New approaches for predicting protein retention time in hydrophobic interaction chromatography

PEP-Patch: Electrostatics in Protein-Protein Recognition, Specificity, and Antibody Developability

The electrostatic properties of proteins arise from the number and distribution of polar and charged residues. Electrostatic interactions in proteins play a critical role in numerous processes such as molecular recognition, protein solubility, viscosity, and antibody developability. Thus, characterizing and quantifying electrostatic properties of a protein are prerequisites for understanding these processes. Here, we present PEP-Patch, a tool to visualize and quantify the electrostatic potential on the protein surface in terms of surface patches, denoting separated areas of the surface with a common physical property. We highlight its applicability to elucidate protease substrate specificity and antibody-antigen recognition and predict heparin column retention times of antibodies as an indicator of pharmacokinetics.

SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody

Therapeutic antibodies play a crucial role in the treatment of various diseases. However, the success rate of antibody drug development is low partially because of unfavourable biophysical properties of antibody drug candidates such as the high aggregation tendency, which is mainly driven by hydrophobic interactions of antibody molecules. Therefore, early screening of the risk of hydrophobic interaction of antibody drug candidates is crucial. Experimental screening is laborious, time-consuming, and costly, warranting the development of efficient and high-throughput computational tools for prediction of hydrophobic interactions of therapeutic antibodies. In the present study, 131 antibodies with hydrophobic interaction experiment data were used to train a new support vector machine-based ensemble model, termed SSH2.0, to predict the hydrophobic interactions of antibodies. Feature selection was performed against CKSAAGP by using the graph-based algorithm MRMD2.0. Based on the antibody sequence, SSH2.0 achieved the sensitivity and accuracy of 100.00 and 83.97%, respectively. This approach eliminates the need of three-dimensional structure of antibodies and enables rapid screening of therapeutic antibody candidates in the early developmental stage, thereby saving time and cost. In addition, a web server was constructed that is freely available at http://i.uestc.edu.cn/SSH2/.

SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences

Therapeutic antibodies are one of the most important parts of the pharmaceutical industry. They are widely used in treating various diseases such as autoimmune diseases, cancer, inflammation, and infectious diseases. Their development process however is often brought to a standstill or takes a longer time and is then more expensive due to their hydrophobicity problems. Hydrophobic interactions can cause problems on half-life, drug administration, and immunogenicity at all stages of antibody drug development. Some of the most widely accepted and used technologies for determining the hydrophobic interactions of antibodies include standup monolayer adsorption chromatography (SMAC), salt-gradient affinity-capture self-interaction nanoparticle spectroscopy (SGAC-SINS), and hydrophobic interaction chromatography (HIC). However, to measure SMAC, SGAC-SINS, and HIC for hundreds of antibody drug candidates is time-consuming and costly. To save time and money, a predictor called SSH is developed. Based on the antibody's sequence only, it can predict the hydrophobic interactions of monoclonal antibodies (mAbs). Using the leave-one-out crossvalidation, SSH achieved 91.226% accuracy, 96.396% sensitivity or recall, 84.196% specificity, 87.754% precision, 0.828 Mathew correlation coefficient (MCC), 0.919 f-score, and 0.961 area under the receiver operating characteristic (ROC) curve (AUC).

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.

Accounting for host cell protein behavior in anion-exchange chromatography

Host cell proteins (HCP) are a problematic set of impurities in downstream processing (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning followed by two-dimensional electrophoresis provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error value for each database. This measure never exceeded a value of 0.045 (fraction of protein populating each of the multiple separation fractions) for AEX. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1453-1463, 2016.

Prediction of protein retention times in hydrophobic interaction chromatography by robust statistical characterization of their atomic-level surface properties

The correlation between the dimensionless retention times (DRT) of proteins in hydrophobic interaction chromatography (HIC) and their surface properties were investigated. A ternary atomic-level hydrophobicity scale was used to calculate the distribution of local average hydrophobicity across the proteins surfaces. These distributions were characterized by robust descriptive statistics to reduce their sensitivity to small changes in the three-dimensional structure. The applicability of these statistics for the prediction of protein retention behaviour was looked into. A linear combination of robust statistics describing the central tendency, heterogeneity and frequency of highly hydrophobic clusters was found to have a good predictive capability (R2  = 0.78), when combined a factor to account for protein size differences. The achieved error of prediction was 35% lower than for a similar model based on a description of the protein surface on an amino acid level. This indicates that a robust and mathematically simple model based on an atomic description of the protein surface can be used for the prediction of the retention behaviour of conformationally stable globular proteins with a well determined 3D structure in HIC. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:372-381, 2016.

Semi-automated hydrophobic interaction chromatography column scouting used in the two-step purification of recombinant green fluorescent protein

Background

Hydrophobic interaction chromatography (HIC) most commonly requires experimental determination (i.e., scouting) in order to select an optimal chromatographic medium for purifying a given target protein. Neither a two-step purification of untagged green fluorescent protein (GFP) from crude bacterial lysate using sequential HIC and size exclusion chromatography (SEC), nor HIC column scouting elution profiles of GFP, have been previously reported.

