Basis set dependence of solute-solvent interaction energy of benzene in water: A HF/DFT study

Solubility determination, dissolution properties and solid transformation of resmetirom (form A) in heptane and seven alcohols

In this work, the solubility of resmetirom (form A) was initially measured in heptane and seven alcohol solvents by gravimetric methods. Then, the transformation temperature between form A and ethanol solvate was determined at 333.76 K. Subsequently, some commonly used models were applied to fit the solubility data, and it was found that the modified Apelblat equation and the Jouyban-Acree-van't Hoff (J-A-V) model achieved the highest correlation accuracy for those in mono-solvents and heptane + propanol, respectively. And the average relative deviation (ARD) values of models were less than 0.5%, indicating a good agreement with the experimental results. Additionally, through density functional theory calculation and the analysis of solvent parameters, it was observed that hydrogen-bonding played primary roles in the dissolution process of resmetirom. The multiple factors such as the polarity of solvent, active site interaction, the molecular size and free volume all affect the solubility of resmetirom. Furthermore, by comparing the experimental and simulated infrared spectra of form A and two alcohol solvates, five characteristic bands were selected for quantification. Partial least squares regression (PLSR), a multivariate statistical analysis method, was used to extract quantitative information. The quantitative analysis model was established based on specific wavelength intervals, which were associated with inter-molecular interactions. Combined with PLSR, a new high-precision quantitative method was established to study the solid transformation process between form A and solvates. From 303.15 to 323.15 K, the rate of transformation from form A to methanol solvate or ethanol solvate was decreased with increasing temperature, revealing that the transformation process was driven by the solubility difference between form A and solvates under the studied conditions. This research will definitely afford necessary solubility data and solvent selection for the design of the crystallization process of resmetirom (form A) in industry, and provide basic data for the production of resmetirom (form A) in the pharmaceutical industry.

Dynamic and static behavior of the H...π and E...π interactions in EH₂ adducts of benzene π-system (E = O, S, Se and Te), elucidated by QTAIM dual functional analysis

Dynamic and static behavior of the interactions in the EH2 adducts of a benzene π-system (E = O, S, Se and Te) is elucidated by applying QTAIM-DFA (QTAIM dual functional analysis). Two types of H-*-π and E-*-π interactions are detected in the adducts, where the asterisk (*) emphasizes the existence of the bond critical point (BCP) on the interaction in question. Total electron energy densities Hb(rc) are plotted versus Hb(rc) -Vb(rc)/2 [=(ℏ(2)/8m)∇(2)ρb(rc)] at BCPs in QTAIM-DFA, where Vb(rc) are the potential energy densities at BCPs. Data from the fully optimized structures are analyzed by polar (R, θ) coordinate representation. Each plot for an interaction, containing data from the perturbed structures with those of the fully optimized one, shows a specific curve, which provides important information. The plot is expressed by (θp, κp): θp corresponds to the tangent line for the plot and κp is the curvature. θ and θp are measured from the y-axis and y-direction, respectively. Moreover, (R, θ) corresponds to the static nature, (θp, κp) represents the dynamic nature of interactions. While θ classifies the interaction in question, θp characterizes it. Both values are less than 90° for all H-*-π and E-*-π interactions examined in this study; therefore, they are all classified by the pure closed-shell interactions and predicted to have the character of vdW nature. However, it is suggested that E-*-π has the nature of the stronger interaction than the case of H-*-π for dynamic behavior in the same species evaluated at the MP2 and M06-2X levels. The nature of the interactions is well analyzed and specified by applying QTAIM-DFA.

Serine and Cysteine π-Interactions in Nature: A Comparison of the Frequency, Structure, and Stability of Contacts Involving Oxygen and Sulfur

Despite many DNA–protein π-interactions in high-resolution crystal structures, only four X–H···π or X···π interactions were found between serine (Ser) or cysteine (Cys) and DNA nucleobase π-systems in over 100 DNA–protein complexes (where X = O for Ser and X = S for Cys). Nevertheless, 126 non-covalent contacts occur between Ser or Cys and the aromatic amino acids in many binding arrangements within proteins. Furthermore, Ser and Cys protein–protein π-interactions occur with similar frequencies and strengths. Most importantly, due to the great stability that can be provided to biological macromolecules (up to –20 kJ mol–1 for neutral π-systems or –40 kJ mol–1 for cationic π-systems), Ser and Cys π-interactions should be considered when analyzing protein stability and function.

Noncovalent Interaction Analysis in Fluctuating Environments

Noncovalent interactions play a central role in many chemical and biological systems. In a previous study, Johnson et al developed a NonCovalent Interaction (NCI) index to characterize and visualize different types of weak interactions. To apply the NCI analysis to fluctuating environments as in solution phase, we here develop a new Averaged NonCovalent Interaction (i.e., aNCI) index along with a fluctuation index to characterize magnitude of interactions and fluctuations. We applied aNCI for various systems including solute-solvent and ligand-protein noncovalent interactions. For water and benzene molecules in aqueous solution, solvation structures and the specific hydrogen bond patterns were visualized clearly. For the Cl^-+CH₃Cl S_N2 reaction in aqueous solution, charge reorganization influences over solvation structure along S_N2 reaction were revealed. For ligand-protein systems, aNCI can recover several key fluctuating hydrogen bond patterns that have potential applications for drug design. Therefore, aNCI, as a complementary approach to the original NCI method, can extract and visualize noncovalent interactions from thermal noise in fluctuating environments.