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. 2024 Jul 5;15(28):7154–7160. doi: 10.1021/acs.jpclett.4c01175

Amino Acids and Their Biological Derivatives Modulate Protein–Protein Interactions in an Additive Way

Xufeng Xu †,*, Francesco Stellacci †,‡,*
PMCID: PMC11261602  PMID: 38967372

Abstract

graphic file with name jz4c01175_0005.jpg

Protein–protein interactions (PPIs) differ when measured in test tubes and cells due to the complexity of the intracellular environment. Free amino acids (AAs) and their derivatives constitute a significant fraction of the intracellular volume and mass. Recently, we have found that AAs have a generic property of rendering protein dispersions more stable by reducing the net attractive part of PPIs. Here, we study the effects on PPIs of different AA derivatives, AA mixtures, and short peptides. We find that all the tested AA derivatives modulate PPIs in solution as effectively as AAs. Furthermore, we show that the modulation effect is additive when AAs form mixtures or are bound into short peptides. Therefore, this study demonstrates the additive effects of a class of small molecules (i.e., AAs and their biological derivatives) on PPIs and provides insights into rationally designing biocompatible molecules for stabilizing protein interactions and consequently tuning protein functions.

Protein–protein interactions (PPIs) are essential for carrying out distinct processes and maintaining homeostasis inside the cell.1 PPIs in cells (in vivo) are very hard to reproduce in test tubes (in vitro) due to the complexity of intracellular organization.2 A map of the interactions between proteins and metabolites reveals the importance of intracellular small molecules in modulating protein activity.3,4 In particular, free amino acids (AAs) were reported to constitute a major component in cellular biomolecules.5 The total AA concentration in the cytosol of a mammalian cell was also reported to reach tens of mM in normal conditions.6,7 The effect of AAs on the protein folding equilibrium between the folded/native and unfolded states (i.e., protein stability) is widely studied (as an important class of osmolytes) by both experimental observation and theoretical descriptions.8,9 For instance, the protein backbone transfer energies10 were experimentally measured, and a quantitative solvation model was brought up to explain the protecting/denaturing effects of different AAs.1119F NMR and binding isotherms were also employed12 to measure the effects of different AAs on the dissociation of a model protein dimer of the B1 domain of the streptococcal immunoglobulin binding protein G. It was found that AAs perturb the dimer dissociation.

Recently, we have found that AAs significantly affect PPIs by reducing the net attraction, and it in turn makes protein dispersions more stable.13 The experimental evidence to substantiate this finding was an increase in the protein second virial coefficient (B22) and a change in the protein potential of mean force. We also proposed a theoretical framework of AAs weakly interacting/binding with proteins to explain the AAs’ general stabilizing effect. Here, we perform a detailed study of the effects on PPIs of different AAs’ biological derivatives, of AA mixtures, and of short peptides. We employ lysozyme and bovine serum albumin (BSA) as protein models. They have distinct molecular weights and isoelectric points (14 000 Da and pH 11 for lysozyme; 66 000 Da and pH 4.5 for BSA). We use the method of sedimentation-diffusion equilibrium analytical ultracentrifugation (SE-AUC)14−17 as an analytical method to measure the second virial coefficient (B22),18,19 which parameter reflects the extent of the solution nonideality (i.e., protein self-interaction here). We find a general modulation effect on PPIs of a variety of AAs’ biological derivatives. The modulation effect is related to their aliphatic chain length, hydrophobicity, and charge. We also demonstrate that the modulation effect is additive not only for the mixtures of AAs or of AAs and salt but also for short peptides up to 8 AA residues. The additivity is however lost when peptides are long (1000–10 000 Da or 40 AA residues). We believe that this study presents a wide class of AA-based molecules capable of stabilizing protein dispersions.

