Here we obtain the data needed to predict chemical interactions of polyethylene glycols (PEGs) and glycerol with proteins and related organic compounds, and thereby interpret or predict chemical effects of PEGs on protein processes. To accomplish this we determine interactions of glycerol and tetraEG with >30 model compounds displaying the major C, N, and O functional groups of proteins. Analysis of these data yields coefficients (α-values) quantifying interactions of glycerol, tetraEG and PEG end (-CH2OH) and interior (-CH2OCH2-) groups with these groups, relative to interactions with water. TetraEG (strongly) and glycerol (weakly) interact favorably with aromatic C, amide N, and cationic N, but unfavorably with amide O, carboxylate O and salt ions. Strongly unfavorable O and salt anion interactions help make both small and large PEGs effective protein precipitants. Interactions of tetraEG and PEG interior groups with aliphatic C are quite favorable, while interactions of glycerol and PEG end groups with aliphatic C are not. Hence tetraEG and PEG 300 favor unfolding of the DNA-binding domain of lac repressor (lacDBD) while glycerol, di- and mono-ethylene glycol are stabilizers. Favorable interactions with aromatic and aliphatic C explain why PEG400 greatly increases the solubility of aromatic hydrocarbons and steroids. PEG400-steroid interactions are unusually favorable, presumably because of simultaneous interactions of multiple PEG interior groups with the fused ring system of the steroid. Using α-values reported here, chemical contributions to PEG m-values can be predicted or interpreted in terms of changes in water-accessible surface area (ΔASA), and separated from excluded volume effects.
The interaction between salts and nucleobases or aromatic compounds plays an important role in noncovalent biopolymer assembly processes such as DNA and RNA helix formation and protein‐nucleic acid interactions. The project determines effects of six salts selected from the Hofmeister series (NaF, KBr, KSCN, NaSCN, NH4Cl, NH4Br) on the solubility of 11 model compounds (nucleic bases, their analogs, and naphthalene) to experimentally quantify salt‐model compound preferential interactions (μ23) using UV‐Vis spectroscopy. Values of μ23 are obtained from the derivative of the logarithm of the model compound solubility with respect to the molal concentration of salt. Salt has the ability to either increase or decrease the solubility of the bases, which can be explained by their differences in interacting with various surface types of the model compounds, namely aromatic C, aromatic N, Sp3 C, Sp2 O, and Sp3 N. Results of this study indicates that for all the 11 model compounds investigated in this study, KSCN has the most favorable interactions whereas NaF has the most unfavorable interactions except with 26DAP. The differences in their ability to decrease (unfavorable interaction) or increase (favorable interaction) the solubility of bases corresponds with their rank orders among the Hofmeister series, in which the Na+ and F− are at the stabilizing end but SCN− ion is at the destabilizing end. Through one‐way analysis, the breakdown of nucleobase's surface types allows us to study the specific interactions between salts and biopolymer surfaces. Setting Na+ as a reference point, relative strength of interaction potentials of Hofmeister ions with aromatic and aliphatic C surfaces are obtained, which in general follows the Hofmeister series. The quantities describing accumulation and exclusion of Hofmeister salts, Kp, are also obtained through calculation. Using Na+ or K+ as a reference, a dissection of salts’ Kp values to get ions’ Kp values illustrates that both aromatic and aliphatic C surfaces strongly exclude cations and accumulate SCN−. Further studies are required to make dissections on other surface types possible. The quantified αi values and water accessible area (ASA) data are used to predict μ23 values. These predictions are compared with both our experimental data and published experimental data. The results confirm the effectiveness of our method and indicate potential applications in pharmaceutical, environmental, and biological studies.
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