Heat-Stable Hazelnut Profilin: Molecular Dynamics Simulations and Immunoinformatics Analysis

Multi-Target Detection of Nuts and Peanuts as Hidden Allergens in Bakery Products through Bottom-Up Proteomics and High-Resolution Mass Spectrometry

Due to the growing global incidence of allergy to nuts and peanuts, the need for better protection of consumers sensitive to those products is constantly increasing. The best strategy to defend them against adverse immunological reactions still remains the total removal of those products from their diet. However, nuts and peanuts traces can also be hidden in other food products, especially processed ones, such as bakery products, because of cross-contamination occurring during production. Precautionary labelling is often adopted by producers to warn allergic consumers, usually without any evaluation of the actual risk, which would require a careful quantification of nuts/peanuts traces. In this paper, the development of a multi-target method based on liquid chromatography-tandem high resolution mass spectrometry (LC-MS, MS/MS), able to detect traces of five nuts species (almonds, hazelnuts, walnuts, cashews and pistachios) and of peanuts in an in-house incurred bakery product (cookie) through a single analysis is described. Specifically, allergenic proteins of the six ingredients were used as the analytical targets, and the LC-MS responses of selected peptides resulting from their tryptic digestion, after extraction from the bakery product matrix, were exploited for quantification, following a bottom-up approach typical of proteomics. As a result, nuts/peanuts could be detected/quantified down to mg·kg^-1 levels in the model cookie, thus opening interesting perspectives for the quantification of hidden nuts/peanuts in bakery products and, consequently, for a more rational use of precautionary labelling.

Functional Characterization of an Arabidopsis Profilin Protein as a Molecular Chaperone under Heat Shock Stress

Profilins (PFNs) are actin monomer-binding proteins that function as antimicrobial agents in plant phloem sap. Although the roles of Arabidopsis thaliana profilin protein isoforms (AtPFNs) in regulating actin polymerization have already been described, their biochemical and molecular functions remain to be elucidated. Interestingly, a previous study indicated that AtPFN2 with high molecular weight (HMW) complexes showed lower antifungal activity than AtPFN1 with low molecular weight (LMW). These were bacterially expressed and purified to characterize the unknown functions of AtPFNs with different structures. In this study, we found that AtPFN1 and AtPFN2 proteins have LMW and HMW structures, respectively, but only AtPFN2 has a potential function as a molecular chaperone, which has never been reported elsewhere. AtPFN2 has better protein stability than AtPFN1 due to its higher molecular weight under heat shock conditions. The function of AtPFN2 as a holdase chaperone predominated in the HMW complexes, whereas the chaperone function of AtPFN1 was not observed in the LMW forms. These results suggest that AtPFN2 plays a critical role in plant tolerance by increasing hydrophobicity due to external heat stress.

An Overview of Molecular Dynamics Simulation for Food Products and Processes

Molecular dynamics (MD) simulation is a particularly useful technique in food processing. Normally, food processing techniques can be optimized to favor the creation of higher-quality, safer, more functional, and more nutritionally valuable food products. Modeling food processes through the application of MD simulations, namely, the Groningen Machine for Chemical Simulations (GROMACS) software package, is helpful in achieving a better understanding of the structural changes occurring at the molecular level to the biomolecules present in food products during processing. MD simulations can be applied to define the optimal processing conditions required for a given food product to achieve a desired function or state. This review presents the development history of MD simulations, provides an in-depth explanation of the concept and mechanisms employed through the running of a GROMACS simulation, and outlines certain recent applications of GROMACS MD simulations in the food industry for the modeling of proteins in food products, including peanuts, hazelnuts, cow’s milk, soybeans, egg whites, PSE chicken breast, and kiwifruit.