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

Journal of Chromatography A, 1218 (2011) 8958–8973

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Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma

High-throughput protein precipitation and hydrophobic interaction chromatography: Salt effects and thermodynamic interrelation Beckley K. Nfor, Nienke N. Hylkema, Koenraad R. Wiedhaup, Peter D.E.M. Verhaert, Luuk A.M. van der Wielen, Marcel Ottens ∗ Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC, Delft, The Netherlands

a r t i c l e

i n f o

Article history: Available online 16 August 2011 Keywords: High throughput experimentation Protein precipitation Hydrophobic interaction chromatography Solvophobic theory Preferential Interaction theory

a b s t r a c t 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. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Recombinant therapeutic proteins, industrial enzymes and food proteins have a growing impact on the global market [1–3]. Salt-induced protein precipitation and hydrophobic interaction chromatography (HIC) are two widely used methods for protein purification. Both methods exploit the surface hydrophobicity of proteins and respond in similar ways to the characteristics of the solvent—pH, temperature, salt type and salt concentration [4,5]. In HIC, high salt concentration in the mobile phase induces hydrophobic interactions between immobilized hydrophobic ligands and the non-polar regions on the protein surface [6]. Similarly, salt-induced protein precipitation is promoted by high salt concentration in much the same way, except that hydrophobic protein–protein interactions predominate [7]. Protein precipitation has widespread application as an isolation procedure in protein recovery on both laboratory and industrial scales. HIC is usually the ideal next step after salt-induced protein precipitation since the latter naturally

∗ Corresponding author. Tel.: +31 0 152782151; fax: +31 0 152782355. E-mail address: [email protected] (M. Ottens). 0021-9673/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2011.08.016

provides the high conductivity feed needed in HIC, without the need for further salt addition. The most important thermodynamic property of interest in protein precipitation is the protein solubility, which is a complex function of solution conditions. In fact, knowledge of protein solubility is equally crucial in other downstream processing steps, such as protein crystallization, chromatography and protein formulation [8]. Current state-of-the-art for protein solubility determination involves the use of high throughput experimentation on 96-well micro-titre plates [8,9]. Even so, the amount of pure protein material required for solubility determination may still be significant for highly soluble proteins, and the time required to reach thermodynamic equilibrium (the incubation time) may be long, varying from several hours to weeks in some cases. Hence, in addition to using high throughput experimentation for protein solubility determination, purification costs could be further reduced by minimizing the need for these experiments in the first place, especially for expensive protein products. One way of achieving this is through the use of theoretical models for predicting protein solubility as a function of the modulator concentration, in which case only a few solubility experiments may be necessary for model characterization and validation. Alternatively, protein solubility could be related to other more

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easily obtainable surrogate quantities such as the protein surface hydrophobicity or the second virial coefficient (B22 ) characterizing pair-wise protein interactions [10–12]. To and Lenhoff correlated protein retention factors in HIC to their B22 trends and found qualitative agreement over a wide range of pH, salt type and concentration, protein and ligand [13]. They concluded that since a correlation exists between protein solubility and B22 [12] and likewise between B22 and HIC retention [13], it is likely that protein solubility and HIC retention could be correlated [13]. However, a more quantitative treatment was hampered by the lack of suitable protein solubility data [13]. From the process design perspective, the prediction of protein solubility from a more easily obtainable surrogate quantity would be most attractive, but then better understanding of the link between protein solubility and any surrogate parameter of interest need to be developed. Here, focus is on the relation between parameters in protein precipitation and HIC. Several theoretical models describing salt effects in protein precipitation and HIC have been developed. Melander and Horváth [4,14] adapted the Solvophobic theory of Sinanogl and co-workers [15] to elucidate the effect of neutral salts on protein precipitation and retention in HIC. They separated the effect of salt on protein solubility or HIC retention into an electrostatic contribution and a hydrophobic contribution, and described the ability of salts to promote the salting out of proteins in terms of their molal surface tension increment [4]. This classification of salts was found to match the lyotropic or Hofmeister series for some but not all salts [16]. For example, salts containing divalent cations have high molal surface tension increments [4] but are known to solubilize and destabilize proteins (i.e. to be chaotropic) at high salt concentrations [17]. Similar discrepancies were observed for ␣-chymotrypsin even with some lyotropic salts [18]. Specific salt–protein interactions [17–19], neglected in the Solvophobic treatment [4], and protein structural pertubations [18] were put forward as possible reasons for these discrepancies. Timasheff and Arakawa described specific salt–protein interactions in aqueous solutions using the Preferential Interaction theory (PIT) [19,20]. According to the PIT, lyotropic salts promote protein salting out by being preferentially excluded from the protein surface, thereby promoting hydration of the surface and enhancing the protein stability. Chaotropic salts, on the other hand, undergo specific interactions with proteins at high salt concentration, increasing the protein solubility and also destabilizing the protein [17]. Perkins et al. [21] extended the PIT to HIC of proteins. Interestingly, the same mechanism by which salts increase or decrease the surface tension of an aqueous solution, i.e. by their exclusion from or accumulation at air–water interfaces, respectively, have been found to explain their propensity to interact with proteins [22,23]. Hence, the Solvophobic theory describing surface tension effects and the PIT describing salt–protein interactions are, in fact, interrelated [19,20]. Other theoretical treatments of protein precipitation and HIC include activity-coefficient-based models [24,25] and those based on molecular thermodynamics or the potential of mean force [26–28]. Given the paucity of protein solubility data in the open literature, the first objective of this study was to generate protein solubility data for a large number of protein–salt combinations by means of the high throughput protein precipitation method. For the same protein–salt combinations, protein retention data were generated in isocratic HIC. The generated protein solubility and HIC retention data were then used to compute the critical thermodynamic parameters in protein precipitation and HIC within the hermeneutics of the Solvophobic theory and the Preferential Interaction theory. Finally, the thermodynamic salting out parameters were compared in order to investigate the existence of a quantitative interrelation between the two separation techniques.

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2. Theory In this section, the key equations describing protein precipitation and HIC in terms of the Solvophobic and Preferential Interaction theories are presented. Where applicable, the subscripts 1, 2 and 3 represent water, protein and salt, respectively. 2.1. The Solvophobic theory applied to protein precipitation On the basis of the Solvophobic theory [15], the free energy change of a protein in solution when only salt concentration is altered, and in the absence of specific interactions between the protein and the salt, is described by [4]: G◦ = Gcav. + Ge.s.

(1)

where Gcav. is the free energy change for formation of a cavity in the solvent to accommodate the solute molecule, and Ge.s. accounts for the free energy change due to electrostatic interactions between the solvent and solute. The free energy change for cavity formation is given by [4]: Gcav. = [N˚ + 4.8N 1/3 (e − 1)V 2/3 ]( 0 + m3 )

(2)

where m3 is the molality of the salt,  is the molal surface tension increment of the salt, ˚ is the surface area per protein molecule that is dehydrated upon precipitation, also called the hydrophobic contact area (HCA),  0 is the surface tension of pure water, N is the Avogadro’s number, V is the molar volume and e corrects the macroscopic surface tension of the solvent to molecular dimensions. Melander and Horváth combined the proper terms from the Debye–Hückel theory for protein ions at low ionic strength and Kirkwood’s theory for the protein dipole at high ionic strength to describe the electrostatic free-energy term [4]: Ge.s. = A −

Bm0.5 3 1 + Cm0.5 3

− Dm3

(3)

where  is the dipole moment of the protein. The constants A and B are proportional to the net charge on the protein, and A is inversely proportional to the size of the protein. C and D are related to the composition and dielectric properties of the mobile phase and protein. The solubility of an aqueous protein solution can be expressed as: ln S2 =

Glat. − G◦ + ln(55.56) RT

(4)

where S2 is the protein solubility in grams per 1000 g of water, and Glat. is the free energy change for lattice formation, which is assumed to be salt-independent. Inserting Eqs. (1)–(3) into Eq. (4) and rearranging by lumping all salt-independent terms together leads to the following equation for protein solubility: ln S2 = ln S2,w +

1 RT



Bm0.5 3



1 + Cm0.5 3

− KS m3

(5)

where the salt-independent term, ln S2,w , is equivalent to the protein solubility in pure water, and KS =

1 (˝ − D) RT

(6)

with ˝ = [N˚ + 4.8N 1/3 (e − 1)V 2/3 ]

(7)

where Ks is the salting out constant, which according to this theory, depends solely on the properties of the protein (hydrophobic surface area and dipole moment) and the salt type (molal surface tension increment), but not on salt concentration. Eq. (5) essentially

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captures the competing effects of electrostatic and hydrophobic interactions in salt-induced protein precipitation. At high salt concentrations, the Debye–Hückel term in Eq. (5) approaches a constant value and the protein solubility becomes linear in salt concentration, analogous to Cohn’s equation for protein solubility [29]. ln S2 = b − KS m3

