  The dynamic nature of proteins is crucial for determining their biological
functions and properties, for which Monte Carlo (MC) and molecular dynamics
(MD) simulations stand as predominant tools to study such phenomena. By
utilizing empirically derived force fields, MC or MD simulations explore the
conformational space through numerically evolving the system via Markov chain
or Newtonian mechanics. However, the high-energy barrier of the force fields
can hamper the exploration of both methods by the rare event, resulting in
inadequately sampled ensemble without exhaustive running. Existing
learning-based approaches perform direct sampling yet heavily rely on
target-specific simulation data for training, which suffers from high data
acquisition cost and poor generalizability. Inspired by simulated annealing, we
propose Str2Str, a novel structure-to-structure translation framework capable
of zero-shot conformation sampling with roto-translation equivariant property.
Our method leverages an amortized denoising score matching objective trained on
general crystal structures and has no reliance on simulation data during both
training and inference. Experimental results across several benchmarking
protein systems demonstrate that Str2Str outperforms previous state-of-the-art
generative structure prediction models and can be orders of magnitude faster
compared to long MD simulations. Our open-source implementation is available at
https://github.com/lujiarui/Str2Str
