Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-05565-1 |
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| author | J. Kyle Brubaker Kyle E. C. Booth Akihiko Arakawa Fabian Furrer Jayeeta Ghosh Tsutomu Sato Helmut G. Katzgraber |
| author_facet | J. Kyle Brubaker Kyle E. C. Booth Akihiko Arakawa Fabian Furrer Jayeeta Ghosh Tsutomu Sato Helmut G. Katzgraber |
| author_sort | J. Kyle Brubaker |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-41373ac3f71849178e18c20fc39bc138 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-41373ac3f71849178e18c20fc39bc1382025-08-20T03:38:12ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-05565-1Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide dockingJ. Kyle Brubaker0Kyle E. C. Booth1Akihiko Arakawa2Fabian Furrer3Jayeeta Ghosh4Tsutomu Sato5Helmut G. Katzgraber6Amazon Advanced Solutions LabAmazon Advanced Solutions LabResearch Division, Chugai Pharmaceutical Co., Ltd.Amazon Advanced Solutions LabAWS Professional ServicesResearch Division, Chugai Pharmaceutical Co., Ltd.Amazon Advanced Solutions LabAbstract 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.https://doi.org/10.1038/s41598-025-05565-1 |
| spellingShingle | J. Kyle Brubaker Kyle E. C. Booth Akihiko Arakawa Fabian Furrer Jayeeta Ghosh Tsutomu Sato Helmut G. Katzgraber Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking Scientific Reports |
| title | Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking |
| title_full | Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking |
| title_fullStr | Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking |
| title_full_unstemmed | Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking |
| title_short | Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking |
| title_sort | quadratic unconstrained binary optimization and constraint programming approaches for lattice based cyclic peptide docking |
| url | https://doi.org/10.1038/s41598-025-05565-1 |
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