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Target-Specific De Novo Peptide Binder Design with DiffPepBuilder.

Fanhao Wang1, Yuzhe Wang1, Laiyi Feng2

  • 1Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.

Journal of Chemical Information and Modeling
|September 12, 2024
PubMed
Summary

DiffPepBuilder designs novel peptide binders using an SE(3)-equivariant diffusion model. This method effectively generates stable peptide sequences and structures, outperforming existing tools in binding affinity and diversity.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Drug discovery

Background:

  • Peptide binder design is challenging due to structural flexibility and limited complex data.
  • Existing *de novo* protein binder design methods struggle with peptide specificity.

Purpose of the Study:

  • To develop a *de novo* method for generating target-specific peptide binders.
  • To improve the stability and binding affinity of designed peptides.

Main Methods:

  • Curated a large synthetic dataset (PepPC-F) from protein-protein interface data.
  • Developed DiffPepBuilder, an SE(3)-equivariant diffusion model for peptide sequence and structure co-design.
  • Incorporated disulfide bonds for enhanced peptide structural stability.

Main Results:

  • DiffPepBuilder successfully recalled native peptide structures and sequences.
  • Generated novel peptide binders with improved binding free energy.
  • Outperformed AfDesign and RFdiffusion in sequence/structure recall, interface quality, and diversity.
  • Disulfide bonds enhanced structural rigidity and binding performance.

Conclusions:

  • DiffPepBuilder is an effective *de novo* peptide binder design tool.
  • The method shows promise for designing peptide binders for specific protein targets.
  • DiffPepBuilder advances the field of peptide-based therapeutics design.