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Structure-based protein function prediction using graph convolutional networks

Nature communications, 2021-05, Vol.12 (1), p.3168-3168, Article 3168 [Peer Reviewed Journal]

The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-021-23303-9 ;PMID: 34039967

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Title:
Structure-based protein function prediction using graph convolutional networks
Author: Gligorijević, Vladimir ; Renfrew, P Douglas ; Kosciolek, Tomasz ; Leman, Julia Koehler ; Berenberg, Daniel ; Vatanen, Tommi ; Chandler, Chris ; Taylor, Bryn C ; Fisk, Ian M ; Vlamakis, Hera ; Xavier, Ramnik J ; Knight, Rob ; Cho, Kyunghyun ; Bonneau, Richard
Subjects: Amino Acid Sequence ; Annotations ; Artificial neural networks ; Automation ; Computational Biology - methods ; Computer applications ; Databases, Protein - statistics & numerical data ; Datasets as Topic ; Deep Learning ; Feature extraction ; Homology ; Language ; Models, Biological ; Models, Molecular ; Neural networks ; Peptide mapping ; Predictions ; Protein structure ; Protein Structure, Tertiary ; Proteins ; Proteins - physiology ; Proteins - ultrastructure ; Structure-Activity Relationship ; Structure-function relationships
Is Part Of: Nature communications, 2021-05, Vol.12 (1), p.3168-3168, Article 3168
Description: 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/ .
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 2041-1723
EISSN: 2041-1723
DOI: 10.1038/s41467-021-23303-9
PMID: 34039967
Source: MEDLINE
PubMed Central
ProQuest Central
DOAJ Directory of Open Access Journals

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Structure-based protein function prediction using graph convolutional networks

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