2.2. The Preferential Interaction theory (PIT)

32 ≡



−RT ln

  2 =



∂2 ∂m3





0

= 2 − 2,w = 2

∂2 ∂m3

(10)

 dm3 T,P,m2



= −32 T,P,m2

∂3 ∂m3

(11)

 T,P,m2

T,P,m2

is an activity coefficient term expressing the non-ideality of the salt in the aqueous solution. The preferential interaction parameter is essentially the net effect of the protein interaction with salts plus its hydration by water molecules and was expressed by the authors [19] as:

v1 m1

¯3 m

(13)

where vi represents the number of moles of species i ¯ 3 is the mean molal bound/released per mole of protein and m salt concentration. From the above formulations by Arakawa and Timasheff [19,20], the protein solubility could be derived in terms of the molal salt concentration and the preferential interaction parameters. This was achieved by combining Eqs. (10)–(13) and rearranging to obtain:



ln

S2 S2,w



=

1 RT

v3 −

v1 m1



¯ 3 m3 m

(15)

 0

v3 −

v1 m1

(16)

ˇ= =

n3 v1

(17)

m1 g

v3

(18)

g



∂ ln m3 ∂ ln a±

 (19) T,P

where vi is defined similarly as vi , n3 is the total moles of ions associated with the salt or electrolyte, and a± is the mean ionic activity of the ions. 2.3. Parameter regression The precipitation and HIC data were fitted to the MH and the PIT models presented above. Parameter regression was performed by the minimization of the sum of squared residuals (SSR) between experimental data and model response using the least squares method [30]. The fitting was performed in MATLAB. SSR =

N 

(yexp,i − ypred,i )2

(20)

i=1

where N is the number of data points, yexp and ypred are the experimental and predicted responses, respectively. 3. Materials and methods

(12)

where 2 and 2,w denote the chemical potentialof the protein  in salt solution and in pure water, respectively, and ∂3 /∂m3

32 = v3 −



where ˛ is a constant,

g=

2.2.1. The PIT applied to protein precipitation The solubility of a protein in salt solution can be expressed in terms of the preferential interaction parameter through the change in the chemical potential of the protein upon its transfer from pure water to the salt solution as given by Arakawa and Timasheff [19,20]: S2 S2,w

1 RT

2.2.2. The PIT applied to HIC Perkins et al. [21] extended the PIT to protein retention in HIC. For salts that promote protein interaction with hydrophobic ligands in HIC, the protein retention factor was expressed in terms of preferential interaction parameters by [21]:

(9) T,1 ,3

where mi is the molal concentration of species i,  is the chemical potential and T is the temperature in Kelvin. A positive value of 32 denotes preferential salt–protein interaction (destabilizing and salting-in effect), and a negative value indicates that the salt is expelled from the protein surface or that the protein is preferentially hydrated (stabilizing and salting-out effect).



ln(S2 ) = ln(S2,w ) +

ln(k ) = ˛ − ˇm3 +  ln(m3 )

When a protein is added to an aqueous salt solution, it affects the distribution of water molecules and salt ions. This effect can be characterized by the preferential interaction coefficient or the preferential interaction parameter, which is a thermodynamic quantity depicting the propensity of a salt to interact with proteins [19,20]: ∂m3 ∂m2

T,P,m2

expression for the protein solubility as a function of molal salt concentration and the preferential interaction parameters:

(8)

where b is a constant. Eq. (5) or Eq. (8) shall henceforth be referred to in this text as the Melander and Horváth (MH) equation or the MH model.



of solution idealwhich after integration, with the assumption  ity with respect to salt, i.e. ∂3 /∂m3 = 1, gave the final

¯3 m

 ∂  3

∂m3

dm3 T,P,m2

(14)

3.1. Materials 3.1.1. Proteins Albumin from bovine serum (≥96%, product no. A2153-10G), albumin from porcine serum (≥98%, product no. A1830-10G), albumin from chicken egg white (≥98%, product no. A550310G), lysozyme from chicken egg white (∼100,000 units/mg, product no. 62970-5G-F), ␣-chymotrypsin from bovine pancreas (≥40 units/mg, product no. C4129-10G) and amyloglucosidase from Aspergillus niger (∼70 units/mg, product no. 10115-5G-F) were purchased from Sigma–Aldrich. 3.1.2. Chemicals Potassium dihydrogen phosphate (≥99%), dipotassium hydrogen phosphate (≥99%), sodium dihydrogen phosphate (≥99%), disodium hydrogen phosphate (≥99%), citric acid monohydrate (≥99.5%), sodium chloride (≥99.5%), hydrochloric acid and sodium hydroxide were purchased from Mallinckrodt Baker, Deventer, The Netherlands. Absolute ethanol, ammonium sulphate (≥99.5%) and potassium chloride (≥99.5%) was purchased from Merck, Darmstadt, Germany. Ammonium monohydrogen phosphate (≥99%), ammonium dihydrogen phosphate (≥98.5%) and potassium citrate

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Table 1 Properties of HIC media used in this studya . Media

Backbone

Ligand type

Ligand density [␮mol/ml medium]

Mean particle diameter [␮m]

Mean pore radiusb [nm]

Total ligand areac [Å2 ]

Butyl Sepharose 4 FF (SB4FF) Phenyl Sepharose 6 FF low sub (SP6FFls) Resource Phenyl (RP)

4% cross-linked agarose 6% cross-linked agarose

Butyl Phenyl

40 25

90 90

– 27.3

191 210

Polystyrene/divinylbenzene

Phenyl

-

15

41.7

–

a b c

Except otherwise indicated, presented data were obtained from resin supplier (www.gelifesciences.com). Data from To and Lenhoff [48]. Data from Perkins et al. [21].

(≥99%) were purchased from Sigma–Aldrich Chemie, Zwijndrecht, The Netherlands. MilliQ water was prepared using a Millipore MilliQ Biocel and verified by a Q-gard 1 conductivity measurement unit. 3.1.3. Equipment A JANUS Robotic Workstation from Perkin-Elmer was used for liquid manipulations. It was equipped with a Varispan 8-fixed tips arm and a 96-tip Modular Dispense Technology (MDT) dispensing arm. Each tip of the Varispan arm can transfer a different volume of liquid and each tip of the MDT arm transfers the same volume of liquid. The Varispan arm was used for plate preparation. The fixed tips were washed using the standard washing protocol before each new liquid to be dispensed. The automated workstation was controlled by WinPrep for Janus. 12 × 10, 12 × 100 and 12 × 300 ␮l Pipetman Concept Multichannel pipettes from Gilson were used to load protein samples into the plate wells. A Synergy 2 Multi-mode Microplate Reader from BioTek with Gen5 software was used for absorbance measurements in the 96well UV plates. Non-UV flat bottom plates, UV flat bottom plates and filter plates (MSGVN2250, with a hydrophilic PVDF membrane and a pore size of 0.22 ␮m) were obtained from Millipore Greiner. The HIC experiments were carried out on an ÄKTA Explorer from GE HealthCare. The pH and the conductivity were measured inline by a pH/C 900 unit. The temperature was measured by the system. The absorbance was measured after the column at 280 nm by a UV-900 unit. The pumping and pressure measurements were carried out by a P-900 pump system. The experimental setup and the measurements were managed by Unicorn 5.0 software. Image analysis was performed with a stereo microscope (M205 FA 7.8-160x) with Leica Application Suite (LAS) version V3.5.0 software from Leica Microsystems B.V. 3.1.4. Columns Three different HIC columns from GE HealthCare were used in this study; two HiTrap pre-packed columns (Phenyl Sepharose 6 Fast Flow low sub and Butyl Sepharose 4 Fast Flow) and a prepacked Resource Phenyl column. All HIC columns had a bed volume of 1 ml. The resin properties are summarized in Table 1. 3.2. Methods 3.2.1. High throughput protein precipitation experiments The high-throughput technique employing 96-well microtitre plates and robotic liquid handling technology was used to establish solubility curves for different combinations of protein and lyotropic salt, at a fixed pH (pH 7) and temperature (25 ± 0.1 ◦ C). All precipitation experiments were performed in duplicates. 3.2.1.1. Preparation of stock solutions. A stock solution of 25 mM sodium phosphate buffer (pH 7) was prepared by dissolving the appropriate amounts of NaH2 PO4 and Na2 HPO4 in MilliQ water. The

