Back

Oxygen partitioning into biomolecular condensates is governed by protein density

Garg, A.; Brasnett, C.; Marrink, S. J.; Koren, K.; Kjaergaard, M.

2024-10-09 biophysics
10.1101/2024.05.03.592328 bioRxiv
Show abstract

Biomolecular condensates form through the self-assembly of proteins and nucleic acids to create dynamic compartments in cells. By concentrating specific molecules, condensates establish distinct microenvironments that regulate biochemical reactions in time and space. Macromolecules and metabolites partition into condensates depending on their interactions with the macromolecular constituents, however, the partitioning of gases has not been explored. We investigated oxygen partitioning into condensates formed by intrinsically disordered repeat proteins with systematic sequence variations using microelectrodes and phosphorescence lifetime imaging microscopy (PLIM). Unlike other hydrophobic metabolites, oxygen is partially excluded from the condensate with partitioning constants more strongly modulated by changes in protein length than hydrophobicity. For repeat proteins, the dense phase protein concentration drops with chain length resulting in a looser condensate. We found that oxygen partitioning is anti-correlated with dense phase protein concentration. Several mechanisms could explain such an anti-correlation including excluded volume or salting out effects. Molecular dynamics simulations suggest that oxygen does not form strong and specific interactions with the scaffold and is dynamic on the nanosecond timescale. Biomolecular condensates thus result in variation of oxygen concentrations on nanometer length-scales, which may tune the oxygen concentration available for biochemical reactions within the cell.

Published in Nature Communications (predicted rank #12) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.