Glycosaminoglycan Recognition of Neutrophil‐Activating Chemokines

Abstract

Neutrophil‐activating chemokines are known for their fundamental and diverse role in recruiting immune cells to the site of inflammation/infection. Extracellular glycosaminoglycans (GAGs), especially heparan sulfate, sequester chemokines and engineer a concentration gradient to regulate the immune cell trafficking. Not much is known about the structural and dynamical aspects of chemokine – GAG interactions. We have studied the interaction of several human and mouse chemokines, including CXCL1, CXCL2, and CXCL5 with heparin/heparan sulfate oligosaccharides using computational (modeling and dynamics) and biophysical (NMR & ITC) techniques. These studies have led to fine atomistic level understanding of GAG recognition of chemokines. First, our studies show that even though these chemokines are structurally homologous, they exhibit striking differences in their recognition of GAGs. Diverse binding geometries have been observed in which a GAG chain may bind each monomer present in a chemokine dimer or a GAG chain may bind two non‐overlapping sites by spanning across the dimer interface. Second, molecular dynamics simulations show large concerted motions of the Lys/Arg side chains bound to sulfate/carboxylates of GAGs, which imply translational motion of a GAG chain over the binding interface leading to considerable plasticity in binding. At the same time, a GAG chain may have restricted motion arising from specific interactions with a group of core amino acid residues. Third, a large number of hydrogen bond contributing residues occur in chemokines, e.g., in N‐loop, 40s turn, β3 strand and C‐helix, which provide strong/weak interactions to stabilize the complex. Interestingly, GAG recognition of chemokines is also brought about by water‐mediated interactions between amino acid side chains and sulfates/carboxylates, which introduce additional stability to GAG's dynamical motion. Fourth, relative importance of individual amino acid residues correlate well with experimental observations, thus implying that computational trajectories can be reliably used to understand GAG–chemokine systems. In addition, the computational trajectories can lead to identification of preferred chain length, mode of binding, and dynamical motions of GAGs in recognition of neutrophil‐activating chemokines. Our work contributes fundamental understanding on the structural biology of GAG–chemokine interactions.Support or Funding InformationThis work was supported in part by grants from the NIH including HL107152, HL090586 and HL128639This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.