exact pH was obtained by adding known amounts of concentrated acid/base. Stock salt solutions were prepared by dissolving known amounts of salt in known amounts of buffer. The final concentration of salt in the salt solution was often close to the maximum solubility of the salt. The pH of the solutions was adjusted by adding known amounts of concentrated acid/base. For lyotropic salts with anions being in multiple states at pH 7 (e.g. ammonium phosphate and potassium phosphate), the ratio of the acidic and basic variants of the salt that gave pH 7 was determined first on a small scale. The pH of the final solutions was adjusted by adding known amounts of the acidic or basic variant of the salt until the desired pH was attained. The density of the salt solution was measured after preparation. Buffer and salt solutions were filtered (0.2 ␮m) and degassed by ultrasonification prior to use. Protein stock solutions were prepared by adding small known portions of protein (50 mg) to known amounts of buffer. Protein was dissolved by vortexing and by centrifuging at 4600 rpm for at least 12 min. The cycle was repeated until no significant protein dissolution could be observed. The pH of the protein solution was adjusted to pH 7 by adding known amounts of concentrated acid/base. To minimize the chance of autolysis [31], ␣chymotrypsin samples were prepared just prior to the precipitation or HIC experiments. All solutions were 0.2 ␮m filtered before use. 3.2.1.2. Sample loading and incubation on 96-well plates. Protein samples of the desired salt and protein concentrations were prepared on 96-well flat-bottom plates by adding a fixed volume of the protein solution to known mixtures of salt and buffer solutions in each well. The total volume of each well was adjusted to 300 ␮l with buffer. The end result was wells with the same initial protein concentration but different salt concentrations. To avoid sudden protein exposure to a high salt concentration, the protein solution was always added last. For the precipitation experiments, 50 ␮l of protein stock solution was found to be optimal. Except for the protein solution, liquid (salt and buffer solutions) handling and pipetting were performed using the robot. Protein solutions were added manually with a multi-channel pipette instead of the robot so as to avoid the need for a large reservoir volume of the protein solution. The multi-channel pipette tips were properly emptied and the solution properly mixed by repeated aspiration and dispensing. The 96-well plates were covered tightly with parafilm and incubated for at least 36 h on a thermomixer at 25 ◦ C with a shaking speed of 500 rpm. 3.2.1.3. Separation of precipitate and supernatant. After the incubation, the protein precipitate and supernatant were first roughly separated by centrifugation in a plate centrifuge at 4000 rpm for 30 min. Depending on the combination of protein and salt, the precipitate either moved to the bottom or the top phase upon centrifugation. In some cases the densities of the two phases were so close that complete separation by centrifugation was not possible. To achieve complete separation of precipitate and supernatant in these cases, 100 ␮l of the supernatant was transferred to a

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Fig. 1. Schematic overview of the high throughput protein precipitation method employed.

MSGVN2250 filter plate. This plate was centrifuged for 5 min at 2800 rpm at 25 ◦ C and the supernatant was collected in a 96-well flat bottom plate. 3.2.1.4. Protein concentration measurement. For protein concentration measurements, 300 ␮l of 60× dilutions of the precipitate-free supernatant was prepared on a UV-plate using buffer. The UV-plate was covered with parafilm and mixed on a plate shaker for 3 min at 1100 rpm. Finally, the UV absorbance at 280 nm of the diluted protein samples was measured on a plate reader. The exact protein concentration in the clear supernatant was back calculated with the use of a calibration curve, prepared by measuring the UVabsorbance at 280 nm of serially diluted protein samples of known concentrations. A schematic overview of the high throughput protein precipitation method is shown in Fig. 1. 3.2.2. Isocratic HIC experiments For all protein–salt combinations examined in precipitation experiments, protein retention data were equally generated by isocratic HIC experiments. HIC experiments are relatively easy to perform and have a good reproducibility. First, the column was equilibrated with at least 6 column volumes (CV) of the buffered

solution of desired salt concentration until the UV, pH and conductivity baselines became constant. Then a pulse of protein sample was injected into the column with the use of a 0.5 ml sample loop. The UV absorbance (280 nm), conductivity, pH and pressure were monitored. Salt concentrations in the range of 0–1.5 M and a protein concentration of 1 mg/ml were used in HIC. Column cleaning solutions included MilliQ water, 1 M sodium hydroxide, and 20% (v/v) ethanol in water. All HIC runs were performed at 25 ◦ C and pH 7, and at a constant flow rate of 0.5 ml/min. The retention volume of a protein was calculated as the first moment of its absorbance peak at 280 nm. The retention factor was determined from the retention volume of the protein by: k =

Vr − V0 V0

(21)

where k is the protein retention factor, Vr is the retention volume, and V0 is the void volume of the column, equivalent to the retention volume of an unretained solute. In this study, V0 was determined by measuring the retention volume of Blue Dextran under nonretaining conditions [32]. Protein recovery was determined from the difference in elution peak areas obtained by injecting the same amount of protein with and without column.

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Table 2 Protein characteristics. Protein

Mr [kDa]

pI

Non-polar area [Å2 ]a

ks [×10−6 cm3 / (g bar)]b

Lysozyme (LYS) ␣-Chymotrypsin (ACT) Porcine serum albumin (PSA) Bovine serum albumin (BSA) Ovalbumin (OVA) Amyloglucosidase (AMY)

14.30 25.60 67.50 66.40 42.80 90.00

11.2 8.7 5.6 5.5 4.9 3.6

3230 5426 – – 8597 –

2.3 2.9 – 7.2 6.4 –

a b

Data from Ref. [43]. Adiabatic compressibility. Average values from Refs. [43,44].

Table 3 Lyotropic salts used in this study. Salt name e

Ammonium phosphate Ammonium sulphate Potassium citrate Potassium phosphatef Potassium chloride Sodium chloride

Formula



gd

(NH4 )x Hy PO4 (NH4 )2 SO4 K3 Citrate Kx Hy PO4 KCl NaCl

– 2.29 ± 0.12a 3.31b 2.7c 1.59 ± 0.13a 1.73 ± 0.17a

2.01 1.68 – 2.15 1.06 1.03

Clearly, the lysozyme solids from this study can be seen as disordered, flaky particles (Fig. 2A) compared to the more structurally organized (hexagonal) lysozyme particles from the crystallization study (Fig. 2B), suggesting that the image in Fig. 2A could be the precipitate form of lysozyme. However, the image analysis results alone would be inconclusive without additional verification of the obtained protein precipitation data. Hence a literature comparison of the precipitation data was undertaken.

a

Data from Pegram et al. [23]. Average of values from Kim et al. and Melander and Horváth [4]. Data from Kim et al. [51]. d Calculated from tabulated data for activity coefficients as a function of salt concentration [52]. e A mixture of (NH4 )2 HPO4 and NH4 H2 PO4 . f A mixture of K2 HPO4 and KH2 PO4 . b c

4. Results and discussion 4.1. Characteristics of resins, proteins and salts used The three HIC media used in this study consist of spherical beads and differ in one or more of the following properties; base matrix, ligand type, ligand density, and particle size, as shown in Table 1. The characteristics of model proteins used are shown in Table 2. The proteins were selected to cover a broad range of molecular masses (Mr ) and isoelectric points (pI). Six lyotropic salts were used, as summarized in Table 3. 4.2. Protein precipitation 4.2.1. Image analysis The occurrence of protein precipitation after sample incubation was verified by image analysis of the protein-rich phase to check for the presence of protein precipitates. As an example, Fig. 2 shows comparison snapshots from image analysis of lysozyme from this study (Fig. 2A) and from a crystallization study (Fig. 2B) [33].

4.2.2. Literature comparison Despite its widespread application, few experimental studies on protein precipitation have been reported and so tabulated protein solubility data are scarce, with the exception of lysozyme. Lysozyme solubility data in ammonium sulphate from this study were compared to solubility data from lysozyme precipitation [34,35] and crystallization studies [35] employing similar conditions as this study (Fig. 3A). Fig. 3A shows that the lysozyme solubility data from this study are in excellent agreement with the solubility data from other precipitation studies, but clearly different from the lysozyme solubility in equilibrium with the crystal form (i.e. data represented by open squares in Fig. 3A). Additionally, data for NaCl-induced lysozyme precipitation from this study were compared to the solubility data from lysozyme crystallization studies [33,36,37], as depicted in Fig. 3B. Similar to Fig. 3A, the lysozyme solubilities in equilibrium with the amorphous precipitate (this study) are higher than those in equilibrium with the crystals (Fig. 3B), in accordance with the general protein phase diagram [8,38]. Observed differences in the solubility curves from the crystallization studies can be attributed to differences in the experimental conditions used (Fig. 3B legend). As shown in Fig. 4, data for ␣-chymotrypsin (ACT) precipitation by ammonium sulphate were also in good agreement with those from Coen et al. [34], suggesting that autolysis of ACT was minimal under the conditions examined in this study. Solution pH did not show any significant influence on protein solubility within the pH range examined in Figs. 3 and 4. The

Fig. 2. (A) Image analysis of lysozyme from this work (pH 7, 25 mM sodium phosphate buffer, 25 ◦ C, 2.5 M NaCl, >100 mg/ml lysozyme, 36 h incubation). (B) Image analysis of lysozyme from crystallization study by Borst [33] (pH 7, 10 mM sodium phosphate buffer, 10 ◦ C, 0.6 M NaCl, 20 mg/ml lysozyme, 2 h incubation).

