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Role of N-glycosylation in activation of proMMP-9. A molecular dynamics simulations study.

Sonu KumarPiotr Cieplak
Published in: PloS one (2018)
Human matrix metalloproteinase proMMP-9 is secreted as latent zymogen, which requires two-steps proteolytic activation. The secreted proMMP-9 is glycosylated at two positions: Asn38 and Asn120 located in the prodomain and catalytic domain, respectively. It has been demonstrated that glycosylation at Asn120 is required for secretion of the enzyme, while the role of Asn38 glycosylation is not well understood, but is usually linked to the activation process. One hypothesis stated that the Asn38 glycosylation might protect against proteolytic activation. However, the activation process occurs with or without the presence of this glycosylation. We conducted molecular dynamics (MD) simulations on the glycosylated and non-glycosylated proMMP-9 to elucidate the effect of Asn38 glycosylation on this two-step activation process. The simulation results suggest that Asn38 glycosylation does not hinder the activation process directly, but induces conformational changes in the vicinity of the first proteolytic region in such a way that E59-M60 cleavage is processed before R106-F107. These results correlate with analysis provided by Boon et al. and experimental data from Ogata et al. who attempted to determine the order of events in activation of proMMP-9. Results from additional MD simulations for the model of glycosylated proMMP-9 bound to galectin-8 N-domain suggest that Gal-8 by interacting with Asn38 glycan might further facilitate processing of the first cleavage between E59-M60. Thus, our simulation results suggest that both Asn38 glycosylation and interaction with Gal-8N may be involved in facilitating and the temporal order of the activation process of pro-MMP9. The aim of this report is to provide an inspiration for future detailed experiments aimed at explaining the role of N-glycosylation in the activation process of prodomain of MMP-9.
Keyphrases
  • molecular dynamics
  • molecular dynamics simulations
  • endothelial cells
  • deep learning
  • cell migration
  • dna binding
  • induced pluripotent stem cells