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Long-Range Changes in Neurolysin Dynamics Upon Inhibitor Binding.

A UyarV T KaramyanAlex Dickson
Published in: Journal of chemical theory and computation (2017)
Crystal structures of neurolysin, a zinc metallopeptidase, do not show a significant conformational change upon the binding of an allosteric inhibitor. Neurolysin has a deep channel where it hydrolyzes a short neuropeptide neurotensin to create inactive fragments and thus controls its level in the tissue. Neurolysin is of interest as a therapeutic target since changes in neurotensin level have been implicated in cardiovascular disorders, neurological disorders, and cancer, and inhibitors of neurolysin have been developed. An understanding of the dynamical and structural differences between apo and inhibitor-bound neurolysin will aid in further design of potent inhibitors and activators. For this purpose, we performed several molecular dynamics (MD) simulations for both apo and inhibitor-bound neurolysin. A machine learning method (Linear Discriminant Analysis) is applied to reveal differences between the apo and inhibitor-bound ensembles in an automated way, and large differences are observed on residues that are far from both the active site and the inhibitor binding site. The effects of inhibitor binding on the collective motions of neurolysin are extensively analyzed and compared using both Principal Component Analysis and Elastic Network Model calculations. We find that inhibitor binding induces additional low-frequency motions that are not observed in the apo form. ENM also reveals changes in inter- and intradomain communication upon binding. Furthermore, differences are observed in the inhibitor-bound neurolysin contact network that are far from the active site, revealing long-range allosteric behavior. This study also provides insight into the allosteric modulation of other neuropeptidases with similar folds.
Keyphrases
  • molecular dynamics
  • machine learning
  • density functional theory
  • binding protein
  • squamous cell carcinoma
  • dna binding
  • genome wide
  • subarachnoid hemorrhage
  • deep learning
  • blood brain barrier
  • cerebral ischemia