Interface-Driven Peptide Folding: Quantum Computations on Simulated Membrane Surfaces

Antimicrobial peptides (AMPs) play important roles in cancer, autoimmunediseases, and aging. A critical aspect of AMP functionality is their targetedinteraction with pathogen membranes, which often possess altered lipidcompositions. Designing AMPs with enhanced therapeutic properties relies on anuanced understanding of these interactions, which are believed to trigger arearrangement of these peptides from random coil to alpha-helicalconformations, essential for their lytic action. Traditional supercomputing hasconsistently encountered difficulties in accurately modeling these structuralchanges, especially within membrane environments, thereby opening anopportunity for more advanced approaches. This study extends an existingquantum computing algorithm to address the complexities of antimicrobialpeptide interactions at interfaces. Our approach enables the prediction of theoptimal conformation of peptides located in the transition region betweenhydrophilic and hydrophobic phases, akin to lipid membranes. The new method hasbeen applied to model the structure of three 10-amino-acid-long peptides, eachexhibiting hydrophobic, hydrophilic, or amphipathic properties in differentmedia and at interfaces between solvents of different polarity. Notably, ourapproach does not demand a higher number of qubits compared to simulations inhomogeneous media, making it more feasible with current quantum computingresources. Despite existing limitations in computational power and qubitaccessibility, our findings demonstrate the significant potential of quantumcomputing in accurately characterizing complex biomolecular processes,particularly the folding of AMPs at membrane models. This research paves theway for future advances in quantum computing to enhance the accuracy andapplicability of biomolecular simulations.