In-silico behavior of dissolved prolamins under electric field effect applied by electrospinning process using molecular dynamics simulation

A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies

Recent advancements in biomaterial research conduct artificial intelligence for predicting diverse material properties. However, research predicting the mechanical properties of biomaterial based on amino acid sequences have been notably absent. This research pioneers the use of classification models to predict ultimate tensile strength from silk fiber amino acid sequences, employing logistic regression, support vector machines with various kernels, and a deep neural network (DNN). Remarkably, the model demonstrates a high accuracy of 0.83 during the generalization test. The study introduces an innovative approach to predicting biomaterial mechanical properties beyond traditional experimental methods. Recognizing the limitations of conventional linear prediction models, the research emphasizes the future trajectory toward DNNs that can adeptly capture non-linear relationships with high precision. Moreover, through comprehensive performance comparisons among diverse prediction models, the study offers insights into the effectiveness of specific models for predicting the mechanical properties of certain materials. In conclusion, this study serves as a pioneering contribution, laying the groundwork for future endeavors and advocating for the seamless integration of AI methodologies into materials research.

Molecular-level insight into the effects of low moisture and trehalose on the thermostability of β-glucosidase

The high temperature induces conformational changes in β-glucosidase, making it inactive and limiting its application field. In this paper, the effect of trehalose on the thermostability of β-glucosidase from low-moisture Hevea brasiliensis seeds was investigated. The results showed that the residual enzyme activities of β-glucosidase supplemented with trehalose after high-temperature treatment were significantly higher than that of the control group. The improvement of thermostability could be explained by low-field nuclear magnetic resonance (LF-NMR) and molecular dynamics (MD) simulations at the molecular level. Moreover, adding trehalose increased the water activity and water content of β-glucosidase, leading to a more stable conformation. Trehalose replaced some water and formed a stable network of hydrogen bonds with protein and surrounding water. The glass formed by trehalose also reduced molecular movement, thus providing good protection for enzymes.

Application of electrospinning to fabricate phycocyanin- and Spirulina extract-loaded gliadin fibers for active food packaging

This study explored the active food packaging application of phycocyanin- and Spirulina extract-loaded gliadin electrospun fibers (GPhy and GSPE5%). SEM findings confirmed that the morphology of fibers was tubular, showing the GPhy and GSPE5% as the optimum fibers. The loading efficiencies of GPhy and GSPE5% were also around 90%, which proved the well-incorporated compounds within the fibers. Simulation results of α-gliadin dissolved in acetic acid illustrated the denaturation of the protein. FTIR and TGA confirmed that after electrospinning the chemical/structural changes and enhanced thermostabilities occurred, respectively. Antibacterial and antioxidant tests detected higher bactericidal and antioxidative effects of GSPE5% than GPhy. In the application part, it was found that GPhy and GSPE5% were able to decrease PV and TBA values as the indications of walnut kernels' protection from lipid oxidation. This work shows a facile and an efficient way to fabricate active food packaging materials using electrospinning and natural compounds.

Conformations of a highly expressed Z19 α-zein studied with AlphaFold2 and MD simulations

α-zeins are amphiphilic maize seed storage proteins with material properties suitable for a multitude of applications e.g., in renewable plastics, foods, therapeutics and additive manufacturing (3D-printing). To exploit their full potential, molecular-level insights are essential. The difficulties in experimental atomic-resolution characterization of α-zeins have resulted in a diversity of published molecular models. However, deep-learning α-zein models are largely unexplored. Therefore, this work studies an AlphaFold2 (AF2) model of a highly expressed α-zein using molecular dynamics (MD) simulations. The sequence of the α-zein cZ19C2 gave a loosely packed AF2 model with 7 α-helical segments connected by turns/loops. Compact tertiary structure was limited to a C-terminal bundle of three α-helices, each showing notable agreement with a published consensus sequence. Aiming to chart possible α-zein conformations in practically relevant solvents, rather than the native solid-state, the AF2 model was subjected to MD simulations in water/ethanol mixtures with varying ethanol concentrations. Despite giving structurally diverse endpoints, the simulations showed several patterns: In water and low ethanol concentrations, the model rapidly formed compact globular structures, largely preserving the C-terminal bundle. At ≥ 50 mol% ethanol, extended conformations prevailed, consistent with previous SAXS studies. Tertiary structure was partially stabilized in water and low ethanol concentrations, but was disrupted in ≥ 50 mol% ethanol. Aggregated results indicated minor increases in helicity with ethanol concentration. β-sheet content was consistently low (∼1%) across all conditions. Beyond structural dynamics, the rapid formation of branched α-zein aggregates in aqueous environments was highlighted. Furthermore, aqueous simulations revealed favorable interactions between the protein and the crosslinking agent glycidyl methacrylate (GMA). The proximity of GMA epoxide carbons and side chain hydroxyl oxygens simultaneously suggested accessible reactive sites in compact α-zein conformations and pre-reaction geometries for methacrylation. The findings may assist in expanding the applications of these technologically significant proteins, e.g., by guiding chemical modifications.

