Major Compatible Solutes and Structural Adaptation of Proteins in Extremophiles

Extremophiles are the most ancient microbes on the Earth and also a center of attraction for the scientific community for research because of their ability to adapt to extreme habitats. Compatible solutes are among those factors which enable these microorganisms to thrive in such extreme habitats. Under osmotic stress, the majority of extremophiles accumulate specific organic solutes such as amino acids, sugars, polyols, and their derivatives. In addition, proteins in extremophiles are found to be evolved by changing their amino acid composition to alter the hydrophobicity of its core and surface charge to maintain activity. This chapter encompasses a comprehensive study about the role of various compatible solutes in the endurance of microorganisms under extremophilic conditions, synthesis of compatible solutes, nature of extremophilic proteins, and their applications. Furthermore, an attempt has been made to cover various strategies adopted by the scientific community while pursuing research on compatible solutes.

AMINO ACID COMPOSITION AND ANTI-TYROSINASE ACTIVITY OF METABOLITES FROM EDIBLE Pleurotus SPECIES FOR THEIR NUTRITIONAL AND THERAPEUTIC POTENTIAL

Mushrooms are being extensively researched due to their nutritional value and medicinal importance. The genus Pleurotus is the second most cultivated mushroom and is known for its high nutritional value, therapeutic properties, taste, flavor, as well as their application in biotechnology and environmental study. Also, tyrosinase is prevalent in most living organisms. The enzyme catalyzes the oxidation of monophenols to ortho-quinones in a two-step reaction process. This study was aimed to assess the amino acid composition and anti-tyrosinase activity of metabolites obtained from edible Pleurotus species. Assessment of the nutritional content and inhibitory studies of mushroom tyrosinase produced from four Pleurotus strains was carried out using high-performance liquid chromatography (RP-HPLC). The results of the study showed that seventeen different amino acids were identified in the crude and partially purified protein metabolites. Also, the crude extract metabolite had the highest quantity of amino acids than the partially purified. The highest and lowest amino acids value Glutamic acid (1343.26 µmol/mL) and valine (0.34 µmol/mL). The anti-tyrosinase inhibition studies of the four Pleurotus strains showed varying results from significantly inhibitory effects to slightly inhibitory effects on mushroom tyrosinase. The highest inhibition was 14.86% (Pleu-W), while the lowest inhibition was 51.42% (Plof-30) respectively. The high point of this study is that the Pleurotus species contains a significant number of amino acids and also, they possess good anti-tyrosinase activity. Therefore, these are a good source of nutritional and therapeutic metabolites and these can be explored further for their nutritional and medicinal importance to man.

Specialized sports nutrition products using protein hydrolysis compositions of collagen-containing fish raw materials

The development of specialized sports nutrition with components of collagen-containing fish raw materials is due to the shortage of domestic products of this group, the problem of fish waste processing, scientific data on bioactive peptides of marine origin. Low-molecular active peptides and protein-mineral compositions in the form of powder and liquid dietary supplements were obtained from the scales of sardine and sardinella by methods of deep hydrolysis. During the hydrolysis of raw materials, fermentolysis was carried out with the Alcalase 2.5L enzyme, thermohydrolysis - at temperatures of 130-140 °C. The chemical composition of the scales of two fish species, the products of its hydrolysis, and the amino acid composition of its proteins have been studied. The amount of low-molecular-weight peptides in hydrolysates of sardine scales was investigated by various methods of hydrolysis. Rational methods of hydrolysis of scales - enzymatic and enzymatic-thermal - have been substantiated. They make it possible to obtain protein supplements with protein content of 83.9-85.2 % with a proportion of active peptides with a molecular weight of less than 10 kDa 91.7-98.1 %. A technology for obtaining protein supplements from fish scales has been developed, and their amino acid composition has been studied. The presence of irreplaceable and ergogenic amino acids, accompanying nitrogenous compounds, valuable in sports nutrition, was shown in the peptide supplement. An increased content of calcium, phosphorus and magnesium in the protein-mineral dietary supplement being important for the musculoskeletal tissues of an athlete has been established. On the basis of protein supplements in dry and liquid forms with the addition of beekeeping products, the technology of chewing marmalade for sports nutrition has been developed. Bioproduct "Apikolltonus" belongs to the class of gainers with a protein content of 20.5 %, a carbohydrate of 41.6 % with an amino acid value of BCAA (isoleucine : leucine : valine) 2 : 1 : 1.5. A multicomponent bar for sports nutrition has been developed using protein hydrolysates of scales, flaxseed and apple cakes, and pine nuts. The bar is functional in terms of the content of calcium and phosphorus, dietary fiber, flavonoids, and vitamin E. Recommendations for the use of new sports nutrition products are substantiated.

Characterization and Prediction of Dengue Virus Targeting Peptides Based on Combined Amino Acid Composition Descriptors Using Random Forest Algorithm.

Dengue virus peptides are emerging as potential therapeutics for dengue infection. Due to the important role of dengue peptides in curbing dengue infection, their identification has proven crucial in terms of infection biology. To calculate differences between amino acids and physiochemical attributes, statistical tests and F-scores were used in this work. The random forest algorithm was used to predict dengue peptides using grouped amino acid composition, transition and distribution. Here, we have used three descriptors; Amino acid content, Grouped Amino acid composition and Composition, transition and distribution features (CTDC). We have created models and compared with combined model. Using the grouped amino acid composition as input parameters for the random forest algorithm, Our classifier's overall accuracy increased to 88.80%, which was the greatest overall accuracy found in this investigation. Our classifier produced superior predicting outcomes when compared to previously developed algorithms. In conclusion, we looked at the differences in amino acids and physiochemical properties between dengue viral peptides, using the grouped amino acid composition to build a classifier that predicts these dengue virus inhibitory peptides.

Structural basis for the peptidoglycan editing activity of YfiH

Bacterial cells are encased in peptidoglycan (PG), a polymer of disaccharide N-acetyl-glucosamine (GlcNAc) and N-acetyl-muramic acid (MurNAc) cross-linked by peptide stems. PG is synthesized in the cytoplasm as UDP-MurNAc-peptide precursors, of which the amino-acid composition of the peptide is unique, with L-Ala added at the first position in most bacteria but L-Ser or Gly in some bacteria. YfiH is a PG-editing factor whose absence causes misincorporation of L-Ser instead of L-Ala into peptide stems; but its mechanistic function is unknown. Here we report the crystal structures of substrate-bound and product-bound YfiH, showing that YfiH is a cytoplasmic amidase that controls the incorporation of the correct amino acid to the nucleotide precursors by preferentially cleaving the nucleotide precursor byproduct UDP-MurNAc-L-Ser. This work reveals an editing mechanism in the cytoplasmic steps of peptidoglycan biosynthesis.

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.