The sugar fucose is expressed on mammalian cell membranes as part of glycoconjugates and mediates essential physiological processes. The aberrant expression of fucosylated glycans has been linked to pathologies such as cancer, inflammation, infection, and genetic disorders. Tools to modulate fucose expression on living cells are needed to elucidate the biological role of fucose sugars and the development of potential therapeutics. Herein, we report a class of fucosylation inhibitors directly targeting de novo GDP-fucose biosynthesis via competitive GMDS inhibition. We demonstrate that cell permeable fluorinated rhamnose 1-phosphate derivatives (Fucotrim I & II) are metabolic prodrugs that are metabolized to their respective GDP-mannose derivatives and efficiently inhibit cellular fucosylation.
Fucose sugars are expressed on mammalian cell
membranes as part of glycoconjugates and mediates essential physiological processes.
The aberrant expression of fucosylated glycans has been linked to pathologies such
as cancer, inflammation, infection, and genetic disorders. Tools to modulate
fucose expression on living cells are needed to elucidate the biological role
of fucose sugars and the development of potential therapeutics. Herein, we
report a novel class of fucosylation inhibitors directly targeting de novo GDP-fucose biosynthesis. We demonstrate that cell permeable
fluorinated mannoside 1-phosphate derivatives (Fucotrim I & II) are
metabolic prodrugs that are metabolized to their respective GDP-mannose
derivatives and efficiently inhibit cellular fucosylation.
When Oleg Ptitsyn and his group published the first secondary structure prediction for a protein sequence, they started a research field that is still active today. Oleg Ptitsyn combined fundamental rules of physics with human understanding of protein structures. Most followers in this field, however, use machine learning methods and aim at the highest (average) percentage correctly predicted residues in a set of proteins that were not used to train the prediction method. We show that one single method is unlikely to predict the secondary structure of all protein sequences, with the exception, perhaps, of future deep learning methods based on very large neural networks, and we suggest that some concepts pioneered by Oleg Ptitsyn and his group in the 70s of the previous century likely are today’s best way forward in the protein secondary structure prediction field.
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