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Microalgae when grown under certain conditions can be rich sources of protein and can complement conventional protein sources like fishmeal and soymeal, in the aquaculture and animal feed industry. In this study, evaluation of four marine microalgae strains (Picochlorum sp., Nannochloris sp., Nannochloropsis sp., and Chlorella sp.) revealed that in vitro protein solubility and digestibility may serve as key indicators in determining the suitability of microalgae as a protein ingredient in feed. The greenhouse areal biomass productivities, protein and lipid concentrations of these strains ranged between 9–17 g m−2 day−1, 30–38% and 22–24%, respectively. Preliminary in vitro assays using undisrupted biomass of Picochlorum sp. revealed that its protein solubility was 47% and 67% less and digestibility was 28% and 22% less compared with fishmeal and de-oiled soy flour (DOSF), respectively. However, disruption of Picochlorum sp. biomass resulted in 2.5- and 1.5-fold increase in protein solubility and digestibility, respectively, as compared with undisrupted biomass. Further in vitro studies indicated that the soluble protein fractions differed significantly among the four experimental microalgae. The highest in vitro protein solubility (%) and soluble protein fractions (g kg−1 biomass) recorded in the four strains were the following: Picochlorum sp. (53%; 176 g kg−1), Nannochloris sp. (57%; 217 g kg−1), Nannochloropsis sp. (71%; 214 g kg−1), and Chlorella sp. (53%; 197 g kg−1). In addition, extracts from all these four strains were tested for the presence of trypsin inhibitors and found that all these strains have significantly lower trypsin inhibiting activity (TIA) compared with DOSF. The methodology presented in this study combines growth, biochemical composition, protein solubility, in vitro protein digestibility, and TIA and thus provides a reliable strategy in selection of microalgae as protein feed ingredient.

Arthrospira platensis has emerged as a novel protein feed source since it contains high protein level and quality. However, this microalga presents a recalcitrant cell wall and its main proteins form protein-pigment complexes attached to the thylakoid membrane. The objective of the present study was to evaluate the influence of mechanical/physical pretreatments (bead milling, extrusion, freeze-drying, heating, microwave and sonication) on A. platensis protein solubility. Total protein content and solubility were assessed by Bradford method and SDS-PAGE quantification. Protein degradation was assessed through quantification of protein fractions (18–26 kDa, 40–48 kDa and others) in SDS-PAGE gels. Peptide formation was evaluated using the o-phthaldialdehyde assay. The results showed a decrease in total protein content in the supernatant with extrusion (0.07 to 1.42 mg/mL) and microwave pretreatments, and in the pellet with extrusion. Therefore, extrusion, followed by microwave, was the most effective pretreatment for A. platensis proteins denaturation and solubility. It is suggested that the extrusion process cause an irreversible denaturation and aggregation of the major microalga proteins (c-phycocyanin and allophycocyanin), with a strong decrease in their solubility. Therefore, extrusion could increase the bioaccessibility of A. platensis proteins and enable the incorporation of this microalga at higher levels in monogastric diets

Background Over the last 20 years in biotechnology, the production of recombinant proteins has been a crucial bioprocess in both biopharmaceutical and research arena in terms of human health, scientific impact and economic volume. Although logical strategies of genetic engineering have been established, protein overexpression is still an art. In particular, heterologous expression is often hindered by low level of production and frequent fail due to opaque reasons. The problem is accentuated because there is no generic solution available to enhance heterologous overexpression. For a given protein, the extent of its solubility can indicate the quality of its function. Over 30% of synthesized proteins are not soluble. In certain experimental circumstances, including temperature, expression host, etc., protein solubility is a feature eventually defined by its sequence. Until now, numerous methods based on machine learning are proposed to predict the solubility of protein merely from its amino acid sequence. In spite of the 20 years of research on the matter, no comprehensive review is available on the published methods. Results This paper presents an extensive review of the existing models to predict protein solubility in Escherichia coli recombinant protein overexpression system. The models are investigated and compared regarding the datasets used, features, feature selection methods, machine learning techniques and accuracy of prediction. A discussion on the models is provided at the end. Conclusions This study aims to investigate extensively the machine learning based methods to predict recombinant protein solubility, so as to offer a general as well as a detailed understanding for researches in the field. Some of the models present acceptable prediction performances and convenient user interfaces. These models can be considered as valuable tools to predict recombinant protein overexpression results before performing real laboratory experiments, thus saving labour, time and cost.

