Prediction of Solubility Behavior of Globular Plant Proteins with

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Prediction of Solubility Behavior of Globular Plant Proteins with Hansen Solubility Parameters: A Conformational Study A. Aghanouri† and G. Sun*,‡ †

Agricultural and Environmental Chemistry, ‡Division of Textiles and Clothing, University of California, One Shields Avenue, Davis, California 95616, United States S Supporting Information *

ABSTRACT: In this study Hansen solubility parameter (HSP) theory was utilized to discover proper solvent systems that can replace solid denaturants, such as urea or guanidinium chloride, in order to prepare processable plant protein solutions as potential sources of new industrial macromolecules. Soybean proteins, glycinin, and β-conglycinin were selected, and their structural effects on solubility in different solvent systems and unfolding status were investigated. The results revealed that certain organic solvent systems with HSPs similar to 6 M urea aqueous solution tend to dissolve both proteins, and a high correlation was observed between their solubility and Ra values. Dynamic light scattering and nanodifferential scanning calorimetry of the proteins in selected solvent systems were measured in order to understand denatured status of the proteins in the solvent systems. It was observed that hydrodynamic radii of the both proteins were increased by decreasing the Ra values, and significant denaturation was detected for the solutions with HSPs closer to 6 M urea solutions. Also, the solvent systems with high Ra values revealed gel-like rheological behavior, while HSPs close samples presented shear-thinning behaviors, an indication of extended protein chains. Lastly, HSPs theory can meaningfully predict the ability of different solvents that can dissolve and denature plant proteins. KEYWORDS: Hansen solubility parameters, Denaturation, Dissolution, Solvent system, Glycinin, β-Conglycinin

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INTRODUCTION The prevalence of synthetic polymeric materials has grown very fast during last century, and they have become crucial parts of modern human life. Polymeric materials provide versatile goods in a wide range of applications with high quality and durability, thanks to their great manufacturing processability and affordable prices.1−5 However, crude oils as resources of synthetic polymers will be depleted in the future, so new and sustainable and renewable resources should be investigated and developed for future demands.6−13 Plant proteins as naturally renewable polymers are one of the potential candidates to replace synthetic polymers. On the one side, soybean, zein, gluten, and peanut proteins are one of the most abundant and inexpensive biomass macromolecules on the earth which are often extracted as a byproduct of cooking oil factories.14−16 Despite of the aforementioned facts, it is rare to find promising industrial products introduced from pure plant proteins,17−28 while this field of research was started by the Ford Company in the 1940s.29 Chaotropic agents, plasticizer, temperature control, acidic/ alkaline solutions, and chemical modifications were utilized to make plant proteins processable for industrial products, but with little success due to the lack of investigation on effects of the agents and parameters on the conformational structure of the proteins.30−35 Plant proteins are mostly considered as © XXXX American Chemical Society

globular proteins with different ordered domains and secondary structures. In polymer sciences the first crucial requirement to have a processable macromolecule is a linear and flexible chainlike polymer, which can provide enough chain entanglement and subsequently bring optimum mechanical and physical properties to the products.36 However, plant proteins have an ordered globular structure; therefore, they need to be converted to their denatured structures. Figure 1 illustrates schematic structures of native and denatured glycinin structures in favored/unfavored solvents. High concentrations of chaotropic agents such as urea and guanidine hydrochloride are widely used to denature proteins. However, the amount of chaotropic agents is higher than the amount of protein in the solution systems in most cases, bringing difficulties in obtaining the recovered protein products from the solutions. When liquid solvent (water) is removed from the mixture of chaotropic agents and protein, the remained protein content will be in minority in comparison with urea or guanidine hydrochloride content, then the final product becomes brittle and useless.38−44 Received: January 4, 2016 Revised: February 3, 2016

