CeLINC, a fluorescence-based protein-protein interaction assay in C. elegans

Interactions among proteins are fundamental for life and determining whether two particular proteins physically interact can be essential for fully understanding a protein’s function. We present C. elegans light-induced co-clustering (CeLINC), an optical binary protein-protein interaction assay to determine whether two proteins interact in vivo. Based on CRY2/CIB1 light-dependent oligomerization, CeLINC can rapidly and unambiguously identify protein-protein interactions between pairs of fluorescently tagged proteins. A fluorescently tagged bait protein is captured using a nanobody directed against the fluorescent protein (GFP or mCherry) and brought into artificial clusters within the cell. Co-localization of a fluorescently tagged prey protein in the cluster indicates a protein interaction. We tested the system with an array of positive and negative reference protein pairs. Assay performance was extremely robust with no false positives detected in the negative reference pairs. We then used the system to test for interactions among apical and basolateral polarity regulators. We confirmed interactions seen between PAR-6, PKC-3, and PAR-3, but observed no physical interactions among the basolateral Scribble module proteins LET-413, DLG-1, and LGL-1. We have generated a plasmid toolkit that allows use of custom promoters or CRY2 variants to promote flexibility of the system. The CeLINC assay is a powerful and rapid technique that can be widely applied in C. elegans due to the universal plasmids that can be used with existing fluorescently tagged strains without need for additional cloning or genetic modification of the genome.

Creation of a functional product based on grain mixture for hardtacks for people with intense loads

As a rule, grain products contain deficient protein in their composition. In order to achieve the reference protein in plant foods, a blending method can be used. The development of the formulation and technology of high-protein hardtacks based on wheat wholemeal flour using high-protein biomass will become indispensable in the field nutrition of climbers, militaries and athletes during the competition period.

Proteogenomics: an analysis of how and when it enhances proteomics

ABSTRACTBackgroundProteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases (i.e., built from reference protein databases only retaining proteins whose transcripts are detected in the sample-matched transcriptome), which were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach.ResultsFirst, we established that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative FDRs are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible.ConclusionIn summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce ambiguity of protein identifications.

Expression of the human molecular chaperone domain Bri2 BRICHOS on a gram per liter scale with an E. coli fed-batch culture

Background The human Bri2 BRICHOS domain inhibits amyloid formation and toxicity and could be used as a therapeutic agent against amyloid diseases. For translation into clinical use, large quantities of correctly folded recombinant human (rh) Bri2 BRICHOS are required. To increase the expression and solubility levels of rh Bri2 BRICHOS it was fused to NT*, a solubility tag derived from the N-terminal domain of a spider silk protein, which significantly increases expression levels and solubility of target proteins. To increase the expression levels even further and reach the g/L range, which is a prerequisite for an economical production on an industrial scale, we developed a fed-batch expression protocol for Escherichia coli. Results A fed-batch production method for NT*-Bri2 BRICHOS was set up and systematically optimized. This gradual improvement resulted in expression levels of up to 18.8 g/L. Following expression, NT*-Bri2 BRICHOS was purified by chromatographic methods to a final yield of up to 6.5 g/L. After removal of the NT*-tag and separation into different oligomeric species, activity assays verified that different assembly states of the fed-batch produced rh Bri2 BRICHOS have the same ability to inhibit fibrillar and non-fibrillar protein aggregation as the reference protein isolated from shake flask cultures. Conclusions The protocol developed in this work allows the production of large quantities of rh Bri2 BRICHOS using the solubility enhancing NT*-tag as a fusion partner, which is required to effectively conduct pre-clinical research.

CeLINC, a fluorescence-based protein-protein interaction assay in C. elegans

Interactions among proteins are fundamental for life and determining whether two particular proteins physically interact can be essential for fully understanding a protein's function. We present C. elegans light-induced co-clustering (CeLINC), an optical binary protein-protein interaction assay to determine whether two proteins interact in vivo. Based on CRY2/CIB1 light-dependent oligomerization, CeLINC can rapidly and unambiguously identify protein-protein interactions between pairs of fluorescently tagged proteins. A fluorescently tagged bait protein is captured using a nanobody directed against the fluorescent protein (GFP or mCherry) and brought into artificial clusters within the cell. Co-localization of a fluorescently tagged prey protein in the cluster indicates a protein interaction. We tested the system with an array of positive and negative reference protein pairs. Assay performance was extremely robust with no false positives detected in the negative reference pairs. We then used the system to test for interactions among apical and basolateral polarity regulators. We confirmed interactions seen between PAR-6, PKC-3, and PAR-3, but observed no physical interactions among the basolateral Scribble module proteins LET-413, DLG-1, and LGL-1. We have generated a plasmid toolkit that allows use of custom promoters or CRY2 variants to promote flexibility of the system. The CeLINC assay is a powerful and rapid technique that can be widely applied in C. elegans due to the universal plasmids that can be used with existing fluorescently tagged strains without need for additional cloning or genetic modification of the genome.

