Synthesis of vanillin derivatives with 1,2,3-triazole fragments and evaluation of their fungicide and fungistatic activities.

Vanillin is the main component of natural vanilla extract and is responsible for its flavoring properties. Besides its well-known applications as an additive in food and cosmetics, it has also been reported that vanillin can inhibit fungi of clinical interest, such as Candida spp., Cryptococcus spp., Aspergillus spp., as well as dermatophytes. Thus, the present work approaches the synthesis of a series of vanillin derivatives with 1,2,3-triazole fragments and the evaluation of their antifungal activities against Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, Cryptococcus neoformans, Cryptococcus gattii, Trichophyton rubrum, and Trichophyton interdigitale strains. Twenty-two vanillin derivatives were obtained, with yields in the range of 60%-91%, from copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) click reaction between two terminal alkynes prepared from vanillin and different benzyl azides. In general, the evaluated compounds showed moderate activity against the microorganisms tested, with minimum inhibitory concentration (MIC) values ranging from 32 to >512 µg mL-1 . Except for compound 3b against the C. gattii R265 strain, all vanillin derivatives showed fungicidal activity for the yeasts tested. The predicted physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties for the compounds indicated favorable profiles for drug development. In addition, a four-dimensional structure-activity relationship (4D-SAR) analysis was carried out and provided useful insights concerning the structures of the compounds and their biological profile. Finally, molecular docking calculations showed that all compounds bind favorably at the lanosterol 14α-demethylase enzyme active site with binding energies ranging from -9.1 to -12.2 kcal/mol.

The use of ANOVA-PCA and DD-SIMCA in the development of corn flour laboratory reference materials.

The development of a collaborative study as a requirement for the preparation of a laboratory reference material candidate is reported in this paper. The evaluation was performed by 13 laboratories invited to quantify the calcium, potassium, magnesium, phosphorus, copper, iron, manganese and zinc; 8 of them presented results for all the analytes under investigation. The data were statistically analyzed by applying the z-score robust technique as recommended by ISO Guide 35. For the potassium element, laboratories 4 and 13 presented questionable results. Laboratory 5 proved to be unsatisfactory for calcium and zinc. ANOVA-PCA and DD-SIMCA were also applied to evaluate stability and interlaboratory studies results, respectively. It has been demonstrated that multivariate data analysis can be successfully applied as an alternative method to the recommendations made by ISO 13528 and ISO Guide 35 with defined confidence intervals.

Leishmanicidal and cytotoxic activities and 4D-QSAR of 2-arylidene indan-1,3-diones.

The indan-1,3-dione and its derivatives are important building blocks in organic synthesis and present important biological activities. Herein, the leishmanicidal and cytotoxicity evaluation of 16 2-arylidene indan-1,3-diones is described. The compounds were evaluated against the leukemia cell lines HL60 and Nalm6, and the most effective ones were 2-(4-nitrobenzylidene)-1H-indene-1,3(2H)-dione (4) and 4-[(1,3-dioxo-1H-inden-2(3H)-ylidene)methyl]benzonitrile (10), presenting IC50 values of around 30 µmol/L against Nalm6. The leishmanicidal activity was assessed on Leishmania amazonensis, with derivative 4 (IC50 = 16.6 µmol/L) being the most active. A four-dimensional quantitative structure-activity analysis (4D-QSAR) was applied to the indandione derivatives, through partial least-squares regression. The statistics presented by the regression models built with the selected field descriptors of Coulomb (C) and Lennard-Jones (L) nature, considering the activities against L. amazonensis, HL60, and Nalm6 leukemia cells, were, respectively, R2 = 0.88, 0.92, and 0.98; Q2 = 0.83, 0.88, and 0.97. The presence of positive Coulomb descriptors near the carbonyl groups indicates that these polar groups are related to the activities. Besides, the presence of positive Lennard-Jones descriptors close to substituents R3 or R1 indicates that bulky nonpolar substituents in these positions tend to increase the activities. This study provides useful insights into the mode of action of indandione derivatives for each biological activity involved.