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DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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properties of denatured plant proteins.57 Also, a preliminary study has been done on feasibility of using HSPs to predict practical solvent systems for plant proteins without considering conformational and denaturation status of proteins in solutions.58 This study focuses on application of HSPs for predicting proper solvent systems to dissolve soy proteins, glycinin and βconglycinin, and proving the feasibility with structural and conformational evidence of the proteins in the solvent systems. A solvent system set was prepared from common organic solvents with varied polarities and functional groups in their structures. Different analytical instruments were utilized to provide assessments of structural changes and properties of the proteins in various solutions. Solubilities of glycinin and βconglycinin were investigated by using the Coomassie reagent along with spectrophotometric analysis. Particle sizes and size distributions of the plant proteins in different solvent systems were measured by using a dynamic light scattering (DLS) analyzer. A nanodifferential scanning calorimeter (nDSC) was used to measure thermal behavior of the glycinin and βconglycinin in specific solutions to shed light on denaturation status of the proteins in the solutions. Also, viscosity of some specific solutions was measured by a cone plate rheometer to measure intermolecular entanglements of denatured proteins in selected solutions. These observations were employed to correlate HSPs of solvent systems to structural characteristics of soy proteins in solutions aiming to find proper and practical solvent systems for dissolving plant proteins.

Figure 1. Schematic structures of native and denatured glycinin structures in favored/unfavored solvents. (The native structure of glycinin was adapted with permission from ref 37. Copyright 2003 National Academy of Sciences, U.S.A.)

Thus, finding organic systems that can completely dissolve and denature the proteins is a necessary and important approach to solve the current difficulty. Traditionally, polymer solubility theories are used to find a proper solvent for a synthetic polymer. These theories are based on a basic assumption that a solvent dissolves a solute with same or similar physicochemical properties while the solvent can overcome all intermolecular interactions between solute molecules and make stronger intermolecular interactions with them. Hildebrand was the first scientist who tried to quantify solubility properties of solvents and solutes. The Hildebrand solubility parameter was hypothesized based on square root of cohesive energy density of the materials, which means a solvent with a same or very close Hildebrand solubility parameter, δ, of a solute potentially has this chance to dissolve the solute.45,46 Hansen further expanded this theory and the dissociated cohesive energy density into three components,

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MATERIALS AND METHODS

Materials and Sample Preparation. Defatted soy flour, containing ∼48% crude protein and 0.5% crude oil, was purchased from MP Biomedicals (Santa Ana, CA). 2-Chloroethanol, 2-butanol, 1,4-dioxane, dimethyl sulfoxide (DMSO), ethanol, 2-propanol, nbutanol, formamide, n-methylformamide, n-methylacetamide, triethanolamine, 2-pyrrolidone, propylene carbonate and glycerol carbonate buffers, sodium bisulfate, sodium chloride, mercaptoethanol, Coomassie (Bradford) protein assay kit, and other reagents were purchased from Fisher Scientific (Pittsburgh, PA). Solvent Systems. A wide range of different organic solvents including alcohols, halogenated alcohols, amines, amides, ethers, and sulfoxides was selected, which could provide different intermolecular interactions such as, polar, nonpolar, protic, aprotic, and with different functional groups in the systems. More than 100 different solvent systems were prepared by mixing the listed solvents in different compositions (volume %) and ratios. Since the major solvent component in 6 M urea solutions is water, then water was considered as a major solvent for designing and preparing all solvent systems; therefore, all solvents are miscible and soluble in water to specific ratios. Some solvent systems were designed based on an assumption that proteins are consisted of different amino acids with various polarity and hydrophilicity, and thus a solvent system should provide proper physiochemical interaction with all amino acids. Also, some solvent systems were prepared with the intention to have close HSPs to 6 M urea solution. HSPs of the mixtures were calculated by

