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UVliPiD: A UVPD-Based Hierarchical Approach for De Novo Characterization of Lipid A Structures.


ABSTRACT: The lipid A domain of the endotoxic lipopolysaccharide layer of Gram-negative bacteria is comprised of a diglucosamine backbone to which a variable number of variable length fatty acyl chains are anchored. Traditional characterization of these tails and their linkages by nuclear magnetic resonance (NMR) or mass spectrometry is time-consuming and necessitates databases of pre-existing structures for structural assignment. Here, we introduce an automated de novo approach for characterization of lipid A structures that is completely database-independent. A hierarchical decision-tree MS(n) method is used in conjunction with a hybrid activation technique, UVPDCID, to acquire characteristic fragmentation patterns of lipid A variants from a number of Gram-negative bacteria. Structural assignments are derived from integration of key features from three to five spectra and automated interpretation is achieved in minutes without the need for pre-existing information or candidate structures. The utility of this strategy is demonstrated for a mixture of lipid A structures from an enzymatically modified E. coli lipid A variant. A total of 27 lipid A structures were discovered, many of which were isomeric, showcasing the need for a rapid de novo approach to lipid A characterization.

SUBMITTER: Morrison LJ 

PROVIDER: S-EPMC4850738 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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UVliPiD: A UVPD-Based Hierarchical Approach for De Novo Characterization of Lipid A Structures.

Morrison Lindsay J LJ   Parker W Ryan WR   Holden Dustin D DD   Henderson Jeremy C JC   Boll Joseph M JM   Trent M Stephen MS   Brodbelt Jennifer S JS  

Analytical chemistry 20160115 3


The lipid A domain of the endotoxic lipopolysaccharide layer of Gram-negative bacteria is comprised of a diglucosamine backbone to which a variable number of variable length fatty acyl chains are anchored. Traditional characterization of these tails and their linkages by nuclear magnetic resonance (NMR) or mass spectrometry is time-consuming and necessitates databases of pre-existing structures for structural assignment. Here, we introduce an automated de novo approach for characterization of li  ...[more]

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