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Design of permeability-optimized target-binding macrocycles via direct preference optimization.

Heqi Sun1, Hong Tan1, Yanyi Chu2

  • 1State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University Shanghai 200040 P. R. China dqwei@sjtu.edu.cn.

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This summary is machine-generated.

We developed CycDiff-DPO, a new AI framework for designing macrocyclic peptides. This method enhances membrane permeability, improving drug delivery for challenging therapeutic targets.

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Area of Science:

  • Medicinal Chemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Macrocyclic peptides show therapeutic promise for difficult targets like protein-protein interactions.
  • Clinical application is hindered by poor membrane permeability, limiting oral bioavailability and intracellular access.
  • Current design methods often fail to optimize permeability alongside binding affinity and structural validity.

Purpose of the Study:

  • To introduce CycDiff-DPO, a novel diffusion framework for designing macrocyclic peptides with enhanced membrane permeability.
  • To integrate a Caco-2 permeability predictor and preference learning into the generative process.
  • To enable co-optimization of target binding and membrane permeability in peptide design.

Main Methods:

  • Developed CycDiff-DPO, a preference-aligned diffusion model for macrocyclic peptide generation.
  • Utilized a Caco-2 permeability predictor to rank and filter generated peptide candidates.
  • Constructed preference pairs to guide the generative distribution towards permeability-favorable chemical space.
  • Benchmarked performance across 56 protein targets against existing structure-based design methods.

Main Results:

  • CycDiff-DPO demonstrated improved predicted Caco-2 and PAMPA permeability across multiple independent predictors.
  • Designs exhibited superior binding energetics compared to baseline methods while maintaining stereochemical quality.
  • Case studies on Keap1-Nrf2 and SPSB2-iNOS showed designs recapitulating key interactions and stable molecular dynamics.
  • The framework successfully balanced target binding competence with enhanced permeability.

Conclusions:

  • CycDiff-DPO offers a robust framework for designing macrocyclic peptides with improved membrane permeability.
  • This approach facilitates the development of peptide therapeutics with enhanced bioavailability and intracellular target access.
  • The method holds broad potential for therapeutic applications targeting challenging diseases.