SolupHred: a server to predict the pH-dependent aggregation of intrinsically disordered proteins

Area of Science:

• Biochemistry
• Computational Biology
• Protein Science

Background:

• Protein aggregation is influenced by environmental conditions, particularly pH.
• Intrinsically disordered proteins (IDPs) are highly sensitive to solvent fluctuations due to exposed aggregation determinants.
• Current computational models often neglect the impact of solvent conditions on protein aggregation.

Purpose of the Study:

• To introduce SolupHred, a novel web server for predicting pH-dependent protein aggregation.
• To provide a tool that incorporates the influence of pH on protein solubility and aggregation propensity.
• To address the gap in computational tools for predicting aggregation under varying pH conditions.

Main Methods:

• Development of a phenomenological model linking protein lipophilicity and charge to solution pH.
• Implementation of the theoretical framework into a user-friendly, web-based interface (SolupHred).
• Validation of the model's ability to accurately anticipate solubility changes in different IDPs.

Main Results:

• SolupHred accurately predicts IDP aggregation propensities as a function of pH.
• The tool is the first of its kind dedicated to predicting pH-dependent protein aggregation.
• The underlying model demonstrated accuracy in anticipating solubility changes.

Conclusions:

• SolupHred offers a valuable resource for researchers studying protein aggregation.
• The tool enhances the understanding of how pH affects protein behavior and aggregation.
• This work highlights the importance of considering solvent conditions in aggregation prediction.