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

• URL: http://arxiv.org/abs/2007.06847v3
• Date: Thu, 3 Sep 2020 02:25:28 GMT
• Title: Sequence-guided protein structure determination using graph convolutional and recurrent networks
• Authors: Po-Nan Li and Saulo H. P. de Oliveira and Soichi Wakatsuki and Henry van den Bedem
• Abstract summary: Single particle, cryogenic electron microscopy (cryo-EM) experiments now routinely produce high-resolution data for large proteins. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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