bioRxivBiophysicsPreprint
Structural basis of lipid-mediated self-regulation of Tissue Factor.
Abstract
Tissue Factor (TF) is a tightly regulated transmembrane protein that maintains its encrypted state on the cell surface with an unknown conformation. Generation of TF conformational ensembles by the co-folding method using alphafold3 and boltz2 software revealed two mechanisms of TF self-regulation and corresponding conformational changes that were validated by Molecular Dynamics simulations. The first mechanism includes tilted and upright conformations of TF, whereas the second mechanism involves the spontaneous formation of TF oligomers, mostly dimers. The structural changes in TF responsible for both mechanisms were reversible and lipid-dependent. TF co-folding with phosphatidylserine resulted in approximately 90% of the TF extracellular domain in an upright conformation, enabling fast Factor VII (FVII) binding, while co-folding with phosphatidylcholine resulted in 60% of TF conformations tilted relative to the membrane surface and with a hidden FVII binding site. TF residues 210-219 (Linker Peptide) serve as a sensor that determines the TF conformation by interacting with the headgroups of phospholipids. TF self-association results in four types of homodimers with distinct relative orientations of extracellular domains and different accessibility of TF FVII binding sites. Approximately half of the dimers had accessible FVII binding sites, whereas in the remaining dimers, the FVII binding sites were partially or completely blocked. Co-folding of two TF sequences in the presence of cholesterol and phospholipids inhibited the dimerization and increased the number of TF monomers capable of FVII binding. These previously unknown TF conformations provide a structural basis for maintaining the encrypted and decrypted states of TF on the cell surface.
- organism: human
- assay: structural biology
- research focus: protein folding & dynamics
- research focus: cell signaling & trafficking
- research focus: host-pathogen & immune response
- approach: deep learning
- approach: statistical model
- Citations
- 0
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