GraphQA: protein model quality assessment using graph convolutional networks

By Federico Baldassarre, David Ménendez Hurtado, Arne Elofsson, Hossein Azizpour. 2020

127 citations recorded by the source corpus.

Abstract Motivation Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein’s structure can be time-consuming, prohibitively expensive and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results. GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance and computational efficiency. Results GraphQA performs similarly to state-of-the-art methods despite using a relatively low number of input features. In addition, the graph network structure provides an improvement over the architecture used in ProQ4 operating on the same input features. Finally, the individual contributions of GraphQA components are carefully evaluated. Availability and implementation PyTorch implementation, datasets, experiments and link to an evaluation server are available through this GitHub repository: github.com/baldassarreFe/graphqa. Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

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Citations in the corpus

Multi-Scale Representation Learning on Proteins (2022, methodology): “, 2017), and protein structure quality prediction (Baldassarre et al., 2021).”
Equivariant Graph Neural Networks for 3D Macromolecular Structure (2021, unknown): “Graph neural networks (GNNs) have enjoyed widespread use on macromolecular structure due to the natural representation of structures as graphs, with residues or atoms as nodes and edges drawn based on bonding or spatial proximity (Ingraham et al., 2019; Baldassarre et al., 2020).”
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021, methodology): “…to two recent hybrid methods: Gligorijevic et al. (2019), who process primary structure with several LSTM cells ﬁrst and then apply GCNN, and Baldassarre et al. (2020), who indirectly process primary and secondary structures by using as input distances along the backbone as edge features…”
Protein Representation Learning by Geometric Structure Pretraining (2022, methodology): “, 2021), model quality assessment (Baldassarre et al., 2021; Derevyanko et al., 2018), and function prediction (Gligorijević et al.”
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, background): “As a sub-task evaluated in protein folding, protein quality assessment estimates the quality of computational protein models in terms of divergence from their native structure, for which GNN architectures have also been applied (Sanyal et al., 2020; Baldassarre et al., 2021).”