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Fig. 3. Comparison of lysozyme (LYS) solubility data: (A) LYS–(NH4 )2 SO4 (This work: pH 7, 25 mM sodium phosphate buffer, 25 ◦ C, 36 h incubation; Coen et al. [34]: pH 6, pH 7, pH 8, 25 ◦ C, 4 h incubation; Moretti et al. [35]: pH 8, 25 ◦ C, 2 h incubation plus 15 h centrifugation); and (B) LYS–NaCl (This work: same as above; Howard et al. [36]: pH 7, 50 mM sodium phosphate buffer, 25 ◦ C, 6–11 weeks incubation; Forsythe et al. [37]: pH 4.5, 100 mM sodium acetate buffer, 22.7 ◦ C; Borst [33]: pH 7, 10 mM sodium phosphate buffer, 10 ◦ C, 5 day incubation).

above literature comparisons thus provide additional verification of the protein precipitation method and data. 4.2.3. Effects of salt concentration and salt type The natural logarithm of protein solubility is plotted against molal salt concentration for different protein–salt combinations in Fig. 5, and the key salting out parameters obtained by fitting the protein solubility data to the MH and the PIT models are summarized in Table 4. As shown in Fig. 5, the protein precipitation data were obtained in the salting-out region of the solubility curve where protein solubility decreases with increasing salt concentration or ionic strength. In general, significant protein salting out was only observed at salt concentrations > 2 molal for most of the protein–salt combinations examined, except for lysozyme precipitation with potassium citrate. The effectiveness of salts in promoting protein precipitation/crystallization is often related to the lyotropic character of the constituent ions according to the Hofmeister series [16] or to the molal surface tension increments of the salts [4]. Based on the latter, the salts used in this study in order of increasing lyotropic effect would be: KCl ( = 1.59) < NaCl ( = 1.73) < (NH4 )2 SO4 ( = 2.29) < Kx Hy PO4 ( = 2.7) < K3 Citrate ( = 3.31). With the exception of AMY, this order is generally conserved with respect to the positions of solubility lines of each protein with different salts (the

Fig. 4. Comparison of data for ␣-chymotrypsin precipitation by ammonium sulphate (This work: pH 7, 25 mM sodium phosphate buffer, 25 ◦ C, 36 h incubation; Coen et al. [34]: pH 5.5, pH 7, pH 8.3, 25 ◦ C, 4 h incubation).

more lyotropic the salt, the more to the left the protein solubility line and the lower the protein solubility at any given salt concentration), but not always with respect to their slopes. While the positions of solubility lines of each protein are clearly distinct for different salt types, their slopes do not differ significantly among salts belonging to the same category (i.e. salts of multivalent or monovalent anions). Hence, in comparing protein solubility data for different salts, a distinction must be made between the steepness or slope of the solubility lines and their positions in the salting out region, since the overall salting out strength of a salt is a combined effect of the two. For example, although the salting out constants (Ks values) of lysozyme are much lower for NaCl and KCl than for multivalent salts (Table 4), these monovalent salts show stronger salting-out properties (lower lysozyme solubility) than the multivalent salts below the intersection points of the respective lysozyme solubility lines (i.e. below ∼2.5 molal for ammonium phosphate or below ∼1.5 molal for ammonium sulphate and potassium phosphate). These results are in agreement with the observation that lysozyme precipitation/crystallization at low salt concentrations and at pH values below its pI follows the reverse Hofmeister series in which the chloride ion is more effective in salting-out the protein than multivalent anions like sulphate and citrate [39,40]. The authors suggested that under these conditions, the anions interact with positively charged residues on the proteins through an adsorption-like mechanism, decreasing the net charge on the protein and consequently the protein solubility [39,40]. However, since electrostatic interactions are nonspecific, it is unlikely that this mechanism alone can explain observed differences between monovalent and multivalent anions, or even between different monovalent anions. Boström and co-workers [41,42] provided a better explanation for the reverse Hofmeister effect by means of the ionic dispersion potential that acts between each salt ion and the protein alongside the existing electrostatic potentials or interactions. The dispersion forces depend on the polarizability of each ion, hence are ionspecific. At the typical ionic strengths encountered in salt-induced protein precipitation/crystallization, the electrostatic potentials become significantly screened, and the ionic dispersion potentials dominate the interactions between the anions and protein molecules. These ion-specific interactions decrease in the reverse order of the Hofmeister series, hence explaining the stronger salting out character of monovalent anions such as Cl− under certain conditions. Similar deviations from the lyotropic series and the Solvophobic theory were observed by Przybycien et al. [18] with ␣-chymotrypsin (ACT), where KSCN showed much higher salting

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Fig. 5. Plots of the natural logarithm of protein solubility versus molal salt concentration. (A) BSA, (B) PSA, (C) AMY, (D) OVA, (E) ACT and (F) LYS. Data points are average values from duplicate experiments. All experiments were carried out at 25 ◦ C and pH 7. Lines show the fitted Melander and Horváth model, Eq. (8).

out characteristics than predicted from its surface tension increment. The authors attributed the discrepancies to structural pertubations and salt–protein interactions. Since, their investigation involved a monovalent anion (SCN− ) and they operated under conditions favorable for reverse Hofmeister effects, i.e. at pH (3) < pI (8.7) of ACT, it is likely that also here, salt–protein interactions are dominated by the ionic dispersion potential. In the case of AMY (Fig. 5C), however, it is not clear why ammonium sulphate is a much better salting out agent than potassium phosphate, even though the operating pH is well above the pI of AMY (pI 3.6) and so the protein and salt anions possess like charges. Here too, one could postulate the occurrence of salt–protein interactions by the same ionic dispersion potential mechanism, but then the ion-specific interactions are now more prominent between the cationic species and the negatively charged protein, and are stronger for the ammonium ion than for the potassium ion. It appears that the lower the parameter b, the lower the protein solubility at any given salt concentration and the stronger the

salting out character of the salt as is the case with salts used for BSA precipitation (see Fig. 5A and Table 4), but in fact, this is not always true. For example, the solubility curve of OVA with potassium citrate has a higher b value than any of the other two salts used (see Table 4), but potassium citrate displays the best salting out character based on the positions of the OVA solubility lines in Fig. 5D. Hence, the hypothetical intercept is not a reliable indicator of the salting out character of different salts. In fact, the parameter b more correctly describes the salting-in properties of different salts, i.e. salts with higher b values will inevitably show better salting in characteristics at low salt concentrations, but not necessarily poor salting out characteristics in the salting out region. 4.2.4. Effects of protein type The effects of protein type on solubility were examined by considering the solubility of LYS, ACT, OVA, BSA and AMY in ammonium sulphate as depicted in Fig. 8A. A comparison of the protein solubility at any given salt concentration indicates the ease of salting

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Table 4 Parameters of the MH and PIT models applied to protein precipitation. Protein

Salt

MH model b

BSA BSA BSA PSA PSA PSA AMY AMY AMY OVA OVA OVA ACT ACT ACT ACT LYS LYS LYS LYS LYS LYS

(NH4 )2 SO4 Kx Hy PO4 K3 Citrate (NH4 )2 SO4 (NH4 )x Hy PO4 Kx Hy PO4 (NH4 )2 SO4 (NH4 )x Hy PO4 Kx Hy PO4 (NH4 )2 SO4 Kx Hy PO4 K3 Citrate (NH4 )2 SO4 (NH4 )x Hy PO4 Kx Hy PO4 K3 Citrate (NH4 )2 SO4 (NH4 )x Hy PO4 Kx Hy PO4 K3 Citrate KCl NaCl

30.2 22.0 16.87 28.6 32.79 40.7 12.71 13.85 17.3 16.0 16.14 16.7 11.81 19.6 15.69 13.13 8.68 13.71 9.70 7.71 4.871 4.170

PIT model KS

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.4 1.4 0.92 1.0 0.69 5.3 0.96 0.64 1.5 1.2 0.69 1.5 0.52 2.0 0.26 0.63 0.26 0.39 0.14 0.10 0.062 0.086

8.47 7.10 6.76 8.47 9.95 13.1 2.93 2.47 3.55 4.71 5.52 7.56 3.41 5.02 4.826 5.13 3.39 4.65 4.274 4.805 1.105 0.797

v1 /RT

ln(S2,w ) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.42 0.46 0.41 0.31 0.21 1.7 0.29 0.13 0.34 0.40 0.27 0.73 0.18 0.54 0.088 0.29 0.12 0.14 0.073 0.072 0.023 0.026