Multitrophic interactions in the rhizosphere microbiome of wheat: from bacteria and fungi to protists.

Plants modulate the soil microbiota by root exudation assembling a complex rhizosphere microbiome with organisms spanning different trophic levels. Here, we assessed the diversity of bacterial, fungal and cercozoan communities in landraces and modern varieties of wheat. The dominant taxa within each group were the bacterial phyla Proteobacteria, Actinobacteria and Acidobacteria; the fungi phyla Ascomycota, Chytridiomycota and Basidiomycota; and the Cercozoa classes Sarcomonadea, Thecofilosea and Imbricatea. We showed that microbial networks of the wheat landraces formed a more intricate network topology than that of modern wheat cultivars, suggesting that breeding selection resulted in a reduced ability to recruit specific microbes in the rhizosphere. The high connectedness of certain cercozoan taxa to bacteria and fungi indicated trophic network hierarchies where certain predators gain predominance over others. Positive correlations between protists and bacteria in landraces were preserved as a subset in cultivars as was the case for the Sarcomonadea class with Actinobacteria. The correlations between the microbiome structure and plant genotype observed in our results suggest the importance of top-down control by organisms of higher trophic levels as a key factor for understanding the drivers of microbiome community assembly in the rhizosphere.

In vitro cytotoxicity and structure-activity relationship approaches of ent-kaurenoic acid derivatives against human breast carcinoma cell line.

In this study, ent-kaurenoic acid derivatives were obtained by microbial transformation methodologies and tested against breast cancer cell lines (MCF-7). A multivariate quantitative-structure activity relationship (QSAR) analysis was performed taking into account both microbial transformation derivatives and other analogues previously reported in literature to give some insight into the main features behind the cytotoxic activity displayed by kaurane-type diterpenes against MCF-7 cells. The partial least square regression (PLS) method was employed in the training set and the best PLS model was built with a factor describing 69.92% of variance and three descriptors (logP, εHOMO and εHOMO-1) selected by the Ordered Predictors Selection (OPS) algorithm. The QSAR model provided reasonable regression (Q2 = 0.64, R2 = 0.72, SEC = 0.29 and SEV = 0.33). The model was validated by leave-N-out cross-validation, y-randomization and external validation (R2pred = 0.89 and SEP = 0.27). The selected descriptors indicated that the activity was mainly related to electronic parameters (HOMO and HOMO-1 molecular orbital energies), as well as to logP. These findings suggest that higher activity values are directly related with both higher logP and frontier orbital energy values. The positive relationship between these orbitals and the activity suggests that the ent-kaurenoic acid analogues interaction with the target involves charge displacement, which is entirely consistent with the literature. Based on these findings, three compounds were proposed and one of them was synthesized and tested. The experimental result confirmed the activity predicted by the model.

"Omics" Prospective Monitoring of Bariatric Surgery: Roux-En-Y Gastric Bypass Outcomes Using Mixed-Meal Tolerance Test and Time-Resolved (1)H NMR-Based Metabolomics.

Roux-en-Y gastric bypass (RYGB) surgery goes beyond weight loss to induce early beneficial hormonal changes that favor glycemic control. In this prospective study, ten obese subjects diagnosed with type 2 diabetes underwent bariatric surgery. Mixed-meal tolerance test was performed before and 12 months after RYGB, and the outcomes were investigated by a time-resolved hydrogen nuclear magnetic resonance ((1)H NMR)-based metabolomics. To the best of our knowledge, no previous omics-driven study has used time-resolved (1)H NMR-based metabolomics to investigate bariatric surgery outcomes. Our results presented here show a significant decrease in glucose levels after bariatric surgery (from 159.80 ± 61.43 to 100.00 ± 22.94 mg/dL), demonstrating type 2 diabetes remission (p < 0.05). The metabolic profile indicated lower levels of lactate, alanine, and branched chain amino acids for the operated subject at fasting state after the surgery. However, soon after food ingestion, the levels of these metabolites increased faster in operated than in nonoperated subjects. The lipoprotein profile achieved before and after RYGB at fasting was also significantly different, but converging 180 min after food ingestion. For example, the very low-density lipoprotein, low-density lipoprotein, N-acetyl-glycoproteins, and unsaturated lipid levels decreased after RYGB, while phosphatidylcholine and high-density lipoprotein increased. This study provides important insights on RYGB surgery and attendant type 2 diabetes outcomes using an "omics" systems science approach. Further research on metabolomic correlates of RYGB surgery in larger study samples is called for.

