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CyclicMPNN: Stable Cyclic Peptide Sequence Generation.

Andrew C Powers1,2, Yanapat Janthana1,3, Parisa Hosseinzadeh1,4

  • 1Department of Bioengineering, University of Oregon, Eugene, OR.

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|February 9, 2026
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Summary
This summary is machine-generated.

We developed CyclicMPNN, a new AI model that designs stable cyclic peptide sequences for therapeutics. This method improves the success rate of developing new cyclic peptide drugs.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Cyclic peptides offer therapeutic advantages like stability and cell permeability.
  • Advanced models like CyclicCAE and RFPeptide accelerate cyclic peptide backbone generation.
  • Designing stable cyclic peptide sequences for specific backbones remains challenging.

Purpose of the Study:

  • To develop an AI-driven method for designing energetically favorable cyclic peptide sequences.
  • To improve the success rate of therapeutic cyclic peptide development.

Main Methods:

  • Fine-tuning the ProteinMPNN deep learning model.
  • Utilizing X-ray crystal structures from the Protein Data Bank.
  • Incorporating in silico generated cyclic peptides for training.

Main Results:

  • CyclicMPNN outperforms ProteinMPNN in cyclic peptide sequence design.
  • Generated sequences exhibit enhanced energetic stability and folding success rates.
  • The model functions effectively for motif-inpainting and de novo sequence design.

Conclusions:

  • CyclicMPNN facilitates rapid design of stable cyclic peptide sequences.
  • This advancement is expected to increase the success rate of therapeutic cyclic peptide development.