Hamiltonian replica exchange augmented with diffusion-based generative models and importance sampling to assess biomolecular conformational basins and barriers

Enhanced sampling techniques are essential for exploring biomolecular conformational dynamics that occur on timescales inaccessible to conventional molecular dynamics (MD) simulations. This study introduces a framework that combines Hamiltonian replica exchange (REST2) with denoising diffusion probabilistic models (DDPMs) and importance sampling to enhance the mapping of conformational free-energy landscapes. Building on previous applications of DDPMs to temperature replica exchange (TREM), we propose two key improvements. First, we adapt the method to REST2 by treating potential energy as a fluctuating variable. This adaptation allows for more efficient sampling in large biomolecular systems. Second, to further improve resolution in high-barrier regions, we develop an iterative scheme combining replica exchange, DDPM, and importance sampling along known collective variables. Benchmarking on the mini-protein CLN025 demonstrates that DDPM-refined REST2 achieves comparable accuracy to TREM while requiring fewer replicas. Application to the enzyme PTP1B reveals a loop transition pathway consistent with prior complex biased simulations, showcasing the approach's ability to uncover high-barrier transitions with minimal computational overhead with respect to conventional replica exchange approaches. Overall, this hybrid strategy enables more efficient exploration of free-energy landscapes, expanding the utility of generative models in enhanced sampling simulations.