δ = δd 2 + δp2 + δ h 2 , where δd is referred to intermolecular dispersion forces, δp is related to intermolecular polar interactions, and δh is corresponding to intermolecular hydrogen bonding.47 Hansen solubility parameters have been widely used in polymer and different sciences during past 20 years. HSPs of solvents and polymers can be measured or predicted based on the chemical structures of repeating units in polymers, which can be employed to find either a single solvent or a solvent system for synthetic or even natural polymers.47−50 Nevertheless, natural proteins and synthetic polymers have fundamental difference in structures, and the proteins mostly are made of 20 different amino acids with a wide range of different physicochemical properties, ranging from polar and ionic interactions to nonpolar and aromatic compounds in their structures. Synthetic polymers contain very simple repeating units with limited types of intermolecular interactions. Therefore, the proteins are more complicated than synthetic polymers in structures and properties. For example, soy proteins contain two major biopolymers, β-conglycinin (7S) and glycinin (11S), globulins in quite different structures and properties. β-Conglycinin is a trimeric glycoprotein consisting of three subunits, α (68 kDa), α′ (72 kDa), and β (52 kDa), associated via hydrophobic interactions.51 Glycinin is a hexamer composed of acidic (∼35kD) and basic (∼20 kDa) polypeptides linked by disulfide bonds.52−56 In our previous studies, we tried to characterize the mechanism of denaturation of soy proteins with urea as well as acidic/alkaline solutions to understand the structural

n

δx =

∑ ai ·δi , x i=1

Where δx is a partial solubility parameter component of a mixture, n is number of solvents in the mixture, ai is the volume fraction of a solvent in the mixture, and δi,x represents a partial solubility parameter component of a solvent in the mixture.47 Also the Ra values were calculated by

Ra = B

4(δd,u − δd,s)2 + (δp,u − δp,s)2 + (δ h,u − δ h,s)2 DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

Research Article

ACS Sustainable Chemistry & Engineering Where Ra is the distance between each solvent system, s, from 6 M urea solution, u, and δd, δp, δh are dispersion, polar, and hydrogen bonding solubility parameters, respectively.47 The solvent system compositions are available in the Supporting Information.58 Also, 6 M urea solution was made to compare with the organic solvent systems. Proteins Isolation. The soy β-conglycinin and glycinin were prepared according to a method reported by Suchkov and Nagano with some modifications.59−61 The purification process has been reported in detail in our previous study.57 Protein Solubility. Soy protein solutions were prepared by adding 20 mg of each protein into 1 mL of each solvent system. Samples were shaken for 24 h and then centrifuged for 10 min (12 000 g at room temperature) to remove any insoluble proteins, the glycinin and βconglycinin solutions were diluted 40-fold by their solvent systems to make appropriate concentration for Bradford tests. Standard curve was obtained by nine standard solutions prepared from bovine serum albumin (BSA) in the range of 0−2000 μg/mL. A 30 μL portion of any standard samples or diluted glycinin and β-conglycinin solutions was mixed well with 1.5 mL of Coomassie reagent, and the absorbance of the prepared samples at 595 nm was measured with a UV spectrophotometer (Evolution 600, Thermo, USA); the concentration of each sample was calculated from the standard curve, based on corresponding absorbance to concentrations of BSA. To narrow down the number of analytical tests the solvent systems which showed more than 40% concentrations of both glycinin and β-conglycinin were selected for further analysis. Particle Size Measurement. Particle sizes of the glycinin and βconglycinin in the solutions were measured by using a dynamic light scattering particle size analyzer (Zetasizer Nano-ZS, Malvern, MA). Protein concentration was used at 1 wt % in these tests. Thermal Analysis. β-Conglycinin or glycinin solutions were diluted to 0.1 wt % with 10 mM sodium phosphate buffer (pH 7.0). Thermodynamic properties of these soy protein samples were determined using an nDSC (TA Instruments, New Castle, DE, USA) at the scanning rate of 1 °C/min for a temperature range of 25− 100 °C. A protein solution (300 μL) was filled into the sample cell, and the reference buffer was placed in the reference cell at constant pressure of 2 atm. For the curve analysis, buffer−buffer tracings were recorded under the same conditions and subtracted from the sample endotherms. Subsequently, curve analysis using NanoAnalyzer Data Analysis software (TA Instruments) was performed to determine the transition temperature at peak maximum (Tm) as well as the calorimetric enthalpy change (ΔHm) of the unfolding process normalized for protein content. ΔHm was obtained by integration of the area of the excess heat capacity endotherm. Viscosity. AR 1000 Rheometer, TA. Instruments (Dover, USA), using Carri-Med software (version 5.3), was employed for measuring viscosity of the samples. The instrument contains a lower Peltier plate with programmed temperature control at 25.00 °C and an upper metal cone (40 mm diameter with 2° cone angle). Solvent systems with 2 wt % protein content were prepared for this experiment.

