Protein content of any source is classically determined through the analysis of its nitrogen content done for more 100 years by the Kjeldahl method, and the obtained result is multiplied by a number named nitrogen conversion factor (NCF). The value of NCF is related to the amino acid composition of the protein source and to the eventual presence of side groups covalently bound to some amino acids of the protein chain. Consequently, the value of NCF cannot be identical for all sources of food proteins. The aim of this paper is to review the available knowledge on the two allowed protein sources for infant food formulas, milk and soybean, in order to bring the right scientific basis which should be used for the revision of both European legislation and Codex Standard for Infant Formulas.
Abstract The cytoplasm of bacterial cells is densely packed with highly polydisperse macromolecules that exhibit size-dependent glassy dynamics. Recent research has revealed that metabolic activities in living cells can counteract the glassy nature of these macromolecules, allowing the cell to maintain critical fluidity for its growth and function. While it has been proposed that the crowded cytoplasm is responsible for this glassy behavior, a detailed analysis of the size-dependent nature of the glassy dynamics and an explanation for how cellular activity induces its fluidization remains elusive. Here, we use a combination of computational models and targeted experiments to show that entropic segregation of the protein synthesis machinery from the chromosomal DNA causes size-dependent spatial organization of molecules within the cell, and the resultant crowding leads to size-dependent glassy dynamics. Furthermore, Brownian dynamics simulations of this in silico system supports a new hypothesis: protein synthesis in living cells contributes to the metabolism-dependent fluidization of the cytoplasm. The main protein synthesis machinery, ribosomes, frequently shift between fast and slow diffusive states. These states correspond to the independent movement of ribosomal subunits and the actively translating ribosome chains called polysomes, respectively. Our simulations demonstrate that the frequent transitions of the numerous ribosomes, which constitute a significant portion of the cell proteome, greatly enhance the mobility of other macromolecules within the bacterial cytoplasm. Considering that ribosomal protein synthesis is the largest consumer of ATP in growing bacterial cells, the translation process can serve as the primary mechanism for fluidizing the cytoplasm in metabolically active cells.
The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc–protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc–protein complexes are available, numerous others have not yet been determined. Protein–protein docking could be used to investigate Fc–protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc–protein complexes. Based on the available set of X-ray structures of Fc–protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc–protein complexes, as well as in the design of peptides and proteins that target Fc. ; This work was supported by grant number BIO2013‐48213‐R from Spanish Government. M.A. is a recipient of an NHMRC Early Career Fellowship (GNT1054245). We acknowledge the computational resources provided by the Australian Government through the Victorian Life Sciences Computational Initiative under the National Computational Merit Allocation Scheme (project dq3). The authors gratefully acknowledge the contribution toward this study fromthe VictorianOperational Infrastructure Support Program received by the Burnet Institute. ; Peer Reviewed ; Postprint (author's final draft)
The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc–protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc–protein complexes are available, numerous others have not yet been determined. Protein–protein docking could be used to investigate Fc–protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc–protein complexes. Based on the available set of X-ray structures of Fc–protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc–protein complexes, as well as in the design of peptides and proteins that target Fc. ; This work was supported by grant number BIO2013‐48213‐R from Spanish Government. M.A. is a recipient of an NHMRC Early Career Fellowship (GNT1054245). We acknowledge the computational resources provided by the Australian Government through the Victorian Life Sciences Computational Initiative under the National Computational Merit Allocation Scheme (project dq3). The authors gratefully acknowledge the contribution toward this study fromthe VictorianOperational Infrastructure Support Program received by the Burnet Institute. ; Peer Reviewed ; Postprint (author's final draft)
Protein content of any source is classically determined through the analysis of its nitrogen content done for more 100 years by the Kjeldahl method, and the obtained result is multiplied by a number named nitrogen conversion factor (NCF). The value of NCF is related to the amino acid composition of the protein source and to the eventual presence of side groups covalently bound to some amino acids of the protein chain. Consequently, the value of NCF cannot be identical for all sources of food proteins. The aim of this paper is to review the available knowledge on the two allowed protein sources for infant food formulas, milk and soybean, in order to bring the right scientific basis which should be used for the revision of both European legislation and Codex Standard for Infant Formulas.
Protein content of any source is classically determined through the analysis of its nitrogen content done for more 100 years by the Kjeldahl method, and the obtained result is multiplied by a number named nitrogen conversion factor (NCF). The value of NCF is related to the amino acid composition of the protein source and to the eventual presence of side groups covalently bound to some amino acids of the protein chain. Consequently, the value of NCF cannot be identical for all sources of food proteins. The aim of this paper is to review the available knowledge on the two allowed protein sources for infant food formulas, milk and soybean, in order to bring the right scientific basis which should be used for the revision of both European legislation and Codex Standard for Infant Formulas.
