  Single particle, cryogenic electron microscopy (cryo-EM) experiments now
routinely produce high-resolution data for large proteins and their complexes.
Building an atomic model into a cryo-EM density map is challenging,
particularly when no structure for the target protein is known a priori.
Existing protocols for this type of task often rely on significant human
intervention and can take hours to many days to produce an output. Here, we
present a fully automated, template-free model building approach that is based
entirely on neural networks. We use a graph convolutional network (GCN) to
generate an embedding from a set of rotamer-based amino acid identities and
candidate 3-dimensional C$\alpha$ locations. Starting from this embedding, we
use a bidirectional long short-term memory (LSTM) module to order and label the
candidate identities and atomic locations consistent with the input protein
sequence to obtain a structural model. Our approach paves the way for
determining protein structures from cryo-EM densities at a fraction of the time
of existing approaches and without the need for human intervention.
