Khawar Muhammad Irfan, Arshad Muhammad, Achterberg Eric P, Nabi Deedar
Institute of Environmental Science and Engineering (IESE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan.
GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstr. 1-3, Kiel 24148, Germany.
J Chem Inf Model. 2024 Dec 23;64(24):9327-9340. doi: 10.1021/acs.jcim.4c01289. Epub 2024 Dec 2.
Linear free energy relationships (LFERs) are pivotal in predicting protein-water partition coefficients, with traditional one-parameter (-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 (-LFER) approach. This study introduces a two-parameter (-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 ) and air-water (log ) partition coefficients. Our -LFER model, utilizing linear combinations of log and log , showed promising results. It accurately predicted structural protein-water (log ) and bovine serum albumin-water (log ) partition coefficients, with values of 0.878 and 0.760 and root mean squared errors (RMSEs) of 0.334 and 0.422, respectively. Additionally, the -LFER model favorably compares with -LFER predictions for neutral per- and polyfluoroalkyl substances. In a multiphase partitioning model parametrized with -LFER-derived coefficients, we observed close alignment with experimental and distribution data for diverse mammalian tissues/organs ( = 137, RMSE = 0.44 log unit) and milk-water partitioning data ( = 108, RMSE = 0.29 log units). The performance of the -LFER is comparable to -LFER and significantly surpasses -LFER. Our findings highlight the utility of the -LFER model in estimating chemical partitioning to proteins based on hydrophobicity, volatility, and solubility, offering a viable alternative in scenarios where -LFER descriptors are unavailable.
线性自由能关系（LFERs）在预测蛋白质 - 水分配系数方面至关重要，传统的单参数（-LFER）模型通常基于辛醇。然而，其有限的范围促使人们转向更全面但参数密集的基于亚伯拉罕溶剂化的多参数（-LFER）方法。本研究引入了一种双参数（-LFER）模型，旨在平衡简单性和预测准确性。我们表明，由六个亚伯拉罕溶质描述符定义的复杂六维分子间相互作用空间可以有效地简化为两个关键维度。这些维度由辛醇 - 水（log ）和空气 - 水（log ）分配系数有效表示。我们的 -LFER模型利用log 和log 的线性组合，显示出有前景的结果。它准确预测了结构蛋白 - 水（log ）和牛血清白蛋白 - 水（log ）分配系数， 值分别为0.878和0.760，均方根误差（RMSEs）分别为0.334和0.422。此外，-LFER模型与中性全氟和多氟烷基物质的 -LFER预测相比具有优势。在一个用 -LFER衍生系数参数化的多相分配模型中，我们观察到与多种哺乳动物组织/器官的实验 和 分布数据（ = 137，RMSE = 0.44对数单位）以及乳 - 水分配数据（ = 108，RMSE = 0.29对数单位）紧密吻合。-LFER的性能与 -LFER相当，且显著超过 -LFER。我们的研究结果突出了 -LFER模型在基于疏水性、挥发性和溶解性估计化学物质在蛋白质中的分配方面的实用性，在 -LFER描述符不可用的情况下提供了一个可行的替代方案。
