  Accurate protein structure prediction from amino-acid sequences is critical
to better understanding the protein function. Recent advances in this area
largely benefit from more precise inter-residue distance and orientation
predictions, powered by deep neural networks. However, the structure
optimization procedure is still dominated by traditional tools, e.g. Rosetta,
where the structure is solved via minimizing a pre-defined statistical energy
function (with optional prediction-based restraints). Such energy function may
not be optimal in formulating the whole conformation space of proteins. In this
paper, we propose a fully-differentiable approach for protein structure
optimization, guided by a data-driven generative network. This network is
trained in a denoising manner, attempting to predict the correction signal from
corrupted distance matrices between Ca atoms. Once the network is well trained,
Langevin dynamics based sampling is adopted to gradually optimize structures
from random initialization. Extensive experiments demonstrate that our EBM-Fold
approach can efficiently produce high-quality decoys, compared against
traditional Rosetta-based structure optimization routines.
