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Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide

J Kyle Brubaker1, Kyle E C Booth2, Akihiko Arakawa3

  • 1Amazon Advanced Solutions Lab, Seattle, WA, 98170, USA.

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|July 1, 2025
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Summary
This summary is machine-generated.

Quadratic unconstrained binary optimization (QUBO) can model peptide-protein docking, but struggles with larger problems. Constraint programming offers a more scalable solution for this structural biology challenge.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Drug Design

Background:

  • Peptide-protein interactions are crucial for biological processes and drug development.
  • Accurate modeling of these interactions is essential for rational drug design.
  • The peptide-protein docking problem presents significant computational challenges.

Purpose of the Study:

  • To explore the application of quantum-amenable Quadratic Unconstrained Binary Optimization (QUBO) for peptide-protein docking.
  • To incorporate peptide cyclization and docking constraints within a QUBO framework.
  • To compare the performance of a novel QUBO approach against a constraint programming (CP) method.

Main Methods:

  • Developed a resource-efficient QUBO encoding for peptide-protein docking on a tetrahedral lattice.
  • Implemented an end-to-end framework for evaluating QUBO and CP methods using Protein Data Bank (PDB) instances.
  • Utilized a classical simulated annealing solver for the QUBO approach.
  • Developed a novel constraint programming (CP) approach for benchmarking.

Main Results:

  • The QUBO approach successfully modeled feasible conformations for small peptide-protein docking problems (up to 6 peptide residues).
  • QUBO performance degraded significantly with increasing problem size (e.g., PDB 3WNE, 5LSO).
  • The CP approach demonstrated superior scalability, solving larger instances (up to 11 peptide residues, PDB 2F58).

Conclusions:

  • QUBO can be applied to peptide-protein docking but exhibits scaling limitations.
  • Constraint programming shows stronger performance and scalability for larger peptide-protein docking instances.
  • CP may be a more suitable method than QUBO for complex peptide-protein docking problems in drug design.