Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling

Area of Science:

• Environmental Chemistry
• Physical Chemistry
• Biophysics

Background:

• Linear free energy relationships (LFERs) are crucial for predicting chemical partitioning.
• Traditional one-parameter LFERs (1p-LFER) based on octanol have limitations.
• Abraham solvation-based poly-parameter LFERs (pp-LFER) are comprehensive but complex.

Purpose of the Study:

• To introduce a simplified two-parameter LFER (2p-LFER) model.
• To balance predictive accuracy with model simplicity.
• To evaluate the 2p-LFER model's performance against established methods.

Main Methods:

• Developed a 2p-LFER model using octanol-water (log Kow) and air-water (log Kaw) partition coefficients.
• Applied the model to predict protein-water (log Kpw) and bovine serum albumin-water (log KBSA) partition coefficients.
• Compared 2p-LFER predictions with pp-LFER and 1p-LFER for various chemical partitioning scenarios.

Main Results:

• The 2p-LFER model accurately predicted log Kpw (R²=0.878) and log KBSA (R²=0.760).
• Model performance was comparable to pp-LFER for neutral per- and polyfluoroalkyl substances.
• Multiphase partitioning models using 2p-LFER showed good agreement with experimental in vivo/in vitro tissue distribution and milk-water partitioning data.

Conclusions:

• The 2p-LFER model offers a viable and accurate alternative to pp-LFER for estimating chemical partitioning.
• It effectively simplifies the six-dimensional Abraham solute descriptor space into two key dimensions.
• The model's performance surpasses 1p-LFER, providing a more robust prediction tool.