Prediction of retention times of proteins in hydrophobic interaction chromatography using only their amino acid composition

Predictive chromatography of peptides and proteins as a complementary tool for proteomics

In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications.

Adsorption of Proteins on Colloidal Lignin Particles for Advanced Biomaterials

Coating of colloidal lignin particles (CLPs), or lignin nanoparticles (LNPs), with proteins was evaluated in order to establish a safe, self-assembly mediated modification technique to tune their surface chemistry. Gelatin and poly- l-lysine formed the most pronounced protein corona on the CLP surface, as determined by dynamic light scattering (DLS) and zeta potential measurements. Spherical morphology of individual protein coated CLPs was confirmed by transmission electron (TEM) and atomic force (AFM) microscopy. A mechanistic adsorption study with several random coiled and globular model proteins was carried out using quartz crystal microbalance with dissipation monitoring (QCM-D). The three-dimensional (3D) protein fold structure and certain amino acid interactions were decisive for the protein adsorption on the lignin surface. The main driving forces for protein adsorption were electrostatic, hydrophobic, and van der Waals interactions, and hydrogen bonding. The relative contributions of these interactions were highly dependent on the ionic strength of the surrounding medium. Capillary electrophoresis (CE) and Fourier transform infrared spectroscopy (FTIR) provided further evidence of the adsorption-enhancing role of specific amino acid residues such as serine and proline. These results have high impact on the utilization of lignin as colloidal particles in biomedicine and biodegradable materials, as the protein corona enables tailoring of the CLP surface chemistry for intended applications.

Electrospun poly(l-lactide)/zein nanofiber mats loaded with Rana chensinensis skin peptides for wound dressing

Electrospun nanofiber mats can display impressive performance as an ideal wound dressing. In this study, poly(l-lactide)(PLLA)/zein nanofiber mats loaded with Rana chensinensis skin peptides (RCSPs) were successfully produced by two different electrospinning techniques, blend and coaxial, with the goal of developing a wound dressing material. The nanofiber mats were investigated by environmental scanning electron microscope (ESEM), transmission electron microscopy (TEM), fourier transform infrared spectroscopy (FTIR), differential scanning calorimeter (DSC), water contact angle, mechanical tests and cell viability. The resulting nanofiber mats exhibited smooth surfaces, tiny diameters and different cross-sectional shapes from pure PLLA and zein nanofibers. The FTIR result showed that PLLA, zein and RCSPs were well dispersed, without chemical interactions. Compared with coaxial nanofiber mats, blending zein-RCSPs with PLLA enhanced hydrophilicity but decreased mechanical properties. Adding RCSPs into the electrospun nanofibers significantly improved the mechanical properties of the mats. Cell viability studies with human foreskin fibroblasts demonstrated that cell growth on PLLA/zein-RCSPs nanofiber mats was significantly higher than that on PLLA/zein nanofiber mats. The results indicate that nanofiber mats containing RCSPs are potential candidates for wound dressing.

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.

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.

Microfluidic chips with reversed-phase monoliths for solid phase extraction and on-chip labeling

The integration of sample preparation methods into microfluidic devices provides automation necessary for achieving complete micro total analysis systems. We have developed a technique that combines on-chip sample enrichment with fluorescence labeling and purification. Polymer monoliths made from butyl methacrylate were fabricated in cyclic olefin copolymer microdevices and used for solid phase extraction. We studied the retention of fluorophores, amino acids and proteins on these columns. The retained samples were subsequently labeled with both Alexa Fluor 488 and Chromeo P503, and unreacted dye was rinsed off the column before sample elution. Additional purification was obtained from the differential retention of proteins and fluorescent labels. A linear relation between the eluted peak areas and concentrations of on-chip labeled heat shock protein 90 samples demonstrated the utility of this method for on-chip quantitation. Our fast and simple method of simultaneously concentrating and labeling samples on-chip is compatible with miniaturization and desirable for automated analysis.