Blood Metabolome Changes Before and After Bariatric Surgery: A (1)H NMR-Based Clinical Investigation.

Excessive body fat and obesity have adverse health effects and result in significant morbidity such as type 2 diabetes mellitus. The health burden of obesity can be reduced with the Roux-en-Y gastric bypass (RYGB) weight-loss bariatric surgery. Little is known on the molecular changes that occur at the metabolome level before and after bariatric surgery, with a view to clinical biomarker development. Hence, we employed a metabolomics approach in 10 obese diabetic patients who underwent bariatric surgery. Metabolomics data were obtained by T2- and diffusion-edited hydrogen nuclear magnetic resonance ((1)H NMR) spectra to monitor the metabolic and lipoprotein profiles, and gas chromatography-mass spectrometry (CG-MS) to access the fatty acid profile before and 12 months after RYGB. Using hierarchical partial least squares discriminant analysis, we found that RYGB induces several key metabolic alterations associated with glucose homeostasis, as well as fatty acid and amino acid metabolism. The levels of lactate (Krebs' intermediate cycle) decreased after RYGB. The leucine, isoleucine, valine, lactate, and glucose levels were higher in the samples before RYGB (p<0.05). Additionally, the levels of very low-density lipoprotein, unsaturated lipids, and N-acetyl-glycoprotein were higher before RYGB. By contrast, levels of the high-density lipoprotein and phosphatidylcholine were higher after bariatric surgery. These results collectively offer important holistic integrative biology data to develop future clinically relevant metabolomics biomarkers related to bariatric surgery in connection with obesity.

The influence of R and S configurations of a series of amphetamine derivatives on quantitative structure-activity relationship models.

Chiral molecules need special attention in drug design. In this sense, the R and S configurations of a series of thirty-four amphetamines were evaluated by quantitative structure-activity relationship (QSAR). This class of compounds has antidepressant, anti-Parkinson and anti-Alzheimer effects against the enzyme monoamine oxidase A (MAO A). A set of thirty-eight descriptors, including electronic, steric and hydrophobic ones, were calculated. Variable selection was performed through the correlation coefficients followed by the ordered predictor selection (OPS) algorithm. Six descriptors (CHELPG atomic charges C3, C4 and C5, electrophilicity, molecular surface area and logP) were selected for both configurations and a satisfactory model was obtained by PLS regression with three latent variables with R(2)=0.73 and Q(2)=0.60, with external predictability Q(2)=0.68, and R(2)=0.76 and Q(2)=0.67 with external predictability Q(2)=0.50, for R and S configurations, respectively. To confirm the robustness of each model, leave-N-out cross validation (LNO) was carried out and the y-randomization test was used to check if these models present chance correlation. Moreover, both automated or a manual molecular docking indicate that the reaction of ligands with the enzyme occurs via pi-pi stacking interaction with Tyr407, inclined face-to-face interaction with Tyr444, while aromatic hydrogen-hydrogen interactions with Tyr197 are preferable for R instead of S configurations.

The receptor-dependent LQTA-QSAR: application to a set of trypanothione reductase inhibitors.