Protein content of any source is classically determined through the analysis of its nitrogen content done for more 100 years by the Kjeldahl method, and the obtained result is multiplied by a number named nitrogen conversion factor (NCF). The value of NCF is related to the amino acid composition of the protein source and to the eventual presence of side groups covalently bound to some amino acids of the protein chain. Consequently, the value of NCF cannot be identical for all sources of food proteins. The aim of this paper is to review the available knowledge on the two allowed protein sources for infant food formulas, milk and soybean, in order to bring the right scientific basis which should be used for the revision of both European legislation and Codex Standard for Infant Formulas.
Intro -- Contents -- Preface -- Chapter 1 -- Soybean in India: At a Glance -- Abstract -- Abbreviations -- 1. Introduction -- 2. Methodology for Selecting Model through Arima -- 2.1. ARIMA Model -- 2.2. The Box-Jenkins Modeling Procedure -- 3. Resullts and Discussion -- 4. History of Development for Soybean Industry -- 5. Reason for Low Productivity -- Conclusion -- Recommendations -- References -- Chapter 2 -- Value Addition in Soya Bean: Production of Soya Sauce -- Abstract -- 1. Introduction -- 2. Soy Sowing and Harvesting of Soybeans -- 3. Important Varieties of Soybeans -- 3.1. Quality Characteristics of Soybeans -- 4. Production of Soy Sauce -- 4.1. Raw Materials -- 4.2. Types of Microorganisms and Fermentation Stages of Soy Sauce -- 4.3. Composition of Fermentation Media for Production of Soy Sauce -- 4.3.1. Growth Media for A. oryzae -- 4.3.2. Growth Media for Lactobacillus Delbrueckii -- 4.3.3. Growth Media for S. rouxii (salt tolerant yeast) -- 4.4. Fermentation and Application of Soy Sauce -- 5. Value Addition in Soy Proteins -- 5.1. Nutritional Compositions of Soy Sauces -- 5.2. Benefits of Soy Sauce Consumption -- Conclusion and Future Work -- References -- Chapter 3 -- Biological Effects of Soy Protein and Isoflavones: A Review -- Abstract -- 1. Introduction -- 1.1. Soy Intake across Different Countries -- 1.2. Nutritional Value of Soybeans -- 1.3. Absorption of Soy Protein -- 1.4. Metabolism of Soy Protein -- 2. Effects of Soy Protein on Some Health Parameters -- 2.1. Effect of Soy Protein and Isoflavones on Lipids -- 2.2. Effect of Soy Food on Breast and Prostate Cancer -- 2.3. Effect of Soy Food on Bones and Menopausal Symptoms -- Conclusion and Current Status -- References -- Chapter 4 -- Modified and Unmodified Soy Protein: A Versatile Protein Supplement -- Abstract -- 1. Introduction -- 2. Habitat and History
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Protein phosphorylation is a key mechanism in cellular signaling. This volume presents a state-of-the-art survey of one of the most rapidly developing fields of biochemical research. Written by leading experts, it presents the latest results for some of the most important cellular pathways. Color plates illustrate structural or functional relationships, numerous references provide links to the original literature.
Structural characterization of protein–protein interactions can provide essential details to understand biological functions at the molecular level and to facilitate their manipulation for biotechnological and biomedical purposes. Unfortunately, the 3D structure is available for only a small fraction of all possible protein–protein interactions, due to the technical limitations of high-resolution structural determination methods. In this context, low-resolution structural techniques, such as small-angle X-ray scattering (SAXS), can be combined with computational docking to provide structural models of protein–protein interactions at large scale. In this chapter, we describe the pyDockSAXS web server (https://life.bsc.es/pid/pydocksaxs), which uses pyDock docking and scoring to provide structural models that optimally satisfy the input SAXS data. This server, which is freely available to the scientific community, provides an automatic pipeline to model the structure of a protein–protein complex from SAXS data ; This work was supported by the Spanish Ministry of Science (grant BIO2016-79930-R), the European Union H2020 programme (grant MuG 676566), and the Labex EpiGenMed, an "Investissements d'avenir" program (ANR-10-LABX-12-01). The CBS is a member of France-BioImaging (FBI) and the French Infrastructure for Integrated Structural Biology (FRISBI), two national infrastructures supported by the French National Research Agency (ANR-10-INSB-04-01 and ANR-10-INSB-05, respectively). ; Peer Reviewed ; Postprint (author's final draft)
