Investigating protein properties using PEG precipitation assays

This article and associated images are based on a poster originally authored by Laurine Achimo, Dr. Leon F. Willis, and Dr. David J. Brockwell and presented at ELRIG Drug Discovery 2026 in affiliation with the University of Leeds.

This poster is being hosted on this website in its raw form, without modifications. It has not undergone peer review but has been reviewed to meet AZoNetwork's editorial quality standards. The information contained is for informational purposes only and should not be considered validated by independent peer assessment.

Polyethylene glycol (PEG) precipitation has been used for decades as a protein solubility assay. Understanding protein solubility is fundamentally important and has applications in antibody formulation¹. The slope of the precipitation curve (β) (Figure 1a, left) is indicative of cooperativity, and a link has been proposed between β and protein hydrodynamic radius (rh) (Figure 1b, right).

Figure 1. (a) Representative precipitation curve fitted with the Boltzmann sigmoidal model and (b) proposed relationship between rh and β. Image Credit: Gibson, T.J., et al. (2011); Atha, D.H. and Ingham, K.C. (1981); Pons Royo, M.D.C. and Jungbauer, A. (2025)^2-4

• Research gap: Most studies report that protein rh and molecular weight (Mw) are the primary factors dictating protein precipitation behavior while ignoring the dynamic behavior of proteins.
• Study aim: Optimize and validate the PEG precipitation assay and screen a panel of model proteins (with known dynamic properties) to assess whether the slope and midpoints are correlated with the rh or molecular weight of proteins. Panel included: IgGs (Adalimumab, Fulranumab) and corresponding scFv-Fc fusions, Glucose oxidase, MBP WT and MBP 4A.

Methods

• Assay optimization:

Image Credit: Image courtesy of Laurine Achimo et al., in partnership with ELRIG (UK) Ltd.

• The optimized assay was then used to determine the β values and midpoint values of different proteins. Proteins were purchased or expressed in E.coli (MBP WT and 4A) or in CHO cells (IgGs from AstraZeneca). Preparation involved SEC or AKTA purification. Protein rh was determined by FIDA neo.

Key findings

• The PEG precipitation assay was successfully optimized over the range 0–20% PEG (w/v). The significant negative correlation between β and protein hydrodynamic radius (Log rh) was in sharp contrast to the previously reported positive association^3,4,7.
• The data suggests that more dynamic proteins have higher β values and thus exhibit higher cooperativity. No significant association was observed between Mw and either β or PEG50%.

Results

Figure 2. Progressive optimization of the PEG-precipitation assay reduced variability within the transition region. Light scattering is plotted against PEG concentration (% w/v) for nephelometry measurements (A–C) and for the optimized assay soluble protein concentration (D) determined by absorbance (NanoDrop) following centrifugation and plotted against PEG concentration (% w/v).

The optimized assay reduced normalized mean absolute error from 7.4% to 3.6% and improved estimates of PEG50% and cooperativity (β). MEDI IgG was used throughout assay optimization. Image Credit: Image courtesy of Laurine Achimo et al., in partnership with ELRIG (UK) Ltd.

Figure 3. Correlation analysis between protein hydrodynamic radius (Log rh) measured using FIDA neo and cooperativity (β). β showed a significant negative association with rh (Spearman’s ρ = -0.714, p = 0.047). Image Credit: Image courtesy of Laurine Achimo et al., in partnership with ELRIG (UK) Ltd.

Figure 4. Spearman’s rank-rank correlation graphs for Log rh and β values. This helps to visualize the strong negative monotonic association observed between the Rank β and Rank Log rh (Spearman’s ρ = -0.714, p = 0.047). Image Credit: Image courtesy of Laurine Achimo et al., in partnership with ELRIG (UK) Ltd.

Conclusion

• These findings suggest that protein size alone may not adequately describe PEG-induced precipitation behavior, highlighting the potential contribution of protein dynamics to precipitation cooperativity. However, limited protein availability limited the dataset's size and diversity, and protein dynamics were inferred rather than measured directly.
• A larger protein panel combined with direct measurements of conformational dynamics will be required to test this hypothesis.

Download the original poster

References and further reading

1. Willis, L.F., et al. (2024). Biophysical analysis of therapeutic antibodies in the early development pipeline. Biologics : targets & therapy, 18, pp.413–432. DOI:10.2147/BTT.S486345. https://www.dovepress.com/biophysical-analysis-of-therapeutic-antibodies-in-the-early-developmen-peer-reviewed-fulltext-article-BTT.
2. Gibson, T.J., et al. (2011). Application of a high-throughput screening procedure with PEG-Induced precipitation to compare relative protein solubility during formulation development with IgG1 monoclonal antibodies. Journal of Pharmaceutical Sciences, 100(3), pp.1009–1021. DOI:10.1002/jps.22350. https://jpharmsci.org/article/S0022-3549(15)32264-4/abstract.
3. Atha, D.H. and Ingham, K.C. (1981). Mechanism of precipitation of proteins by polyethylene glycols. Analysis in terms of excluded volume. Journal of Biological Chemistry, 256(23), pp.12108–12117. DOI:10.1016/s0021-9258(18)43240-1. https://www.sciencedirect.com/science/article/pii/S0021925818432401.
4. Pons Royo, M.D.C. and Jungbauer, A. (2025). Polyethylene glycol precipitation: Fundamentals and recent advances. Preparative biochemistry & biotechnology, 55(8), pp.935–954. DOI:10.1080/10826068.2025.2470220. https://www.tandfonline.com/doi/full/10.1080/10826068.2025.2470220.
5. BMG Labtech (2026). Nephelometry measurements on plate readers | BMG labtech. Available at: https://www.bmglabtech.com/en/nephelometry/.
6. Toprani, V.M., et al. (2016). A micro–polyethylene glycol precipitation assay as a relative solubility screening tool for monoclonal antibody design and formulation development. Journal of Pharmaceutical Sciences, 105(8), pp.2319–2327. DOI:10.1016/j.xphs.2016.05.021. https://www.sciencedirect.com/science/article/abs/pii/S0022354916414577.
7. Odijk, T. (2009). Depletion theory and the precipitation of protein by polymer. The Journal of Physical Chemistry B, 113(12), pp.3941–3946. DOI:10.1021/jp806722j. https://pubs.acs.org/jpcbfk/article-abstract/113/12/3941/1423492/Depletion-Theory-and-the-Precipitation-of-Protein?redirectedFrom=fulltext.

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Last Updated: Nov 6, 2026