To evaluate protein–protein interactions (PPIs) in a dispersion, the equation of state (EOS)15,20 (eq 1) was employed

graphic file with name jz4c01175_m001.jpg 1

where Π is the osmotic pressure, ρ is the number density, and kT is the product of the Boltzmann constant (k) and the temperature (T). In the EOS, B indicates the virial coefficient with the number subscripts indicating the component of the dispersion, 1 the solvent, and 2 and 3 the main and minor solute. In this case, B22 is the second virial coefficient that measures the self-interaction among the main solutes (i.e., proteins in this study). A positive change of B22 (ΔB22 > 0) indicates that in the dispersion the net interactions between proteins become more repulsive while a negative change of B22 (ΔB22 < 0) indicates that the net interactions more attractive.15

In practice, we follow a three-step workflow (Figure 1) to measure the effect of small molecules on PPI. 1) In step 1, small molecules at varying concentrations (typically, from 0 up to the solubility limit) are added to a concentrated protein dispersion. 2) In step 2, these as-prepared dispersions are injected into the sample channel of AUC cells by pipettes with the small molecule solution of the same concentration into the reference channel. The AUC cells are then assembled into the AUC rotor. The sedimentation-diffusion equilibrium experiments are performed. After a typical time of approximately 24 h, the sedimentation-diffusion equilibrium is normally achieved, and the raw data detailing the protein concentration gradient along the AUC cell radius are collected. 3) In step 3, we perform the data analysis. It involves (i) calculating osmotic pressure by integrating protein concentration along the radius; (ii) computing the B22 values by assessing the slope of osmotic pressure divided by protein concentration as a function of protein concentration, and (iii) generating a plot of the B22 change (ΔB22) as a function of small molecule concentration (detailed calculation steps and related theoretical equations in Methods). A typical example of data analysis is shown in the red box in Figure 1. The Inline graphicvs ρ curve is above the van’t Hoff line (dashed), which indicates that the net lysozyme-lysozyme interactions in dispersion are repulsive (B22 > 0).15 Then, the value for B22 can be obtained by linear fitting (red dashed) the curve of Inline graphicvs ρ: 1.08 × 10–25 m3. The data in the small ρ region is omitted from the linear fitting due to data noise in low protein concentrations. Using this workflow, the effect of any small molecules on PPIs can be measured. As shown in the blue box in Figure 1, the effects of different AAs on lysozyme-lysozyme interactions are obtained by this workflow in our previous study.13 It is noteworthy that the molecular mass of small molecules has to be significantly smaller than that of proteins to ensure negligible small molecule sedimentation during the sedimentation process of the proteins.

Figure 1.

Figure 1

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Workflow to measure the effect of small molecules on protein–protein interactions. Representative scheme of the workflow (black box) to measure the effect of small molecules on protein–protein interactions (characterized by B22). It consists of three steps: 1. Sample preparation; 2. sedimentation-diffusion equilibrium analytical ultracentrifugation (SE-AUC) experiment, and 3. Data analysis. Created by BioRender.com; A typical example of measuring B22 for lysozyme-lysozyme interactions in 10 mg/mL lysozyme dispersion with 1.2 M proline (red box) and a previous study13 of the effects of different AAs on lysozyme-lysozyme interactions by using this workflow (blue box).

Amino Acid Derivatives. We first studied the effect of chirality. As shown in Figure 2A, we found that the modulation of the two proline enantiomers (d- and l-proline) on ΔB22 for lysozyme-lysozyme interactions basically overlaps. We also tested a 1:1 racemic mixture of d- and l-proline (Figure S1), and the effect is the same as a pure enantiomer (l-proline). Then, we investigated the effect of amine group location on the AA. We employed alpha-alanine, beta-alanine, and gamma-aminobutyric acid (GABA), where the amine group is attached to the alpha-carbon, beta-carbon, and gamma-carbon, respectively. As shown in Figure 2B, we found that ΔB22 does not change when the amine group is switched from the alpha- to beta-carbon. However, changing the amine group location to gamma-carbon (with the addition of one methyl group) increased ΔB22. This indicates that the effect of AAs on ΔB22 may be not dependent on amine group location but on aliphatic chain length. Based on this hypothesis, a further study was conducted by using 3 diols from 1,2-ethylene glycol (2-carbon aliphatic chain) to 1,4-butanediol (4-carbon aliphatic chain) and 1,6-hexanediol (6-carbon aliphatic chain) which only differ in aliphatic chain lengths. As shown in Figure S2, we found that the effect of diols on ΔB22 increases gradually with longer aliphatic chain lengths and 1,6-hexanediol has the best stabilization effect on the protein solution, which could explain the widespread use of 1,6-hexanediol in biological assays to dissolve protein phase separation.21 A diol of a longer aliphatic chain length than 7 carbons could not be tested due to its limited water solubility. We also studied the effect of glucose, a main product of the catabolism of AAs.22 We found a weaker effect on ΔB22 (Figure 2C), which may be explained by a lower hydrophobicity of glucose compared to AAs.

