Structural studies of Bordetella endotoxins (LPSs) have revealed remarkable differences: (i) between their LPSs and those of other bacterial pathogens; (ii) among the LPSs of the seven identified Bordetella species; and (iii) among the LPSs of some Bordetella strains. The lipid As have the "classical" bisphosphorylated diglucosamine backbone but tend to have fewer and species-specific fatty acid components compared to those of other genera. Nevertheless, three strains of B. bronchiseptica have at least three different fatty acid distributions; however, the recently identified B. hinzii and B. trematum LPSs had identical lipid A structures. The B. pertussis core is a dodecasaccharide multi-branched structure bearing amino and carboxylic groups. Another unusual feature is the presence of free amino sugars in the central core region and a complex distal trisaccharide unit containing five amino groups of which four are acetylated and one is methylated. The B. pertussis LPS does not have O-chains and that of B. trematum had only a single O-unit, unlike the LPSs of all the other species of the smooth-type. The O-chain-free cores of non-B. pertussis LPSs were always built on the B. pertussis core model but most were species-specifically incomplete. The LPS structures of three B. bronchiseptica strains were found to be different from each other. The O-chains of B. bronchiseptica and B. parapertussis were almost identical and had some features in common with B. hinzii O-chain. Serological analyses are consistent with the determined LPS structures.
In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become a mainstream practice. However, formal evidence of the relationship between a high-quality predicted model and the chances of experimental success is lacking. We used experimentally characterized de novo designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction (in silico melting), which can reveal differences between designs in terms of present favorable contacts. This study provides insight on how DL-based structure prediction could improve de novo protein design and open the door to the design of increasingly complex proteins; in particular membrane proteins which have historically been lacking basic in silico validation tools.
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