Georgios A. Dalkas

Oleogels based on sterols such as β‐sitosterol blended with the sterol ester γ‐oryzanol are a very interesting class of systems, but there are aspects of their formation and structure that remain elusive. It has previously been shown that a methyl group on the C30 position of the sterol‐ester plays an important role in gelation. This work explored the effect that having C30 methyl groups on both the sterol and the sterol‐ester had on the gelation process and subsequent gel structure. Lanosterol and saponified γ‐oryzanol (which was synthesized as part of this study) were identified as materials of interest, as both feature a methyl group on the C30 position of their steroidal cores. It was observed that both sterols formed gels when blended with γ‐oryzanol, and also that lanosterol gelled sunflower oil without the addition of γ‐oryzanol. All of these gels were significantly weaker than that formed by β‐sitosterol blended with γ‐oryzanol. To explore why, molecular docking simulations along with AFM and SAXS were used to examine these gels on a broad range of length scales. The results suggest that saponified γ‐oryzanol‐γ‐oryzanol gels have a very similar structure to that of β‐sitosterol‐γ‐oryzanol gels. Lanosterol‐γ‐oryzanol gels and pure lanosterol gel, however, form with a totally different structure facilitated by the head‐to‐tail stacking motif exhibited by lanosterol. These results give further evidence that relatively slight changes to the molecular structure of gelators can result in significant differences in subsequent gel properties.

The structure of foods is a complex amalgam of different biomolecules that are structurally diverse and that exhibit heterogeneity within their own biomolecule type. The interactions among these components control the structure and texture of foods, in addition to indirectly influencing other properties such as colour, flavour, and aroma. Molecular modelling has been used increasingly over the past few decades to help understand the interactions that control food structure and, as modelling techniques and hardware improve, will become an ever more important tool for food scientists. In this chapter, we will review aspects of food structure formation that are highly amenable to the modelling approach. The review will concentrate on the modelling of biomolecule self-association and gel formation and how this contributes to food structure and texture. Modelling the development of gelling agents into 3D fibrillar structures is a very important and challenging task and can provide significant insights to food chemists and biologists. This chapter gives an overview of recent studies focusing on how modelling and computer simulations can be used to investigate the relationship between the microscopic and macroscopic properties of different building blocks, such as oleogelators, polysaccharides, or proteins, that are commonly used for formulating food products.

In this work, we have employed docking and atomistic molecular dynamics (MD) simulations supported by complementary experiments using atomic force microscopy, rheology and spectroscopy to investigate the self-assembled structure of β-sitosterol and γ-oryzanol molecules into cylindrical tubules in a non-aqueous solvent. Docking models of several phytosterols, including sitosterol, with oryzanol and other sterol-esters demonstrate that for systems to form tubules, the phytosterol sterane group must be stacked in a wedge shape with the esters sterane group, and a hydrogen bond must form between the hydroxyl group of the phytosterol and the carbonyl group of the ester. Molecular dynamics of the self-assembled structure were initiated with the molecules in a roughly cylindrical configuration, as suggested from previous experimental studies, and the configurations were found to be stable during 50 ns simulations. We performed MD simulations of two tubules in proximity to better understand the aggregation of these fibrils and how the fibrils interact in order to stick together. We found that an interfibril network of non-covalent bonds, in particular van der Waals and π-π contacts, which is formed between the ferulic acid groups of oryzanol through the hydroxyl, methoxy and aromatic groups, is responsible for the surface-to-surface interactions between fibrils; an observation supported by molecular spectroscopy. We believe these interactions are of primary importance in creating a strong organogel network.

This article presents a summary of recent results relating to phytosterol oleogels. Oleogels represent a novel way of replacing saturated fat in food, whilst phytosterols have been shown to actively lower low‐density lipoprotein (LDL)‐ cholesterol levels. There are a number of technical challenges to exploiting phytosterol oleogels, including a high sensitivity to water. To facilitate their incorporation into food, the fundamental physiochemical processes which mediate the formation of these gels and two different approaches to produce phytosterol oleogels that are stable in the presence of water were explored as part of the recent Biotechnology and Biological Sciences Research Council (BBSRC)–Diet and Health Research Industry Club (DRINC)–funded Edible Oleogels for Reduction of Saturated Fat project. This report summarises the findings, which will support the development of healthier food products that are lower in saturated fat and acceptable to consumers.