Protein quality affects nutritional value and functional properties of protein products. It is important to assess protein quality accurately and cost‐effectively. Recently, a new indicator for protein quality, protein solubility index (PSI), was developed (JAOCS, 2022; 99, 855–871). The new method, featuring 5 mM NaOH extraction with magnetic stirring and simultaneous running of multiple samples, was proposed as AOCS method Ba 15‐2023. As part of the AOCS method approval process, a collaborative study was conducted to evaluate its performance. It involved 16 laboratories from 10 countries to measure PSI of the 12 selected samples plus a blind duplicate, including soybeans, pulses, cereals, and their processed products (flours, concentrates and isolates). After rigorous statistical analysis to remove a few outliers, several precision parameters were calculated. Repeatability relative standard deviations (RSDr) ranged 0.6%–11.4%, with 10 samples having RSDr ≤ 5%. Reproducibility RSDR ranged 2.6%–15.7%. The five samples with RSDR ≥ 10% corresponded to protein isolates or those with the lowest N content or the lowest PSI. The study demonstrated robust performance of the proposed AOCS method. A few collaborators carried out additional experiments to address some aspects of the method, leading to further improvement. The results of the present study were presented to the AOCS Uniform Methods Committee for evaluation. Once the method is adopted as the Official Method for measuring PSI in various protein products, it is poised to serve as a unified index for protein quality with respect to both nutritional value and functional properties. Step by step in conducting the AOCS collaborative study on PSI.

Soybean meal was subjected to autoclaving for different durations (0, 5, 10, 15, and 20 min) to alter its protein solubility index (PSI). As a result of autoclaving, the PSI of soybean meal was reduced from 85–64% but the protein quantity was not affected. Among amino acids, methionine and cystine were reduced significantly ( P  < 0.05) beyond autoclaving for 15 min. Trypsin inhibitor was below detectable level after 20 min of autoclave. Saponin and phytic acid were reduced by 0.3–8 and 1–24%, respectively, in treated soybean meal. Five iso-nitrogenous diets were formulated by replacing untreated soybean meal using processed soybean meal, and its effects on growth performance, feed efficiency, and digestibility parameters were assessed in Penaeus vannamei . The results revealed that the growth rate was not affected ( P  > 0.05) in shrimp fed with diets having soybean meal autoclaved up to 10 min (PSI 72%). The similar trend was noticed in feed efficiency parameters. The apparent dry matter and crude protein digestibility parameters were reduced ( P  < 0.05) in shrimp fed diets having soybean meal autoclaved for 15 and 20 min (PSI 68 and 64%). The inclusion of processed soybean meal has not influenced the shrimp carcass composition. The present study showed that though anti-nutritional factors were reduced in prolonged heat treatment, the declined protein solubility has resulted in the reduction of growth parameters and digestibility in those treatments. Hence, the present preliminary results suggest to scrutinize the quality of protein whenever heat is being applied during the processing of soybean meal and also other protein sources.

The solubility of plant protein isolates is a key determinant of their potential application. Two protein isolates (PI) from ethanol-treated industrial rapeseed meal, PI10.5–2.5 and PI2.5–8.5, were prepared by sequential isoelectric precipitation of alkali-extracted proteins (pH 12) starting from pH 10.5 to 2.5 or from pH 2.5 to 8.5, respectively. Biochemical analyses revealed that PI2.5–8.5 contained a higher amount of crude protein (72.84%) than PI10.5–2.5 (68.67%). In the same protein isolate, the level of total phenols (0.71%) was almost two-fold higher than that in PI10.5–2.5 (0.42%). No glucosinolates were established in both protein isolates. SDS-PAGE analysis demonstrated that PI10.5–2.5 contained 10 to 15 kDa protein fractions in a relatively higher amount, while PI2.5–8.5 was enriched in 18 to 29 kDa protein fractions. PI10.5–2.5 exhibited high solubility, varying from 41.74% at pH 4.5 to 65.13% at pH 6.5, while PI2.5–8.5 was almost two-fold less soluble under the same conditions. Up to pH 5.5, the addition of NaCl at 0.03 and 0.25 M diminished the solubility of PI2.5–8.5, while the solubility of PI10.5–2.5 was increased. The supplementation of PI10.5–2.5 with 0.25 M NaCl enhanced the protein solubility to 56.11% at pH 4.5 and 94.26% at pH 6.5. The addition of 0.03 M NaCl also increased the solubility of this protein isolate but to a lower extent. Overall, the approach for sequential precipitation of proteins influenced the biochemical characteristics, protein fractional profile and solubility of prepared protein isolates.