Figure 2. Coordination of primary solvents, prepared solvent systems, water, and 6 M urea solution in HSP space.

proteins in comparison with other solvent systems having further distance to 6 M urea solution location.58 Solvent systems having larger HSP distances to 6 M all exhibited poor solubility to the proteins. Also, there are some exceptions. Solvent systems 114 and 113, which have the smallest Ra values, could not serve as proper solvents for both glycinin and βconglycinin, indicating Ra value only is not sufficient in predicting solvents for proteins. Table 1 shows the solubility values and Ra values for the selected solvent systems together with pure water and 6 M urea solution as solvents for both glycinin and β-conglycinin. Six M urea aqueous solution represents the one with the best solubility behavior while water showed the worst one among all in the table. Among these prepared solvent systems, the best solubility result was achieved by solvent system number 108 for both glycinin and β-conglycinin. In general, β-conglycinin showed slightly higher solubility results in comparison to that of glycinin in the same solvent systems. Solubility results versus Ra values are drawn in Figure 3. It can be seen that the solvent systems with smaller Ra values can possibly dissolve more proteins, while the solvent systems with greater Ra values tend to dissolve less amount of the proteins. Solubility is linearly and inversely correlated to the Ra value. Glycinin shows higher correlation factor between solubility and Ra than β-conglycinin. DLS Analysis. Solubility test itself cannot provide any details about the conformational structures of glycinin and βconglycinin in the solvents. Good solvents should dissolve and denature protein chains and produce dissolved random coil forms of the biomacromolecules in the systems. The solvents should be able to overcome all intramolecular and intermolecular interactions of the biomolecules and provide more stable interactions with the protein domains instead. Circular dichroism (CD) can provide valuable information about the changes in secondary and tertiary structures of the proteins, however, due to the UV absorptivity of some organic solvents, CD could not be used for measuring conformational structures of these proteins in the solution systems. Instead, DLS was used in order to shed light on molecular size and size

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RESULTS AND DISCUSSION Solubility test. Solubility test was carried out on some selected samples. Both glycinin and β-conglycinin were added to the solvent systems, and the solutions were agitated for 24 h. Samples with no obvious precipitations were selected to measure concentrations of the protein by following the Bradford assay. Among over 100 prepared solvent systems only solvent systems number 58, 99, 102, 103, 108, 109, 111, and 112 showed clear solutions. Only these solvent systems were selected for further analysis. Figures 2 shows the status of the selected samples in comparison with other solvent systems based on distance from 6 M urea solution in HSPs in three different attributes, respectively. Generally speaking, the solvent systems which have the closest HSPs to 6 M urea solution, a universal solvent for proteins, could be good solvents for the C

DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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Table 1. Solubility, Hydrodynamic Radius, PDI, ΔHm, and Tm Results of Glycinin and β-Conglycinin in Selected Solvent Systems and 6 M Urea Solution solvent system water 6 M urea 58 99 102 103 108 109 110 111 112

protein glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin glycinin β-conglycinin

solubility (%) 27.02 55.65 86.18 94.99 43.57 64.67 49.01 77.00 68.91 85.22 53.18 76.97 85.38 94.22 68.81 79.50 69.51 75.21 80.80 93.51 62.78 69.72

