Sunday, May 18, 2025
5/18/2025
Data-driven modeling of the vibrational spectroscopy of ion channels - Data-driven modeling of the vibrational spectroscopy of ion channels Abstract The long-term goals of this research program are (1) to develop new computational methods for accurate sim- ulations of linear and two-dimensional infrared (2D IR) spectra of proteins and use the developed methods to simulate recent and design new 2D IR experiments to (2) investigate the mechanisms of ion transport and molec- ular origins of selectivity in the KcsA ion channel and (3) elucidate the conformational and hydrational changes of the voltage-sensing domain of the KvAP channel during voltage activation. Despite decades of research, we still don’t have the direct information on ion channel dynamics and the effects of an applied voltage on ion channel structures. 2D IR spectroscopy is an emerging analytical technique that probes protein dynamics with chemi- cal bond-specific spatial and high temporal resolution. 2D IR spectroscopy is analogous to NMR spectroscopy, except that it uses pulses of infrared light to measure vibrations rather than pulsed magnetic fields for nuclear spins. New methodology improvements expand the frontiers of 2D IR spectroscopy, permitting the study of com- plex biological systems in their native environments. Particularly interesting are systems for which NMR and X-ray crystallography are difficult to apply, such as ion channels. Interpreting congested 2D IR spectra is difficult without simulations that can quantitatively connect spectral features to atomistic structural models. Currently, 2D IR spectra of proteins are modeled using model-driven, mostly empirical, spectroscopic maps that correlate solvent-induced electric field and backbone dihedral angles to vibrational frequencies and couplings. This ap- proach, however, lacks systematic improvability, has limited transferability, provides qualitative accuracy at best, and is inaccurate for peptides in heterogeneous environments. Shifting away from the model-driven paradigm, we will use ab initio-based data-driven approaches based on Graph Neural Networks to accurately model the vibrational spectra of proteins in realistic environments. The proposed methods will provide computational sup- port for the ongoing and future 2D IR experiments on ion channels. The results of the proposed studies will significantly enhance our understanding of the molecular-level mechanisms of function of ion channels. A large spectrum of neurological, cardiovascular, and muscle disorders result from defective ion channel functioning. A better understanding of the origins of these diseases will pave the way for improved therapeutics that target ion channels.
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