Work

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

Year: 2024

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide 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 K ow ) and air–water (log K aw ) partition coefficients. Our 2p -LFER model, utilizing linear combinations of log K ow and log K aw, showed promising results. It accurately predicted structural protein–water (log K pw ) and bovine serum albumin–water (log K BSA ) partition coefficients, with R 2 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.

Source: Journal of Chemical Information and Modeling

DOI: 10.1021/acs.jcim.4c01289

Authors: Muhammad Irfan Khawar, Muhammad Arshad, Eric P. Achterberg, Deedar Nabi

Institutions: National University of Sciences and Technology, GEOMAR Helmholtz Centre for Ocean Research Kiel

Language: English

Topic: Protein Interaction Studies and Fluorescence Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funders: GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, National University of Sciences and Technology