An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement

Oeller, Marc; Sormanni, Pietro; Vendruscolo, Michele

doi:10.1038/s41598-021-01126-4

Cited by 24 publications

(50 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental protocol was adapted from the method reported by Chai et al 6 and Oeller et al 44 Solutions of 10 mM histidine, pH 6.0, containing varying polyethylene glycol 3350 (PEG 3350) concentrations (v/v) (from 4 to 36%) were prepared using a Formulator (Formulatrix, Bedford, MA). All the mAb samples were buffer-exchanged and diluted to 1 mg/mL and plated (50 µL/well) onto the wells of 96-well polystyrene, V-bottomed assay plates (Greiner).…”

Section: Methodsmentioning

confidence: 99%

Development of QSAR models for in silico screening of antibody solubility

Han

Shih

Lin

et al. 2022

mAbs

View full text Add to dashboard Cite

Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R 2 of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.

show abstract

Section: Methodsmentioning

confidence: 99%

Development of QSAR models for in silico screening of antibody solubility

Han

Shih

Lin

et al. 2022

mAbs

View full text Add to dashboard Cite

show abstract

“…A mild drop in soluble concentration was observed only for the WT at 33% PEG ( Fig. S7 ), a concentration at which the pipetting of the automated robot starts to become less accurate because of the very high viscosity of the PEG solution (62). While this result hints at a higher relative solubility of the quadruple mutant, it shows that PEG precipitation cannot be used to measure the relative solubility of all designed variants.…”

Section: Resultsmentioning

confidence: 99%

“…We then attempted to measure relative solubility with polyethylene glycol (PEG) precipitation, using a recently introduced method that only requires roughly 270 µg of purified protein material (62). However, a first experiment carried out with WT and quadruple mutant revealed that these nanobodies do not precipitate in PEG-6K at PEG concentrations up to 30% (weight/volume).…”

Section: Experimental Validation On a Nanobodymentioning

confidence: 99%

Automated optimisation of solubility and conformational stability of antibodies and proteins

Rosace

Bennett

Oeller³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Developability issues, such as aggregation, low expression yield, or instability over long-term storage often hamper the development of biologics. Developability is underpinned by a complex interplay of biophysical properties, including conformational stability and solubility. Advances have been made in the optimisation of individual properties, however, multi-trait optimisation remains highly challenging, as mutations that improve one property often negatively impact the others. Here, we introduce a fully automated computational strategy for the rational design of conformationally stable and soluble protein and antibody variants harbouring multiple mutations. This pipeline, called CamSol Combination, leverages phylogenetic information to reduce false positive predictions, and combines a rapid method of predicting solubility changes with an empirical energy function. We experimentally validate the method’s predictions on a nanobody isolated with yeast-display from a synthetic library, by producing 12 designs ranging from single-point mutants to a quadruple mutant. All 12 designed variants retained antigen binding, had improved thermal stability, and decreased propensity to precipitate (increased relative solubility), and 8 also had reduced cross-reactivity. The melting temperature was improved by 13.6 ºC with 4 rationally designed mutations. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.ukSignificanceProtein-based biologics, such as antibodies and enzymes, are crucial reagents in research, industrial biotechnology, and diagnostics, and are increasingly used to treat a wide range of diseases as biotherapeutics. Often, biologics with suitable functionality are discovered, but their development into practically useful molecules is impeded by developability issues. Conformational stability and solubility are arguably the most important biophysical properties underpinning developability potential, as they determine colloidal stability and aggregation, and correlate with expression yield and poly-specificity. Here we introduce and experimentally validate a fully automated computational method and associated webserver for the rational design of proteins and antibodies with improved stability and solubility. Computational methods are rapid, inexpensive, and have no material requirements, which makes their implementation into biologic development pipelines particularly attractive.

show abstract

“…Solubility assay. The relative solubility of proteins was measured using a recently developed polyethylene glycol (PEG) solubility assay 25 . In brief, PEG 6000 is used as a crowding agent and titrated to final PEG concentrations of 0-33% and final protein concentration of 1 mg/mL, starting from PEG and protein stocks in the same buffer at the same pH, which typically needed to be re-adjusted after dissolving the PEG.…”

Section: Experimental Methodsmentioning

confidence: 99%

Sequence-based pH-dependent prediction of protein solubility using CamSol

Oeller

Kang

Sormanni

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Solubility is a property of central importance for the use of proteins in research and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are resource-intensive and time-consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the extension of one of such methods, CamSol, to include in the predictions the effect of the pH of the solubility. We illustrate the accuracy of the pH-dependent predictions on a variety of antibodies and other proteins.

show abstract

An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement

Cited by 24 publications

References 33 publications

Development of QSAR models for in silico screening of antibody solubility

Development of QSAR models for in silico screening of antibody solubility

Automated optimisation of solubility and conformational stability of antibodies and proteins

Sequence-based pH-dependent prediction of protein solubility using CamSol

Contact Info

Product

Resources

About