseradish peroxidase (HRP) was immobilized onto both plasticized and unplasticized polyvinylchloride (PVC) and ultrahigh molecular weight polyethylene (UHMWPE). Plasma immersion ion implantation (PIII) in a nitrogen plasma with 20 kV bias was used to facilitate covalent immobilization and to improve the wettability of the surfaces. The surfaces and immobilized protein were studied using attenuated total reflection infrared (ATR-IR) spectroscopy and water contact angle measurements. Protein elution on exposure to repeated sodium dodecyl sulfate (SDS) washing was used to assess the strength of HRP immobilization. The presence of low molecular weight components (plasticizer, additives in solvent, unreacted monomers, adsorbed molecules on surface) was found to have a major influence on the strength of immobilization and the conformation of the protein on the samples not exposed to the PIII treatment. A phenomenological model considering interactions between the low molecular weight components, the protein molecule, and the surface is developed to explain these observations.

Classification of protein binding in hydroxyapatite chromatography: synergistic interactions on the molecular scale

A protein library exhibiting a range of properties was employed to study protein binding behavior in hydroxyapatite systems. Chromatographic retention on ceramic hydroxyapatite (CHT) chromatography was determined using a sodium chloride gradient in the presence of different phosphate concentrations. Results from the column experiments were then analyzed using various quantitative structure property relationship (QSPR) based modeling approaches. Using the experimental data set in concert with new molecular descriptors, QSPR classification models were generated to provide improved understanding of protein binding in CHT systems. In addition, nonlinear SVM QSPR prediction models were generated and employed as a predictive tool for protein affinity in CHT. Interestingly, a class of descriptors which describe synergistic binding with both metal chelation and cation exchange interactions on the angstrom length scale was found to play a vital role for protein binding in all of the models developed for CHT. The importance of this descriptor suggests the importance of synergistic binding in CHT, which has not been previously described in the literature. This study provides a deeper understanding into the mechanisms and selectivity of protein adsorption in CHT and will help to create predictive models which could be used for methods development in bioseparation processes.

Methods of calculating protein hydrophobicity and their application in developing correlations to predict hydrophobic interaction chromatography retention

Hydrophobic interaction chromatography (HIC) is a key technique for protein separation and purification. Different methodologies to estimate the hydrophobicity of a protein are reviewed, which have been related to the chromatographic behavior of proteins in HIC. These methodologies consider either knowledge of the three-dimensional structure or the amino acid composition of proteins. Despite some restrictions; they have proven to be useful in predicting protein retention time in HIC.

Informatics for peptide retention properties in proteomic LC-MS

Retention times in HPLC yield valuable information for the identification of various analytes and the prediction of peptide retention is useful for the identification of peptides/proteins in LC-MS-based proteomics. Informatics methods such as artificial neural networks and support vector machines capable of solving nonlinear problems made possible the accurate modeling of quantitative structure-retention relationships of peptides (including large polymers) up to 5 kDa to which classical linear models cannot be applied, as well as the proteome-wide prediction of peptide retention. Proteome-wide retention prediction and accurate mass-information facilitate the identification of peptides in complex proteomic samples. In this review, we address recent developments in solid informatics methods and their application to peptide-retention properties in 'bottom-up' shotgun proteomics. We also describe future prospects for the standardization and application of retention times.

Purification of an Fc-fusion biologic: Clearance of multiple product related impurities by hydrophobic interaction chromatography

An hydrophobic interaction chromatography step was developed for the large-scale production of an Fc-fusion biologic. Two abundant product-related impurities were separated from the active monomer using a Butyl resin and a simple step-wash and step-elution strategy. Capacity and resolution of the HIC step was optimal when sodium sulfate was employed as the lyotropic salt and pore size of the Butyl resin was 750A. Factorial analysis identified critical parameters for the Butyl chromatography and an operating window capable of delivering high product quality and yield over a broad column loading range.

Protein adsorption isotherm behavior in hydrophobic interaction chromatography

The adsorption behavior of proteins in hydrophobic interaction chromatography (HIC) was evaluated by determining the isotherms of a wide range of proteins on various HIC resin systems. Parallel batch experiments were carried out with eleven proteins on three hydrophobic resins with different ligand chemistries and densities. The effects of salt concentration, resin chemistry and protein properties on the isotherms were also examined. The resulting isotherms exhibited unique patterns of adsorption behaviors. For certain protein-resin combinations, a "critical salt behavior" was observed where the amount of protein bound to the resin increased significantly above this salt concentration. Proteins that exhibited this behavior tended to be relatively large with more solvent accessible hydrophobic surface area. Further, calculations indicated that under these conditions the occupied surface area of the adsorbed protein layer could exceed the accessible surface area. The establishment of unique classes of adsorption behavior may shed light on our understanding of the behavior of proteins in HIC systems.

Generalizing a two-conformation model for describing salt and temperature effects on protein retention and stability in hydrophobic interaction chromatography

A two-conformation adsorption model that includes the effects of salt concentration and temperature on both stability and adsorption has been developed to describe the effects of secondary protein unfolding on hydrophobic interaction chromatography (HIC). The model has been applied to a biotech protein and to beta-lactoglobulin on Phenyl Sepharose 6FF low sub HIC media. Thermodynamic property models for adsorption and protein stability with parameters obtained from experimental chromatographic data successfully describe observed chromatographic behavior over ranges of temperature and salt concentration, provide predictions of distribution among different conformers, and give a basis for calculating trends in retention strength and stability with changing conditions, that might prove useful in HIC process development.

Optimal operation conditions for protein separation in hydrophobic interaction chromatography

Protein retention in hydrophobic interaction chromatography is determined by protein physicochemical properties and by system characteristics. In this paper we present an attempt to determine the optimal operation conditions that would allow the separation of binary protein mixtures. The statistically significant system variables were determined, and then empirical models were obtained which explained more than 92% of variability in dimensionless retention time based on salt properties, ionic strength of the initial eluent and substitution degree of the resin. These variables were optimized in order to achieve the maximum retention time difference between two proteins in a mixture. The optimum operation conditions as predicted by the models were tested experimentally, showing a good agreement with predicted separation. We concluded that it would be possible to determine the system conditions that allow the maximum separation of two proteins based on the main system properties. The methodology proposed here presents potential to be applied to partially characterized systems, however, it could be improved if protein's properties were included explicitly in the models.

Current insights on protein behaviour in hydrophobic interaction chromatography

This paper gives a summary of different aspects for predicting protein behaviour in hydrophobic interaction chromatography (HIC). First, a brief description of HIC, hydrophobic interactions, amino acid and protein hydrophobicity is presented. After that, several factors affecting protein chromatographic behaviour in HIC are described. Finally, different approaches for predicting protein retention time in HIC are shown. Using all this information, it could be possible to carry out computational experiments by varying the different operating conditions for the purification of a target protein; and then selecting the best conditions in silico and designing a rational protein purification process involving an HIC step.