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We developed a multiscale quantum-classical simulation workflow integrating QM/MM with correlated wavefunction methods and a 54-qubit QPU, alongside 1,200 H100 GPUs, to model ADGRG1 autoproteolysis within a million-atom membrane environment. This approach overcomes the computational barrier of applying high-accuracy electronic structure methods to large, biologically relevant systems, enabling the study of previously intractable reaction mechanisms. The simulations successfully captured the autoproteolysis reaction of ADGRG1, demonstrating the feasibility of using quantum processors as accelerators in complex scientific workflows.
A million-atom protein simulation with correlated electronic structure methods is now possible, powered by a hybrid quantum-classical architecture.
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.