Unraveling the Mechanical Property Decrease of Electrospun Spider Silk: A Molecular Dynamics Simulation Study

This study investigated the impact of electric fields on Nephila clavipes spider silk using molecular dynamics modeling. Electric fields with varying amplitudes and directions were observed to disrupt the β sheet structure of spider silk and reduce its mechanical properties. However, a notable exception was observed when a 0.1 V/nm electric field was applied in the antiparallel direction, resulting in improvements in Young's modulus and ultimate tensile strength. The antiparallel direction was observed to be particularly sensitive to electric fields, causing disruptions in beta sheets and hydrogen bonds, which significantly influence the mechanical properties. This study demonstrates that spider silk maintains its structural integrity at 0.1 V/nm. Possibly, lowering the power levels of typical electrospinning machines can prevent secondary structural disruption. These findings provide valuable insights for enhancing silk fiber production and applications using natural silk proteins while shedding light on the impact of electric fields on other silk proteins. Finally, this study opens up possibilities for optimizing electrospinning processes to enhance performance in various silk electrospinning applications.

Integration of physicochemical, molecular dynamics, and in vitro evaluation of electrosprayed γ-oryzanol-loaded gliadin nanoparticles

Electrospraying is a technique to improve the application and stability of bioactive compounds in food. Here, electrospraying was applied to fabricate gliadin particles incorporated γ-oryzanol. The round particles were obtained, with an average diameter of 481.56 ± 283.74 nm, from scanning electron microscopy. Simulations demonstrated how γ-oryzanol-loaded gliadin particles were unfolded in acetic acid and culminated in their globular shape under an electric field. The results also revealed that γ-oryzanol was present in gliadin particles. Moreover, there was a successful formation of particles with a homogeneous distribution and an enhanced thermostabilization of γ-oryzanol. In food simulants, γ-oryzanol demonstrated an initial burst release, followed by a subsequent, slower release that occurred gradually. Finally, MTT assays showed concentration- and time-dependent inhibitions of γ-oryzanol-loaded gliadin particles on HT-29 cells, with IC₅₀ values of 0.47 and 0.40 mg/mL for 24 and 48 h, respectively. This study described a protocol for developing γ-oryzanol-loaded gliadin particles with enhanced stability, valuable release-behavior, and decreased HT-29 proliferation.

Molecular modeling of lactoferrin for food and nutraceutical applications: insights from in silico techniques

Lactoferrin is a protein, primarily found in milk that has attracted the interest of the food industries due to its health properties. Nevertheless, the instability of lactoferrin has limited its commercial application. Recent studies have focused on encapsulation to enhance the stability of lactoferrin. However, the molecular insights underlying the changes of structural properties of lactoferrin and the interaction with protectants remain poorly understood. Computational approaches have proven useful in understanding the structural properties of molecules and the key binding with other constituents. In this review, comprehensive information on the structure and function of lactoferrin and the binding with various molecules for food purposes are reviewed, with a special emphasis on the use of molecular dynamics simulations. The results demonstrate the application of modeling and simulations to determine key residues of lactoferrin responsible for its stability and interactions with other biomolecular components under various conditions, which are also associated with its functional benefits. These have also been extended into the potential creation of enhanced lactoferrin for commercial purposes. This review provides valuable strategies in designing novel nutraceuticals for food science practitioners and those who have interests in acquiring familiarity with the application of computational modeling for food and health purposes.