Classical Simulations on Quantum Computers: Interface-Driven Peptide Folding on Simulated Membrane Surfaces

Antimicrobial peptides (AMPs) are crucial in the fight against infections and play significant roles in various health contexts, including cancer, autoimmune diseases, and aging. A key aspect of AMP functionality is their selective interaction with pathogen membranes, which often exhibit altered lipid compositions. These interactions are thought to induce a conformational shift in AMPs from random coil to alpha-helical structures, essential for their lytic activity. Traditional computational approaches have faced challenges in accurately modeling these structural changes, especially in membrane environments, thereby opening and opportunity for more advanced approaches.

This study extends an existing quantum computing algorithm, initially designed for peptide folding simulations in homogeneous environments, to address the complexities of AMP interactions at interfaces. Our approach enables the prediction of the optimal conformation of peptides located in the transition region between hydrophilic and hydrophobic phases, resembling lipid membranes. The new method was tested on three 10-amino-acid-long peptides, each characterized by distinct hydrophobic, hydrophilic, or amphipathic properties, across different media and at interfaces between solvents of different polarity.

The developed method successfully modeled the structure of the peptides without increasing the number of qubits required compared to simulations in homogeneous media, making it more feasible with current quantum computing resources. Despite the current limitations in computational power and qubit availability, the findings demonstrate the significant potential of quantum computing in accurately characterizing complex biomolecular processes, particularly AMP folding at membrane models.

This research highlights the promising applications of quantum computing in biomolecular simulations, paving the way for future advancements in the development of novel therapeutic agents. We aim to offer a new perspective on enhancing the accuracy and applicability of biomolecular simulations in the context of AMP interactions with membrane models.