instruction:
In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
question:
Uniform polymeric nanoparticles with various morphologies of projection coronas like the viruses in the coronavirus group have been formed by the self-organization of macromolecular chains polymerizing in a dispersion system of styrene (St), acrylonitrile (AN) and poly(ethylene glycol) monomethoxymonomethacrylate (PEGm) in a polar solvent (water/ethanol). An increase in the water composition reduced the crystallization degree of AN units, resulting in a variety of the nanoparticle morphology such as the increased particle size, the reduced projection size, the increased projection number, and the decreased inter-projection distance. The difference in the projection morphology strongly affected a dispersibility in water. q
answer:
One-step nanomorphology control of self-organized projection coronas in uniform polymeric nanoparticles *


question:
Currently available diagnostic assays for leptospirosis cannot differentiate vaccine from infection serum antibody. Several leptospiral proteins that are upregulated during infection have been described, but their utility as a diagnostic marker is still unclear. In this study, we undertook a lipidomics approach to determine if there are any differences in the serum lipid profiles of horses naturally infected with pathogenic Leptospira spp. and horses vaccinated against a commercially available bacterin. Utilizing a high-resolution mass spectrometry serum lipidomics analytical platform, we demonstrate that cyclic phosphatidic acids, diacylglycerols, and hydroperoxide oxidation products of choline plasmalogens are elevated in the serum of naturally infected as well as vaccinated horses. Other lipids of interest were triacylglycerols that were only elevated in the serum of infected horses and sphingomyelins that were increased only in the serum of vaccinated horses. This is the first report looking at the equine serum lipidome during leptospiral infection and vaccination.
answer:
Lipidomic analysis of immune activation in equine leptospirosis and Leptospira- vaccinated horses


question:
In this contribution, the classification of protein binding sites using the physicochemical properties exposed to their pockets is presented. We recently introduced Cavbase, a method for describing and comparing protein binding pockets on the basis of the geometrical and physicochemical properties of their active sites. Here, we present algorithmic and methodological enhancements in the Cavbase property description and in the cavity comparison step. We give examples of the Cavbase similarity analysis detecting pronounced similarities in the binding sites of proteins unrelated in sequence. A similarity search using SARS M pro protease subpockets as queries retrieved ligands and ligand fragments accommodated in a physicochemical environment similar to that of the query. This allowed the characterization of the protease recognition pockets and the identification of molecular building blocks that can be incorporated into novel antiviral compounds. A cluster analysis procedure for the functional classification of binding pockets was implemented and calibrated using a diverse set of enzyme binding sites. Two relevant protein families, the a-carbonic anhydrases and the protein kinases, are used to demonstrate the scope of our cluster approach. We propose a relevant classification of both protein families, on the basis of the binding motifs in their active sites. The classification provides a new perspective on functional properties across a protein family and is able to highlight features important for potency and selectivity. Furthermore, this information can be used to identify possible cross-reactivities among proteins due to similarities in their binding sites.
answer:

From the Similarity Analysis of Protein Cavities to the Functional Classification of Protein Families Using Cavbase Keywords: protein binding pockets; classification of protein binding pockets; cluster analysis of protein binding pockets; protein kinases; SARS protease