Elucidating Protein\nStructures in the Gas Phase: Traversing\nConfiguration Space with Biasing Methods

Achieving accurate characterization of protein structures\nin the\ngas phase continues to be a formidable challenge. To tackle this issue,\nthe present study employs Molecular Dynamics (MD) simulations in tandem\nwith enhanced sampling techniques (methods designed to efficiently\nexplore protein conformations). The objective is to identify suitable\nstructures of proteins by contrasting their calculated Collision Cross-Section\n(CCS) with those observed experimentally. Significant discrepancies\nwere observed between the initial MD-simulated and experimentally\nmeasured CCS values through Ion Mobility–Mass Spectrometry\n(IMS-MS). To bridge this gap, we employed two distinct enhanced sampling\nmethods, Harmonic Biasing Potential and Adaptive Biasing Force, which\nhelp the proteins overcome energy barriers to adopt more compact configurations.\nThese techniques leverage the radius of gyration as a reaction coordinate\n(guiding parameter), guiding the system toward compressed states that\npotentially match experimental configurations more closely. The guiding\nforces are only employed to overcome existing barriers and are removed\nto allow the protein to naturally arrive at a potential gas phase\nconfiguration. The results demonstrated close alignment (within ∼4%)\nbetween simulated and experimental CCS values despite using different\nstrengths and/or methods, validating their efficacy. This work lays\nthe groundwork for future studies aimed at optimizing biasing methods\nand expanding the collective variables used for more accurate gas-phase\nstructural predictions.