Figure 2.

Figure 2

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The effect on protein–protein interactions of different AA derivatives. Plots of ΔB22vs concentration of small molecules. A. Chirality: the effect of two proline enantiomers (d- and l-proline) on lysozyme-lysozyme interactions; B. Amine group location: the effect of alanine, beta-alanine, and GABA on lysozyme-lysozyme interactions; C. Metabolism product: the effect of glucose on lysozyme-lysozyme interactions, compared with three AAs (alanine, serine, and glycine); D. Post-translational modification: the effect of proline and hydroxyl-proline on lysozyme-lysozyme interactions; the effect of serine, phospho-serine, and acetyl-serine on lysozyme-lysozyme interactions and BSA-BSA interactions.

Proteins normally change their properties by post-translational modifications (PTMs),23 where different functional groups are covalently bonded to AAs. Three main PTMs of AAs were studied here. As shown in Figure 2D, we compared the effects of proline and hydroxyl-proline (the most frequently hydroxylated AA residue in the human body24). We found that the hydroxylation decreases the effect of proline on ΔB22, which may be due to lower hydrophobicity after the hydroxylation modification.24 Both the phosphorylation and acetylation of AAs were also investigated as they introduce negative charge to AAs. We found that both PTMs enhance the stabilization effect on lysozyme-lysozyme interactions (Figure 2D). This may be due to more negative charge after the PTMs, enhancing the binding of these AAs on positively charged lysozyme. The destabilization effect was found when negatively charged BSA was employed since the more negative charge by the phosphorylation and acetylation, the less binding to negatively charged BSA (−) (Figure 2D). The same phenomenon was observed for the effects of negatively charged arginine (−) on positively charged lysozyme (+) and negatively charged BSA (−) (Figure S3).

Amino Acid Mixtures. As shown in Figure 3A, we found that the effect of a binary mixture of glycine (0.6 M) and proline (0.5 M) equals the addition of the separate effects from the two AAs. A quinary AA mixture was also employed. It consisted of glycine, alanine, asparagine, proline, and serine 0.1 M each (the same composition as in Gibco MEM Non-Essential Amino Acids Solution for cell culture media). We found that the effect of this complex AA mixture at a total AA concentration of 0.5 M equals the effect of proline at a concentration of 0.5 M (Figure 3B). In a previous report,13 we showed that when adding proline to a solution that contained NaCl we could counter the salt destabilization with the stabilization effect of proline. Here we revisit the same system and show that the salt and proline effects are indeed simply additive. As shown in Figures 3C and 3D, we mixed 1 M proline with 0.2 or 0.3 M NaCl, respectively. We found that the overall effect of the mixture is roughly the addition of the effects from proline and NaCl. Therefore, the modulation effect of AAs is shown to be additive when different AAs are mixed or AAs are mixed with salt.

Figure 3.

Figure 3

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The effect on protein–protein interactions of different AA mixtures. Plots of ΔB22vs mixture concentration. The effect on lysozyme-lysozyme interactions of A. 0.6 M glycine, 0.5 M proline, and the mixture; B. a complex AA mixture used in MEM Non-Essential Amino Acids Solution, consisting of glycine, alanine, asparagine, proline, and serine of 0.1 M and proline of 0.5 M; C. 0.2 M NaCl, 1 M proline, and the mixture; D. 0.3 M NaCl, 1 M proline, and the mixture.

Peptides. In our previous publication,13 we showed that peptides made of three of four proline residues have a roughly additive effect when compared to proline. Here we further investigated this effect. First, we show in Figure 4A, that the effect of a poly(proline) being an additive is not only true in (proline)3 and (proline)4 but also in (proline)8. However, the depletion interactions25 take over when the molecular weight for the poly(proline) increases to 1000–10 000 Da. The volume available for the polymeric chains of the long peptides increases as the depletion layers of protein particles overlap. The free energy of the long peptides is minimized by the states where the protein particles become closer together.26 Thus, the effect leads to more attractive interaction between protein particles, and ΔB22 becomes negative, in stark contrast to positive ΔB22 (repulsive PPI) for proline at the same mass concentration (Figure 4B). We also investigated the effect of hetero dipeptides made of proline and glycine, including H-pro-gly-OH, H-gly pro-OH, and cyclo-(pro-gly). As shown in Figure 4C, the effects of these three dipeptides are similar to the additive effects of proline and glycine. However, when the peptide length is increased to 40 amino acid residues, the depletion interactions again took over. As shown in Figure 4D, ΔB22 becomes negative for poly(pro-gly)20 compared to positive ΔB22 for proline. These two experiments on polyAA and (poly)dipeptides indicate that the modulation effect is still additive when AAs are bound into short peptides. However, the additivity does not hold when the peptide becomes long due to depletion attraction.