Increasingly, consumers are moving towards a more plant-based diet. However, some consumers are avoiding common plant proteins such as soy and gluten due to their potential allergenicity. Therefore, alternative protein sources are being explored as functional ingredients in foods, including pea, chickpea, and other legume proteins. The factors affecting the functional performance of plant proteins are outlined, including cultivars, genotypes, extraction and drying methods, protein level, and preparation methods (commercial versus laboratory). Current methods to characterize protein functionality are highlighted, including water and oil holding capacity, protein solubility, emulsifying, foaming, and gelling properties. We propose a series of analytical tests to better predict plant protein performance in foods. Representative applications are discussed to demonstrate how the functional attributes of plant proteins affect the physicochemical properties of plant-based foods. Increasing the protein content of plant protein ingredients enhances their water and oil holding capacity and foaming stability. Industrially produced plant proteins often have lower solubility and worse functionality than laboratory-produced ones due to protein denaturation and aggregation during commercial isolation processes. To better predict the functional performance of plant proteins, it would be useful to use computer modeling approaches, such as quantitative structural activity relationships (QSAR).

Background Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work. Results In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects. Conclusions DeepSoluE is suitable for the prediction of protein solubility in E. coli ; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .

Proteins are marginally stable, and an understanding of the sequence determinants for improved protein solubility is highly desired. For enzymes, it is well known that many mutations that increase protein solubility decrease catalytic activity. These competing effects frustrate efforts to design and engineer stable, active enzymes without laborious high-throughput activity screens. To address the trade-off between enzyme solubility and activity, we performed deep mutational scanning using two different screens/selections that purport to gauge protein solubility for two full-length enzymes. We assayed a TEM-1 beta-lactamase variant and levoglucosan kinase (LGK) using yeast surface display (YSD) screening and a twin-arginine translocation pathway selection. We then compared these scans with published experimental fitness landscapes. Results from the YSD screen could explain 37% of the variance in the fitness landscapes for one enzyme. Five percent to 10% of all single missense mutations improve solubility, matching theoretical predictions of global protein stability. For a given solubility-enhancing mutation, the probability that it would retain wild-type fitness was correlated with evolutionary conservation and distance to active site, and anticorrelated with contact number. Hybrid classification models were developed that could predict solubility-enhancing mutations that maintain wild-type fitness with an accuracy of 90%. The downside of using such classification models is the removal of rare mutations that improve both fitness and solubility. To reveal the biophysical basis of enhanced protein solubility and function, we determined the crystallographic structure of one such LGK mutant. Beyond fundamental insights into trade-offs between stability and activity, these results have potential biotechnological applications.

Dry fractionated faba bean protein-rich flour (FPR) produced by milling/air classification, and faba bean protein isolate (FPI) produced by acid extraction/isoelectric precipitation were compared in terms of composition, techno-functional properties, nutritional properties and environmental impacts. FPR had a lower protein content (64.1%, dry matter (DM)) compared to FPI (90.1%, DM), due to the inherent limitations of air classification. Of the two ingredients, FPR demonstrated superior functionality, including higher protein solubility (85%), compared to FPI (32%) at pH 7. Foaming capacity was higher for FPR, although foam stability was similar for both ingredients. FPR had greater gelling ability compared to FPI. The higher carbohydrate content of FPR may have contributed to this difference. An amino acid (AA) analysis revealed that both ingredients were low in sulfur-containing AAs, with FPR having a slightly higher level than FPI. The potential nutritional benefits of the aqueous process compared to the dry process used in this study were apparent in the higher in vitro protein digestibility (IVPD) and lower trypsin inhibitor activity (TIA) in FPI compared to FPR. Additionally, vicine/convicine were detected in FPR, but not in FPI. Furthermore, much lower levels of fermentable oligo-, di- and monosaccharides, and polyols (FODMAPs) were found in FPI compared to FPR. The life cycle assessment (LCA) revealed a lower environmental impact for FPR, partly due to the extra water and energy required for aqueous processing. However, in a comparison with cow’s milk protein, both FPR and FPI were shown to have considerably lower environmental impacts.