Molecular dynamics simulation of the truncated NMDA receptor in a partially restrained active-like state

The GluN1/GluN2A N-methyl-D-aspartate receptor (NMDAR) is a critical ligand-gated ion channel in the central nervous system, playing essential roles in synaptic plasticity, learning, and memory. Understanding its dynamics in the open-channel active state is paramount for deciphering its physiological functions and for developing targeted therapeutics. Despite many past efforts, the active state has not yet been fully resolved at atomic resolutions. To elucidate the molecular mechanism of the NMDAR activation, computer modeling and simulation are instrumental in providing detailed information about the dynamics and energetics of the receptor in various functional states. In this study, we started from a previously built active-like model of a truncated NMDAR (with its ligand-binding domains restrained), and explored its energetics and dynamics with extensive molecular dynamics (MD) simulation (total simulation time is 5.6 µs). Based on the MD simulation, we employed an array of analysis tools to study the fluctuations/motions at the levels of individual residues, the channel pore, and the global structure, and identified a dynamic network of polar/nonpolar interactions between residues. Furthermore, we used machine learning to identify key interactions and residues associated with channel pore opening/closing and annotated them with evolutionary conservation grades and known disease mutations in NMDAR. Taken together, our study provided rich structural/dynamic information and specific predictions for key residues/interactions which will inform future functional studies of the NMDA receptor.