Figure 4.

Figure 4

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The effect on protein–protein interactions of different peptides. Plots of ΔB22vs peptide concentration. A. The effect on lysozyme-lysozyme interactions of three homopeptides of proline (including (proline)3, (proline)4, and (proline)8); B. The effect on BSA-BSA interactions of proline and of the polypeptide of proline, poly(proline) with Mw = 1000–10 000 Da; C. The effect on lysozyme-lysozyme interactions of the three dipeptides made of proline and glycine, including H-pro-gly-OH, H-gly pro-OH, and cyclo-(pro-gly) compared to the additive effects of proline and glycine; D. The effect on BSA-BSA interactions of proline and of the polypeptide made of glycine and proline poly(glypro)20. BSA was chosen for studying the effects of poly(proline) (1000–10 000 Da) and poly(gly-pro)20 due to the significantly larger molecular weight of BSA (66 000 Da) than that of peptides.

Solution pH. In this study, we have consistently found that when the addition of small molecules changes the solution pH, the ΔB22 measured reflects the change of electrostatic repulsion between proteins rather than the effect of small molecules on PPIs. For instance, we found that the effect of a heptapeptide (H-Ser-Leu-Ser-Leu-Ser-Pro-Gly-OH) is more significant than the additive effects of all the AA residues, and the difference is more pronounced at higher concentrations (Figure S4A). This discrepancy is due to the buffer pH change after the addition of the peptide. As shown in Figure S4B, the buffer pH becomes lower with the addition of the peptide, which makes the lysozyme more electrostatically repulsive. This explains the significantly larger ΔB22 value for the peptide compared to the addition of the effects of all AA residues. Therefore, it is crucial to ensure an unchanged buffer pH after the addition of any small molecules for the investigation of any potential effect of small molecules on PPI.

In this study, all the biological derivatives of AAs were shown to modulate PPI. The change in the AA chirality and the amine group location does not affect AAs’ modulation on PPIs. Instead, the aliphatic chain length of AAs is found to affect their modulation on PPIs, which is further illustrated by using a series of diols of 2, 4, and 6 carbon chain lengths. The PTMs including phosphorylation and acetylation introduce more charge to AAs and affect their modulation of PPIs: the opposite charge between AAs and proteins helps AAs stabilize the PPI, while the same charge destabilizes the PPI. Moreover, we show that AAs have an additive effect when they form mixtures or short peptides. These findings can be explained by our recently proposed theory on the stabilization of proteins by weakly bound AAs.13 We showed that weakly bound AAs modulate protein–protein colloidal interactions by effectively screening a fraction of their net interaction potential. AAs bind easily to proteins of opposite charge and thus effectively screen PPIs. The theory also predicts that the property shown by AAs is a generic small-molecule property, which only requires weak interactions with proteins. Therefore, all the different types of AA derivatives also show similar modulation effects. Additionally, to a first approximation, the theory also predicts that the interaction scales linearly with the number of AAs either in the AA mixture or in peptides. This could explain the additive effect we observe for AA mixtures or short peptides. However, long peptides destabilize PPIs by introducing depletion attraction. Special cautions should be taken for plausible buffer pH change after the addition of small molecules as the buffer pH change will significantly affect electrostatic repulsion between proteins. Overall, this study demonstrated the general effects of a wide class of small molecules (i.e., AAs and their biological derivatives) on PPIs. The simple additivity principle can also extend the modulation effect to AA mixtures and short peptides. Therefore, this knowledge improves our understanding of the fundamental importance of AAs and their derivatives in cells and their influence on intracellular protein interactions and functions. Furthermore, the additivity of the modulation effects from AAs to short peptides will allow a further design and screening of a large library of short peptides for stabilizing proteins in vitro and in vivo, which may find potentials in the treatment of aging-related neurological diseases.

