Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations.

The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure-function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.

Benchmarking a Fast Proton Titration Scheme in Implicit Solvent for Biomolecular Simulations.

pH is a key parameter for technological and biological processes, intimately related to biomolecular charge. As such, it controls biomolecular conformation and intermolecular interactions, for example, protein/RNA stability and folding, enzyme activity, regulation through conformational switches, protein-polyelectrolyte association, and protein-RNA interactions. pH also plays an important role in technological systems in food, brewing, pharma, bioseparations, and biomaterials in general. Predicting the structure of large proteins and complexes remains a great challenge experimentally, industrially, and theoretically, despite the variety of numerical schemes available ranging from Poisson-Boltzmann approaches to explicit solvent based methods. In this work we benchmark a fast proton titration scheme against experiment and several theoretical methods on the following set of representative proteins: [HP36, BBL, HEWL (triclinic and orthorhombic), RNase, SNASE (V66K/WT, V66K/PHS, V66K/Δ+PHS, L38D/Δ+PHS, L38E/Δ+PHS, L38K/Δ+PHS), ALAC, and OMTKY3]; routinely used in similar tests due to the diversity of their structural features. Our scheme is rooted in the classical Tanford-Kirkwood model of impenetrable spheres, where salt is treated at the Debye-Hückel level. Treating salt implicitly dramatically reduces the computation time, thereby circumventing sampling difficulties faced by other numerical schemes. In comparison with experimental measurements, our calculated pKa values have the average, maximum absolute, and root-mean-square deviations of 0.4-0.9, 1.0-5.2, and 0.5-1.2 pH units, respectively. These values are within the ranges commonly observed in theoretical models. They are also in the large majority of the cases studied here more accurate than the NULL model. For BBL, ALAC, and OMTKY3, the predicted pKa are closer to experimental results than other analyzed theoretical data. Despite the intrinsic approximations of the fast titration scheme, its robustness and ability to properly describe the main system physics is confirmed.