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The change of quality characteristics in snakehead fillets were investigated during brining, ultrasound, and ultrasound-assisted brining processing. Results showed that ultrasound and brine had significantly impact on the tissue microstructure and the color parameter of fillets. Compared to 60-min marination in deionized water, the shear force was reduced by 17.67 g by ultrasound, compared to 80-min marination in deionized water, the shear force was reduced by 28.68 g by brine. Brine significantly increased the water-holding capacity of fish fillets. Ultrasound resulted in increased random coils, β-turn and hydrophobic interaction, while brine significantly promoted the formation of the α-helix structure. The increase of the thermal stability of the myosin head was due to the synergistic effect of ultrasound and brine, but the decrease of the thermal stability of actin only associated with brine. The study provides the reference for the application of ultrasound-assisted brining technology to aquatic industry. [ABSTRACT FROM AUTHOR]

Proteins hold multispectral patterns of different kinds of physicochemical features of amino acids in their structures, which can help understand proteins’ behavior. Here, we propose a method based on the graph-wavelet transform of signals of features of amino acids in protein residue networks derived from their structures to achieve their abstract numerical representations. Such abstract representations of protein structures hand in hand with amino-acid features can be used for different purposes, such as modelling the biophysical property of proteins. Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. Even though our method does not predict deleterious mutations, it can summarize the effect of an amino acid based on its location and neighbourhood in protein-structure using graph-wavelet to estimate its influence on the biophysical property of proteins. Such an estimate of the influence of amino-acid has the potential to explain the mechanism of the effect of deleterious non-synonymous mutations. Thus, our approach can reveal patterns of distribution of amino-acid properties in the structure of the protein in the context of a biophysical property for better classification and more insightful understanding.

This study investigated the relationship between nitrogen soluble index (NSI), bulk density, gel storage modulus of soybean protein isolate and torque during high‐moisture (50%) extrusion, and analyzed the influence to extrudate texture by the moisture content and the expansion degree of extrudate. The results showed that in the range of NSI of 21–74%, bulk density of 0.41–0.47 g/cm3, and gel storage modulus of 3,800–5,400 Pa, the torque and specific mechanical energy raised with the decrease in NSI and the increase in bulk density and gel storage modulus. The lower the moisture content of the extrudate was, the higher its hardness. Interestingly, as the extrudate expands, bubble cavities formed on the surface of the extrudate could cause its hardness to drop from 13,124 g/s to 11,736 g/s and 4,840 g/s. Overall, the hard extrudates can be produced from soy protein isolates with low NSI (<21%), high bulk density (>0.47 g/cm3), and gel storage modulus (>5,380 Pa) via high‐moisture extrusion, at the same time appearing in an expanded structure that reduces the hardness test value of the extrudate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the field of protein engineering, the function and structure of proteins are key to understanding cellular mechanisms, biological evolution, and biodiversity. With the advancement of high-throughput sequencing technologies, we have accumulated a vast amount of protein sequence data, yet the protein properties and functional information contained within these data have not been fully deciphered. Predicting protein properties is crucial for revealing how proteins function within complex biological systems and also offers possibilities for the early diagnosis of diseases and the development of new drugs. However, due to the complexity of protein properties and functions, traditional experimental methods face significant challenges in terms of cost, time, and accuracy. In recent years, machine learning techniques have become a powerful tool for addressing these challenges due to their ability to learn patterns and relationships from large-scale data. Machine learning methods have demonstrated outstanding performance in areas such as protein structure prediction, function annotation, interaction recognition, and physicochemical property prediction. This survey reviews the application of machine learning in protein property prediction. Current research progress, challenges in the field, and future development directions have been discussed, highlighting the significance and potential of machine learning methods in advancing protein science research and applications. • Comprehensive review of machine learning methods. • Deep learning innovations in protein science, especially from 2019 to 2023. • Role of pre-trained models in protein property prediction. • Integration of advanced machine learning techniques in protein property prediction. • Representation learning from protein sequences and structures. [ABSTRACT FROM AUTHOR]

Rice protein has attracted considerable attention recently due to its physiological effects. This study extracted the proteins from paddy rice (PR) and germinated paddy rice (GPR) using three methods i.e., alkaline, sodium dodecyl sulfate (SDS) reagent and enzymatic extractions. The extracted proteins or protein fractions were assessed for their properties using various techniques. Data were analyzed by 2′3 factorial design experiment. It was found that germination and extraction methods significantly affected the concentration of protein fractions when analyzed by Bradford assay. Average protein fraction concentration of the GPR was lower than that of PR. SDS-PAGE patterns of protein fractions obtained from PR and GPR using any extraction method displayed similar protein profiles. Three major protein bands at about 13 kDa (prolamin), 22–23 kDa (basic glutelin) and 37–39 kDa (acidic glutelin) with small amount of 57 kDa proglutelin were observed. For amino acid profile, germination increased the content of most amino acids, resulting in the higher content of amino acids in GPR, excepted for some amino acids. When processed with in vitro digestion, protein fractions from GPR exhibited a higher level of digestibility than those from PR as evidenced by the less intensity of the protein bands obtained from SDS-PAGE. Alkaline and SDS reagent extractions provided more digestible protein fractions than enzymatic extraction. Extraction methods also influenced phase transition of protein fractions as investigated by a DSC. Alkaline extraction resulted in protein fractions with higher phase transition temperature than the other methods. For antioxidant capacity, extraction methods as well as germination significantly affected antioxidant capacity of the protein fractions. Enzymatic extraction provided protein fractions with the best antioxidant capacity.

