Multiscale Quantum-Classical Simulation of Protein Autoproteolysis in a Million-Atom Membrane Environment
Abstract
Predicting chemical reactions in biomolecules is challenging because reactive centers are embedded within complex environments that strongly influence their energetics and mechanisms. Here, we present a hybrid quantum-classical workflow that combines molecular dynamics, density functional theory, and correlated wavefunction methods, tightly integrating CPUs, GPUs, and quantum processing units (QPUs) to enable simulations across multiple length and accuracy scales. We apply this approach to ADGRG1 autoproteolysis, a maturation reaction in an adhesion G protein-coupled receptor that has not previously been studied using correlated electronic-structure methods. Our simulations treat a biologically realistic system containing more than one million atoms, including the full-length uncleaved receptor embedded in an endoplasmic-reticulum membrane, while incorporating a 54-qubit IQM quantum processor and up to 1,200 NVIDIA H100 GPUs. These results demonstrate how quantum computers can serve as accelerators within scalable scientific workflows, extending correlated electronic-structure methods to biologically relevant systems.
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BibTeX
@misc{orlov2026multiscale,
title = {{Multiscale Quantum-Classical Simulation of Protein Autoproteolysis in a Million-Atom Membrane Environment}},
author = {Alexander Orlov and Alexis Ralli and Tim Weaving and Michael Williams de la Bastida and Reuben Martin and Zohim Chandani and Minh Chung and P. V. Sriluckshmy and Fedor Simkovic IV and Luigi Iapichino and Peter J. Love and Christian B. Mendl and Andrea Townsend-Nicholson and Peter V. Coveney},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23108},
eprint = {2610.23108},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23108},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Multiscale Quantum-Classical Simulation of Protein Autoproteolysis in a Million-Atom Membrane Environment
AU - Alexander Orlov
AU - Alexis Ralli
AU - Tim Weaving
AU - Michael Williams de la Bastida
AU - Reuben Martin
AU - Zohim Chandani
AU - Minh Chung
AU - P. V. Sriluckshmy
AU - Fedor Simkovic IV
AU - Luigi Iapichino
AU - Peter J. Love
AU - Christian B. Mendl
AU - Andrea Townsend-Nicholson
AU - Peter V. Coveney
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.23108
UR - https://arxiv.org/abs/2610.23108
AB - Predicting chemical reactions in biomolecules is challenging because reactive centers are embedded within complex environments that strongly influence their energetics and mechanisms. Here, we present a hybrid quantum-classical workflow that combines molecular dynamics, density functional theory, and correlated wavefunction methods, tightly integrating CPUs, GPUs, and quantum processing units (QPUs) to enable simulations across multiple length and accuracy scales. We apply this approach to ADGRG1 autoproteolysis, a maturation reaction in an adhesion G protein-coupled receptor that has not previously been studied using correlated electronic-structure methods. Our simulations treat a biologically realistic system containing more than one million atoms, including the full-length uncleaved receptor embedded in an endoplasmic-reticulum membrane, while incorporating a 54-qubit IQM quantum processor and up to 1,200 NVIDIA H100 GPUs. These results demonstrate how quantum computers can serve as accelerators within scalable scientific workflows, extending correlated electronic-structure methods to biologically relevant systems.
ER -