Materials and Methods

Materials. The powders of bovine serum albumin (BSA) and amino acids (including l-proline, l-alanine, l-serine l-glycine, d-proline, beta-alanine, gamma-aminobutyric acid (GABA), hydroxyl-proline, arginine-HCl, phospho-serine, and acetyl-serine) were purchased from Sigma-Aldrich. 1,2-Ethylene glycol, 1,4-butanediol, and 1,6-hexanediol were purchased from Fisher Scientific. The powder of lysozyme was purchased from Carl Roth. All the peptides (including (pro)3, (pro)4, (pro)8, H-pro-gly-OH, H-gly-pro-OH, cyclo-(pro-gly), and H-Ser-Leu-Ser-Leu-Ser-Pro-Gly-OH) were purchased from Bachem. Poly(proline) (Mw: 1,000–10,000) was purchased from Sigma-Aldrich. Poly(gly-pro)20 was purchased from GenScript.

Methods. Sedimentation-Diffusion Equilibrium. In a typical SE experiment,15,27 an analytical ultracentrifuge (Beckman Coulter ProteomeLab XL-I/XL-A) and titanium double sector cells of 3 mm path length were used. Protein solutions (e.g., 10 mg/mL lysozyme solution) with different concentrations of AAs in 50 mM phosphate buffer (pH 7) were prepared. The solution of an appropriate volume (e.g., 60 μL) was added to the sample channel, and the AA solution of the same concentration and volume in the buffer was added to the reference channel in an AUC cell by a micropipette. SE experiments were then performed overnight at 20 °C with scan intervals of 2 h using interference and absorbance optics (radial steps: 3 μm) at an appropriate angular velocity (44,000 rpm for lysozyme and 24,000 rpm for BSA). Typically, a sedimentation-diffusion equilibrium was reached after 24 h, when the concentration profile stayed unchanged for at least 6 h.

Data Analysis. The raw data from an SE-AUC experiment is the fringe difference (ΔJ) versus radial positions (r). ΔJ was first converted to the concentration difference (Δc) by using eq 2,27,28 where λ = 655 nm for the laser diode light source embedded in the Optima XLI, and dn/dc is the refractive index increment, which the abbe refractometer can measure. a is the path length of the AUC cell, which equals 3 mm.

graphic file with name jz4c01175_m004.jpg 2

The concentration gradient (Δc versus r) was then converted to an equation of state curve (osmotic pressure Π versus protein number density ρ) by using eq 3(20,29) where ω is the angular velocity, and Δm is the protein buoyant mass.

graphic file with name jz4c01175_m005.jpg 3

B22 was calculated by the equation of state (EOS) (eq 1).

The final step was to calculate Inline graphicand the slope for Inline graphicversus ρ2 equals B22(15) (eq 4).

graphic file with name jz4c01175_m008.jpg 4

The B22 variation (ΔB22) was calculated by using eq 5 where B022 is the value of B22 without any AA addition.

graphic file with name jz4c01175_m009.jpg 5

Acknowledgments

X.X. and F.S. acknowledge the support of the European Union’s Horizon 2020 Research and Innovation program under grant agreement no. 101017821 (LIGHT-CAP).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpclett.4c01175.

  • The effect of chirality (Figure S1), diols of different aliphatic chain lengths (Figure S2) on ΔB22 for lysozyme; The effect of arginine on ΔB22 for lysozyme and BSA (Figure S3); The effect of the heptapeptide (H-Ser-Leu-Ser-Leu-Ser-Pro-Gly-OH) on ΔB22 for lysozyme and on solution pH (Figure S4) (PDF)

  • Transparent Peer Review report available (PDF)

The authors declare no competing financial interest.