The combined effect of tumbling marination methods (vacuum continuous tumbling marination, CT; vacuum intermittent tumbling marination, IT) and effective tumbling time (4, 6, 8, and 10 h) on the water status and protein properties of prepared pork chops was investigated. Results showed that regardless of tumbling time, CT method significantly decreased the muscle fiber diameter (MD) and significantly increased the total moisture content, product yield, salt soluble proteins (SSP) solubility, immobilized water component (p

• Dead-stop operation can provide more evidence for the fibrous formation process. • Surface hydrophobicity of the protein was the lowest in zone 1 with water injection. • The lowest value of extractable protein was in zone 3 (150 °C). • The protein ΔH was reduced from feeding zone to zone 3 and recovered in cooling die. • The secondary structure of protein was more ordered after than before extrusion. To provide additional evidence and better understand the high-moisture texturing extrusion process of soybean protein isolate (SPI), the physico-chemical changes of SPI during extrusion were investigated. SPI in the extruder feeding zone, barrel zones 1 (80 °C) to 4 (135 °C), and the cooling die (80 °C) were obtained from a dead-stop operation. The lowest values associated with the denaturation enthalpy and the extractable protein occurred in zone 3 (150 °C). The minimum level of the protein subunit content was identified in zone 4. The highest value of the average protein molar mass occurred in zone 3. The β-sheet ratio in the protein increased, and the unordered ratio decreased after extrusion. The surface hydrophobicity of the protein decreased with water injection in zone 1; however, it increased in zone 2 (110 °C). Overall, SPI undergoes swelling, denaturation, aggregation, and depolymerization due to water injection, heating, and shearing during high-moisture extrusion. [ABSTRACT FROM AUTHOR]

BACKGROUND Recently, in consideration of the rapidly increasing demand for meat products and the trend towards fast food consumption, the prepared boneless pork chop, as a new style of value-added product, is cut from pork loins and welcomed by consumers after marinating or further cooking. The effect of different tumbling marinade treatments (control group, CG; conventional static marinade, SM; vacuum continuous tumbling marinade, CT; vacuum intermittent tumbling marinade, IT) on the water status and protein properties of prepared pork chops was investigated. RESULTS The CT treatment significantly increased T22 peak area ratio compared with other treatments ( P < 0.05). Histological microstructure results showed that CT treatment significantly increased solubility of salt-soluble proteins and decreased muscle fiber diameter compared with other treatments ( P < 0.05). The net enthalpy (Δ H) of IT treatment was significantly lower than SM treatment ( P < 0.05), whereas the protein denaturation temperature ( Tm3) and Δ H3 of CT treatment were lower than IT treatment. CONCLUSION These results suggest that CT treatment was more effective in improving the water mobility and distribution, promoting the denaturation of muscle protein and accelerating the marinade efficiency of pork chops compared with other treatments. Thus CT treatment should be adopted. © 2014 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Aggregation changes of whey protein induced by high-pressure microfluidization (HPM) treatment have been investigated in relation with their functional properties. Whey protein was treated with HPM under pressure from 40 to 160 MPa. Functional properties (solubility, foaming, and emulsifying properties) of whey protein concentrate (WPC) ultrafiltered from fluid whey were evaluated. The results showed significant modifications in the solubility (30% to 59%) and foaming properties (20% to 65%) of WPC with increasing pressure. However, emulsifying property of WPC treated at different pressures was significantly worse than untreated sample. To better understand the mechanism of the modification by HPM, the HPM-induced aggregation changes were examined using particle size distribution, scanning electron microscopy, and hydrophobicity. It was indicated that HPM induced 2 kinds of aggregation changes on WPC: deaggregation and reaggregation of WPC, which resulted in the changes of functional properties of WPC modified by HPM. [ABSTRACT FROM AUTHOR]

Arabidopsis thaliana is an important model species for studies of plant gene functions. Research on Arabidopsis has resulted in the generation of high-quality genome sequences, annotations and related post-genomic studies. The amount of annotation, such as gene-coding regions and structures, is steadily growing in the field of plant research. In contrast to the genomics resource of animals and microorganisms, there are still some difficulties with characterization of some gene functions in plant genomics studies. The acquisition of information on protein structure can help elucidate the corresponding gene function because proteins encoded in the genome possess highly specific structures and functions. In this study, we calculated multiple physicochemical and secondary structural parameters of protein sequences, including length, hydrophobicity, the amount of secondary structure, the number of intrinsically disordered regions (IDRs) and the predicted presence of transmembrane helices and signal peptides, using a total of 208,333 protein sequences from the genomes of six representative plant species, Arabidopsis thaliana, Glycine max (soybean), Populus trichocarpa (poplar), Oryza sativa (rice), Physcomitrella patens (moss) and Cyanidioschyzon merolae (alga). Using the PASS tool and the Rosetta Stone method, we annotated the presence of novel functional regions in 1,732 protein sequences that included unannotated sequences from the Arabidopsis and rice proteomes. These results were organized into the Plant Protein Annotation Suite database (Plant-PrAS), which can be freely accessed online at http://plant-pras.riken.jp/. [ABSTRACT FROM AUTHOR]