Membrane protein diffusion without more molecular dynamics: a neural stochastic model stretches trajectories to 40,000 frames while holding near-Brownian motion
Coarse-grained molecular dynamics gives short membrane-protein trajectories, but long-time diffusion statistics are costly to obtain. A neural stochastic model combines a transformer encoding of protein configurations, recurrent memory at multiple time scales and a stochastic latent process with learned memory to generate correlated displacements autoregressively. In the seven-PIEZO system it yields a mean anomalous diffusion exponent of 1.048±0.012 against 1.049±0.022 for MD, where the earlier VAR-SDE model gives 1.563±0.016, and its trajectories extend to 40,000 frames.