30.2 22.0 16.87 28.6 32.79 40.7 12.7 13.85 17.3 16.0 16.14 16.7 11.72 19.6 15.69 13.14 8.68 13.71 9.70 7.71 4.871 4.170

out a particular protein. In this respect, salting out of the proteins increased in the order AMY < BSA < OVA < ACT < LYS as shown in Fig. 8A. To try to understand these trends, three different molecular properties of proteins that can potentially influence their precipitation were considered; molecular mass or size, non-polar area and adiabatic compressiblity (ks ), as summarized in Table 2. In general, larger proteins also have larger non-polar surface areas and so would be expected to be more easily precipitated. However, no correlation was observed between protein precipitation and molecular size/non-polar area. In fact, the smaller proteins were even precipitated more easily with ammonium sulphate. A quantitative measure of the extent to which proteins undergo conformational changes is given by the adiabatic compressibility of the proteins [43,44]. The higher the adiabatic compressibility of a protein, the more flexible the protein and the greater the extent to which it undergoes conformational changes [43,44]. Literatureobtained ks values of BSA, OVA, ACT and LYS [43,44] were used to investigate the influence of protein conformational changes. The ease of protein precipitation did not follow the expected increasing trend with adiabatic compressibility. These results point to additional hidden factors that may be playing a significant role, such as the distribution of hydrophobic patches on the surface of proteins and the potential influence of protein hydration on the adiabatic compressibility. The adiabatic compressibility of globular proteins has two main contributions; the intrinsic compressibility arising from the imperfect packing of the amino acid residues within the solvent-inaccessible core of the protein, and the hydration compressibility due to hydration of the protein surface [45]. While the intrinsic compressibility is fairly constant for each protein, the hydration contribution varies with the surface characteristics of the protein [45]. Hence, it is likely that the adiabatic compressibility is not constant but varies to different extents for different proteins based their hydration characteristics at given solution conditions. In fact, a strong correlation was observed between the number of hydrating water molecules per unit non-polar area of proteins, here coined the ‘specific surface hydration’, and the ease of protein precipitation or salting out. The specific surface hydration decreased in the order LYS (45.66 A˚ −2 ) > ACT (18.91 A˚ −2 ) > OVA (15.30 A˚ −2 ), which is also the order of increasing non-polar area or molecular mass. The decreasing specific surface hydration is in excellent agreement with the observed order in which the proteins

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.6 1.6 0.99 1.1 0.84 7.5 1.2 0.69 1.6 1.3 0.73 1.7 0.77 2.1 0.28 0.70 0.28 0.43 0.15 0.10 0.064 0.090

v3 /RT

79.569 75.293 97.448 82.319 91.610 129.11 33.496 20.019 29.754 53.095 71.166 117.927 41.418 46.655 56.1270 78.791 59.5226 55.7749 77.1509 123.3985 23.1758 16.2214

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.029 0.028 0.018 0.020 0.015 0.13 0.021 0.012 0.028 0.023 0.013 0.031 0.014 0.039 0.0050 0.013 0.0050 0.0076 0.0026 0.0018 0.0011 0.0016

−3.70 −3.02 −2.86 −3.71 −4.44 −6.0 −0.94 −0.69 −1.23 −1.83 −2.24 −3.26 −1.15 −1.98 −1.887 −2.05 −1.18 −1.80 −1.620 −1.890 −0.030 0.130

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.48 0.52 0.44 0.35 0.25 2.5 0.35 0.14 0.37 0.43 0.28 0.84 0.26 0.59 0.095 0.32 0.13 0.15 0.077 0.075 0.024 0.027

¯3 m

− 32 /RT

3.33 3.01 2.22 3.22 3.34 3.04 3.31 4.93 4.32 3.01 2.56 2.02 2.95 3.62 2.91 2.18 2.07 2.84 1.91 1.31 2.58 3.18

8.47 7.10 6.76 8.47 9.95 13.08 2.93 2.47 3.55 4.71 5.52 7.56 3.34 5.02 4.83 5.13 3.39 4.65 4.27 4.80 1.11 0.80

precipitated as shown in Fig. 8A. This correlation supports the view that differences in hydrophobic surface distribution and/or changes in protein compressibility or flexibility based on their unique surface hydration characteristics could have led to the observed trends. 4.2.5. Salting out constant and the preferential interaction parameter From Eq. (8), the salting-out constant can be expressed as:



Ks = −

∂ ln S2 ∂m3



(22) T,P,m2

and from Eq. (15),



∂ ln S2 ∂m3



= T,P,m2

1 RT



v3 −

v1 m1

¯3 m

≡

32 RT

(23)

Combining Eq. (22) and Eq. (23) provides a quantitative interrelation between the salting-out constant and the preferential interaction parameter: Ks = −

32 RT

(24)

Eqs. (8) and (15) were each fitted to the protein solubility data. Eq. (23) was then used to compute the preferential interaction parameter (− 32 /RT) for each protein–salt type combination for comparison with the Ks values from the MH model. The results are summarized in Table 4. Obtaining the number of water molecules and salt ions per protein molecule (v1 and v3 , respectively) from Table 4 is straightforward (T = 298 K and R = 8.314 J mol−1 K−1 ). As expected, calculated values of − 32 /RT are in excellent agreement with the fitted Ks values, as are the intercepts b and ln(S2,w ), since both solubility equations are linear in salt concentration. Nevertheless, the results show that the salting-out constant is, in effect, the net result of preferential interaction of a protein with water molecules and salt ions in its vicinity [19,20], shedding more light into the protein salting out mechanism. The signs of v1 /RT and v3 /RT (or v1 and v3 ) give the direction of movement of water molecules and salt ions, respectively, with respect to the protein surface. With the exception of LYS–NaCl, salt ions are generally expelled from the protein (negative v3 values) while water molecules hydrate the protein surface (positive

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v1 values) as expected from the PIT for lyotropic salts. However, the overall effect on the protein (i.e. salting in or salting out) is dictated by the sum total of the magnitude and direction of movement of water molecules and salt ions, i.e. the preferential interaction parameter (see Table 4). Calculated preferential interaction parameters are all negative (i.e. preferential salt exclusion or protein hydration) even for LYS–NaCl with a slightly positive v3 /RT, indicating that the salts used in this study are all protein stabilizing and salting out salts, in agreement with the PIT [19,20]. Thus, although vi gives the individual contributions of water molecules and salt ions in the vicinity of the protein and are useful for interpreting the precipitation mechanism, their overall effect as given by the preferential interaction parameter must also be considered in interpreting protein solubility data. In fact, the preferential interaction parameter characterizing salt–protein interactions could also find applications in areas such as protein stability studies, formulation and storage, just like the second virial coefficient characterizing protein–protein interactions [46]. 4.3. Hydrophobic interaction chromatography With the objective of comparing salt effects in protein precipitation and HIC, isocratic protein retention data in HIC were obtained for the same protein–salt combinations examined in precipitation experiments, and for two or three different HIC resins. The protein retention data were fitted to the PIT model. The obtained parameters are summarized in Table 5. Plots of the natural logarithm of protein retention factor against molal salt concentration for the different proteins, salts and resins are given in Figs. 6–8. As shown in the figures, the PIT model could sufficiently describe the protein retention data over the investigated salt concentration range. Here, the salt effects in HIC in comparison to protein precipitation are discussed. The influence of resin chemistry is also briefly examined. More in-depth coverage of the influence of salt and resin on protein retention in HIC can be found elsewhere [21,32,47–50]. 4.3.1. Effects of salt concentration and salt type To visualize the effects of different salts in HIC, the retention data of each protein for the different salt types were plotted together for two of the three HIC resins, as depicted in Figs. 6 and 7. In general, much lower salt concentrations were required in HIC than in protein precipitation due to the presence of a more hydrophobic auxiliary phase, the HIC ligand. As expected, the effect of salt concentration on protein retention in HIC is opposite to that in protein precipitation, i.e. the protein retention factor decreases with decreasing salt concentration in the salting out region, and a minimum point is reached beyond which the retention factor starts increasing with further decrease in salt concentration due to the salting-in effect. However, much of the data lie in the salting out region, which is of interest for the purpose of the study. The maximum salt concentration used was determined by the protein recovery. A minimum protein recovery of 90% was required. Lower protein recoveries were not considered due to the associated risk of protein unfolding/conformational changes. A comparison of the influence of different salts based on the position and slope of the plots for different salts as presented above for protein precipitation is more difficult in HIC due to the additional influence of the resin type. However, the order of salts in increasing lyotropic effect in HIC is generally similar to that found in protein precipitation: NaCl < KCl < (NH4 )2 SO4 < Kx Hy PO4 < K3 Citrate, with the position of ammonium phosphate varying between ammonium sulphate and potassium phosphate. Observed differences in the lyotropic order of salt between protein precipitation and HIC can be attributed