[Application of peptide retention time in proteome research]

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has been one of the most popular approaches in proteome analysis. As an independent parameter to mass spectrometry information, peptide retention time has been utilized to facilitate protein identification and quantification. In the field of peptide identification, the prediction of the retention time combined with routine tandem mass spectrometry database searching methods could help improve the confidence of identification. The sensitivity of identification could also be improved by matching peaks with both the accurate mass and retention time in multiple aligned LC-MS runs. Meanwhile, because small changes of liquid chromatography conditions lead to variability in retention times unavoidably, retention time alignment is crucial to label-free quantification. Additionally, post-translational modifications (PTM) could be identified by combining retention time shifts and mass deviation information.

A new thermodynamic model describes the effects of ligand density and type, salt concentration and protein species in hydrophobic interaction chromatography

A new thermodynamic model is derived that describes both loading and pulse-response behavior of proteins in hydrophobic interaction chromatography (HIC). The model describes adsorption in terms of protein and solvent activities, and water displacement from hydrophobic interfaces, and distinguishes contributions from ligand density, ligand type and protein species. Experimental isocratic response and loading data for a set of globular proteins on Sepharose resins of various ligand types and densities are described by the model with a limited number of parameters. The model is explicit in ligand density and may provide insight into the sensitivity of protein retention to ligand density in HIC as well as the limited reproducibility of HIC data.

Separation of product associating E. coli host cell proteins OppA and DppA from recombinant apolipoprotein A-I(Milano) in an industrial HIC unit operation

We have shown how product associating E. coli host cell proteins (HCPs) OppA and DppA can be substantially separated from apolipoprotein A-I(Milano) (apo A-I(M)) using Butyl Sepharose hydrophobic interaction chromatography (HIC). This work illustrates the complex problems that frequently arise during development and scale-up of biopharmaceutical manufacturing processes. Product association of the HCPs is confirmed using co-immunoprecipitation and Western blotting techniques. Two-dimensional gel electrophoresis and mass spectrometry techniques are used to confirm the identity of OppA and DppA. In this example, clearance of these difficult to separate HCPs decreased significantly when the process was scaled to a 1.4 m diameter column. Laboratory-scale experimentation and trouble shooting identified several key parameters that could be further optimized to improve HCP clearance. The key parameters included resin loading, peak cut point on the ascending side, wash volume, and wash salt concentration. By implementing all of the process improvements that were identified, it was possible to obtain adequate HCP clearance so as to meet the final specification. Although it remains speculative, it is believed that viscosity effects may have contributed to the lower HCP clearance observed early in the manufacturing campaign.

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.

Probing protein surface accessibility of amino acid substitutions using hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) has been used to determine the influence of amino acid substitutions on protein retention and thereby their accessibility on the protein surface. The retentions of mutants of green fluorescent protein (GFPuv) and human hemoglobin (Hb) were studied on multimodal HIC media and compared to the hydrophobicities from known hydrophobicity scales with respect to the accessible surface area. For GFPuv, the theoretical and experimental results of three hydrophobicity scales correlated well (R(2)>0.85), which clearly indicate that the results can be used for protein retention prediction as well as probing surface properties of protein variants.

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.

Classification of protein adsorption and recovery at low salt conditions in hydrophobic interaction chromatographic systems

There is significant interest in establishing appropriate bioprocessing conditions for protein adsorption in hydrophobic interaction chromatographic (HIC) systems without the need for high salt concentrations. In this paper, the adsorption and recovery of proteins under low salt conditions in HIC systems was investigated using a variety of experimental and computational techniques. Parallel batch screening was employed to determine protein adsorption and recovery. Experiments were carried out with twenty six proteins using five resins with different ligand chemistry, ligand density and backbone chemistry. Proteins were classified based on various combinations of adsorption and recovery behavior. In order to gain insight into the effect of protein properties on this behavior, molecular descriptors were computed based on protein crystal structure and primary sequence information as well as a set of hydrophobicity descriptors based on the solvent accessible surface area of the proteins. Finally, classification software CART was employed to determine the key molecular descriptors associated with various types of adsorption behavior.