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

0.45 0.71 0.35 0.23 0.81 0.47 0.59 0.43 0.73 0.29 0.65 0.35 0.51 0.31 0.62 0.38 0.66 0.37 0.44 0.28 0.49 0.44

hydrodynamic radius (nm) 29.83 15.98 9.81 7.70 8.83 6.53 8.60 6.43 8.77 6.95 8.46 6.14 9.04 7.13 8.86 6.81 8.65 6.88 9.11 7.36 8.89 6.86

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

0.26 0.14 0.05 0.03 0.09 0.08 0.10 0.08 0.08 0.07 0.12 0.06 0.07 0.02 0.06 0.04 0.06 0.05 0.05 0.05 0.07 0.04

PDI

ΔHm (kJ/mol)

Tm (°C)

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

618.31 ± 6.74 357.18 ± 4.18 no peak observed no peak observed 451.64 ± 8.11 262.73 ± 4.23 509.97 ± 10.19 249.11 ± 3.41 480.12 ± 7.32 269.30 ± 2.97 588.41 ± 7.39 291.32 ± 2.80 169.57 ± 0.91 95.35 ± 0.37 220.9 ± 2.16 128.13 ± 3.29 413.74 ± 5.83 254.49 ± 0.78 153.89 ± 3.51 89.18 ± 0.83 319.17 ± 0.88 189.52 ± 1.83

68.99 ± 0.65 59.24 ± 0.29

0.792 0.332 0.275 0.374 0.340 0.318 0.312 0.358 0.369 0.294 0.346 0.339 0.341 0.353 0.339 0301 0.361 0.341 0.311 0.329 0.373 0.322

0.086 0.017 0.024 0.017 0.051 0.033 0.047 0.028 0.057 0.029 0.034 0.035 0.039 0.014 0.043 0.25 0.041 0.021 0.028 0.020 0.030 0.023

59.38 52.59 63.15 61.75 69.93 60.78 61.85 56.37 56.30 48.41 62.37 58.39 62.85 57.81 59.34 49.56 61.88 55.51

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.90 1.16 0.42 0.43 1.08 0.68 0.59 0.55 0.39 0.27 0.23 0.61 0.61 0.53 0.82 0.41 0.18 0.20

Figure 3. Correlation between solubility and Ra.

Figure 4. Correlation between hydrodynamic radius and solubility.

distribution of the dissolved proteins so as to get information on the conformational changes of the molecules. Glycinin and β-conglycinin possess quaternary structures, in addition to secondary and tertiary ones, but can dissociate to smaller building protein blocks such as acidic and basic units or α and β units by denaturants in the solutions. Thus, the measured hydrodynamic radius of glycinin and β-conglycinin become significantly smaller changing from water to 6 M urea solutions due to dissociation of the proteins to these building protein units by the denaturant of urea. Also, the same dissociative behavior was observed by all selected solvent systems. Among 6 M urea solution and selected good solvent systems, the biggest hydrodynamic radius was achieved by 6 M urea solutions, suggesting that 6 M urea solution can denature soybean proteins more effectively and also result in more open and random structure than the other solvent systems. The solvent systems having hydrodynamic radii closest to 6 M urea solution were the systems of 111 and 108 for both proteins, and both demonstrated high solubility as well, suggesting that there might be a good correlation existing between solubility and denaturation of proteins. Figure 4 represents the correlation between solubility and hydrodynamic radius, showing some

level of relationship. On the other hand, HSPs have provided improved correlation between molecular sizes and Ra values in the solvent systems. Figure 5 shows the plots of Ra values and hydrodynamic radii of both soybean proteins in the solvents, proving that HSP theory can explain and predict protein dissolution behavior in different solvent systems. Here, the

