Finding a needle in a haystack: the role of electrostatics in target lipid recognition by PH domains

Journal article

Interactions between protein domains and lipid molecules play key roles in controlling cell membrane signalling and trafficking. The pleckstrin homology (PH) domain is one of the most widespread, binding specifically to phosphatidylinositol phosphates (PIPs) in cell membranes. PH domains must locate specific PIPs in the presence of a background of approximately 20% anionic lipids within the cytoplasmic leaflet of the plasma membrane. We investigate the mechanism of such recognition via a multiscale procedure combining Brownian dynamics (BD) and molecular dynamics (MD) simulations of the GRP1 PH domain interacting with phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P3). The interaction of GRP1-PH with PI(3,4,5)P3 in a zwitterionic bilayer is compared with the interaction in bilayers containing different levels of anionic ‘decoy’ lipids. BD simulations reveal both translational and orientational electrostatic steering of the PH domain towards the PI(3,4,5)P3-containing anionic bilayer surface. There is a payoff between non-PIP anionic lipids attracting the PH domain to the bilayer surface in a favourable orientation and their role as ‘decoys’, disrupting the interaction of GRP1-PH with the PI(3,4,5)P3 molecule. Significantly, approximately 20% anionic lipid in the cytoplasmic leaflet of the bilayer is nearly optimal to both enhance orientational steering and to localise GRP1-PH proximal to the surface of the membrane without sacrificing its ability to locate PI(3,4,5)P3 within the bilayer plane. Subsequent MD simulations reveal binding to PI(3,4,5)P3, forming protein-phosphate contacts comparable to those in X-ray structures. These studies demonstrate a computational framework which addresses lipid recognition within a cell membrane environment, offering a link between structural and cell biological characterisation.

Authors: Lumb, C; Sansom, MS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Computational Biology
• Volume: 8
• Issue: 7
• Pages: ARTN e1002617
• Publication date: 2012-07-26
• Acceptance date: 2012-06-01
• DOI: 10.1371/journal.pcbi.1002617
• EISSN: 1553-7358
• ISSN: 1553-734X
• Language: English
• Keywords: SBTMR