TITLE: SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning 

ABSTRACT: Despite recent advancements in deep learning methods for protein structure
prediction and representation, little focus has been directed at the
simultaneous inclusion and prediction of protein backbone and sidechain
structure information. We present SidechainNet, a new dataset that directly
extends the ProteinNet dataset. SidechainNet includes angle and atomic
coordinate information capable of describing all heavy atoms of each protein
structure. In this paper, we provide background information on the availability
of protein structure data and the significance of ProteinNet. Thereafter, we
argue for the potentially beneficial inclusion of sidechain information through
SidechainNet, describe the process by which we organize SidechainNet, and
provide a software package (https://github.com/jonathanking/sidechainnet) for
data manipulation and training with machine learning models.
Despite recent advancements in deep learning methods for protein structure
prediction and representation, little focus has been directed at the
simultaneous inclusion and prediction of protein backbone and sidechain
structure information. We present SidechainNet, a new dataset that directly
extends the ProteinNet dataset. SidechainNet includes angle and atomic
coordinate information capable of describing all heavy atoms of each protein
structure. In this paper, we provide background information on the availability
of protein structure data and the significance of ProteinNet. Thereafter, we
argue for the potentially beneficial inclusion of sidechain information through
SidechainNet, describe the process by which we organize SidechainNet, and
provide a software package (https://github.com/jonathanking/sidechainnet) for
data manipulation and training with machine learning models.
SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning
http://arxiv.org/abs/2010.08162v2