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

TL;DR

This paper compares QUBO and constraint programming approaches for peptide-protein docking, demonstrating that CP outperforms QUBO in scalability and feasibility for larger problem instances.

Contribution

It introduces a resource-efficient QUBO encoding for peptide docking and benchmarks it against a novel CP approach on real biological data.

Findings

QUBO finds feasible solutions for up to 6 peptide residues

CP solves problems with up to 13 peptide residues

QUBO has scaling limitations compared to CP

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, but has trouble scaling beyond this problem size. In contrast, the CP approach can solve problems with up to 13 peptide residues and 34 target protein residues. 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.