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to the resin (e.g. PSA in Figs. 5 and 6). The same general observation regarding the slopes of plots involving multivalent anions versus those of monovalent anions can be made, i.e. the former being steeper than the latter and the two cross each other at some point, leading to the reverse Hofmeister effect in which higher protein retention is observed with the monovalent anion than with the multivalent anions at salt concentrations below the crossing point, as shown for lysozyme in Fig. 7E and F. Another interesting observation is that the lyotropic order of salts with AMY is conserved in both processes (Figs. 5C and 6E–F), confirming the deviation from the lyotropic series observed in protein precipitation. 4.3.2. Effects of resin chemistry The effect of ligand type was examined using Phenyl Sepharose 6 FF (SP6FFls) and Butyl Sepharose 4 FF (SB4FF) which possess the same backbone but different ligands. For each protein and salt type, ln(k ) is generally higher for SP6FFls than for SB4FF in the saltingout region; even though the ligand density of the latter is 1.6 times higher (see Table 1). Furthermore, the number of released water molecules is generally larger with SP6FFls than with SB4FF for the same protein and salt type, in accordance with the PIT, with a few exceptions (PSA–(NH4 )x Hy PO4 , PSA–Kx Hy PO4 , LYS–Kx Hy PO4 , LYS–KCl, LYS–NaCl). Hence, the stronger phenyl ligand overrides the ligand density advantage of the butyl ligand. Similar observations were made by comparing the average retention times of different proteins on these two resins [47]. The authors [47] used the high sub Phenyl Sepharose which has a higher ligand density (40 ␮mol/ml) than the low sub variant used in this study. It must be pointed out, however, that ligand density only gives the moles of ligand per unit volume of medium but not the available ligand surface for protein interaction. As shown in Table 1, SP6FFls possesses a larger ligand area than SB4FF. Hence, the observed differences between SP6FFls and SB4FF were likely due to a combined effect of the ligand type and the available ligand area for protein interaction. The influence of resin backbone was equally investigated by comparing the protein retention on SP6FFls and Resource Phenyl (RP), both of which possess the phenyl ligand but different backbones (see Table 1). For Resource Phenyl (RP), only data for LYS and OVA in ammonium sulphate were available, making these the obvious choice for the comparison, as shown in Fig. 8. For both proteins, the plot of ln(k ) for SP6FFls lies above that for RP, indicating that SP6FFls has stronger retention properties for these proteins than RP (Fig. 8). Although the ligand density and ligand area of RP were not available, RP has a much wider pore size (Table 1) and size distribution than SP6FFls [48]. Hence, it is likely that the observed differences between the two resins are due to a combined effect of backbone chemistry, ligand area and ligand density rather than the backbone chemistry alone. In terms of resin selectivity, however, no appreciable difference was observed between the two resins in spite of the smaller particle size of RP. 4.3.3. Effects of protein type To compare the effects of protein type in protein precipitation and HIC, the same proteins and salt type were considered, as shown in Fig. 8. HIC retention data on SP6FFls or RP were used (Fig. 8B). As shown in Fig. 8B, protein retention increased in the order AMY < OVA < LYS < ACT, and except for the pair LYS–ACT, no correlation with molecular size/non-polar area was observed. This is in agreement with Horváth and co-workers [53], who compared estimated molecular surface areas in HIC to the molecular sizes of different proteins. It has been shown that for proteins with similar molecular masses, those with higher adiabatic compressibility (ks ) were more strongly retained in HIC [48]. The explanation provided for this was that upon adsorption in HIC,

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Table 5 Parameters of the PIT models applied to HIC and comparison of v1 and v1 a . Protein

BSA BSA BSA BSA BSA BSA BSA PSA PSA PSA PSA PSA PSA AMY AMY AMY AMY AMY AMY OVA OVA OVA OVA OVA OVA OVA OVA OVA ACT ACT ACT ACT ACT ACT ACT ACT LYS LYS LYS LYS LYS LYS LYS LYS LYS LYS LYS LYS LYS a

Salt

(NH4 )2 SO4 (NH4 )x Hy PO4 (NH4 )x Hy PO4 Kx Hy PO4 Kx Hy PO4 K3 Citrate K3 Citrate (NH4 )2 SO4 (NH4 )2 SO4 (NH4 )x Hy PO4 (NH4 )x Hy PO4 Kx Hy PO4 Kx Hy PO4 (NH4 )2 SO4 (NH4 )2 SO4 (NH4 )x Hy PO4 (NH4 )x Hy PO4 Kx Hy PO4 Kx Hy PO4 (NH4 )2 SO4 (NH4 )2 SO4 (NH4 )2 SO4 (NH4 )x Hy PO4 (NH4 )x Hy PO4 Kx Hy PO4 Kx Hy PO4 K3 Citrate K3 Citrate (NH4 )2 SO4 (NH4 )2 SO4 (NH4 )x Hy PO4 (NH4 )x Hy PO4 Kx Hy PO4 Kx Hy PO4 K3 Citrate K3 Citrate (NH4 )2 SO4 (NH4 )2 SO4 (NH4 )2 SO4 (NH4 )x Hy PO4 (NH4 )x Hy PO4 Kx Hy PO4 Kx Hy PO4 K3 Citrate K3 Citrate KCl KCl NaCl NaCl

Resin

SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF RP SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF RP SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF SP6FFls SB4FF

PIT model ˛

−ˇ

−

v1

v3

|v1 /v1 |

−0.01 ± 0.11 −0.20 ± 0.26 −0.11 ± 0.15 −0.227 ± 0.082 −0.54 ± 0.26 −1.90 ± 0.63 -0.91 ± 0.32 −0.17 ± 0.15 2.17 ± 0.20 0.59 ± 0.15 0.28 ± 0.11 −0.20 ± 0.55 −0.279 ± 0.097 −2.13 ± 0.41 −0.072 ± 0.054 −2.65 ± 0.45 −1.71 ± 0.47 −2.13 ± 0.50 −0.46 ± 0.21 −3.06 ± 0.47 −1.48 ± 0.31 −5.23 ± 0.76 −1.77 ± 0.73 −3.67 ± 0.46 −0.11 ± 0.12 −0.08 ± 0.10 −1.29 ± 0.45 −0.25 ± 0.11 0.66 ± 0.32 −0.08 ± 0.23 0.379 ± 0.059 0.028 ± 0.060 0.62 ± 0.13 0.005 ± 0.066 0.19 ± 0.20 −1.50 ± 0.46 −2.12 ± 0.34 −1.97 ± 0.39 −2.19 ± 0.42 −2.41 ± 0.29 −2.34 ± 0.34 −3.26 ± 0.17 −4.51 ± 0.66 −1.08 ± 0.17 −0.94 ± 0.21 0.500 ± 0.070 −0.67 ± 0.56 0.497 ± 0.090 −0.28 ± 0.16

0.87 ± 0.16 1.62 ± 0.39 1.07 ± 0.20 1.98 ± 0.10 1.75 ± 0.38 7.0 ± 1.2 3.26 ± 0.57 2.31 ± 0.21 2.10± 0.21 0.37 ± 0.22 0.86 ± 0.15 1.79 ± 0.99 2.02 ± 0.17 2.97 ± 0.42 0.669 ± 0.066 3.18 ± 0.44 2.26 ± 0.46 2.81 ± 0.52 1.04 ± 0.23 4.31 ± 0.50 2.38 ± 0.33 5.72 ± 0.76 4.6 ± 1.1 4.49 ± 0.45 0.95 ± 0.17 0.85 ± 0.12 3.79 ± 0.69 1.49 ± 0.18 1.92 ± 0.50 1.36 ± 0.36 1.96 ± 0.10 0.970 ± 0.081 1.89 ± 0.20 1.16 ± 0.10 4.58 ± 0.52 4.33 ± 0.71 3.97 ± 0.35 3.35 ± 0.41 3.32 ± 0.43 3.85 ± 0.28 3.40 ± 0.33 5.23 ± 0.17 6.09 ± 0.66 4.30 ± 0.27 3.30 ± 0.32 1.261 ± 0.068 2.03 ± 0.54 1.375 ± 0.087 1.76 ± 0.15

0.148 ± 0.049 0.12 ± 0.10 0.147 ± 0.066 0.149 ± 0.045 0.34 ± 0.11 0.70 ± 0.23 0.42 ± 0.12 0.283 ± 0.064 0.81 0.154 ± 0.064 0.025 ± 0.044 0.14 ± 0.21 0.195 ± 0.037 1.29 ± 0.28 0.183 ± 0.029 1.80 ± 0.37 1.42 ± 0.39 1.28 ± 0.34 0.41 ± 0.14 1.87 ± 0.32 1.07 ± 0.21 2.96 ± 0.59 0.75 ± 0.30 2.84 ± 0.38 0.082 ± 0.049 0.189 ± 0.051 0.53 ± 0.19 0.229 ± 0.042 0.07 ± 0.13 0.205 ± 0.088 0.148 ± 0.023 0.168 ± 0.026 0.059 ± 0.053 0.171 ± 0.025 0.170 ± 0.060 0.74 ± 0.20 1.45 ± 0.23 1.40 ± 0.26 1.20 ± 0.26 1.90 ± 0.24 1.93 ± 0.28 2.26 ± 0.13 3.20 ± 0.51 0.574 ± 0.073 0.543 ± 0.089 0.383 ± 0.055 1.04 ± 0.44 0.449 ± 0.083 0.71 ± 0.14

27.2 ± 5.0 45 ± 11 29.8 ± 5.7 59.0 ± 3.0 52 ± 11 – – 71.8 ± 6.6 65.5 ± 3.3 10.3 ± 6.2 24.2 ± 4.2 54 ± 30 60.4 ± 5.0 92 ± 13 20.8 ± 2.0 89 ± 12 63 ± 13 84 ± 16 30.9 ± 6.8 134 ± 15 74 ± 10 178 ± 24 130 ± 30 125 ± 13 28.4 ± 5.0 25.4 ± 3.7 – – 60 ± 16 42 ± 11 54.7 ± 2.9 27.1 ± 2.3 56.5 ± 6.0 34.7 ± 3.0 – – 124 ± 11 104 ± 13 103 ± 14 107.5 ± 7.9 94.9 ± 9.3 156.2 ± 5.0 181.9 ± 20 – – 37.2 ± 2.0 60 ± 16 39.4 ± 2.5 50.3 ± 4.4