Sitosterol and oryzanol self-assemble to form very firm gels in a range of organic solvents. However, due to the formation of sitosterol hydrate crystals, these gels are unstable in the presence of water, prohibiting the dispersal of water droplets throughout the gel matrix. We demonstrate that by using glycerol as the polar phase rather than water, droplets may be dispersed throughout the oil phase without disrupting the self-assembly of the gel. As increasing volumes of water are added to the glycerol, the G′ values decrease. This can be correlated to both a drop in water activity, and also the stability of the fibrils in the presence of glycerol compared to water, as elucidated by molecular dynamics simulations. We explore how changing the total volume of polar droplets, and changing the water content of these droplets alters the strength of 15% w/w sterol gels. We find that gels exhibit G′ values of ∼1 × 10⁷ Pa even with ∼30% w/w glycerol dispersed throughout the matrix. At higher glycerol loadings, complex multiple emulsion morphologies can form.

We demonstrate that by mixing the phytosterol-ester oryzanol with lecithin in an organic solvent, both components may be dispersed at much higher concentrations than they may be individually. Dynamic light scattering and molecular dynamics simulations show that the mechanism for this is the formation of r ∼ 4 nm mixed micelles. Infrared spectroscopy and simulations suggest that these micelles are formed due in part to hydrogen bonding of the phosphate of the lecithin head-group, and the phenol group of the oryzanol. Rheology shows that by mixing these materials at an equimolar ratio, highly viscous suspensions are created. Furthermore, by adding water to these samples, a solid-like gel may be formed which offers mechanical properties close to those desired for a margarine type spread, whilst still solubilizing the oryzanol.

Rheology and atomic force microscopy (AFM) were employed to examine the microstructure of β-sitosterol:γ-oryzanol organogels in sunflower oil. Using time-resolved rheology, we followed gel formation, paying specific attention to the fibril aggregation process, which had not been studied in detail previously for this system. Using AFM, we observed gel structures directly and obtained detailed information on the gel structure, far exceeding previous studies. Our analysis suggests that though gels are formed by the self-assembly and aggregation of one-dimensional fibrils, the manner in which these fibrils aggregate into ribbons results in complex structures of higher dimensionality. We emphasize that it is a surprise to find ribbons and not twisted strands. Comparing AFM images of 10% w/w and 20% w/w gelator systems, we observed differences in the degree of branching which are consistent with the rheology. We also observed the individual self-assembled fibrils which make up these gels with much greater clarity than in previous microscopy studies, and the fibril diameters of ∼9.8 nm we measured agree excellently with those obtained from existing small-angle neutron scattering data. These results provide new insight into the structure and formation kinetics of this important organogel system.

The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale. We developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a naïve Bayesian and a random forest classifier, through a voting algorithm that exploits the advantages of both. It is based on 13 sequence-based features, whose values in a 9-residue sequence window are compiled to predict the epitope/non-epitope state of the central residue. The features are related to the type of amino acid, its conservation in homologous proteins, and its tendency of being exposed to the solvent, soluble, flexible, and disordered. The highest signal is obtained from statistical amino acid preferences, but all 13 features contribute non-negligibly in the predictor. SEPIa’s average prediction accuracy is limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set. It is nevertheless slightly higher than that of other methods evaluated on the same test set. SEPIa was applied to a test protein whose epitopes are known, human β2 adrenergic G-protein-coupled receptor, with promising results. Although the actual AUC score is rather low, many of the predicted epitopes cluster together and overlap the experimental epitope region. The reasons underlying the limitations of SEPIa and of all other B-cell epitope predictors are discussed.