Computational studies of CXCR1, the receptor of IL-8/CXCL8, using molecular dynamics and electrostatics

Peptides derived from CXCL8 based on in silico analysis inhibit CXCL8 interactions with its receptor CXCR1

Chemokine CXCL8 is crucial for regulation of inflammatory and immune responses via activating its cognate receptor CXCR1. In this study, molecular docking and binding free energy calculations were combined to predict the initial binding event of CXCL8 to CXCR1 for peptide drug design. The simulations reveal that in the initial binding, the N-loop of CXCL8 interacts with the N-terminus of CXCR1, which is dominated by electrostatic interactions. The derived peptides from the binding region of CXCL8 are synthesized for further confirmation. Surface plasmon resonance analyses indicate that the CXCL8 derived peptide with 14 residues is able to bind to the receptor CXCR1 derived peptide with equilibrium KD of 252 μM while the peptide encompassing a CXCL8 K15A mutation hardly binds to CXCR1 derived peptide (KD = 1553 μM). The cell experiments show that the designed peptide inhibits CXCL8-induced and LPS-activated monocytes adhesion and transmigration. However, when the peptides were mutated on two lysine residues (K15 and K20), the inhibition effects were greatly reduced indicating these two amino acids are key residues for the initial binding of CXCL8 to CXCR1. This study demonstrates that in silico prediction based functional peptide design can be effective for developing anti-inflammation drugs.

In silico analysis reveals sequential interactions and protein conformational changes during the binding of chemokine CXCL-8 to its receptor CXCR1

Chemokine CXCL-8 plays a central role in human immune response by binding to and activate its cognate receptor CXCR1, a member of the G-protein coupled receptor (GPCR) family. The full-length structure of CXCR1 is modeled by combining the structures of previous NMR experiments with those from homology modeling. Molecular docking is performed to search favorable binding sites of monomeric and dimeric CXCL-8 with CXCR1 and a mutated form of it. The receptor-ligand complex is embedded into a lipid bilayer and used in multi ns molecular dynamics (MD) simulations. A multi-steps binding mode is proposed: (i) the N-loop of CXCL-8 initially binds to the N-terminal domain of receptor CXCR1 driven predominantly by electrostatic interactions; (ii) hydrophobic interactions allow the N-terminal Glu-Leu-Arg (ELR) motif of CXCL-8 to move closer to the extracellular loops of CXCR1; (iii) electrostatic interactions finally dominate the interaction between the N-terminal ELR motif of CXCL-8 and the EC-loops of CXCR1. Mutation of CXCR1 abrogates this mode of binding. The detailed binding process may help to facilitate the discovery of agonists and antagonists for rational drug design.

Automated computational framework for the analysis of electrostatic similarities of proteins

Charge plays an important role in protein-protein interactions. In the case of excessively charged proteins, their electrostatic potentials contribute to the processes of recognition and binding with other proteins or ligands. We present an automated computational framework for determining the contribution of each charged amino acid to the electrostatic properties of proteins, at atomic resolution level. This framework involves computational alanine scans, calculation of Poisson-Boltzmann electrostatic potentials, calculation of electrostatic similarity distances (ESDs), hierarchical clustering analysis of ESDs, calculation of solvation free energies of association, and visualization of the spatial distributions of electrostatic potentials. The framework is useful to classify families of mutants with similar electrostatic properties and to compare them with the parent proteins in the complex. The alanine scan mutants introduce perturbations in the local electrostatic properties of the proteins and aim in delineating the contribution of each mutated amino acid in the spatial distribution of electrostatic potential, and in biological function when electrostatics is a dominant contributing factor in protein-protein interactions. The framework can be used to design new proteins with tailored electrostatic properties, such as immune system regulators, inhibitors, and vaccines, and in guiding experimental studies. We present an example for the interaction of the immune system protein C3d (the d-fragment of complement protein C3) with its receptor CR2, and we discuss our data in view of a binding site controversy.

MIF-chemokine receptor interactions in atherogenesis are dependent on an N-loop-based 2-site binding mechanism

Macrophage migration inhibitory factor (MIF) is a cytokine that mediates inflammatory diseases. MIF promotes atherogenic leukocyte recruitment through a promiscuous, yet highly affine, interaction with CXCR2 and CXCR4. Binding to CXCR2 is dependent on a pseudo-(E)LR motif in MIF, but a second interaction site has been elusive. Here we identified an N-like loop in MIF, suggesting that MIF binding to CXCR2 follows the 2-site binding mode of bona fide chemokines. For MIF, the model predicts interactions between the N-like loop and the CXCR2 N domain (site 1) and pseudo-(E)LR and extracellular loops (ELs) of CXCR2 (site 2). Applying biophysical and peptide array analysis, we demonstrated an interaction between MIF and the CXCR2 N domain, which was pseudo-(E)LR independent. Peptide array analysis also indicated that the pseudo-(E)LR motif is responsible for MIF binding to EL2 and 3. Notably, peptides MIF-(40-49) and MIF-(47-56), representing N-like-loop-derived peptides, but not a scrambled control peptide, significantly blocked MIF/CXCR2 binding, MIF-mediated monocyte arrest under flow on aortic endothelial cells in vitro (IC(50): 1.24×10(-6) M), and MIF-dependent monocyte adhesion to atherosclerotic mouse carotid arteries in vivo. Thus, the N-like loop in MIF is critical for MIF's noncognate interaction with CXCR2 and proatherogenic functions. The 2-site binding model that explains chemokine receptor activation also applies to MIF.