A new Receptor-Dependent LQTA-QSAR approach, RD-LQTA-QSAR, is proposed as a new 4D-QSAR method. It is an evolution of receptor independent LQTA-QSAR. This approach uses the free GROMACS package to carry out molecular dynamics simulations and generates a conformational ensemble profile for each compound. Such an ensemble is used to build molecular interaction field-based QSAR models, as in CoMFA. To show the potential of this methodology, a set of 38 phenothiazine derivatives that are specific competitive T. cruzi trypanothione reductase inhibitors, was chosen. Using a combination of molecular docking and molecular dynamics simulations, the binding mode of the phenotiazine derivatives was evaluated in a simulated induced fit approach. The ligands alignments were performed using both ligand and binding site atoms, enabling unbiased alignment. The models obtained were extensively validated by leave-N-out cross-validation and y-randomization techniques to test for their robustness and absence of chance correlation. The final model presented Q(2) LOO of 0.87 and R² of 0.92 and a suitable external prediction of [Formula: see text]= 0.78. The adapted binding site obtained is useful to perform virtual screening and ligand structure-based design and the descriptors in the final model can aid in the design new inhibitors.

Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology.

Despite highly active antiretroviral therapy (HAART) implementation, there is a continuous need to search for new anti-HIV agents. HIV-1 integrase (HIV-1 IN) is a recently validated biological target for AIDS therapy. In this work, a four-dimensional quantitative structure-activity relationship (4D-QSAR) study using the new methodology named LQTA-QSAR approach with a training set of 85 HIV-1 IN strand transfer inhibitors (INSTI), containing the ß-diketo acid (DKA) substructure, was carried out. The GROMACS molecular dynamic package was used to obtain a conformational ensemble profile (CEP) and LQTA-QSAR was employed to calculate Coulomb and Lennard-Jones potentials and to generate the field descriptors. The partial least-squares (PLS) regression model using 14 field descriptors and 8 latent variables (LV) yielded satisfactory statistics (R2= 0.897, SEC = 0.270, and F = 72.827), good performance in internal (QLOO2 = 0.842 and SEV = 0.314) and external prediction (Rpred2 = 0.839, SEP = 0.384, AREpred = 4.942%, k = 0.981, k' = 1.016, and |R02 ­ R0'2 = 0.0257). The QSAR model was shown to be robust (leave-N-out cross validation; average QLNO2 = 0.834) and was not built by chance (y-randomization test; R2 intercept = 0.109; Q2 intercept = -0.398). Fair chemical interpretation of the model could be traced, including descriptors related to interaction with the metallic cofactors and the hydrophobic loop. The model obtained has a good potential for aid in the design of new INSTI, and it is a successful example of application of LQTA-QSAR as an useful tool to be used in computer-aided drug design (CADD).

The use of near-infrared spectroscopy and chemometrics for determining the shelf-life of products.

In this work a procedure for determining the shelf-life of products by merging near-infrared (NIR) spectroscopy, multivariate techniques of data analysis, and kinetic theory is presented. This procedure allows information from several sources (sensory, physical chemical, and instrumental) to be merged via the multivariate accelerated shelf-life test (MASLT) algorithm. The MASLT is a multivariate approach that relies on soft modeling via an unfolding principal component analysis (u-PCA) and hard modeling, through the conventional kinetic theory, for determining the final shelf-life of products. The procedure was successfully applied to a consumer goods product (a body lotion), whose shelf-life was determined to be 3 years and 9 months, in accordance with results previously obtained using conventional analytical techniques and univariate methods of data analysis.

LQTA-QSAR: a new 4D-QSAR methodology.

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Support vector regression for functional data in multivariate calibration problems.

Quantitative analyses involving instrumental signals, such as chromatograms, NIR, and MIR spectra have been successfully applied nowadays for the solution of important chemical tasks. Multivariate calibration is very useful for such purposes and the commonly used methods in chemometrics consider each sample spectrum as a sequence of discrete data points. An alternative way to analyze spectral data is to consider each sample as a function, in which a functional data is obtained. Concerning regression, some linear and nonparametric regression methods have been generalized to functional data. This paper proposes the use of the recently introduced method, support vector regression for functional data (FDA-SVR) for the solution of linear and nonlinear multivariate calibration problems. Three different spectral datasets were analyzed and a comparative study was carried out to test its performance with respect to some traditional calibration methods used in chemometrics such as PLS, SVR and LS-SVR. The satisfactory results obtained with FDA-SVR suggest that it can be an effective and promising tool for multivariate calibration tasks.

