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The suitability of a given protein for use in food products depends heavily on characteristics such as foaming capacity, emulsifiability, and solubility, all of which are affected by the protein structure. Notably, protein structure, and thus characteristics related to food applications, can be altered by treatment with high-power ultrasound (HUS). Almonds are a promising source of high-quality vegetable protein for food products, but their physicochemical and functional properties remain largely unexplored, limiting their current applications in foods. Here, we tested the use of HUS on almond protein isolate (API) to determine the effects of this treatment on API functional properties. Aqueous almond protein suspensions were sonicated at varying power levels (200, 400, or 600 W) for two durations (15 or 30 min). The molecular structure, protein microstructure, solubility, and emulsifying and foaming properties of the resulting samples were then measured. The results showed that HUS treatment did not break API covalent bonds, but there were notable changes in the secondary protein structure composition, with the treated proteins showing a decrease in α-helices and β-turns, and an increase in random coil structures as the result of protein unfolding. HUS treatment also increased the number of surface free sulfhydryl groups and decreased the intrinsic fluorescence intensity, indicating that the treatment also led to alterations in the tertiary protein structures. The particle size in aqueous suspensions was decreased in treated samples, indicating that HUS caused the dissociation of API aggregates. Finally, treated samples showed increased water solubility, emulsifying activity, emulsifying stability, foaming capacity, and foaming stability. This study demonstrated that HUS altered key physicochemical characteristics of API, improving critical functional properties including solubility and foaming and emulsifying capacities. This study also validated HUS as a safe and environmentally responsible tool for enhancing desirable functional characteristics of almond proteins, promoting their use in the food industry as a high-quality plant-based protein.

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model—the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.

Soybean meal (SBM) is a high-quality vegetable protein, whose application is greatly limited due to its high molecular weight and anti-nutritional properties. The aim of this study was to modify the protein of soybean meal via solid-state fermentation of Bacillus subtilis. The fermentation conditions were optimized as, finally, the best process parameters were obtained, namely fermentation temperature of 37 °C, inoculum amount of 12%, time of 47 h, and material-liquid ratio of 1:0.58, which improved the content of acid-soluble protein. To explore the utilization of modified SBM as a food ingredient, the protein structure and properties were investigated. Compared to SBM, the protein secondary structure of fermented soybean meal (FSBM) from the optimal process decreased by 8.3% for α-helix content, increased by 3.08% for β-sheet, increased by 2.71% for β-turn, and increased by 2.51% for random coil. SDS-PAGE patterns showed that its 25–250 KDa bands appeared to be significantly attenuated, with multiple newborn peptide bands smaller than 25 KDa. The analysis of particle size and zeta potential showed that fermentation reduced the average particle size and increased the absolute value of zeta potential. It was visualized by SEM and CLSM maps that the macromolecular proteins in FSBM were broken down into fragmented pieces with a folded and porous surface structure. Fermentation increased the solubility, decreased the hydrophobicity, increased the free sulfhydryl content, decreased the antigenicity, improved the protein properties of SBM, and promoted further processing and production of FSBM as a food ingredient.

The present study aimed to explore the effects of ultra-high pressure (UHP) on the cathepsin (B, D, H, and L) activities, protein oxidation, and degradation properties as well as quality characteristics of iced shrimp (Litopenaeus vannamei). Fresh shrimps were vacuum-packed, treated with UHP (100–500 MPa for 5 min), and stored at 0 °C for 15 days. The results showed that the L* (luminance), b* (yellowness), W (whiteness), ΔE (color difference), hardness, shear force, gumminess, chewiness, and resilience of shrimp were significantly improved by UHP treatment. Moreover, the contents of surface hydrophobicity, myofibril fragmentation index (MFI), trichloroacetic acid (TCA)-soluble peptides, carbonyl, dityrosine, and free sulfhydryl of myofibrillar protein (MP) were significantly promoted by UHP treatment. In addition, UHP (above 300 MPa) treatment enhanced the mitochondrial membrane permeability but inhibited the lysosomal membrane stability, and the cathepsin (B, D, H, and L) activities. UHP treatment notably inhibited the activities of cathepsins, delayed protein oxidation and degradation, as well as texture softening of shrimp during storage. Generally, UHP treatment at 300 MPa for 5 min effectively delayed the protein and quality deterioration caused by endogenous enzymes and prolonged the shelf life of shrimp by 8 days.

