| Home > Publications database > Using quantum annealing to design lattice proteins |
| Journal Article | FZJ-2024-01696 |
; ; ;
2024
APS
College Park, MD
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Please use a persistent id in citations: doi:10.1103/PhysRevResearch.6.013162 doi:10.34734/FZJ-2024-01696
Abstract: Quantum annealing has shown promise for finding solutions to difficult optimization problems, includingprotein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problemin a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups:hydrophobic (H) and polar (P). Using a set of 22 HP sequences with up to 64 amino acids, we demonstratedthe fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of theabove, where the task is to predict protein sequences that fold to a given structure. Here, we approach the designproblem by a two-step procedure implemented and executed on a D-Wave machine. In the first step, we performa pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequenceswhich minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin-glassrepresentation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structureswith 30–64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences fromthe first step according to their ability to fold to the intended structure. In addition, we try solving the sequenceoptimization problem using only the quantum processing unit (QPU), which confines us to sizes 20, due toexponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects ofcontrol errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numericallyanalyzing the Schrödinger equation. We find that the simulated success rates in the presence of control noisesemiquantitatively reproduce the modest pure QPU results for larger chains.
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