Extraction and Estimation of Protein from Ginger (Zingiber officinale) and its Interaction with Glucose Molecule

Aim: To evaluate the protein composition in the ginger rhizome and its interaction with glucose molecule. Place and Duration of Study: Department of Biochemistry, Sokoto State University, Sokoto, Nigeria, between April 2021 and May 2021. Methodology: Protein was extracted from the rhizome of ginger using 0.1 M phosphate buffer. The protein concentration of the sample was estimated using Biuret method while using xanthoproteic test, the presence of aromatic amino acids was ascertained. The crude protein sample was interacted with glucose using UV interaction study. Results: The protein concentration of the sample (2 g) was found to be 1.702 mg/ml, it was identified that ginger rhizome contains aromatic amino acids. UV interaction study between the crude protein sample and glucose molecule showed an increase in absorbance at the range of 280 nm. Conclusion: The interaction of ginger with glucose shows that it possesses a hypoglycemic effect.

GlyNet: A Multi-Task Neural Network for Predicting Protein-Glycan Interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fun-damental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet's output of continuous values provides more detailed results than classification models. Such continuous outputs are easily converted by a following classifier, and in this form GlyNet outperforms reported classifiers. GlyNet is the first multi-output regression model for protein-glycan interactions and will serve as an important benchmark, facilitating development of quantitative computational glycobiology.

Advanced Biocrystallogenesis

Nowadays, X-ray crystallography is one of the most popular structural biology methods. Successful crystallization depends not only on the quality of the protein sample, precipitant composition, pH or other biophysical and biochemical parameters, but also largely on the use of crystallization technique. Some proteins are difficult to be crystallized using basic crystallization methods; therefore, several advanced methods for macromolecular crystallization have been developed. This chapter briefly reviews the most promising advanced crystallization techniques and strategies as one of the efficient tools for crystallization of macromolecules. Crystallization in capillaries, gels, microfluidic chips, electric and magnetic fields as well as crystallization under microgravity condition and crystallization in living cells are briefly described.

Methods for Obtaining Better Diffractive Protein Crystals: From Sample Evaluation to Space Crystallization

In this paper, we present a summary on how to obtain protein crystals from which better diffraction images can be produced. In particular, we describe, in detail, quality evaluation of the protein sample, the crystallization conditions and methods, flash-cooling protection of the crystal, and crystallization under a microgravity environment. Our approach to protein crystallization relies on a theoretical understanding of the mechanisms of crystal growth. They are useful not only for space experiments, but also for crystallization in the laboratory.

2D-DIGE A Powerful Tool for Proteome Analysis

In the recent past, two dimensional gel electrophoresis has emerged as a powerful molecular biology tool for the comparative expression profiling of complex protein sample. It involves the separation as well as the resolution of diverse proteins sample on the basis of isoelectric points and molecular mass of protein in two dimension ways. In this way, it reflects the view of overall proteome status including differentiation in protein expression levels, post-translational modifications etc. Moreover, this allows the identification of novel biological signatures, which may give a particular identity of pathological background to cells or tissues associated with various types of cancers and neurological disorders. Therefore, by utilizing such tools, one can clearly investigate and compare the effects of particular drugs on cells of tissues and also one can analyze the effects of disease on the basis of variations in protein expression profile at broad spectrum. Recently, to get more error-less and accurate proteome profile, conventional 2-D gel electrophoresis has been enhanced with the inclusion of different types of protein labeling dyes which enables a more comparative analysis of diverse protein sample in a single 2-D gel. In this advanced technique (2-D-DIGE), protein samples are labeled with three different types of CyDyes (Cy2, Cy3, and Cy5) separately and combined and further resolved on the same gel. This will facilitate the more accurate spot matching on a single gel platform and will also minimize the experimental variations as commonly reported in the conventional 2D-gel electrophoresis. Therefore, in the present proteomic research era, 2D-DIGE has proved to be an extremely powerful tool with great sensitivity for up to 125 ng of proteins in clinical research volubility especially, neurological and cancer related disorders.