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

The peptide-protein docking problem is an important problem in structural biology that facilitates rational and efficient drug design. In this work, we explore modeling and solving this problem with the quantum-amenable quadratic unconstrained binary optimization (QUBO) formalism. Our work extends r...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 20395 - 11
Main Authors: Brubaker, J. Kyle, Booth, Kyle E. C., Arakawa, Akihiko, Furrer, Fabian, Ghosh, Jayeeta, Sato, Tsutomu, Katzgraber, Helmut G.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.07.2025
Nature Portfolio
Subjects:
631/114
631/154
631/337
631/57
Algorithms
Databases, Protein
Humanities and Social Sciences
Molecular Docking Simulation - methods
multidisciplinary
Peptides, Cyclic - chemistry
Peptides, Cyclic - metabolism
Protein Binding
Protein Conformation
Proteins - chemistry
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:The peptide-protein docking problem is an important problem in structural biology that facilitates rational and efficient drug design. In this work, we explore modeling and solving this problem with the quantum-amenable quadratic unconstrained binary optimization (QUBO) formalism. Our work extends recent efforts by incorporating the objectives and constraints associated with peptide cyclization and peptide-protein docking in the two-particle model on a tetrahedral lattice. We propose a “resource efficient” QUBO encoding for this problem, and baseline its performance with a novel constraint programming (CP) approach. We implement an end-to-end framework that enables the evaluation of our methods on instances from the Protein Data Bank (PDB). Our results show that the QUBO approach, using a classical simulated annealing solver, is able to find feasible conformations for problems with up to 6 peptide residues and 34 target protein residues (PDB 3WNE, 5LSO), but has trouble scaling beyond this problem size. In contrast, the CP approach can solve the largest instance in our test set, containing 11 peptide residues and 49 target protein residues (PDB 2F58). We conclude that while QUBO can be used to successfully tackle this problem, its scaling limitations and the strong performance of the CP method suggest that it may not be the best choice.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-05565-1