Figure 5. Correlation between Ra and hydrodynamic radius. D

DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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while no peak was identified for glycinin and β-conglycinin in the 6 M urea solution. The transition peaks in nDSC thermograms are considered as the amount of adsorbed thermal energy by a mole of protein that its native and ordered structures are denatured and opened up and become disordered structures (ΔHm). Also, Tm is considered as a denaturing temperature of a protein where the maximum denaturation happens at that temperature. ΔHm of 618.31 and 357.18 (kJ/mol) were achieved for glycinin and β-conglycinin in water, while Tm of 68.99 and 59.24 (°C) were obtained for glycinin and β-conglycinin, respectively. ΔHm of these molecules indicates that glycinin has more stable molecules than β-conglycinin and requires more thermal energy to start unfolding due to larger structure and stronger intermolecular and intramolecular interactions. Also, a sharp peak was detected for β-conglycinin, while a broad peak was obtained by glycinin. Higher Tm was detected for glycinin than that of β-conglycinin meaning that the ordered structures of glycinin are more stable than β-conglycinin and require higher temperature and more energy to be thermally denatured. The denaturation information on glycinin and β-conglycinin in water by nDSC are consistent with the ones reported by other researchers.62 A few small peaks appeared at lower temperatures prior to the major peaks for both proteins due to the some insignificant structural changes or structural dissociations. Also, no peak was observed for glycinin and β-conglycinin in 6 M urea solutions which means they are fully denatured in this denaturant solution and no more ordered structures exist. Table 1 shows ΔHm and Tm for both glycinin and βconglycinin in the selected solvent systems. For all selected solvent systems transition peaks were observed for both proteins with different intensities and at various locations in thermograms. The lowest ΔHm was detected for the solvent system of number 111 for both proteins that means the solvent 111 is the strongest denaturant among the solvent systems. This result is in agreement with DLS experiment results where solvent system 111 possessed the largest structures among all selected samples. Figures 8 and 9 illustrate the correlation of

dissolution and denaturation processes of protein biopolymers are different from simple dissolution of regular synthetic polymers. The different correlations shown in both Figures 4 and 5 reveal the different concepts. Solvent characteristics also affect size distributions of the proteins in the solutions, DLS was used to measure polydispersity indexes (PDI) of the samples, and the obtained results are shown in Figure 6. PDI measured by DLS is a

Figure 6. Correlation between Ra and PDI.

numerical value between 0 and a maximum value of 1.0. A PDI value of 1.0 indicates that the sample has a very broad size distribution and may contain large particles or aggregates that come from sedimentation of smaller ones, while PDI values close to zero are corresponded to narrow size distributions. Figure 6 declares that there is no meaningful correlation between Ra values and PDIs. In fact, PDI provides distribution of protein sizes in solutions and it does not directly represents the size or denaturation of protein samples. Thermal Analysis. Although hydrodynamic radius and PDI can provide some clues about the openness of the protein molecules, they cannot provide direct evidence on conformational changes of macromolecules in regards to the existing intramolecular ordered structures. nDSC was used to monitor the unfolding of both proteins in order to provide more information about the denaturation of the proteins in the selected solvent systems. nDSC with a continuous capillary sample cell is designed to minimize the contribution of aggregation of small molecules in comparison with common DSC devices. Figure 7 shows glycinin and β-conglycinin thermograms in water and 6 M urea solution. One significant endothermal transition was detected for both proteins in water,

Figure 8. Correlation between Ra and ΔHm.

ΔHm and Tm as denaturation parameters versus HSP distance, R, respectively. As Figure 8 depicts there is a high correlation between structural denaturation parameter, ΔHm, and the solubility parameter distance in 6 M urea solutions (R2 = 0.8862 and 0.8771 for glycinin and β-conglycinin, respectively). These results confirm that the closer solvent system solubility parameters to the center of 6 M urea solution coordination, the higher structural denaturation can be expected for a protein to be dissolved in that solvent system. Eventually it can be

Figure 7. Glycinin and β-conglycinin thermograms in water and 6 M urea solution. E

DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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Figure 9. Correlation between Ra and Tm.

