Department of Biochemistry and ‡Department of Chemistry, University of Wisconsin , Madison, Wisconsin 53706, United States.
Biochemistry. 2013 Sep 3;52(35):5997-6010. doi: 10.1021/bi400683y. Epub 2013 Aug 21.
To quantify interactions of the osmolyte l-proline with protein functional groups and predict their effects on protein processes, we use vapor pressure osmometry to determine chemical potential derivatives dμ2/dm3 = μ23, quantifying the preferential interactions of proline (component 3) with 21 solutes (component 2) selected to display different combinations of aliphatic or aromatic C, amide, carboxylate, phosphate or hydroxyl O, and amide or cationic N surface. Solubility data yield μ23 values for four less-soluble solutes. Values of μ23 are dissected using an ASA-based analysis to test the hypothesis of additivity and obtain α-values (proline interaction potentials) for these eight surface types and three inorganic ions. Values of μ23 predicted from these α-values agree with the experiment, demonstrating additivity. Molecular interpretation of α-values using the solute partitioning model yields partition coefficients (Kp) quantifying the local accumulation or exclusion of proline in the hydration water of each functional group. Interactions of proline with native protein surfaces and effects of proline on protein unfolding are predicted from α-values and ASA information and compared with experimental data, with results for glycine betaine and urea, and with predictions from transfer free energy analysis. We conclude that proline stabilizes proteins because of its unfavorable interactions with (exclusion from) amide oxygens and aliphatic hydrocarbon surfaces exposed in unfolding and that proline is an effective in vivo osmolyte because of the osmolality increase resulting from its unfavorable interactions with anionic (carboxylate and phosphate) and amide oxygens and aliphatic hydrocarbon groups on the surface of cytoplasmic proteins and nucleic acids.
为了量化渗透溶质 l-脯氨酸与蛋白质功能基团的相互作用,并预测它们对蛋白质过程的影响,我们使用蒸汽压渗透法来确定化学势导数 dμ2/dm3 = μ23,定量地描述了脯氨酸(第 3 组分)与 21 种溶质(第 2 组分)之间的优先相互作用,这些溶质被选择来展示不同的脂肪族或芳香族 C、酰胺、羧酸盐、磷酸盐或羟基 O、酰胺或阳离子 N 表面的组合。溶解度数据为四种溶解度较低的溶质产生了 μ23 值。使用基于 ASA 的分析来剖析 μ23 值,以检验加和性假说,并获得这 8 种表面类型和 3 种无机离子的α值(脯氨酸相互作用势)。从这些α值预测的 μ23 值与实验值相符,证明了加和性。使用溶质分配模型对α值进行分子解释,得到了分配系数(Kp),定量地描述了脯氨酸在每个功能基团的水化水中的局部积累或排斥。从α值和 ASA 信息预测脯氨酸与天然蛋白质表面的相互作用以及脯氨酸对蛋白质展开的影响,并与实验数据进行比较,与甘氨酸甜菜碱和尿素的结果进行比较,并与转移自由能分析的预测进行比较。我们的结论是,脯氨酸通过与展开过程中暴露的酰胺氧和脂肪族碳氢表面的不利相互作用稳定蛋白质,并且脯氨酸是一种有效的体内渗透溶质,因为它与带负电荷的(排斥)阴离子(羧酸盐和磷酸盐)和酰胺氧以及细胞质蛋白质和核酸表面的脂肪族碳氢基团之间的不利相互作用导致渗透压增加。