The interaction between Glycophorin A (GPA) and Band 3 in the formation of the Wright b (Wrb ) antigen

Effects of Cholesterol and PIP2 on Interactions between Glycophorin A and Band 3 in Lipid Bilayers

In the erythrocyte membrane, the interactions between Glycophorin A (GPA) and Band 3 are associated strongly with the biological function of the membrane and several blood disorders. In this work, using coarse-grained molecular dynamics simulation, we systematically investigate the effects of cholesterol and phosphatidylinositol-4,5-bisphosphate (PIP2) on the interactions of GPA with Band 3 in the model erythrocyte membranes. We examine the dynamics of the interactions of GPA with Band 3 in different lipid bilayers on the microsecond time scale and calculate the binding free energy between GPA and Band 3. The results indicate that cholesterols thermodynamically favor the binding of GPA to Band 3 by increasing the thickness of the lipid bilayer and by producing an effective attraction between the proteins due to the depletion effect. Cholesterols also slow the kinetics of the binding of GPA to Band 3 by reducing the lateral mobility of the lipids and proteins and may influence the binding sites between the proteins. The anionic PIP2 lipids prefer binding to the surface of the proteins through electrostatic attraction between the PIP2 headgroup and the positively charged residues on the protein surface. Ions in the solvent facilitate the PIP2 aggregation which promotes the binding of GPA to Band 3.

Computational modeling - an approach to the development of blood grouping reagents

Introduction: Blood group antigens are defined by an immune response that generates antibodies against a red blood cell molecule. Antibodies against these antigens can be associated with hemolytic transfusion reactions. However, difficulties can arise when developing antibodies against antigens through the use of peptide sequences alone. Three-dimensional representations (models) of the molecular structure of antigen-bearing proteins can provide valuable insights into tertiary structures and their consequent antigenicity. This can be achieved through predictive computational modeling to produce both structural and molecular dynamics models of blood group proteins.Areas covered: Authors discuss the use of molecular dynamic simulations on existing structures, as well as the use of computational modeling techniques in the development of protein models lacking preexisting data. Finally, the authors discuss specific examples of the use of computationally derived models of the MNS blood group system and its use in attempts to produce antibodies against MNS proteins.Expert opinion: Although in silico techniques have limitations, computer-based predictive models can inform the direction of research into blood group proteins. It is to be expected that as computer-based techniques grow more powerful these contributions will be even more significant.