Simple quantitative structure-property relationship (QSPR) modeling of 17O carbonyl chemical shifts in substituted benzaldehydes compared to DFT and empirical approaches.

The geometry of 50 substituted benzaldehydes was optimized at the semiempirical PM3 level, and various electronic and steric descriptors accounting for properties of the benzene ring, aldehyde group, and their connecting carbon-carbon bond were calculated. Quantitative structure-property relationships (QSPR) between (17)O carbonyl chemical shifts and these descriptors were established using partial least-squares regression and principal component regression. These two parsimonious QSPR models were comparable with the literature empirical model and DFT (density functional theory) and capable of predicting (17)O chemical shifts for 10 benzaldehydes. Principal component analysis, hierarchical cluster analysis, and crystal structure data retrieved from the Cambridge Structural Database were additional methods for chemical verification of the regression models. The QSPR models are recommended as being more reliable than and superior to the empirical and DFT models due to the results of all validations, simplicity, and short time that regressions need for (17)O shift prediction.

Theoretical study of radical and neutral intermediates of artemisinin decomposition.

Four artemisinin reductive decomposition routes A, B1, B2, and B3 with 13 species (QHS, 1/2, 3, 4, 5, 5a, 6, 7, 18, 18a, 19, 20, and 21) were studied at the B3LYP/6-31G** level. Structures of the species were analyzed in terms of geometrical parameters, Löwdin bond orders, partial atomic charges and spin densities, electronic and free energies, and entropy. Searches in the Cambridge Structural Database for high-level quality artemisinin-related structures were also performed. Principal Component and Hierarchical Cluster analyses were performed on selected electronic and structural variables to rationalize relationships between the routes. The A and B1 routes are possibly interconnected. Structural and electronic features of all species show that there are two clusters: A-B1 and B2-B3. The latter cluster is thermodynamically more favorable (DeltaDeltaG is -64 to -88 kcal mol(-1)) than the former (DeltaDeltaG is -58 to -59 kcal mol(-1)), but kinetical preference may be the opposite. Along the artemisinin decomposition routes, especially B2 and B3, larger structural changes including formation of branched structures and CO2 release are related to increased exothermicity of the conversions, weakened attractive oxygen-oxygen interactions, and increased entropy of the formed species. The intermediate 4 definitely belongs to some minor artemisinin decomposition route.

Classification of commercial Catuaba samples by NMR, HPLC and chemometrics.

For over a century, Catuaba has been used in Brazilian folk medicine as an aphrodisiac even though the identity of the plant material employed is often uncertain. The species recommended by the Brazilian Pharmacopeia is Anemopaegma arvense (Bignoniaceae), but many other plants, regionally known as Catuaba, are commercialised. Frequently, the quality control of such a complex system is based on chemical markers that do not supply a general idea of the system. With the advent of the metabolomics approach, a global analysis of samples becomes possible. It appears that (1)H-NMR is the most useful method for such application, since it can be used as a wide-spectrum chemical analysis technique. Unfortunately, the generated spectra is complex so a possible approach is to look at the metabolite profile as a whole using multivariate methods, for example, by application of principal component analysis (PCA). In the present paper, we describe for the first time a proton high-resolution magic angle spinning nuclear magnetic resonance ((1)H-HR-MAS NMR) method coupled with PCA for the metabolomic analysis of some commercial Catuaba samples, which provided a reduction in the time required for such analysis. A comparative study of HPLC, HR-MAS and liquid-NMR techniques is also reported.

Optimization of extraction of high-ester pectin from passion fruit peel (Passiflora edulis flavicarpa) with citric acid by using response surface methodology.

A central composite design was employed to optimize the extraction of pectin with citric acid. The independent variables were citric acid concentration (0.086-2.91% w/v) and extraction time (17-102 min). The combined effect of these variables on the degree of esterification was investigated. Results have shown that the generated regression models adequately explained the data variation and significantly represented the actual relationship between the independent variables and the responses. Besides that, the citric acid concentration was the most important factor to affect the degree of esterification, as it exerted a significant influence on the dependent variable. Lower citric acid concentration increased the pectin degree of esterification. The surface response showed the relationships between the independent variables, and thus responses were generated. Through this surface, the satisfactory condition of 0.086% w/v citric acid for 60 min was established for extraction of high-ester yellow passion fruit pectin.

