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Structural Modeling of NTPDase-Substrate Complexes Preserving Catalytic Experimental Features.

João Victor B de Moraes1,2, Marcelo D Polêto3, Raissa B de Castro4

  • 1General Biology Department, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570 900, Brazil.

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Ecto-nucleoside triphosphate diphosphohydrolases (E-NTPDases) are key in cell signaling and have therapeutic potential. A new computational method models E-NTPDase-substrate complexes, aiding enzyme optimization for drug development.

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Enzymology

Background:

  • Ecto-nucleoside triphosphate diphosphohydrolase (E-NTPDase) enzymes regulate purinergic and pyrimidinergic signaling by hydrolyzing nucleoside phosphates.
  • E-NTPDases hold significant therapeutic potential, but limited structural insights into their substrate complexes hinder enzyme optimization.
  • Existing molecular docking methods often fail to accurately represent experimentally observed substrate conformations.

Purpose of the Study:

  • To develop a computational strategy for modeling E-NTPDase-substrate complexes that preserves experimentally validated substrate features.
  • To leverage conserved active site features across the E-NTPDase family for accurate modeling.
  • To generate reliable structural models of human E-NTPDases (HsNTPDases) complexed with various nucleotide substrates.

Main Methods:

  • Developed a computational strategy integrating conserved active site characteristics with experimentally observed substrate conformations.
  • Identified a canonical linear-like substrate conformation common across E-NTPDase structures, including the phosphate tail and nucleobase.
  • Applied the method to model Homo sapiens NTPDases (HsNTPDase1-8) with ATP, ADP, GTP, GDP, UTP, and UDP.

Main Results:

  • The computational strategy successfully modeled HsNTPDases complexed with multiple nucleotide substrates.
  • Models accurately positioned essential metal ion cofactors and catalytic water molecules within the active site.
  • The identified canonical substrate conformation provides a conserved feature for accurate enzyme-substrate interaction studies.

Conclusions:

  • The developed computational approach offers a reliable framework for studying E-NTPDase-substrate interactions.
  • These accurate models facilitate rational enzyme engineering for therapeutic applications.
  • The findings pave the way for advancing the therapeutic exploration of E-NTPDases.