ABSTRACT
C-phycocyanin (C-PC) a blue color phycobiliproteins used as a food colorant, therapeutics, medicines, health food and biomarkers. In the present study, morphological property of encapsulated C-PC and its stability under various conditions like temperature, pH conditions are discussed. Microencapsulated droplets formed by extrusion found to be spherical with average size 1.2 ± 0.1 mm. SEM micrographs of freeze dried encapsulate confirmed the spherical shape. The effect of droplet formation with varying alginate percentage (1.5%, 2.0% and 2.5% w/v) was studied. In the stability test at 70 °C and 80 °C relative concentration (CR %) was found to be 86.89 and 88.19%, respectively. The encapsulated C-PC showed a slow degradation at higher temperature compared to without encapsulated C-PC which was confirmed by UV-visible absorbance. At 45 °C and 55 °C temperatures the stability was studied at various pH conditions (pH 4.5, 5.5, 6.5, and 7.0) and reported. Aggregation of C-PC protein will not change during encapsulation was confirmed by SDS-PAGE. FTIR analysis of encapsulate and the alginate depicted similar characteristics of the compound compared to that of native C-phycocyanin colorant. Microencapsulation improves the stability and increases the shelf life of colorant.
ABSTRACT
Virtual screening emerged as an important tool in our quest to access novel drug like compounds. There are a wide range of comparable and contrasting methodological protocols available in screening databases for the lead compounds. The number of methods and software packages which employ the target and ligand based virtual screening are increasing at a rapid pace. However, the general understanding on the applicability and limitations of these methodologies is not emerging as fast as the developments of various methods. Therefore, it is extremely important to compare and contrast various protocols with practical examples to gauge the strength and applicability of various methods. The review provides a comprehensive appraisal on several of the available virtual screening methods to-date. Recent developments of the docking and similarity based methods have been discussed besides the descriptor selection and pharmacophore based searching. The review touches upon the application of statistical, graph theory based methods machine learning tools in virtual screening and combinatorial library design. Finally, several case studies are undertaken where the virtual screening technology has been applied successfully. A critical analysis of these case studies provides a good platform to estimate the applicability of various virtual screening methods in the new lead identification and optimization.
