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Stressed Lipid Droplets: How Neutral Lipids Relieve Surface Tension and Membrane Expansion Drives Protein Association.

Siyoung KimMyong In OhJessica M J Swanson
Published in: The journal of physical chemistry. B (2021)
Lipid droplets (LDs) are intracellular storage organelles composed of neutral lipids, such as triacylglycerol (TG), surrounded by a phospholipid (PL) monolayer decorated with specific proteins. Herein, we investigate the mechanism of protein association during LD and bilayer membrane expansion. We find that the neutral lipids play a dynamic role in LD expansion by further intercalating with the PL monolayer to create more surface-oriented TG molecules (SURF-TG). This interplay both reduces high surface tension incurred during LD budding or growth and also creates expansion-specific surface features for protein recognition. We then show that the autoinhibitory (AI) helix of CTP:phosphocholine cytidylyltransferase, a protein known to target expanding monolayers and bilayers, preferentially associates with large packing defects in a sequence-specific manner. Despite the presence of three phenylalanines, the initial binding with bilayers is predominantly mediated by the sole tryptophan due to its preference for membrane interfaces. Subsequent association is dependent on the availability of large, neighboring defects that can accommodate the phenylalanines, which are more probable in the stressed systems. Tryptophan, once fully associated, preferentially interacts with the glycerol moiety of SURF-TG in LDs. The calculation of AI binding free energy, hydrogen bonding and depth analysis, and in silico mutation experiments support the findings. Hence, SURF-TG can both reduce surface tension and mediate protein association, facilitating class II protein recruitment during LD expansion.
Keyphrases
  • protein protein
  • binding protein
  • amino acid
  • fatty acid
  • artificial intelligence
  • machine learning
  • molecular dynamics simulations
  • small molecule
  • optical coherence tomography
  • deep learning
  • quantum dots