Small-molecule properties define partitioning into biomolecular condensates.

Area of Science:

• Cell Biology
• Biophysics

Background:

• Biomolecular condensates are membrane-less organelles crucial for cellular function.
• Their function depends on selective molecule enrichment and exclusion.
• Understanding small molecule composition is key to condensate function.

Purpose of the Study:

• To quantify the partitioning of small molecules into various biomolecular condensates.
• To identify the driving forces behind small molecule partitioning.
• To develop a predictive model for small molecule behavior within condensates.

Main Methods:

• Quantified partitioning of ~1,700 small molecules across different macromolecule-based condensates.
• Investigated compound-macromolecule interactions under non-condensing conditions.
• Developed and validated a machine learning model using physicochemical properties.

Main Results:

• Small molecule partitioning varied significantly (million-fold) but was consistent across different condensates.
• Partitioning was not primarily driven by high-affinity binding to macromolecules.
• A machine learning model accurately predicted partitioning based on solubility and hydrophobicity.

Conclusions:

• Condensate formation creates a hydrophobic environment that dictates small molecule partitioning.
• Physicochemical properties, particularly hydrophobicity, govern small molecule behavior in condensates.
• This provides a framework for understanding condensate composition and function.