Predicting the behaviour of proteins in hydrophobic interaction chromatography

This paper focuses on the prediction of the dimensionless retention time (DRT) of proteins in hydrophobic interaction chromatography (HIC) by means of mathematical models based on the statistical description of the amino acid surface distribution. Previous models characterises the protein surface as a whole. However, most of the time it is not the whole protein but some of its specific regions that interact with the environment. It seems much more natural to use local measurements of the characteristics of the surface. Therefore, the statistical characterisation of the distribution of an amino acid property on the protein surface was carried out from the systematic calculation of the local average of this property in a neighbourhood placed sequentially on each of the amino acids on the protein surface. This process allowed us to characterise the distribution of this property quantitatively using three main statistics: average, standard deviation and maximum. In particular, if the property considered is a hydrophobicity scale, these statistics allowed us to characterise the average hydrophobicity and the hydrophobic content of the most hydrophobic cluster or hotspot, as well as the heterogeneity of the hydrophobicity distribution on the protein surface. We tested the performance of the DRT predictive models based on these statistics on a set of 15 proteins. We obtained better predictive results with respect to the models previously reported. The best predictive model was a linear model based on the maximum. This statistic was calculated using an index of the mobilities of amino acids in chromatography. The predictive performance of this model (measured as the Jack Knife MSE) was 26.9% better than those obtained by the best model which does not consider the amino acid distribution and 19.5% better than the model based on the hydrophobic imbalance (HI). In addition, the best performance was obtained by a linear multivariable model based on the HI and the maximum. The difference between the experimental data and the prediction carried out by this model was smaller than those observed previously. In fact, this model obtained better predictive capacities than a previous linear multivariable model decreasing the Jack Knife MSE in 8.7%. In addition, this model allowed us to diminish the number of variables required, increasing, in this way, the degrees of freedom of the model.

Predicting the behaviour of proteins in hydrophobic interaction chromatography

This paper focuses on the prediction of the dimensionless retention time of proteins (DRT) in hydrophobic interaction chromatography (HIC) by means of mathematical models based on characteristics of the surface hydrophobicity distribution. We introduce a new parameter, called hydrophobic imbalance (HI), obtained from the three-dimensional structure of proteins. This parameter quantifies the displacement of the superficial geometric centre of the protein when the effect of the hydrophobicity of each amino acid is considered. This parameter is simpler and less expensive than those reported previously. We use HI as a way to incorporate information about the surface hydrophobicity distribution in order to improve the prediction of DRT. We tested the performance of our DRT predictive models in a set of 15 proteins. This set includes four proteins whose DRTs are known as very difficult to predict. By means of the variable HI, it was possible to improve the predictive characteristics obtained by models based on the average surface hydrophobicity (ASH) by 9.1%. Also, we studied linear multivariable models based on characteristics determined from the HI. By using this multivariable model, a correlation coefficient of 0.899 was obtained. With this model, we managed to improve the predictive characteristics shown by previous models based on ASH by 31.8%.

New approaches for predicting protein retention time in hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) is an important technique for the purification of proteins. In this paper, we review three different approaches for predicting protein retention time in HIC, based either on a protein's structure or on its amino-acidic composition, and we have extended one of these approaches. The first approach correlates the protein retention time in HIC with the protein average surface hydrophobicity. This methodology is based on the protein three-dimensional structure data and considers the hydrophobic contribution of the exposed amino acid residues as a weighted average. The second approach, which we have extended, is based on the high correlation level between the average surface hydrophobicity of a protein's hydrophobic interacting zone and its retention time in HIC. Finally, a third approach carries out a prediction of the average surface hydrophobicity of a protein, using only its amino-acidic composition, without knowing its three-dimensional structure. These models would make it possible to test different operating conditions for the purification of a target protein by computer simulations, and thus make it easier to select the optimal conditions, contributing to the rational design and optimization of the process.