0.249 ± 0.082 0.24 ± 0.21 0.30 ± 0.13 0.32 ± 0.10 0.74 ± 0.24 – – 0.48 ± 0.11 1.4 ± 0.30 0.31 ± 0.13 0.050 ± 0.088 0.29 ± 0.44 0.420 ± 0.080 2.17 ± 0.47 0.307 ± 0.049 3.61 ± 0.75 2.84 ± 0.78 2.76 ± 0.73 0.89 ± 0.30 3.15 ± 0.54 1.80 ± 0.35 4.98 ± 0.99 1.51 ± 0.61 5.72 ± 0.77 0.18 ± 0.11 0.41 ± 0.11 – – 0.11 ± 0.21 0.34 ± 0.15 0.298 ± 0.047 0.338 ± 0.055 0.13 ± 0.11 0.367 ± 0.055 – – 2.44 ± 0.38 2.36 ± 0.44 2.02 ± 0.44 3.82 ± 0.48 3.87 ± 0.57 4.86 ± 0.28 6.9 ± 1.1 – – 0.406 ± 0.059 1.10 ± 0.47 0.463 ± 0.085 0.73 ± 0.14

7261.87 – – 3159.81 3573.25

2841.93 3115.23 22116.40 9399.47 5973.91 5290.49 898.11 3988.11 559.00 785.85 877.02 2382.43 980.88 1779.37 738.60 – – 6217.06 6932.15 – – 1720.60 2429.22 2112.69 4267.56 2460.73 4013.59 – – 1194.13 1413.53 1428.65 1285.39 1455.51 1223.72 1050.89 – – 1545.82 960.34 1021.21 798.76

To obtain v1 from v1 /RT, T = 298 K (25 ◦ C) and R = 8.314 J mol−1 K−1 , and for v1 , m1 = 55.56 m and g from Table 3.

the more flexible proteins with high ks can spread and unfold to expose internal hydrophobic surfaces, resulting in higher retention, whereas the more rigid tertiary structure of proteins with low ks limits their ability for conformational change upon adsorption [48]. However, protein retention did not increase with adiabatic compressibility, except for the LYS–ACT pair. Even for LYS and ACT, the difference in their average ks values is insignificant when considering the large differences in measured ks values reported for LYS [43]. As mentioned earlier, a protein recovery requirement of ≥ 90% was set, and so it is unlikely to ascribe the observed retention patterns to low protein recoveries. In general, the order of protein retention strength in HIC (Fig. 8B) matches that of the protein solubility data in Fig. 8A, suggesting that similar factors could be in play in both processes, i.e. the distribution of non-polar surface area, as shown by Mahn et al. [54], and changes in protein compressibility upon hydration. Clearly, further studies to elucidate these factors are needed.

4.4. Interrelating protein precipitation and HIC The fundamental molecular property exploited in protein precipitation and HIC is the surface hydrophobicity of the protein, which in the light of the Solvophobic theory is represented by the hydrophobic contact area (HCA) of the protein. The question, however, is whether there exists any interrelation between the two processes through the HCA or a surrogate parameter such as the B22 . If so, then it could be possible to predict the salting out constant in protein precipitation from HIC retention data, or vice versa. To and Lenhoff derived an expression for protein retention factor in HIC as a function of protein solubility [13]. However, they could not test their interrelation due to the lack of protein solubility data or a suitable interrelation to B22 for which data was available [13]. Nevertheless, the trends of protein retention factors in HIC qualitatively matched the B22 trends for some proteins, showing that protein solution thermodynamics also plays an important role in HIC [13].

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Fig. 6. Plots of the natural logarithm of protein retention factor versus molal salt concentration. (A) BSA–SP6FFls, (B) BSA–SB4FF, (C) PSA–SP6FFls, (D) PSA–SB4FF, (E) AMY–SP6FFls, and (F) AMY–SB4FF. All experiments were carried out at 25 ◦ C and pH 7. Lines show the fitted PIT model, Eq. (16).

Melander and Horváth assumed that the surface area per protein molecule that is dehydrated upon precipitation ( ) does not vary with the nature of the salt, Eqs. (6-7). If this assumption is valid, then for a given protein, plots of KS versus ␴ of different salts should yield a straight line with  as the slope, from which the HCA can be calculated. However, this approach assumes that the salting-out strengths of the salts (and hence Ks values for each protein) follows the lyotropic series or their surface tension increments, which was not always the case as discussed above. An alternative approach would be to estimate the HCA from the number of released water molecules obtained from the PIT, since this is unique for each protein and salt type. This approach assumes that the released water molecules previously formed a monolayer on the hydrophobic surface of the protein, and so the corresponding HCA can be estimated from the number of released water molecules and the effective area occupied by a water molecule [21]. However, calculations performed by the authors for ovalbumin and lysozyme on various HIC adsorbents showed that the HCAs estimated in this way were

significantly different from those estimated from the protein size and ligand density [21], suggesting that the monolayer assumption cannot be generalized. Hence, to avoid the uncertainties associated with estimating HCAs from protein precipitation or HIC data using either of the approaches outlined above, we choose to directly compare the salting out parameters in protein precipitation (Ks ) and in HIC (ˇ) or the number of released water molecules in both methods. In this way, the salting-out parameters and/or the interaction parameters in both methods could be interrelated. The comparisons between Ks and -ˇ for different proteins, salts and resins are depicted in Fig. 9. With a few exceptions (e.g. AMY-(NH4 )x Hy PO4 , LYS–Kx Hy PO4 , LYS–KCl, LYS–NaCl), the magnitude of the parameters generally increase in the order–ˇ (SB4FF) < –ˇ (SP6FFls) < Ks within the specified standard errors. For some protein–salt combinations, the–ˇ values are more or less comparable for both resins, but significantly different from the salting-out constant (e.g. PSA with all salts, OVA-Kx Hy PO4 , BSA and ACT with all salts but K3 Citrate). In other cases, the three are more

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Fig. 7. Plots of the natural logarithm of protein retention factor versus molal salt concentration. (A) OVA–SP6FFls, (B) OVA–SB4FF, (C) ACT–SP6FFls, (D) ACT–SB4FF, (E) LYS–SP6FFls, and (F) LYS–SB4FF. All experiments were carried out at 25 ◦ C and pH 7. Lines show the fitted PIT model, Eq. (16).

Fig. 8. Effects of protein type in protein precipitation and HIC. (A) Plot of the natural logarithm of protein solubility versus molal (NH4 )2 SO4 concentration. Lines show the fitted MH model, Eq. (8). (B) Plot of the natural logarithm of protein retention factor versus molal (NH4 )2 SO4 concentration showing the influence of resin backbone and protein type. Lines show the fitted PIT model, Eq. (16). All experiments were carried out at 25 ◦ C and pH 7.

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Fig. 9. Comparison of salting out parameters in protein precipitation (Ks ) and in HIC (−ˇ) for different protein–salt–resin combinations. (A) BSA, (B) PSA, (C) AMY, (D) OVA, (E) ACT, and (F) LYS. Ammonium sulphate (AmS), ammonium phosphate (AmP), potassium phosphate (PotP), potassium citrate (PotCit), potassium chloride (PotCl), sodium chloride (SodCl).

or less comparable (e.g. LYS–(NH4 )2 SO4 , AMY–(NH4 )x Hy PO4 and ACT–K3 Citrate), and in some cases, the salting-out constant is more comparable to −ˇ for SP6FFls but significantly different from that of SB4FF (e.g. BSA–K3 Citrate, AMY–(NH4 )2 SO4 , AMY–Kx Hy PO4 , OVA–(NH4 )2 SO4 , LYS–KCl). Only for ACT and PSA, can a regular pattern be observed between the two parameters across the different resins and salt types. For lysozyme, both Ks and −ˇ values are clearly higher for the multivalent salts than for the monovalent salts. Similar trends can be expected from a comparison of the number of released water molecules or salt ions in both methods. Just like in protein precipitation, the number of released water molecules in HIC was generally ∼1–2 orders of magnitude higher than the number of salt ions, in agreement with previous investigations [21,48] and in support of the view of an entropically driven process involving the release of water molecules [55]. However, as shown in Table 5, the number of released water molecules was significantly higher in protein precipitation than in HIC. This could be due to more surface hydrophobic patches being involved

in protein–protein interactions in protein precipitation than in protein–ligand interactions in HIC where the ligand is stationary, coupled to the more extreme salt and protein concentrations used in protein precipitation. Furthermore, the ratio of the released water molecules in both processes is highly protein, salt and resin type dependent. It can thus be concluded from the above, that no general quantitative interrelation exists between the salting out parameters or the preferential interaction parameters in protein precipitation and HIC. In other words, protein solubility and HIC retention factor could not be quantitatively interrelated, although for some proteins (e.g. PSA, ACT), regular trends could be observed across different resins and salt types. The likely reasons for the lack of a quantitative interrelation are: (1) the inherent difference between the two techniques, i.e. although both are based on the same fundamental principle, different interaction partners are involved (i.e. protein–protein interactions in protein precipitation versus protein–ligand interactions in HIC), and (2) the inability of the

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applied theoretical treatments to capture other important phenomena such as the reverse Hofmeister effect and the effects of protein conformational changes. Furthermore, the current adaptation of the PIT to HIC [21] fails to incorporate properties of the HIC adsorbent (e.g. ligand density), which would be useful in accounting for differences between resins. Recent thermodynamic treatments of protein retention in HIC by Fernandez and co-workers [56] and by Mollerup [57] address this concern by incorporating both ligand and protein properties.

