Using quantum annealing to design lattice proteins
Irbäck, Anders LU
; Knuthson, Lucas
LU
; Mohanty, Sandipan
LU
and Peterson, Carsten
LU
(2024)
In Physical Review Research
6(1).
- Abstract
- Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in 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 demonstrated the 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 the above, where the task is to predict protein sequences that fold to a given structure. Here, we... (More)
- Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in 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 demonstrated the 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 the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin-glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30–64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the quantum processing unit (QPU), which confines us to sizes ≤20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schrödinger equation. We find that the simulated success rates in the presence of control noise semiquantitatively reproduce the modest pure QPU results for larger chains. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0ae4ca34-d47a-410a-9c02-65dff2b6689f
- author
- Irbäck, Anders
LU
; Knuthson, Lucas
LU
; Mohanty, Sandipan
LU
and Peterson, Carsten
LU
- organization
- publishing date
- 2024-02-13
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- protein design, quantum computing, quantum annealing, biophysical, Ising model
- in
- Physical Review Research
- volume
- 6
- issue
- 1
- article number
- 013162
- publisher
- American Physical Society
- external identifiers
-
- scopus:85185410575
- ISSN
- 2643-1564
- DOI
- 10.1103/PhysRevResearch.6.013162
- language
- English
- LU publication?
- yes
- id
- 0ae4ca34-d47a-410a-9c02-65dff2b6689f
- date added to LUP
- 2024-02-14 10:32:07
- date last changed
- 2024-04-18 15:56:10
@article{0ae4ca34-d47a-410a-9c02-65dff2b6689f,
abstract = {{Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in 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 demonstrated the 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 the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin-glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30–64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the quantum processing unit (QPU), which confines us to sizes ≤20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schrödinger equation. We find that the simulated success rates in the presence of control noise semiquantitatively reproduce the modest pure QPU results for larger chains.}},
author = {{Irbäck, Anders and Knuthson, Lucas and Mohanty, Sandipan and Peterson, Carsten}},
issn = {{2643-1564}},
keywords = {{protein design; quantum computing; quantum annealing; biophysical; Ising model}},
language = {{eng}},
month = {{02}},
number = {{1}},
publisher = {{American Physical Society}},
series = {{Physical Review Research}},
title = {{Using quantum annealing to design lattice proteins}},
url = {{http://dx.doi.org/10.1103/PhysRevResearch.6.013162}},
doi = {{10.1103/PhysRevResearch.6.013162}},
volume = {{6}},
year = {{2024}},
}