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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Type 2 diabetes involves metabolic inflammation driven by the Chrebp/Txnip axis, promoting NLRP3 inflammasome assembly and pancreatic beta-cell dysfunction.
  • The Chrebp-14-3-3 protein-protein interaction (PPI) is a relevant therapeutic target but remains underexplored, with few known modulators.

Purpose of the Study:

  • To develop an AI-driven generative design framework for de novo discovery of selective PPI-targeting compounds against the Chrebp-14-3-3 interaction.
  • To identify and validate novel small-molecule modulators of this PPI for potential type 2 diabetes therapeutics.

Main Methods:

  • Utilized a conditional recurrent neural network (QSPR-GEN) pretrained on a large corpus and fine-tuned on target-specific data for de novo compound generation.
  • Incorporated selectivity-oriented physicochemical descriptors and structure-based refinement focusing on a unique alpha-helical epitope.
  • Employed virtual screening, molecular dynamics, and MM/PBSA calculations to prioritize lead candidates with favorable energetics and pharmacokinetics.

Main Results:

  • Achieved high scaffold uniqueness (94.6%) in generated compounds, biased away from promiscuous chemotypes.
  • Prioritized lead candidates T2 and T7 with favorable binding and pharmacokinetic profiles.
  • Candidate T7 significantly suppressed Txnip and NLRP3 expression, reduced IL-1β secretion, and attenuated pyroptotic cell death in macrophages under metabolic stress, outperforming a reference inhibitor.

Conclusions:

  • Presented a robust computational framework for the inverse design of challenging PPIs.
  • Demonstrated successful identification and experimental validation of mechanistically precise lead compounds (T2, T7) targeting the Chrebp-14-3-3 interaction.
  • Highlighted the potential of AI-driven approaches for discovering novel therapeutics for metabolic diseases like type 2 diabetes.