5. Conclusions Salt-induced protein precipitation and hydrophobic interaction chromatography (HIC) are two widely used methods for protein purification. Both methods exploit the surface hydrophobicity of proteins and respond in similar ways to the characteristics of the solvent—salt type, salt concentration, pH and temperature. This study was conducted to compare salt effects in protein precipitation and HIC and to investigate the existence of a quantitative interrelation between the critical thermodynamic parameters in both techniques when described within the hermeneutics of the Solvophobic theory and the Preferential Interaction theory (PIT). For this purpose, a large amount of experimental data was generated for both protein precipitation and HIC. The protein precipitation data were obtained for 22 protein–salt combinations (involving six protein and six lyotropic salts) by means of a high-throughput technique employing 96-well microtitre plates and robotic liquid handling technology. The model proteins differed widely in their molecular masses and isoelectric points (BSA, porcine serum albumin, amyloglucosidase, ovalbumin, ␣-chymotrypsin and lysozyme). The lyotropic salts used were ammonium sulphate, ammonium phosphate, potassium phosphate, potassium citrate, sodium chloride and potassium chloride. For the same protein–salt combinations, isocratic HIC retention data were generated on two or three of the following HIC resins: Phenyl Sepharose 6 FF low sub (SP6FFls), Butyl Sepharose 4 FF (SB4FF) and Resource Phenyl (RP). All experiments were performed at 25 ◦ C and at pH 7. In general, similar salt effects and deviations from the lyotropic series were observed in protein precipitation and HIC. For example, although the salting out constants of lysozyme for NaCl and KCl were lower than those of the multivalent salts, these monovalent salts showed stronger salting-out properties (lower lysozyme solubility) than the multivalent salts at low salt concentrations. This was also confirmed by the higher lysozyme retention factors in HIC for NaCl and KCl at low salt concentrations. This is in accordance with the reverse Hofmeister effect reported for lysozyme below its isoelectric point. The effects of HIC resin chemistry were equally investigated. In terms of ligand strength, the phenyl group of SP6FFls was generally more hydrophobic than the butyl group of SB4FF, but the observed differences were likely due to a combined effect of the ligand type and the available ligand area for protein interaction, which is higher in SP6FFls. Likewise, SP6FFls was generally more hydrophobic than RP, suggesting a combined effect of backbone chemistry and ligand density, since the two resins possess the same ligand type. No trend was observed between the strength of hydrophobic interaction and the sizes of the model proteins. For the same salt type, proteins with higher specific surface hydration, i.e. the number of hydrating water molecules per unit non-polar area of protein, showed better salting out or lower solubilities. Changes in adiabatic compressibility and the distribution of surface hydrophobic patches could be responsible for observed effects of protein type in protein precipitation and HIC.

A quantitative interrelation between the salting out constant from the Melander and Horváth model and the preferential interaction parameter from the PIT model was investigated. In protein precipitation, the salting out constant could be expressed in terms of the preferential interaction parameters, 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. Finally, salting out parameters (Ks and −ˇ) as well as the number of released water molecules (v1 and v1 ) in protein precipitation and HIC were compared. With a few exceptions, the magnitude of salting out parameters generally increased in the order −ˇ (SB4FF) < −ˇ (SP6FFls) < Ks (precipitation), within the specified standard errors. Significantly more water molecules were released in protein precipitation than in HIC. However, no general quantitative interrelation was found between the salting out parameters or the number of released water molecules in both separation methods, as this depended on the specific protein, salt and resin combination. In other words, protein solubility and HIC retention factor could not be quantitatively interrelated. Possible reasons for this are the inherent difference between the two techniques in terms of the interaction partners involved and the limitations of the applied theoretical treatments in capturing some important phenomena such as the reverse Hofmeister effect and protein conformational changes. Acknowledgement This project was financially supported by the Netherlands Ministry of Economic Affairs and the B-Basic partner organizations (www.b-basic.nl) through B-Basic, a public-private NWO-ACTS programme (ACTS = Advanced Chemical Technologies for Sustainability). References [1] U. Gottschalk, Biopharm. Int. (2006) 8. [2] G. Walsh, Appl. Microbiol. Biotechnol. 67 (2005) 151. [3] B.K. Nfor, P.D.E.M. Verhaert, L.A.M. van der Wielen, J. Hubbuch, M. Ottens, Trends Biotechnol. 27 (2009) 673. [4] W. Melander, C. Horváth, Arch. Biochem. Biophys. 183 (1977) 200. [5] W.R. Melander, Z. El Rassi, C. Horváth, J. Chromatogr. 469 (1989) 3. [6] J.L. Fausnaugh, F.E. Regnier, J. Chromatogr. 359 (1986) 131. [7] Y.-C. Shih, J.M. Prausnitz, H.W. Blanch, Biotechnol. Bioeng. 40 (1992) 1155. [8] M. Wiendahl, C. Volker, I. Husemann, J. Krarup, A. Staby, S. Scholl, J. Hubbuch, Chem. Eng. Sci. 64 (2009) 3778. [9] M. Stenvall, J. Steen, M. Uhlén, S. Hober, J. Ottosson, Biochim. Biophys. Acta: Proteins Proteom. 1752 (2005) 6. [10] B.L. Neal, D. Asthagiri, A.M. Lenhoff, Biophys. J. 75 (1998) 2469. [11] T. Ahamed, M. Ottens, B.K. Nfor, G.W.K. van Dedem, L.A.M. van der Wielen, Fluid Phase Equilib. 241 (2006) 268. [12] B. Guo, S. Kao, H. McDonald, A. Asanov, L.L. Combs, W. William Wilson, J. Cryst. Growth 196 (1999) 424. [13] B.C.S. To, A.M. Lenhoff, J. Chromatogr. A 1141 (2007) 235. [14] C. Horváth, W. Melander, I. Molnar, J. Chromatogr. 125 (1976) 129. [15] O. Sinanogl, S. Abdulnur, Feder. Proc. 24 (1965) S12. [16] F. Hofmeister, Archiv. Exp. Pathol. Pharmak. 24 (1888) 1. [17] T. Arakawa, S.N. Timasheff, Biochemistry 23 (1984) 5912. [18] T.M. Przybycien, J.E. Bailey, Enzyme Microb. Technol. 11 (1989) 264. [19] S.N. Timasheff, T. Arakawa, J. Cryst. Growth 90 (1988) 39. [20] T. Arakawa, S.N. Timasheff, H.W. Wyckoff, C.H.W.H. Serge, N. Timasheff, Methods Enzymology, Academic Press, 1985, p. 49. [21] T.W. Perkins, D.S. Mak, T.W. Root, E.N. Lightfoot, J. Chromatogr. A 766 (1997) 1. [22] M.T. Record, C.F. Anderson, Biophys. J. 68 (1995) 786. [23] L.M. Pegram, M.T. Record, J. Phys. Chem. B 111 (2007) 5411. [24] J.A.P. Coutinho, F.L.P. Pessoa, Fluid Phase Equilib. 222–223 (2004) 127. [25] A. Staby, J. Mollerup, J. Chromatogr. A 734 (1996) 205. [26] H.W. Blanch, J.M. Prausnitz, R.A. Curtis, D. Bratko, Fluid Phase Equilib. 194–197 (2002) 31. [27] Y.C. Chiew, D. Kuehner, H.W. Blanch, J.M. Prausnitz, AIChE J. 41 (1995) 2150. [28] J.M. Prausnitz, J. Chem. Thermodyn. 35 (2003) 21. [29] E.J. Cohn, Chem. Rev. 28 (1941) 395. [30] D.W. Marquardt, SIAM J. Appl. Math. 11 (1963) 431. [31] S. Kumar, G.E. Hein, Biochemistry 9 (1970) 291. [32] C. Machold, K. Deinhofer, R. Hahn, A. Jungbauer, J. Chromatogr. A 972 (2002) 3. [33] R.T. Borst, Master thesis, Life Science & Technology, TU Delft, 2010.

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