RT Journal Article
SR Electronic
T1 ProteinGCN: Protein model quality assessment using Graph Convolutional Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.06.028266
DO 10.1101/2020.04.06.028266
A1 Sanyal, Soumya
A1 Anishchenko, Ivan
A1 Dagar, Anirudh
A1 Baker, David
A1 Talukdar, Partha
YR 2020
UL http://biorxiv.org/content/early/2020/04/07/2020.04.06.028266.abstract
AB 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, ProteinGCN, 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 ProteinGCN 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