concluded that HSP theory is a powerful tool to search and predict proper solvents for proteins. In contrast, weak correlation was achieved between Tm and HSP distance. Although Tm is one of the thermal denaturation parameters and provides some ideas about the denaturation status, it just demonstrates the temperature that maximum denaturation occurred, and it does not directly provide how much denaturation happens in the systems. Rheometry. Rheometry was utilized in order to investigate about processability of the denatured protein solutions. When shear forces are applied to a macromolecular solution which the macromolecules possess random coils in the solution, macromolecules can slide over each other and make elongated and aligned conformation in the solution under a shear force. All linear processable synthetic polymers in rheology are classified as shear-thinning polymers, where viscosity of the macromolecular solutions is decreased by increasing shear force or shear rate. Suspension or gel can be produced by adding protein to poor solvents which they do not represent shearthinning behavior. As a result, denatured protein solutions are capable of showing shear-thinning characteristics. Rheometry study can indirectly provide very useful information about the solvent and solute interactions and unfolding status of the protein structures. However, rheometer tests require high concentration of protein solutions and large quantity of materials, thus, we decided to directly use soy protein isolate, which contains both glycinin and β-conglycinin and also provide same level of viscoelastic behaviors of the polymers. Figure 10 represents viscosity changes of soybean protein dissolved in the selected solvent systems under different shear rates. Figure 10a shows samples with low viscosity ranges, and Figure 10b illustrates those samples with higher viscosity ranges. At the first look, all the selected solvent systems have shear-thinning rheological properties. Soy proteins in solvent systems 58, 103, and 110 demonstrated the highest viscosity, while they have Ra values of 5,92, 6.31, and 5.60, respectively, not very close to 6 M urea solution. A systematic analysis of nDSC, DLS, and solubility results of these solvent systems suggest that a gel status of the protein was formed at high concentrations; therefore, they exhibit with higher viscosity in comparison with the other selected solvent systems. The significant drop in viscosity values at the very low shear rates occurred confirming the gelation behavior for these solvent systems. Also, the solvent system 99 in Figure 10a illustrates the lowest viscosity among all selected solvent systems while it does have the largest Ra value of 6.41. This solvent system shows very weak shear thinning behavior, almost close to Newtonian fluid behavior. The rheometry experiment results represent that

Figure 10. Viscosity changes of soybean proteins dissolved in selected solvent systems under different shear rates. (a) Samples (solvent system numbers 99, 102, 108, 109, 111, and 112) with low viscosity ranges. (b) Other samples (solvent system numbers 58, 103, and 110).

the solvent systems with greater Ra values or weaker solvency properties make solutions with weak pseudoplastic behavior and processability. Medium viscosity range with perfect pseudoplastic behavior were detected for other solvent systems with smaller Ra values.

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CONCLUSION In summary Hansen solubility theory was utilized to explore alternative solvent systems for dissolution of plant proteins. Solution properties of the solvent systems were measured by solubility test, DLS, nDSC, and rheometry. It was observed that the solvent systems that are in a Hansen solubility distance value (Ra) close to 6 M Urea solution have tendency to dissolve glycinin and β-conglycinin. It is necessary to have small Ra value to search for proper solvents, but it is not sufficient. Physicochemical similarity of solvent system to peptide chemistry and urea should be considered. The results demonstrated that Hansen solubility theory has a reasonable accuracy in prediction of a solvent system for dissolution and denaturation of proteins. Thermal denaturation energy (ΔHm) F

DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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ACS Sustainable Chemistry & Engineering

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from nDSC experiment showed a strong correlation with solvent system Ra values. However, very weak correlation was obtained from PDI and Tm versus solvent systems Ra values. Also, rheometry results revealed that solvent systems having Ra far from 6 M urea solution coordination tend to make gel-like solution or weak shear-thinning behaviors, while the solvent systems with small Ra values represent ideal pseudoplastic properties. The results provide useful information for finding solvents to denature and dissolve plant proteins.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssuschemeng.6b00022. Table S1. Solvent systems compositions (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: +1 530(752)0840. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS A.A. acknowledges Jastro Shields graduate student research fellowship for partial financial support. Mara Bryan at the Energy Biosciences Institute at University of California kindly helped the nDSC experiments.

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REFERENCES

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DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

Research Article

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DOI: 10.1021/acssuschemeng.6b00022 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX