Distribution Of Amino Acids Research Articles

Genetic diversity of Rhombomys opimus and Meriones meridianus with potential divergence of plague resistance in the Junggar Basin plague focus based on RT1-Db1*exon1

In the Junggar Basin plague focus, Rhombomys opimus and Meriones meridianus live together, and their parasitic fleas have a similar community structure. However, R. opimus has significantly higher positive rates of Yersinia pestis and anti-F1 antibody compared with M. meridianus. In this study, Y. pestis- and antiF1 antibody-negative R. opimus and M. penicilliger were collected in Qitai county, Fukang city and Mulei county of the eastern part of the Junggar Basin. The genomic DNA was extracted from their spleen tissues, and RT1-Db1*exon1 was amplified through PCR procedure and then sequenced. Sequence analysis was performed and molecular diversity parameters were calculated and compared. The results showed that there were significant differences in nucleotide composition, amino acid composition, number and distribution of single nucleotide polymorphism (SNP) sites and number of haplotypes between R. opimus and M. penicilliger. The nucleotide diversity (π) for R. opimus was 0.00420 ± 0.00139, the haplotype diversity (h) was 0.833 ± 0.086, and the average number of nucleotide differences (K) was 2.02564. The π for M. penicilliger was 0.06569 ± 0.02524, and the h was 1.000 ± 0.045, and the K was 10.4444. The fixation index (FST) value between R. opimus and M. penicilliger was 0.9207. Furthermore, the FST value within R. opimus (0.0275) was significantly lower than that within M. penicilliger (0.2106), indicating a greater genetic variation of M. penicilliger compared with R. opimus. In conclusion, the genetic diversity analysis based on RT1-Db1*exon1 showed that M. penicilliger had higher gene polymorphism and greater genetic differentiation compared with R. opimus in the Junggar Basin plague focus, which might be associated with the low infection rate of Y. pestis.

Algebraic and toroidal representation of the genetic code

The genetic code is a set of regulatory principles that control the translation of information encoded in messenger RNA (mRNA) into a sequence of amino acids. This study proposes a model starting from the relationships between its physicochemical properties of nucleobase in the codons. We employed a binary metric and the Kronecker product to represent the physicochemical properties of nucleobases in a vector space. This state space has a hierarchical order, with self-similarity and symmetry properties in the hydrogen bonds of triplets. The state space can be mapped under linear transformations to a toroidal geometry with allometric properties. Furthermore, this geometric representation highlights a charge symmetry that exists in amino acids and in the distribution of essential and non-essential amino acids. These results describe a state space between codon-anticodon interactions that can be interpreted as Bell states. This suggests that there is a quantum phenomenon involved in the mechanisms of information storage in DNA. In addition, toroidal geometry can be used to represent the sequences of codons of the mRNA that encode the sequence of amino acids of the proteins to find similarities or homologies between evolutionary related species.

Reversing the relative time courses of the peptide bond reaction with oligopeptides of different lengths and charged amino acid distributions in the ribosome exit tunnel

The kinetics of the protein elongation cycle by the ribosome depends on intertwined factors. One of these factors is the electrostatic interaction of the nascent protein with the ribosome exit tunnel. In this computational biology theoretical study, we focus on the rate of the peptide bond formation and its dependence on the ribosome exit tunnel electrostatic potential profile. We quantitatively predict how oligopeptides of variable lengths can affect the peptide bond formation rate. We applied the Michaelis-Menten model as previously extended to incorporate the mechano-biochemical effects of forces on the rate of reaction at the catalytic site of the ribosome. For a given pair of carboxy-terminal amino acid substrate at the P- and an aminoacyl-tRNA at the A-sites, the relative time courses of the peptide bond formation reaction can be reversed depending on the oligopeptide sequence embedded in the tunnel and their variable lengths from the P-site. The reversal is predicted to occur from a shift in positions of charged amino acids upstream in the oligopeptidyl-tRNA at the P-site. The position shift must be adjusted by clever design of the oligopeptide probes using the electrostatic potential profile along the exit tunnel axial path. These predicted quantitative results bring strong evidence of the importance and relative contribution of the electrostatic interaction of the ribosome exit tunnel with the nascent peptide chain during elongation.

One-step high efficiency separation of prolyl endopeptidase from Aspergillus niger and its application

Prolyl endopeptidase from Aspergillus niger (An-PEP) is an enzyme that recognizes C-terminal peptide bonds of amino acid chains and cleaves them by hydrolysis. An aqueous two-phase system (ATPS) was used to separate An-PEP from fermentation broth. Through single factor experiments, the ATPS containing 16 % (w/w) PEG2000 and 15 % (w/w) (NH4)2SO4 at pH 6.0 obtained the recovery of 79.74 ± 0.16 % and the purification coefficient of 7.64 ± 0.08. It was then used to produce soy protein isolate peptide (SPIP) by hydrolysis of soy protein isolate (SPI), and SPIP-Ferrous chelate (SPIP-Fe) was prepared with SPIP and Fe2+. The chelation conditions were optimized by RSM, as the chelation time was 30 min, chelation temperature was 25 °C, SPIP mass to VC mass was two to one and pH was 6.0. The obtained chelation rate was 82.56 ± 2.30 %. The change in the structures and functional features of SPIP before and after chelation were investigated. The FTIR and UV–Vis results indicated that the chelation of Fe2+ and SPIP depended mainly on the formation of amide bonds. The fluorescence, SEM and amino acid composition analysis results indicated that Fe2+ could induce and stabilize the surface conformation and change the amino acid distribution on the surfaces of SPIP. The chelation of SPIP and Fe2+ resulted in the enhancement of radical scavenging activities and ACE inhibitory activities. This work provided a new perspective for the further development of peptide-Fe chelates for iron supplement.

CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder

Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.

Comparative Analysis of Deep Generative Model for Industrial Enzyme Design

Abstract: Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the design of novel enzymes tailored to specific conditions and requirements. It is beneficial to de novo design industrial enzymes using computational methods. Currently, only one tool explicitly designed for the enzyme-only generation performs unsatisfactorily. We have selected several general protein sequence design tools and systematically evaluated their effectiveness when applied to specific industrial enzymes. We investigated the literature related to protein generation. We summarized the computational methods used for sequence generation into three categories: structure-conditional sequence generation, sequence generation without structural constraints, and co-generation of sequence and structure. To effectively evaluate the ability of six computational tools to generate enzyme sequences, we first constructed a luciferase dataset named Luc_64. Then we assessed the quality of enzyme sequences generated by these methods on this dataset, including amino acid distribution, EC number validation, etc. We also assessed sequences generated by structure-based methods on existing public datasets using sequence recovery rates and root-mean-square deviation (RMSD) from a sequence and structure perspective. In the functionality dataset, Luc_64, ABACUS-R, and ProteinMPNN stood out for producing sequences with amino acid distributions and functionalities closely matching those of naturally occurring luciferase enzymes, suggesting their effectiveness in preserving essential enzymatic characteristics. Across both benchmark datasets, ABACUS-R and ProteinMPNN, have also exhibited the highest sequence recovery rates, indicating their superior ability to generate sequences closely resembling the original enzyme structures. Our study provides a crucial reference for researchers selecting appropriate enzyme sequence design tools, highlighting the strengths and limitations of each tool in generating accurate and functional enzyme sequences. ProteinMPNN and ABACUS-R emerged as the most effective tools in our evaluation, offering high accuracy in sequence recovery and RMSD and maintaining the functional integrity of enzymes through accurate amino acid distribution. Meanwhile, the performance of protein general tools for migration to specific industrial enzymes was fairly evaluated on our specific industrial enzyme benchmark.

Development of Transmission Ambient Pressure Laser Desorption Ionization/Postphotoionization Mass Spectrometry Imaging.

Laser-based high-resolution mass spectrometry imaging at ambient conditions has promising applications in life science. However, the ion yield during laser desorption/ablation is poor. Here, transmission atmospheric pressure laser desorption ionization combined with a compact postphotoionization (t-AP-LDI/PI) assembly with a krypton discharge lamp was developed for the untargeted imaging of various biomolecules. The spatial distributions of numerous lipid classes, fatty acids, neurotransmitters, and amino acids in the subregions of mouse cerebellum tissue were obtained. Compared with single laser ablation, the sensitivities for most analytes were increased by 1 to 3 orders of magnitude by dopant-assisted postphotoionization. After careful optimization, a spatial resolution of 4 μm could be achieved for the metabolites in mouse hippocampus tissue. Finally, the melanoma tissue slices were analyzed using t-AP-LDI/PI MSI, which revealed the metabolic heterogeneity of the melanoma microenvironment and exhibited the phenomenon of abnormal proliferation and invasion trends in tumor cells.

Agro-nutritionnel Characterisation of Quinoa (Chenopodium quinoa, Willd.)

Quinoa (Chenopodium quinoa Willd.) originated in the Andean region of South America; is a dicotyledonous species for seeds and, therefore, is not known as a cereal grain and is a pseudograin, which is introduced nowadays as a new crop in the world. Quinoa is an annual herbaceous plant belongs to Amaranthaceae family, but formerly placed in Chenopodiaceae family. It’s well adapted to a wide range of climatic conditions and has significant potential for increased production as a new crop in the Mediterranean region and in other parts of the world, including northern Europe, North America, Asia, and Africa. Quinoa is associated with exceptional grain nutritional quality and is highly valued for its ability to tolerate abiotic stresses. The protein content of quinoa grains is higher than other cereals while it has better distribution of essential amino acids. It can be used as an alternative to milk proteins. Additionally, quinoa contains a high amount of essential fatty acids, minerals, vitamins, dietary fibers, and carbohydrates with beneficial hypoglycemic effects while being gluten-free. e. In addition, it is found to contain a number of bioactive chemical compounds whose various therapeutic properties are actively studied in the scientific field. Quinoa is sought in several industries whether it is the food industry, pharmacology, cosmetics, etc., as a plant (use of leaves, seeds, or stem) or these extracts (saponins, vegetable oil and essential).

The free amino acid profiling of milk- and cereal-based Kyrgyz ethnic fermented beverages and their contribution to taste formation

The present study aimed to determine the content of 18 free amino acids (FAA), including 10 essential amino acids, in traditional fermented beverages and their umami, sweet and bitter taste potential. The FAAs of traditional dairy and cereal-based fermented beverages were measured using high-performance liquid chromatography (HPLC) with a diode array detector and derivatization with diethyl ethoxymethylenemalonate (DEEMM). FAA content and taste attributes with a focus on umami, sweet and bitter were presented for koumiss, ayran, carbonated ayran, boza, boza with sea buckthorn, and maksym. The ratio of free essential amino acids (EFAA) to nonessential (NEFAA) was in the range of 0.41-1.12 except for ayran (0.28), indicating that each type of beverage has a rich source of their respective essential FAA profile. FAA represented 24.68% of the total protein, with the highest value for koumiss. The respective taste property contribution of bitter FAAs was high in koumiss (51%) and carbonated ayran (56%), whereas sweet-tasting FAAs were dominant in jarma (52%). The distribution of taste-active amino acids in the formation of the corresponding tastes was distinct.

Amino acid pattern reveals multi-functionality of ORF3 protein from HEV.

The smallest open reading frame (ORF) encoded protein ORF3 of hepatitis E virus (HEV), recently, has been demonstrated to perform multiple functions besides accessory roles. ORF3 could act as a target for vaccine against HEV infections. The IDR (intrinsically disordered region); IDP (ID protein)/IDPR (ID protein region), plays critical role in various regulatory functions of viruses. The dark proteome of HEV-ORF3 protein including its structure and function was systematically examined by computer predictors to explicate its role in viral pathogenesis and drug resistance beyond its functions as accessory viral protein. Amino acid distribution showed ORF3 enrichment with disorder-promoting residues (Ala, Pro, Ser, Gly) while deficiency in order-promoting residues (Asn, Ile, Phe, Tyr and Trp). Initial investigation revealed ORF3 as IDP (entirely disordered protein) or IDPR (proteins consisting of IDRs with structured globular domains). Structural examination revealed preponderance of disordered regions interpreting ORF3 as moderately/highly disordered protein. Further disorder predictors categorized ORF3 as highly disordered protein/IDP. Identified sites and associated-crucial molecular functions revealed ORF3 involvement in diverse biological processes, substantiating them as targets of regulation. As ORF3 functions are yet to completely explored, thus, data on its disorderness could help in elucidating its disorder related functions.

PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants

BackgroundLysine crotonylation (Kcr) is a crucial protein post-translational modification found in histone and non-histone proteins. It plays a pivotal role in regulating diverse biological processes in both animals and plants, including gene transcription and replication, cell metabolism and differentiation, as well as photosynthesis. Despite the significance of Kcr, detection of Kcr sites through biological experiments is often time-consuming, expensive, and only a fraction of crotonylated peptides can be identified. This reality highlights the need for efficient and rapid prediction of Kcr sites through computational methods. Currently, several machine learning models exist for predicting Kcr sites in humans, yet models tailored for plants are rare. Furthermore, no downloadable Kcr site predictors or datasets have been developed specifically for plants. To address this gap, it is imperative to integrate existing Kcr sites detected in plant experiments and establish a dedicated computational model for plants.ResultsMost plant Kcr sites are located on non-histones. In this study, we collected non-histone Kcr sites from five plants, including wheat, tabacum, rice, peanut, and papaya. We then conducted a comprehensive analysis of the amino acid distribution surrounding these sites. To develop a predictive model for plant non-histone Kcr sites, we combined a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and attention mechanism to build a deep learning model called PlantNh-Kcr. On both five-fold cross-validation and independent tests, PlantNh-Kcr outperformed multiple conventional machine learning models and other deep learning models. Furthermore, we conducted an analysis of species-specific effect on the PlantNh-Kcr model and found that a general model trained using data from multiple species outperforms species-specific models.ConclusionPlantNh-Kcr represents a valuable tool for predicting plant non-histone Kcr sites. We expect that this model will aid in addressing key challenges and tasks in the study of plant crotonylation sites.

Sour cherry seed proteins devoted to covalently bonded carbohydrate moieties: efficacy of transaction and carbohydrate type on amino acid distribution and emulsifier behavior

This paper handled the Maillard reaction under controlled conditions to conjugate sour cherry seed protein isolate (SCPI) with gum Arabic (GA), carboxymethyl cellulose (CMC), and pectin (P) polysaccharides. Characteristic investigations for conjugates were conducted by browning intensity, glycation degree, amino acid, FTIR, SEM, and TGA analyses. After glycation, the level of lysine and arginine declined. Conjugation process displayed beneficial efficacy on techno-functional attributes, namely solubility, water holding capacity, oil holding capacity, foaming capacity/stability and emulsion activity/stability of SCPI but maximum values in terms of these attributes were variable according to conjugate type. Stability index of emulsions stabilized by using conjugates including SCPI-GA (0.87 R), SCPI-CMC (0.94 R), and SCPI-P (0.95 R) was superior than that of SCPI alone (0.60 R). Centrifugal precipitation rate was 59.17, 36.04, 32.61, and 30.92% for emulsions prepared in the presence of SCPI, SCPI-GA, SCPI-CMC, and SCPI-P, respectively. Resilience to freeze-thawed, pH-shifting, various salt concentrations, and different temperature applications of emulsions were investigated to evaluate emulsifier behavior of protein and conjugates in food systems. Findings showed that emulsifying properties of the conjugates prepared using P and CMC came to the fore compared to SCPI alone and SCPI-GA.

Amino acid distribution in blood following high-intensity interval exercise: a preliminary study.

This study investigated the effect of high-intensity interval exercise on total and individual amino acid concentrations in red blood cells (RBCs) and plasma. Seven males (31 ± 13yr) provided venous blood samples at rest, immediately and 15min and 30min following an 8-min high-intensity exercise bout. The exercise bout was 16 × 15s cycle efforts at 0.4N/kg of body mass and 90rpm, interspersed with 15s passive recovery. Total and individual amino acid concentrations of RBC and plasma and blood cell parameters were analysed. No significant differences for total amino acid concentrations between RBC and plasma were found. Individual amino acid analyses showed significant interaction effects for alanine and α-aminoadipic acid (P < 0.05), with plasma alanine significantly increased from baseline across the recovery period (P < 0.001). Blood fraction (group) effects showed greater concentrations of glycine, serine, asparagine, aspartic acid, glutamic acid, α-aminoadipic acid and ornithine in RBC, while greater concentrations of alanine, α-aminobutyric acid, valine, leucine, isoleucine, threonine, proline, phenylalanine, glutamine, tryptophan and cystine were found in plasma (P < 0.05). Comparable levels of histidine, lysine and tyrosine were observed between blood fractions. Significant differences in the variation of total amino acids in RBC were reported with higher variance at rest compared to following exercise (P = 0.01). Haemoglobin, pack cell volume and white blood cell count significantly increased immediately following exercise (P < 0.05) but returned to baseline after 15min recovery. These results support the notion of individualised amino acid transportation roles for RBC and plasma during exercise.

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.

Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial role, and numerous successful methods have been developed. Notably, deep learning-based protein sequence design methods have experienced significant advancements in recent years. However, a comprehensive and systematic comparison and evaluation of these methods have been lacking, with indicators provided by different methods often inconsistent or lacking effectiveness. To address this gap, we have designed a diverse set of indicators that cover several important aspects, including sequence recovery, diversity, root-mean-square deviation of protein structure, secondary structure, and the distribution of polar and nonpolar amino acids. In our evaluation, we have employed an improved weighted inferiority-superiority distance method to comprehensively assess the performance of eight widely used deep learning-based protein sequence design methods. Our evaluation not only provides rankings of these methods but also offers optimization suggestions by analyzing the strengths and weaknesses of each method. Furthermore, we have developed a method to select the best temperature parameter and proposed solutions for the common issue of designing sequences with consecutive repetitive amino acids, which is often encountered in protein design methods. These findings can greatly assist users in selecting suitable protein sequence design methods. Overall, our work contributes to the field of protein sequence design by providing a comprehensive evaluation system and optimization suggestions for different methods.

Effects of slaughter weight on carcass characteristics, meat quality, and metabolomics profiling in the longissimus dorsi muscle of Tianfu finishing pigs.

In order to investigate the effect of slaughter weight (SW) on carcass characteristics and meat quality, we measured the carcass characteristics, meat quality, and amino acid metabolomics characteristics of longissimus dorsi (LD) muscle from Tianfu finishing (TF) pigs. Based on SW, 13 pigs were divided into three groups (100-kg group, 125-kg group, and 150-kg group with 3, 5, 5 pigs in each group, respectively). Raising SW to 125 kg or 150 kg increased average backfat thickness (P < 0.01) and intramuscular fat content (P < 0.01), and decreased shear force (P < 0.01). A total of 231 amino acid metabolome from three amino acid classes identified with metabolomics were analyzed, and 93 differentially expressed metabolites (DEMs) were identified (69 up-regulated DEMs and 24 down-regulated DEMs). The DEMs, including urea, 3-iodo-L-tyrosine, N-glycyl-L-leucine, and N, N-dimethylglycine with amino acid metabolism, were significantly induced (P < 0.01). KEGG pathway analysis showed that these DEMs were significantly enriched (P < 0.01) in 135 metabolism pathways, including pathways related to amino acid metabolism, such as arginine and proline metabolism, glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, tryptophan metabolism, and beta-alanine metabolism. Our research findings provided new insights into the impact of SW on amino acid distribution and theoretical support for genetic breeding of meat quality of TF pigs. However, raising SW to 125 kg, or more, decreased the carcass leanness of live TF pigs and had no benefits to pork quality attributes.

Evaluating the capability of soybean peptides as calcium ion carriers: a study through sequence analysis and molecular dynamics simulations.

Calcium homeostasis imbalance in the body can lead to a variety of chronic diseases. Supplement efficiency is essential. Peptide calcium chelate, a fourth-generation calcium supplement, offers easy absorption and minimal side effects. Its effectiveness relies on peptide's calcium binding capacity. However, research on amino acid sequences in peptides with high calcium binding capacity (HCBC) is limited, affecting the efficient identification of such peptides. This study used soybean peptides (SP), separated and purified by gel chromatography, to obtain HCBC peptide (137.45 μg mg-1) and normal peptide (≤95.78 μg mg-1). Mass spectrometry identified the sequences of these peptides, and an analysis of the positional distribution of characteristic amino acids followed. Two HCBC peptides with sequences GGDLVS (271.55 μg mg-1) and YEGVIL (272.54 μg mg-1) were discovered. Molecular dynamics showed that when either aspartic acid is located near the N-terminal's middle, or glutamic acid is near the end, or in cases of continuous Asp or Glu, the binding speed, probability, and strength between the peptide and calcium ions are superior compared to those at other locations. The study's goal was to clarify how the positions of characteristic amino acids in peptides affect calcium binding, aiding in developing peptide calcium chelates as a novel calcium supplement.

Hemp (Cannabis sativa L.) protein: Impact of extraction method and cultivar on structure, function, and nutritional quality

Hemp (Cannabis sativa L.) is increasingly gaining traction as a novel and sustainable source of plant protein. Accordingly, the aim of this study was to investigate the effectiveness of two protein extraction methods, alkaline extraction coupled with isoelectric precipitation (AE-IEP) and salt extraction coupled with ultrafiltration (SE-UF) in producing hemp protein isolates (pH-HPI and salt-HPI) with high purity and yield. Structural characterization as impacted by extraction method and cultivar was performed and related to functional performance and nutritional quality. Both extraction methods, with carefully selected parameters, resulted in HPI with high purity (86.6–88.1% protein) and protein extraction yields (81.6–87.3%). All HPI samples had poor solubility (∼9–20%) at neutral pH compared to commercial soy protein and pea protein isolates (cSPI, cPPI). A relatively high surface hydrophobicity and low surface charge contributed to such poor solubility of HPI. However, HPI demonstrated similar solubility at acidic pH (50–67%) and comparable gel strength (up to 24 N) to cSPI. Comparing experimental amino acid composition to the theoretical amino acid distribution in hemp protein provided insights to the functional performance of the protein isolates. While pH-HPI demonstrated better functionality than salt-HPI, minimal structural, functional, and nutritional differences were noted among the pH-HPI samples extracted from four different cultivars. Overall, results from this work could be used to guide future attempts to further develop successful protein extraction processes, and to provide valuable insights to propel breeding efforts that target enhanced hemp protein characteristics for food applications.

Age-Specific Reference Values for Amino Acid Content in Dried Blood Spots in Children in Western Kazakhstan, Measured by Tandem Mass Spectrometry

Measuring the level of amino acids in the blood is one of the stages in the early diagnosis of inborn errors of metabolism (IEM), implying timely initiation of therapeutic measures. Tandem mass spectrometry (MS/MS) is now replacing traditional IEM screening methods. Dried blood spot amino acid reference values developed for the pediatric population are crucial for interpreting test results and diagnosing aminoacidopathies. The study aims to establish reference values for amino acid (AAs) concentrations in samples of dried blood spots from newborns in Western Kazakhstan using LC-MS/MS (liquid chromatography-tandem mass spectrometry) technology. Methods: The cross-sectional study included 250 healthy newborns of Western Kazakhstan aged 1-3 days, born at term and breastfed, 49.2% male and 50.2% female. To establish the age-specific reference values for AAs, newborns were divided into three groups: (1) 1 day, (2) 2 days, and (3) 3 days. Blood samples on Guthrie cards were collected on days 1–3 of life and quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nonparametric statistical approaches were used to generate the 2.5th–97.5th percentile distributions for newborns. Results: 3 of the 15 DBS amino acid distributions were dependent on gender. There was a statistically significant difference in the mean level of alanine, citrulline, and glutamic acid in males and females. The highest values were determined in the female group. Age-related differences in glutamic acid, leucine, ornithine, tyrosine, and valine concentration levels were observed. No significant correlations were found between the concentrations of 15 amino acids in dried blood spots and the body weight of newborns. Conclusion: The present study established amino acid concentrations that can be utilized as reference standards in Kazakhstan's newborn screening program for inherited metabolic diseases.

Comprehensive amino acid composition analysis of seed storage proteins of cereals and legumes: identification and understanding of intrinsically disordered and allergenic peptides

The seed storage proteins of cereal and legumes are the primary source of amino acids which are required for sustaining the nitrogen and carbon demands during germination and growth. Humans derive most of their dietary proteins from storage proteins in form of a wide variety of foods, for consumption. The amino acid content of most of these proteins is biased and the need for this biasness is not understood. The high abundance of proline, glutamine, and cysteine in cereals makes the gluten fraction viscoelastic. The cereal proteins have less charge and legume proteins have more charge on them. Their non-polar amino acid distribution has large variations. These characteristics are strongly responsible for the partial and complete unfolding of several domains of the storage proteins. Many of the storage proteins share a highly conserved structural feature within the cupin superfamily spread across all kingdoms of life. The intrinsically disordered viscoelastic proteins help in making dough which is vital for the quality of bread. Unfolded regions harbor more immunogenic sequences and cause food-related allergies and intolerance. We have discussed these properties in terms of comparison of cereal and legume storage protein sequences and allergy. Our study supports the findings that large disordered regions contain allergen-representative peptides. Interestingly, a high number of allergen-representative peptides were cleavable by digestive enzymes. Furthermore, unfolded storage proteins mimic microbial immunogens to induce a memory immune response. Results findings can be used to guide the understanding of immunological characteristics of storage proteins and may assist in treatment decisions for food allergy. Communicated by Ramaswamy H. Sarma

Biochemical repercussions of light spectra on nitrogen metabolism in spinach (Spinacia oleracea) under a controlled environment.

Selecting appropriate light spectra of light-emitting diodes (LEDs) and optimal nutrient composition fertilizers has become integral to commercial controlled environment agriculture (CEA) platforms. This study explored the impact of three LED light regimes (BR: Blue17%, Green 4%, Red 63%, Far-Red 13% and infrared 3%, BGR; Blue 20%, Green 23%, Red 47%, Far-Red 8% and infrared 2%; and GR; Blue 25%, Green 41%, Red 32%, and Far-Red 2%) and nitrogen levels (3.6 and 14.3 mM N) on spinach (Spinacea oleracea). Under limited nitrogen (3.6 mM), BGR light increased the fresh shoot (32%) and root (39%) biomass than BR, suggesting additional green light's impact on assimilating photosynthates under suboptimal nitrogen availability. Reduced chlorophyll (a and b) and carotenoid accumulation, electron transport rate (ETR), and higher oxalates under limited nitrogen availability highlighted the adverse effects of red light (BR) on spinach productivity. Increased activities of nitrogen-associated enzymes (GOGAT; Glutamate synthase, GDH; NADH-Glutamate dehydrogenase, NR; Nitrate reductase, and GS; Glutamine synthetase) in spinach plants under BGR light further validated the significance of green light in nitrogen assimilation. Amino acid distributions remained unchanged across the light spectra, although limited nitrogen availability significantly decreased the percent distribution of glutamine and aspartic acid. Overall, this study demonstrated the favorable impacts of additional green light on spinach productivity, as demonstrated under BGR, than GR alone in response to nitrogen perturbation. However, the exact mechanisms underlying these impacts still need to be unveiled. Nevertheless, these outcomes provided new insights into our understanding of light spectra on spinach nitrogen metabolism.