Comparison of Dual Isotope and Ileal Sampling Methods for the Determination of the Amino Acid Digestibility of Pea Protein and Casein in Adult Humans

Objectives The measurement of amino acid (AA) digestibility of protein through direct ileal sampling is highly invasive and inappropriate for vulnerable populations, such as children or elderly. The new dual tracer method relies on comparing meal and plasma isotopic ratios of 1/a test protein 2/a reference protein (or AA mix) of known digestibility, each one being labelled with a different isotope. The aim of this study was to compare this new indirect dual tracer method to standard ileal method, for the determination of AA digestibility of pea protein and milk casein. Methods Fifteen healthy adult volunteers completed the study and were equipped with a naso-ileal tube. They were given 9 portions of mashed potatoes containing either pea protein or casein isolates that were intrinsically labelled with 15N and 2H. A 13C algal free AA mix was added in the meals as the reference for dual tracer method. Plasma samples were collected regularly from before the first ingestion to 8-h later, while ileal digesta were collected continuously. For ileal sampling method, the AA digestibility (RIDAA) was determined using the recovery a non-absorbable marker (PEG-4000) perfused in the ileum, and the measurement of 15N enrichment of the digesta. For the dual tracer method, the amount of AA absorbed (AbAA) was calculated by the ratio of 2H/13C enrichments in plasma and in meals. The isotopic enrichments were evaluated in digesta, plasma samples and meals by GC-C-IRMS. The AA content was measured in digesta and meals by U-HPLC. Results Mean AbAA and RIDAA of pea protein were 102.2 ± 3.1% and 94.3 ± 1.5%, respectively. Mean AbAA and RIDAA of casein were 91.9 ± 2.0% and 97.1 ± 0.8%, respectively. The dual tracer method overestimated by 10% and 5% the AA digestibility of pea protein and casein, respectively, and the variability was high. The mean ileal AA digestibility of the 13C free AA mix was high (98.1 ± 1.1%), which validated our choice to use it as the reference ‘protein’ in the dual tracer method. Conclusions Several AA digestibilities obtained with dual tracer method were in the same range as the digestibilities from ileal sampling method. The variability was high and the effect of the protein source was inconsistent. After further research and validation, the dual tracer method could lead to notable advances in the determination of protein quality in humans. Funding Sources Roquette.

Optimization of food compositions according to the ideal protein profile

The paper emphasizes the importance of not only the quantitative but also qualitative composition of protein in nutrition. The authors propose protein classification into three main groups according to the concept of reference (ideal) protein. A mathematical model is examined to solve the task of rational mixture production upon the given profile of reference protein. Two variants of the criterion for formation of optimal composition are described. One of them presents the classical sum of squares of the residual for essential amino acid scores and 1. The second also presents the sum of squares of the residual for essential amino acid scores and 1 but with regard to only those amino acids, which scores are less than 1. The minima of these criteria at the set of variants for the content of ingredients are taken as targeted functions. The algorithm and the program of calculation were realized in the program environment Builder C++ 6.0. The macro flowchart of the algorithm is presented and detailed description of each block is given. The program interface before and after the start of the calculation module is shown. The main windows and interpretation of the presented data are described. An example of realization of the proposed mathematical apparatus when calculating a food model composition is given. Plant components (white kidney beans, flax, peanut, grit “Poltavskaya», dry red carrot) were used as an object of the research. Most plant proteins were incomplete. It is possible to regulate the chemical composition including correction of a protein profile by combination of plant raw materials. Analysis of alternative variants demonstrated that minimum essential amino acid score in the first composition was 0.79 (by the first criterion), in the second 1.0 (by the second criterion); the reference protein proportion in the mixture was 10.8 and 13.5, respectively, according to the first and second criterion. The comparative results by other quality indicators for protein in the mixture are also presented: the coefficient of amino acid score difference (CAASD), biological value (BV), coefficient of utility, essential amino acids index (IEAA).

PrecisionProDB: improving the proteomics performance for precision medicine

Summary As the next-generation sequencing technology becomes broadly applied, genomics and transcriptomics are becoming more commonly used in both research and clinical settings. However, proteomics is still an obstacle to be conquered. For most peptide search programs in proteomics, a standard reference protein database is used. Because of the thousands of coding DNA variants in each individual, a standard reference database does not provide perfect match for many proteins/peptides of an individual. A personalized reference database can improve the detection power and accuracy for individual proteomics data. To connect genomics and proteomics, we designed a Python package PrecisionProDB that is specialized for generating a personized protein database for proteomics applications. PrecisionProDB supports multiple popular file formats and reference databases, and can generate a personized database in minutes. To demonstrate the application of PrecisionProDB, we generated human population-specific reference protein databases with PrecisionProDB, which improves the number of identified peptides by 0.34% on average. In addition, by incorporating cell line-specific variants into the protein database, we demonstrated a 0.71% improvement for peptide identification in the Jurkat cell line. With PrecisionProDB and these datasets, researchers and clinicians can improve their peptide search performance by adopting the more representative protein database or adding population and individual-specific proteins to the search database with minimum increase of efforts. Availabilityand implementation PrecisionProDB and pre-calculated protein databases are freely available at https://github.com/ATPs/PrecisionProDB and https://github.com/ATPs/PrecisionProDB_references. Supplementary information Supplementary data are available at Bioinformatics online.

Technology for the production of pates by replacing raw meat with vegetable components

The article is devoted to the development of functional meat food products made by partially replacing beef and poultry meat with lentils and carrots. When developing the pate, a partial replacement of grade 2 beef and chicken fillet with lentils and carrots is provided. A technology for the production of pate with vegetable raw materials based on the use of sprouted lentils obtained by processing it with electroactivated solutions of sodium and ammonium chlorides has been developed. Studies of functional, technological and organoleptic parameters of the developed pates have been conducted, which have shown that the proposed pate recipe allows replacing expensive ingredients – grade 2 beef and chicken fillet with lentils and carrots, to get a product that is not inferior in quality to the standard pate preparation technology. The article describes the detailed amino acid composition of the pate under study and compares it with the reference protein.