ProteinGCN: Protein model quality assessment using GraphConvolutional Networks

Sanyal, S.; Anishchenko, I.; Dagar, A.; Baker, D.; Talukdar, P.

doi:10.1101/2020.04.06.028266

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bioRxiv · 10.1101/2020.04.06.028266

ProteinGCN: Protein model quality assessment using GraphConvolutional Networks

Sanyal, S.·Anishchenko, I.·Dagar, A.·Baker, D.·Talukdar, P.

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Abstract

Blind estimation of local (per-residue) and global (for the whole structure) accuracies in protein structure models is an essential step in many protein modeling applications. With the recent developments in deep-learning, single-model quality assessment methods have been also advanced, primarily through the use of 2D and 3D convolutional deep neural networks. Here we explore an alternative approach and train a graph convolutional network with nodes representing protein atoms and edges connecting spatially adjacent atom pairs on the dataset Rosetta-300k which contains a set of 300k conformations from 2,897 proteins. We show that our proposed architecture, PO_SCPLOWROTEINC_SCPLOWGCN, is capable of predicting both local and global accuracies in protein models at state-of-the-art levels. Further, the number of free parameters in PO_SCPLOWROTEINC_SCPLOWGCN is almost 1-2 orders of magnitude smaller compared to the 3D convolutional networks proposed earlier. We provide the source code of our work to encourage reproducible research.1

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Sanyal, S., Anishchenko, I., Dagar, A., Baker, D., Talukdar, P.. 2020-04-07. ProteinGCN: Protein model quality assessment using GraphConvolutional Networks. https://doi.org/10.1101/2020.04.06.028266

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