Parameters modulating the maximum insertion pressure of proteins and peptides in lipid monolayers

The lipid monolayer model membrane is useful for studying the parameters responsible for protein and peptide membrane binding. Different approaches have been used to determine the extent of protein and peptide binding to lipid monolayers. This review focuses on the use of the “maximum insertion pressure” (MIP) to estimate the extent of protein and peptide penetration in lipid monolayers. The MIP data obtained with different proteins and peptides have been reviewed and discussed which allowed to draw conclusions on the parameters modulating the monolayer binding of proteins and peptides. In particular, secondary structure components such as amphipathic α-helices of proteins and peptides as well as electrostatic interactions play important roles in monolayer binding. The MIPs have been compared to the estimated lateral pressure of biomembranes which allowed to evaluate the possible association between proteins or peptides with natural membranes. For example, the MIP of a membrane-anchored protein with a glycosylphosphatidylinositol (GPI) was found to be far below the estimated lateral pressure of biomembranes. This allowed us to conclude that this protein is probably unable to penetrate the membrane and should thus be hanged at the membrane surface by use of its GPI lipid anchor. Moreover, the values of MIP obtained with myristoylated and non-myristoylated forms of calcineurin suggest that the myristoyl group does not contribute to monolayer binding. However, the acylation of a peptide resulted in a large increase of MIP. Finally, the physical state of lipid monolayers can have a strong effect on the values of MIP such that it is preferable to perform measurements with lipids showing a single physical state. Altogether the data show that the measurement of the maximum insertion pressure provides very useful information on the membrane binding properties of proteins and peptides although uncertainties must be provided to make sure the observed differences are significant.