Computational approaches for the classification of seed storage proteins

Sulfate transport and metabolism: strategies to improve the seed protein quality

Sulfur-containing amino acids (SAA), namely methionine, and cysteine are crucial essential amino acids (EAA) considering the dietary requirements of humans and animals. However, a few crop plants, especially legumes, are characterized with suboptimal levels of these EAA thereby limiting their nutritive value. Hence, improved comprehension of the mechanistic perspective of sulfur transport and assimilation into storage reserve, seed storage protein (SSP), is imperative. Efforts to augment the level of SAA in seed storage protein form an integral component of strategies to balance nutritive quality and quantity. In this review, we highlight the emerging trends in the sulfur biofortification approaches namely transgenics, genetic and molecular breeding, and proteomic rebalancing with sulfur nutrition. The transgenic 'push and pull strategy' could enhance sulfur capture and storage by expressing genes that function as efficient transporters, sulfate assimilatory enzymes, sulfur-rich foreign protein sinks, or by suppressing catabolic enzymes. Modern molecular breeding approaches that adopt high throughput screening strategies and machine learning algorithms are invaluable in identifying candidate genes and alleles associated with SAA content and developing improved crop varieties. Sulfur is an essential plant nutrient and its optimal uptake is crucial for seed sulfur metabolism, thereby affecting seed quality and yields through proteomic rebalance between sulfur-rich and sulfur-poor seed storage proteins.

In order to lower the antinutritional activity of serine protease inhibitors, we need to understand their role in seed development

Proteases, including serine proteases, are involved in the entire life cycle of plants. Proteases are controlled by protease inhibitors (PI) to limit any uncontrolled or harmful protease activity. The role of PIs in biotic and abiotic stress tolerance is well documented, however their role in various other plant processes has not been fully elucidated. Seed development is one such area that lack detailed work on the function of PIs despite the fact that this is a key process in the life cycle of the plant. Serine protease inhibitors (SPI) such as the Bowman-Birk inhibitors and Kunitz-type inhibitors, are abundant in legume seeds and act as antinutrients in humans and animals. Their role in seed development is not fully understood and present an interesting research target. Whether lowering the levels and activity of PIs, in order to lower the anti-nutrient levels in seed will affect the development of viable seed, remains an important question. Studies on the function of SPI in seed development are therefore required. In this Perspective paper, we provide an overview on the current knowledge of seed storage proteins, their degradation as well as on the serine protease-SPI system in seeds and what is known about the consequences when this system is modified. We discuss areas that require investigation. This includes the identification of seed specific SPIs; screening of germplasms, to identify plants with low seed inhibitor content, establishing serine protease-SPI ratios and lastly a focus on molecular techniques that can be used to modify seed SPI activity.

Improvements in Visual Aspects and Chemical, Techno-Functional and Rheological Characteristics of Cricket Powder (Gryllus bimaculatus) by Solvent Treatment for Food Utilization

This study aimed to improve the visual aspects and chemical, techno-functional and rheological characteristics of Gryllus bimaculatus cricket powder through the use of different solvents, with the objective of using it as a protein source in food production. Four treatments (pH 5 aqueous solution, ethanol 20%, ethanol 99.5%, and hexane) were applied to the powder, and analyses were conducted to assess changes in the previously mentioned parameters. The results showed that the treatments led to an increase in protein concentration (from 55.4 to 72.5%) and a decrease in fat concentration (from 33.0 to 6.8%) in ethanol 99.5% treated powder, as well as a reduction in anti-nutritional compounds concentration, such as tannins (from 13.3 to 5.9 g/kg), in pH 5 treated powder, which is important for the nutritional value of the final product. The color of the powders was improved, being lighter after hexane and ethanol 99.5% treatments due to the removal of melanin with the defatting process. Flowability, water, and oil holding capacity were also improved in the defatted powders. All the results suggest that the main composition of the powder directly influences the analyzed parameters. These findings suggest that cricket powder treated with solvents can be used as a protein source in different food applications.

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high ("H") and low ("L") level of saccharification a chosen carrier (potato maltodextrin).

Predicting the quality of soybean seeds stored in different environments and packaging using machine learning

The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optimize the processes and obtain accurate results for decision-making in the seed storage process. This study aimed to analyze the performance of ML algorithms from variables monitored during seed conditioning (temperature and packaging) and storage time to predict the physical and physiological quality of stored soybean seeds. Data analysis was performed using the Artificial Neural Networks, decision tree algorithms REPTree and M5P, Random Forest, and Linear Regression. In predicting seed quality, the combination of the input variables temperature and storage time for REPTree and Random Forest algorithms outperformed the linear regression, providing higher accuracy indices. Among the most important results, it was observed for apparent specific mass that T + P + ST, T + ST, P + ST, and ST had the highest r means and the lowest MAE means, however, Person's r coefficient for these inputs was 0.63 and the MAE between 9.59 to 10.47. The germination results for inputs T + P + ST and T + ST had the best results (r = 0.65 and r = 0.67, respectively) in the ANN, REPTree, M5P and RF models. Using computational intelligence algorithms is an excellent alternative to predict the quality of soybean seeds from the information of easy-to-measure variables.

