Structure-based protein function prediction using graph convolutional networks

By Vladimir Gligorijevic, P. Renfrew, T. Kościółek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, Ian Fisk, H. Vlamakis and 4 more. 2021

926 citations recorded by the source corpus.

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/. The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, the authors introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures.

Subfields: Molecular Biology

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “…other protein representations have been proposed for (graph) DL methods for individual structure prediction (Jing et al., 2020), protein-protein interactions (Dai & Bailey-Kellogg, 2021; Eismann et al., 2020; Townshend et al., 2019), or protein function prediction (Gligorijevi´c et al., 2021).”
Clustering predicted structures at the scale of the known protein universe (2023, methodology): “2b, including the top predicted pocket and coloured by the residue importance given by DeepFRI for this predicted function.”
SaProt: Protein Language Modeling with Structure-aware Vocabulary (2024, methodology): “, 2017) Subcellular Acc 8747 2191 2747 Binary Acc 5477 1336 1731 Protein Annotation Prediction EC (Gligorijević et al., 2021) Fmax 13089 1465 1604 GO (Gligorijević et al.”
Protein Representation Learning by Geometric Structure Pretraining (2022, methodology): “This line of works seeks to encode spatial information in protein structures by 3D CNNs (Derevyanko et al., 2018) or graph neural networks (GNNs) (Gligorijevi´c et al., 2021; Baldassarre et al., 2021; Jing et al., 2021; Wang et al., 2022a; Aykent & Xia, 2022).”