Nabi, Deedar , Achterberg, Eric P. , Khawar, Muhammad and Arshad, Muhammad (2024) Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling. Journal of Chemical Information and Modeling, 64 (24). pp. 9327-9340. DOI 10.1021/acs.jcim.4c01289.

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Abstract

Linear free energy relationships (LFERs) are pivotal in predicting protein–water partition coefficients, with traditional one-parameter (1p-LFER) models often based on octanol. However, their limited scope has prompted a shift toward the more comprehensive but parameter-intensive Abraham solvation-based poly-parameter (pp-LFER) approach. This study introduces a two-parameter (2p-LFER) model, aiming to balance simplicity and predictive accuracy. We showed that the complex six-dimensional intermolecular interaction space, defined by the six Abraham solute descriptors, can be efficiently simplified into two key dimensions. These dimensions are effectively represented by the octanol–water (log Kow) and air–water (log Kaw) partition coefficients. Our 2p-LFER model, utilizing linear combinations of log Kow and log Kaw, showed promising results. It accurately predicted structural protein–water (log Kpw) and bovine serum albumin–water (log KBSA) partition coefficients, with R2 values of 0.878 and 0.760 and root mean squared errors (RMSEs) of 0.334 and 0.422, respectively. Additionally, the 2p-LFER model favorably compares with pp-LFER predictions for neutral per- and polyfluoroalkyl substances. In a multiphase partitioning model parametrized with 2p-LFER-derived coefficients, we observed close alignment with experimental in vivo and in vitro distribution data for diverse mammalian tissues/organs (n = 137, RMSE = 0.44 log unit) and milk–water partitioning data (n = 108, RMSE = 0.29 log units). The performance of the 2p-LFER is comparable to pp-LFER and significantly surpasses 1p-LFER. Our findings highlight the utility of the 2p-LFER model in estimating chemical partitioning to proteins based on hydrophobicity, volatility, and solubility, offering a viable alternative in scenarios where pp-LFER descriptors are unavailable.

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