Supplementary Material

jz4c01175_si_001.pdf (383.6KB, pdf)
jz4c01175_si_002.pdf (380KB, pdf)

References

  1. Hein M. Y.; Hubner N. C.; Poser I.; Cox J.; Nagaraj N.; Toyoda Y.; Gak I. A.; Weisswange I.; Mansfeld J.; Buchholz F.; Hyman A. A.; Mann M. A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances. Cell 2015, 163 (3), 712–723. 10.1016/j.cell.2015.09.053. [DOI] [PubMed] [Google Scholar]
  2. Cohen R. D.; Pielak G. J. A Cell Is More than the Sum of Its (Dilute) Parts: A Brief History of Quinary Structure. Protein Sci. 2017, 26 (3), 403–413. 10.1002/pro.3092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Piazza I.; Kochanowski K.; Cappelletti V.; Fuhrer T.; Noor E.; Sauer U.; Picotti P. A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication. Cell 2018, 172 (1), 358–372. e23 10.1016/j.cell.2017.12.006. [DOI] [PubMed] [Google Scholar]
  4. Gerosa L.; Sauer U. Regulation and Control of Metabolic Fluxes in Microbes. Curr. Opin. Biotechnol. 2011, 22 (4), 566–575. 10.1016/j.copbio.2011.04.016. [DOI] [PubMed] [Google Scholar]
  5. Dar F.; Pappu R. Restricting the Sizes of Condensates. eLife 2020, 9, e59663 10.7554/eLife.59663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Gauthier-Coles G.; Vennitti J.; Zhang Z.; Comb W. C.; Xing S.; Javed K.; Bröer A.; Bröer S. Quantitative Modelling of Amino Acid Transport and Homeostasis in Mammalian Cells. Nat. Commun. 2021, 12 (1), 5282. 10.1038/s41467-021-25563-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bergström J.; Fürst P.; Norée L. O.; Vinnars E. Intracellular Free Amino Acid Concentration in Human Muscle Tissue. J. Appl. Physiol. 1974, 36 (6), 693–697. 10.1152/jappl.1974.36.6.693. [DOI] [PubMed] [Google Scholar]
  8. Rose G. D.; Fleming P. J.; Banavar J. R.; Maritan A. A Backbone-Based Theory of Protein Folding. Proc. Natl. Acad. Sci. U. S. A. 2006, 103 (45), 16623–16633. 10.1073/pnas.0606843103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hu C.; Rösgen J.; Pettitt B. M.. Osmolyte Influence on Protein Stability: Perspectives of Theory and Experiment. In Modeling Solvent Environments; John Wiley & Sons, Ltd, 2010; pp 77–92. 10.1002/9783527629251.ch4. [DOI] [Google Scholar]
  10. Auton M.; Bolen D. W. Additive Transfer Free Energies of the Peptide Backbone Unit That Are Independent of the Model Compound and the Choice of Concentration Scale. Biochemistry 2004, 43 (5), 1329–1342. 10.1021/bi035908r. [DOI] [PubMed] [Google Scholar]
  11. Street T. O.; Bolen D. W.; Rose G. D. A Molecular Mechanism for Osmolyte-Induced Protein Stability. Proc. Natl. Acad. Sci. U. S. A. 2006, 103 (38), 13997–14002. 10.1073/pnas.0606236103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Rydeen A. E.; Brustad E. M.; Pielak G. J. Osmolytes and Protein-Protein Interactions. J. Am. Chem. Soc. 2018, 140 (24), 7441–7444. 10.1021/jacs.8b03903. [DOI] [PubMed] [Google Scholar]
  13. Mao T.; Xu X.; Winkler P. M.; Siri C.; Poliukhina E.; Silva P. J.; Luo Z.; Ong Q.; Katz A.-A.; Stellacci F.. Amino Acids Stabilizing Effect on Protein and Colloidal Dispersions. arXiv April 17, 2024. 10.48550/arXiv.2404.11574. [DOI]
  14. Schuck P.; Braswell E. H. Measuring Protein-Protein Interactions by Equilibrium Sedimentation. Current Protocols in Immunology 2000, 40 (1), 18.8.1–18.8.22. 10.1002/0471142735.im1808s40. [DOI] [PubMed] [Google Scholar]
  15. Xu X.; Ong Q.; Mao T.; Silva P. J.; Shimizu S.; Rebecchi L.; Kriegel I.; Stellacci F. Experimental Method to Distinguish between a Solution and a Suspension. Advanced Materials Interfaces 2022, 9 (19), 2200600. 10.1002/admi.202200600. [DOI] [Google Scholar]
