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Single particle, cryogenic electron microscopy (cryo-EM) experiments nowroutinely 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 humanintervention and can take hours to many days to produce an output. Here, wepresent a fully automated, template-free model building approach that is basedentirely on neural networks. We use a graph convolutional network (GCN) togenerate an embedding from a set of rotamer-based amino acid identities andcandidate 3-dimensional C$\alpha$ locations. Starting from this embedding, weuse a bidirectional long short-term memory (LSTM) module to order and label thecandidate identities and atomic locations consistent with the input proteinsequence to obtain a structural model. Our approach paves the way fordetermining protein structures from cryo-EM densities at a fraction of the timeof existing approaches and without the need for human intervention.

Sequence-guided protein structure determination using graph convolutional and recurrent networks