ProteinGCN: Protein model quality assessment using GraphConvolutional Networks
Sanyal, S.; Anishchenko, I.; Dagar, A.; Baker, D.; Talukdar, P.
Show 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
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding 98%
- SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction 98%
- Estimating Protein Complex Model Accuracy Using Graph Transformers and Pairwise Similarity Graphs 97%
Similar papers in this journal
Similar papers in this journal
- Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions 97%
- Multi-Head Attention-based U-Nets for Predicting Protein Domain Boundaries Using 1D Sequence Features and 2D Distance Maps 97%
- A Comprehensive Survey of Scoring Functions for Protein Docking Models 96%
Similar papers in this journal
- BAGEL: Protein Engineering via Exploration of an Energy Landscape 97%
- Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins 97%
- Paying Attention to Attention: High Attention Sites as Indicators of Protein Family and Function in Language Models 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.