Suchergebnisse

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

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)

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.

1 Introduction -- 1.1 The problem -- 1.2 Overall steps in protein biosynthesis -- 2 The molecules involved in protein biosynthesis -- 2.1 Messenger RNA -- 2.2 Ribosomes -- 2.3 Transfer RNA -- 2.4 Initiator tRNA -- 2.5 Amino-acyl-tRNA synthetases -- 2.6 Elongation factors -- 2.7 Peptidyl transferase -- 2.8 Initiation factors -- 2.9 Termination factors -- References -- 3 The mechanism of protein biosynthesis and its control -- 3.1 Introduction -- 3.2 mRNA metabolism -- 3.3 Initiation complex formation -- 3.4 Elongation -- 3.5 Termination of protein biosynthesis and post-translational modification -- 3.6 RNA phage protein synthesis -- References.

Protein-based therapeutics are highly successful in clinic and currently enjoy unprecedented recognition of their potential. More than 100 genuine and similar number of modified therapeutic proteins are approved for clinical use in the European Union and the USA with 2010 sales of US$108 bln; monoclonal antibodies (mAbs) accounted for almost half (48%) of the sales. Based on their pharmacological activity, they can be divided into five groups: (a) replacing a protein that is deficient or abnormal; (b) augmenting an existing pathway; (c) providing a novel function or activity; (d) interfering with a molecule or organism; and (e) delivering other compounds or proteins, such as a radionuclide, cytotoxic drug, or effector proteins. Therapeutic proteins can also be grouped based on their molecular types that include antibody-based drugs, Fc fusion proteins, anticoagulants, blood factors, bone morphogenetic proteins, engineered protein scaffolds, enzymes, growth factors, hormones, interferons, interleukins, and thrombolytics. They can also be classified based on their molecular mechanism of activity as (a) binding non-covalently to target, e.g., mAbs; (b) affecting covalent bonds, e.g., enzymes; and (c) exerting activity without specific interactions, e.g., serum albumin. Most protein therapeutics currently on the market are recombinant and hundreds of them are in clinical trials for therapy of cancers, immune disorders, infections, and other diseases. New engineered proteins, including bispecific mAbs and multispecific fusion proteins, mAbs conjugated with small molecule drugs, and proteins with optimized pharmacokinetics, are currently under development. However, in the last several decades, there are no conceptually new methodological developments comparable, e.g., to genetic engineering leading to the development of recombinant therapeutic proteins. It appears that a paradigm change in methodologies and understanding of mechanisms is needed to overcome major challenges, including resistance to therapy, access to ...