  16. Xu X.; With G. de; Cölfen H. Self-Association and Gel Formation during Sedimentation of like-Charged Colloids. Materials Horizons 2022, 9 (4), 1216–1221. 10.1039/D1MH01854J. [DOI] [PubMed] [Google Scholar]
  17. Xu X.; Cölfen H. Ultracentrifugation Techniques for the Ordering of Nanoparticles. Nanomaterials 2021, 11 (2), 333. 10.3390/nano11020333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Wills P. R.; Scott D. J.; Winzor D. J. The Osmotic Second Virial Coefficient for Protein Self-Interaction: Use and Misuse to Describe Thermodynamic Nonideality. Anal. Biochem. 2015, 490, 55–65. 10.1016/j.ab.2015.08.020. [DOI] [PubMed] [Google Scholar]
  19. Le Brun V.; Friess W.; Schultz-Fademrecht T.; Muehlau S.; Garidel P. Lysozyme-Lysozyme Self-Interactions as Assessed by the Osmotic Second Virial Coefficient: Impact for Physical Protein Stabilization. Biotechnology Journal 2009, 4 (9), 1305–1319. 10.1002/biot.200800274. [DOI] [PubMed] [Google Scholar]
  20. Page M. G.; Zemb T.; Dubois M.; Cölfen H. Osmotic Pressure and Phase Boundary Determination of Multiphase Systems by Analytical Ultracentrifugation. ChemPhysChem 2008, 9 (6), 882–890. 10.1002/cphc.200700668. [DOI] [PubMed] [Google Scholar]
  21. Kroschwald S.; Maharana S.; Simon A. Hexanediol: A Chemical Probe to Investigate the Material Properties of Membrane-Less Compartments. Matters 2017, 10.19185/matters.201702000010. [DOI] [Google Scholar]
  22. Newsholme P.; Stenson L.; Sulvucci M.; Sumayao R.; Krause M.. 1.02 - Amino Acid Metabolism. In Comprehensive Biotechnology (Second ed.); Moo-Young M., Ed.; Academic Press: Burlington, 2011; pp 3–14. 10.1016/B978-0-08-088504-9.00002-7. [DOI] [Google Scholar]
  23. Tak I.-R.; Ali F.; Dar J. S.; Magray A. R.; Ganai B. A.; Chishti M. Z.. Chapter 1 - Posttranslational Modifications of Proteins and Their Role in Biological Processes and Associated Diseases. In Protein Modificomics; Dar T. A.; Singh L. R., Eds.; Academic Press, 2019; pp 1–35. 10.1016/B978-0-12-811913-6.00001-1. [DOI] [Google Scholar]
  24. Raju T. S.Hydroxylation of Proteins. In Co- and Post-Translational Modifications of Therapeutic Antibodies and Proteins; John Wiley & Sons, Ltd, 2019; pp 119–131. 10.1002/9781119053354.ch10. [DOI] [Google Scholar]
  25. Tuinier R.Introduction to Depletion Interaction and Colloidal Phase Behaviour. In Soft Matter at Aqueous Interfaces; Lang P.; Liu Y., Eds.; Springer International Publishing: Cham, 2016; pp 71–106. 10.1007/978-3-319-24502-7_3. [DOI] [Google Scholar]
  26. Lekkerkerker H. N.; Tuinier R.. Colloids and the Depletion Interaction; Springer Netherlands, 2011. [Google Scholar]
  27. Xu X.; Franke T.; Schilling K.; Sommerdijk N. A. J. M.; Cölfen H. Binary Colloidal Nanoparticle Concentration Gradients in a Centrifugal Field at High Concentration. Nano Lett. 2019, 19 (2), 1136–1142. 10.1021/acs.nanolett.8b04496. [DOI] [PubMed] [Google Scholar]
  28. Maechtle W.; Börger L.. Analytical Ultracentrifugation of Polymers and Nanoparticles; Springer laboratory; Springer: Berlin; New York, 2006. [Google Scholar]
  29. van Rijssel J.; Peters V. F.; Meeldijk J. D.; Kortschot R. J.; van Dijk-Moes R. J.; Petukhov A. V.; Erne B. H.; Philipse A. P. Size-Dependent Second Virial Coefficients of Quantum Dots from Quantitative Cryogenic Electron Microscopy. J. Phys. Chem. B 2014, 118 (37), 11000–11005. 10.1021/jp5056182. [DOI] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

jz4c01175_si_001.pdf (383.6KB, pdf)
jz4c01175_si_002.pdf (380KB, pdf)

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