Crosstalk during the Carbon-Nitrogen Cycle That Interlinks the Biosynthesis, Mobilization and Accumulation of Seed Storage Reserves

Carbohydrates are the major storage reserves in seeds, and they are produced and accumulated in specific tissues during the growth and development of a plant. The storage products are hydrolyzed into a mobile form, and they are then translocated to the developing tissue following seed germination, thereby ensuring new plant formation and seedling vigor. The utilization of seed reserves is an important characteristic of seed quality. This review focuses on the seed storage reserve composition, source–sink relations and partitioning of the major transported carbohydrate form, i.e., sucrose, into different reserves through sucrolytic processes, biosynthetic pathways, interchanging levels during mobilization and crosstalk based on vital biochemical pathways that interlink the carbon and nitrogen cycles. Seed storage reserves are important due to their nutritional value; therefore, novel approaches to augmenting the targeted storage reserve are also discussed.

Predicting ATP-Binding Cassette Transporters Using the Random Forest Method

ATP-binding cassette (ABC) proteins play important roles in a wide variety of species. These proteins are involved in absorbing nutrients, exporting toxic substances, and regulating potassium channels, and they contribute to drug resistance in cancer cells. Therefore, the identification of ABC transporters is an urgent task. The present study used 188D as the feature extraction method, which is based on sequence information and physicochemical properties. We also visualized the feature extracted by t-Distributed Stochastic Neighbor Embedding (t-SNE). The sample based on the features extracted by 188D may be separated. Further, random forest (RF) is an efficient classifier to identify proteins. Under the 10-fold cross-validation of the model proposed here for a training set, the average accuracy rate of 10 training sets was 89.54%. We obtained values of 0.87 for specificity, 0.92 for sensitivity, and 0.79 for MCC. In the testing set, the accuracy achieved was 89%. These results suggest that the model combining 188D with RF is an optimal tool to identify ABC transporters.

Modeling to Understand Plant Protein Structure-Function Relationships-Implications for Seed Storage Proteins

Proteins are among the most important molecules on Earth. Their structure and aggregation behavior are key to their functionality in living organisms and in protein-rich products. Innovations, such as increased computer size and power, together with novel simulation tools have improved our understanding of protein structure-function relationships. This review focuses on various proteins present in plants and modeling tools that can be applied to better understand protein structures and their relationship to functionality, with particular emphasis on plant storage proteins. Modeling of plant proteins is increasing, but less than 9% of deposits in the Research Collaboratory for Structural Bioinformatics Protein Data Bank come from plant proteins. Although, similar tools are applied as in other proteins, modeling of plant proteins is lagging behind and innovative methods are rarely used. Molecular dynamics and molecular docking are commonly used to evaluate differences in forms or mutants, and the impact on functionality. Modeling tools have also been used to describe the photosynthetic machinery and its electron transfer reactions. Storage proteins, especially in large and intrinsically disordered prolamins and glutelins, have been significantly less well-described using modeling. These proteins aggregate during processing and form large polymers that correlate with functionality. The resulting structure-function relationships are important for processed storage proteins, so modeling and simulation studies, using up-to-date models, algorithms, and computer tools are essential for obtaining a better understanding of these relationships.

Advances in the Biology of Seed and Vegetative Storage Proteins Based on Two-Dimensional Electrophoresis Coupled to Mass Spectrometry

Seed storage proteins play a fundamental role in plant reproduction and human nutrition. They accumulate during seed development as reserve material for germination and seedling growth and are a major source of dietary protein for human consumption. Storage proteins encompass multiple isoforms encoded by multi-gene families that undergo abundant glycosylations and phosphorylations. Two-dimensional electrophoresis (2-DE) is a proteomic tool especially suitable for the characterization of storage proteins because of their peculiar characteristics. In particular, storage proteins are soluble multimeric proteins highly represented in the seed proteome that contain polypeptides of molecular mass between 10 and 130 kDa. In addition, high-resolution profiles can be achieved by applying targeted 2-DE protocols. 2-DE coupled with mass spectrometry (MS) has traditionally been the methodology of choice in numerous studies on the biology of storage proteins in a wide diversity of plants. 2-DE-based reference maps have decisively contributed to the current state of our knowledge about storage proteins in multiple key aspects, including identification of isoforms and quantification of their relative abundance, identification of phosphorylated isoforms and assessment of their phosphorylation status, and dynamic changes of isoforms during seed development and germination both qualitatively and quantitatively. These advances have translated into relevant information about meaningful traits in seed breeding such as protein quality, longevity, gluten and allergen content, stress response and antifungal, antibacterial, and insect susceptibility. This review addresses progress on the biology of storage proteins and application areas in seed breeding using 2-DE-based maps.