Structure-based protein function prediction using graph convolutional networks

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 ext...

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Veröffentlicht in:	Nature communications Jg. 12; H. 1; S. 3168 - 14
Hauptverfasser:	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
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 26.05.2021 Nature Publishing Group Nature Portfolio
Schlagworte:	631/114/1305 631/114/2410 631/114/2411 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 Humanities and Social Sciences Language Models, Biological Models, Molecular multidisciplinary Neural networks Peptide mapping Predictions Protein structure Protein Structure, Tertiary Proteins Proteins - physiology Proteins - ultrastructure Science Science (multidisciplinary) Structure-Activity Relationship Structure-function relationships
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-3
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-021-23303-9