The fouling of biomaterials (e.g., membranes) by plasma proteins has always garnered attention because it renders biomedical devices ineffective and can jeopardize the patient’s well-being. Modeling the fouling process sheds light on its mechanisms and helps improve the biocompatibility of biomaterials. Assuming proteins to be hard spheres with uniform surface properties reduces the modeling complexity, but it seriously deviates from the accurate, real perspective. One reason for the inaccuracy is that proteins’ properties tend to change as environmental factors such as pH and ionic strength are varied. This study critically reviews the pH-induced changes in protein properties, namely size, charge, conformity, hydrophobicity, and denaturation. Though these properties may be interrelated, they are addressed individually to allow for a thorough discussion. The study illustrates the necessity of incorporating the protein property changes resulting from pH alteration to better explain and model the fouling process. The discussion is focused on human serum albumin and fibrinogen. Human serum albumin is the most abundant plasma protein, while fibrinogen plays a major role in blood clotting and triggering of the thrombogenic response.

The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.

Background While protein-protein docking is fundamental to our understanding of how proteins interact, scoring protein-protein complex conformations is a critical component of successful docking programs. Without accurate and efficient scoring functions to differentiate between native and non-native binding complexes, the accuracy of current docking tools cannot be guaranteed. Although many innovative scoring functions have been proposed, a good scoring function for docking remains elusive. Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. Results In this study, we perform a comprehensive survey of the state-of-the-art scoring functions by considering the most popular and highly performant approaches, both classical and deep learning-based, for scoring protein-protein complexes. The methods were also compared based on their runtime as it directly impacts their use in large-scale docking applications. Conclusions We evaluate the strengths and weaknesses of classical and deep learning-based approaches across seven public and popular datasets to aid researchers in understanding the progress made in this field.

To provide recommendations to users regarding which thawing method for frozen instant sea cucumbers entails lower quality losses, in this study we compared the water retention, mechanical properties, protein properties, and microstructures of frozen instant sea cucumbers post-thawing by means of different thawing approaches, including refrigerator thawing (RT), air thawing (AT), water immersion thawing (WT), and ultrasound-assisted thawing (UT). The results indicated that UT took the shortest time. RT samples exhibited the best water-holding capacity, hardness and rheological properties, followed by UT samples. The α-helix and surface hydrophobicity of WT and AT samples were significantly lower than those of the first two methods (p < 0.05). The lowest protein maximum denaturation temperature (Tmax) was obtained by means of WT. AT samples had the lowest maximum fluorescence emission wavelength (λmax). Based on these results, WT and AT were more prone to the degradation of protein thermal stability and the destruction of the protein structure. Similarly, more crimping and fractures of the samples after WT and AT were observed in the sea cucumbers’ microstructures. Overall, we observed that UT can be used to maintain the quality of frozen instant sea cucumbers in the shortest time.

Proteins are the basic building blocks of life and perform fundamental functions in biology. Predicting protein properties based on amino acid sequences and 3D structures has become a key approach to accelerating drug development. In this study, we propose a novel sequence- and structure-based framework, SST-ResNet, which consists of the multimodal language model ProSST and a multi-scale information integration module. This framework is designed to deeply explore the latent relationships between protein sequences and structures, thereby achieving superior synergistic prediction performance. Our method outperforms previous joint prediction models on Enzyme Commission (EC) numbers and Gene Ontology (GO) tasks. Furthermore, we demonstrate the necessity of multi-scale information integration for these two types of data and illustrate its exceptional performance on key tasks. We anticipate that this framework can be extended to a broader range of protein property prediction problems, ultimately facilitating drug development.

Gel-based meat products have appealing market potential due to their unique texture, elasticity, and tender taste. Sodium chloride (NaCl) is commonly used in these products to enhance flavor, improve texture, ensure food safety, and extend shelf life. However, excessive long-term NaCl intake is connected with health issues such as hypertension and cardiovascular diseases, raising concerns about its impact on human health. As a result, the reduction of NaCl in these products, while maintaining their flavor and texture, has become a key area in the food industry. Salt reduction strategies often compromise product quality, limiting the search for substitutes. Consequently, there is growing interest in developing new salt substitutes. Recently, basic amino acids (BAA) have emerged as a viable alternative to NaCl in low-salt gel-based meat products. Studies have shown that BAAs not only enhance the solubility, gelation, and emulsification properties of salt-soluble proteins but also reduce protein and lipid oxidation in low-salt conditions, improving sensory characteristics and texture. When combined with chloride salts, BAAs can further lower salt content while improving the quality of the products. In addition, adding modern processing techniques (such as ultrasound, pulsed electric fields) has indicated positive effects on the taste and texture of low-salt meat products. Future studies should deploy advanced tools to dissect the micro-/macro-level impacts of BAAs on low-salt gel products. Furthermore, integrating modern food processing and information technologies could lead to the development of personalized, intelligent low-salt meat products that satisfy consumer demands for both health and taste.