Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods

Structure-Thermodynamics-Antioxidant Activity Relationships of Selected Natural Phenolic Acids and Derivatives: An Experimental and Theoretical Evaluation

<div><p>Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH^• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH^•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the <i>ortho</i> position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media.</p></div

Spin density values of phenoxy radicals of 20 investigated phenolic compounds and phenol calculated at the B3LYP/6-311++G(d,p) levels of theory in ethanol.

<p>Spin density values of phenoxy radicals of 20 investigated phenolic compounds and phenol calculated at the B3LYP/6-311++G(d,p) levels of theory in ethanol.</p

Spin density values of phenoxy radicals of 20 investigated phenolic compounds and phenol calculated at the B3LYP/6-311++G(d,p) levels of theory in ethanol.

<p>Spin density values of phenoxy radicals of 20 investigated phenolic compounds and phenol calculated at the B3LYP/6-311++G(d,p) levels of theory in ethanol.</p

The energy difference (ΔE in kcal/mol) caused by the hydrogen bond between the O^• center and the <i>meta</i> OH for 7 radicals calculated at the B3LYP/6-311++G(d,p) levels of theory in 4 reaction media.

<p>The energy difference (ΔE in kcal/mol) caused by the hydrogen bond between the O^• center and the <i>meta</i> OH for 7 radicals calculated at the B3LYP/6-311++G(d,p) levels of theory in 4 reaction media.</p

HOMO of 20 investigated phenolic compounds calculated at the B3LYP/6-311++G(d,p) levels of theory in ethanol.

<p>The numbers indicates atomic polar tensor charges.</p

The performance comparison among different QSAR models of BTDs.

a<p>The <i>A</i> is the number of principal component.</p>b<p>The <i>R</i>² is the cumulative multiple correlation coefficient.</p>c<p>The <i>Q</i>² is a cross validation square of cumulative multiple correlation coefficient by a leave-one-out procedure.</p>d<p>The <i>RMS</i> is the root mean square error of modeling simulation.</p>e<p>The nd shows that the correlative value is not given out.</p>f<p>Two numbers separated by slashes denote the number of samples in training and test sets, respectively.</p

Plot of the 50-random-permutation validation for the GA-PLS model of ACE inhibitors.

<p>The intercepts of the <i>R</i>²- and <i>Q</i>²- lines with the ordinate axis are 0.029 and −0.270, which are below limits of <i>R</i>²<0.300 and <i>Q</i>²<0.050, respectively.</p

Loading and communality of 155 variables on 6 factors.

<p>Loading and communality of 155 variables on 6 factors.</p

Plot of the 50-random-permutation validation for the GA-PLS model of BTDs.

<p>The intercepts of the <i>R</i>²- and <i>Q</i>²-regression lines with the ordinate axis are −0.026 and −0.183, which are below limits of <i>R</i>²<0.300 and <i>Q</i>²<0.050, respectively.</p