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Dipeptidyl Peptidase 4 Inhibitors01:23

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GPU-Accelerated Virtual Screening and Molecular Dynamics Simulations for Identification of Novel DPP‑4 Inhibitors.

Nathaly Vasquez-Martínez1, Jonathan Trapala2, Laura I Álvarez-Añorve1

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Novel computational methods identified new dipeptidyl peptidase 4 (DPP-4) inhibitors for type 2 diabetes. Compounds EPZ005687, OSU-03012, and bemcentinib show high efficacy and improved cellular absorption compared to existing drugs.

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Area of Science:

  • Computational chemistry and drug discovery
  • Pharmacology and medicinal chemistry
  • Biochemistry and molecular biology

Background:

  • Dipeptidyl peptidase 4 (DPP-4) inhibition is a key strategy for managing type 2 diabetes mellitus (T2DM).
  • Existing DPP-4 inhibitors can cause adverse effects, necessitating the search for safer alternatives.
  • Computational approaches offer a promising avenue for accelerating the discovery of novel therapeutic agents.

Purpose of the Study:

  • To identify novel, selective, and safer DPP-4 inhibitors using an integrated computational pipeline.
  • To evaluate the binding affinity, stability, and cellular permeability of potential drug candidates.
  • To demonstrate the effectiveness of a computational workflow for drug discovery.

Main Methods:

  • GPU-accelerated molecular docking of 30,699 compounds against the DPP-4 active site.
  • Molecular dynamics (MD) simulations (100 ns) to assess the stability of protein-ligand complexes.
  • MM-GBSA calculations for binding affinity and Umbrella Sampling/MD for membrane permeability analysis.

Main Results:

  • Identified EPZ005687, OSU-03012, and bemcentinib as promising DPP-4 inhibitor candidates.
  • These compounds demonstrated superior binding affinity and favorable interactions compared to alogliptin.
  • EPZ005687, OSU-03012, and bemcentinib exhibited enhanced cellular permeability over alogliptin.

Conclusions:

  • The integrated computational strategy effectively accelerates the discovery of potential antidiabetic agents.
  • EPZ005687, OSU-03012, and bemcentinib are promising candidates for developing safer and more effective T2DM treatments.
  • Rigorous filtering of public databases combined with computational analysis is crucial for identifying viable drug leads.