Methods and Results

Bacterial lysate expressing recombinant GFP was sequentially adsorbed to commercially available HIC columns containing butyl, octyl, and phenyl-based HIC ligands coupled to matrices of varying bead size. The lysate was fractionated using a linear ammonium phosphate salt gradient at constant pH. Collected HIC eluate fractions containing retained GFP were then pooled and further purified using high-resolution preparative SEC. Significant differences in presumptive GFP elution profiles were observed using in-line absorption spectrophotometry (A₃₉₅) and post-run fluorimetry. SDS-PAGE and western blot demonstrated that fluorometric detection was the more accurate indicator of GFP elution in both HIC and SEC purification steps. Comparison of composite HIC column scouting data indicated that a phenyl ligand coupled to a 34 µm matrix produced the highest degree of target protein capture and separation.

Conclusions

Conducting two-step protein purification using the preferred HIC medium followed by SEC resulted in a final, concentrated product with >98% protein purity. In-line absorbance spectrophotometry was not as precise of an indicator of GFP elution as post-run fluorimetry. These findings demonstrate the importance of utilizing a combination of detection methods when evaluating purification strategies. GFP is a well-characterized model protein, used heavily in educational settings and by researchers with limited protein purification experience, and the data and strategies presented here may aid in development other of HIC-compatible protein purification schemes.

Solubility evaluation of murine hybridoma antibodies

The successful development of antibody therapeutics depends on the molecules having properties that are suitable for manufacturing, as well as use by patients. Because high solubility is a desirable property for antibodies, screening for solubility has become an essential step during the early candidate selection process. In considering the screening process, we formed a hypothesis that hybridoma antibodies are filtered by nature to possess high solubility and tested this hypothesis using a large number of murine hybridoma-derived antibodies. Using the cross-interaction chromatography (CIC) method, we screened the solubility of 92 murine hybridoma-derived monoclonal antibodies and found that all of these molecules exhibited CIC profiles that are indicative of high solubility (>100mg/mL). Further investigations revealed that variable region N-linked glycosylation or isoelectric parameters are unlikely to contribute to the high solubility of these antibodies. These results support the general hypothesis that hybridoma monoclonal antibodies are highly soluble.

Structure-based engineering of a monoclonal antibody for improved solubility

Protein aggregation is of great concern to pharmaceutical formulations and has been implicated in several diseases. We engineered an anti-IL-13 monoclonal antibody CNTO607 for improved solubility. Three structure-based engineering approaches were employed in this study: (i) modifying the isoelectric point (pI), (ii) decreasing the overall surface hydrophobicity and (iii) re-introducing an N-linked carbohydrate moiety within a complementarity-determining region (CDR) sequence. A mutant was identified with a modified pI that had a 2-fold improvement in solubility while retaining the binding affinity to IL-13. Several mutants with decreased overall surface hydrophobicity also showed moderately improved solubility while maintaining a similar antigen affinity. Structural studies combined with mutagenesis data identified an aggregation 'hot spot' in heavy-chain CDR3 (H-CDR3) that contains three residues ((99)FHW(100a)). The same residues, however, were found to be essential for high affinity binding to IL-13. On the basis of the spatial proximity and germline sequence, we reintroduced the consensus N-glycosylation site in H-CDR2 which was found in the original antibody, anticipating that the carbohydrate moiety would shield the aggregation 'hot spot' in H-CDR3 while not interfering with antigen binding. Peptide mapping and mass spectrometric analysis revealed that the N-glycosylation site was generally occupied. This variant showed greatly improved solubility and bound to IL-13 with affinity similar to CNTO607 without the N-linked carbohydrate. All three engineering approaches led to improved solubility and adding an N-linked carbohydrate to the CDR was the most effective route for enhancing the solubility of CNTO607.

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.

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.

Prediction of retention time of cutinases tagged with hydrophobic peptides in hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) is an important technique for protein purification, which exploits the separation of proteins based on hydrophobic interactions between the stationary phase ligands and hydrophobic regions on the protein surface. One way of enhancing the purification efficiency by HIC is the addition of short sequences of peptide tags to the target protein by genetic engineering, which could reduce the need for extra and expensive chromatographic steps. In the present work, a methodology for predicting retention times of cutinases tagged with hydrophobic peptides in HIC is presented. Cutinase from Fusarium solani pisi fused to tryptophan-proline (WP) tags, namely (WP)2 and (WP)4, and produced in Saccharomyces cerevisiae strains, were used as model proteins. From the simulations, the methodology based on tagged hydrophobic definition proposed by Simeonidis et al. (Phitagged), associated to a quadratic model for predicting dimensionless retention times, showed small differences (RMSE<0.022) between observed and estimated retention times. The difference between observed and calculated retention times being lower than 2.0% (RMSE<0.022) for the two tagged cutinases at three different stationary phases, except for the case of cut_(wp)2 in octyl sepharose-2 M ammonium sulphate. Therefore, we consider that the proposed strategy, based on tagged surface hydrophobicity, allows prediction of acceptable retention times of cutinases tagged with hydrophobic peptides in HIC.