O-Antigen polysaccharides constitute the outer protective layer of most Gram-negative bacteria, important for the bacterium's survival and adaption within its host. Although important for many functions, the three-dimensional structure of the dense polysaccharide coat remains to be elucidated. In this study, we present a systematic numerical investigation of O-antigen polysaccharide chains of Shigella flexneri serotype Y composed of one up to four tetrasaccharide repeat units. To bridge the gap between atomistic and coarse-grained levels of description, we employ a genuine multiscale modeling approach. It reveals that even for a few repeat units polymer-like flexibility emerges, which is furthermore complemented by extreme, hairpin-like conformations. These can facilitate the formation of metastable compact states, but this conclusion depends sensitively on the force field used to model the carbohydrates. Thus, our computational analysis represents an essential prerequisite for developing reliable coarse-grained models that may help visualizing changes in O-antigen coat morphology upon variations in chain length distribution or chemical composition of the polysaccharide characterizing a certain serotype.
conformation, carbohydrates, structure, tetrasaccharide, host, variation, polysaccharide, serotype, O-antigen, analysis, polysaccharides, O antigen, carbohydrate, chain, morphology, Shigella flexneri, level, conformational, bacteria, chemical, Shigella, Gram-negative bacteria, function, gram negative bacteria, Gram-negative, formation, composition, change, protective, distribution, diversity, model, models, modeling, chain length, flexibility, force field, approach, systematic, layer, survival
NCBI PubMed ID: 24559142Publication DOI: 10.1021/jp4111713Journal NLM ID: 101157530Publisher: Washington, DC: American Chemical Society
Correspondence: mark.santer@mpikg.mpg.de
Institutions: Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany
Methods: conformation analysis, MD simulations, molecular modeling, Monte Carlo simulations
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