A study of physicochemical and biopharmaceutical properties of amoxicillin tablets using full factorial design and PCA biplot.

The variables that influence the tablets obtained by direct compression method deserve to be studied to minimize formulations costs and to improve the physicochemical and biopharmaceutical properties of the compacts. Here, we explore the adjuvants effects on amoxicillin tablet formulations considering multiple responses, and indicate the most suitable formulation composition. A 2(3) full factorial design was built to different amoxicillin formulations, each one containing three replicate batches, and eight responses (physicochemical and biopharmaceutical parameters) were obtained. The microcrystalline cellulose (MCC) type Avicel PH-102 (low) or PH-200 (high), the absence (low) or presence (high) of spray-dried lactose (LAC), and the absence (low) or presence (high) of disintegrant (DIS) were the levels investigated. The more relevant responses to the distinct formulations from the experimental design were hardness, friability, and the amount of amoxicillin dissolved during the first hour. PCA biplot indicated high values of amount of drug dissolved in 60 min as advantageous responses for the investigated amoxicillin tablet formulations and high values of friability as not desirable. Considering the individual response evaluation, the most suitable amoxicillin tablet formulation should present in its composition the MCC type Avicel PH-102 (low level) and the superdisintegrant agent (DIS high level), croscarmellose sodium, but no LAC (low level).

Simultaneously calibrating solids, sugars and acidity of tomato products using PLS2 and NIR spectroscopy.

In this work, the development of a robust spectroscopic procedure for determining, simultaneously and non-destructively, relevant quality parameters of processed tomato products (total and soluble solids, total acidity, total sugars, glucose and fructose), is described. Samples of tomato concentrate products with total solids content ranging from 6.9 to 35.9% were collected from Latin America, the US and Europe and NIR spectra were acquired in the 4000-10,000 cm(-1) region. The original spectra were pre-processed by mean-smoothing or by Fourier filter, followed by multiplicative signal correction (MSC) or derivatives. Partial least squares (PLS2 and PLS1) models were built and their predictive abilities were compared through the RMSEP of external validation. The PLS2 regression had better predictive abilities for four out of the six properties under study, namely total solids, total sugars, glucose and fructose. Besides, the model was less complex than the PLS1 models in the sense that only four factors were demanded whilst from 4 to 11 factors were necessary for building the PLS1 models. The standard error of prediction (SEP%) of the PLS2 model for each property was: total solids, 2.67; soluble solids, 1.14; total acidity, 9.60; total sugar, 18.69; glucose, 11.60; and fructose, 13.45.

Conformational study of (8alpha,8'beta)-bis(substituted phenyl)-lignano-9,9'-lactones by means of combined computational, database mining, NMR, and chemometric approaches.

Beta-(3,4-Methylenedioxybenzyl)-gamma-butyrolactone (MDBL) and (-)-hinokinin (HK) were obtained by partial synthesis and characterized by 1H NMR and computational methods (conformational analysis, molecular modeling, structural data mining and chemometrics). Three conformers were detected for MDBL and nine were found for HK. The energy differences are around 1 and 2 kcal mol(-1) and rotation barriers are less than 3 and 5 kcal mol(-1) for MDBL and HK conformers, respectively. The geometries of these conformers, obtained from semiempirical PM3 and density functional theory (DFT) B3LYP 6-31G** calculations agree satisfactorily with 1H NMR data (vicinal proton-proton coupling constants) and structures retrieved from the Cambridge Structural Database (torsion angles). DFT combined with some variants of the Haasnoot-de Leeuuw-Altona equations gives the best predictions for the coupling constants. The molecular conformation of MDBL, of HK, and of related systems depends not only on intramolecular interactions but also on crystal packing forces and solvent-solute interactions, in particular hydrogen bonds and polar interactions. Hydration favors more stable HK conformers, which can be important for their behavior in chemical and biological systems.