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CITATION.cff
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# YAML 1.2
# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/)
cff-version: 1.2.0
message: If you use this software, please cite it as below.
title: 'Structure-based protein function prediction using
graph convolutional networks'
doi: 10.1038/s41467-021-23303-9
authors:
- given-names: Vladimir
family-names: Gligorijević
affiliation: Center for Computational Biology, Flatiron
Institute, New York, NY, USA
orcid: https://orcid.org/0000-0002-5165-0973
- given-names: P. Douglas
family-names: Renfrew
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
- given-names: Tomasz
family-names: Kosciolek
affiliation: Malopolska Centre of Biotechnology, Jagiellonian University,
Krakow, Poland
orcid: https://orcid.org/0000-0002-5693-3593
- given-names: Julia Koehler
family-names: Leman
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
orcid: https://orcid.org/0000-0002-5693-3593
- given-names: Daniel
family-names: Berenberg
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
- given-names: Tommi
family-names: Vatanen
affiliation: Broad Institute of MIT and Harvard, Cambridge, MA, USA
orcid: https://orcid.org/0000-0003-0949-1291
- given-names: Chris
family-names: Chandler
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
- given-names: Bryn C.
family-names: Taylor
affiliation: Biomedical Sciences Graduate Program,
University of California San Diego, La Jolla, CA, USA
- given-names: Ian M.
family-names: Fisk
affiliation: Scientific Computing Core, Flatiron Institute,
Simons Foundation, New York, NY, USA
- given-names: Hera
family-names: Vlamakis
affiliation: Broad Institute of MIT and Harvard, Cambridge, MA, USA
orcid: https://orcid.org/0000-0003-1086-9191
- given-names: Ramnik J.
family-names: Xavier
affiliation: Broad Institute of MIT and Harvard, Cambridge, MA, USA
orcid: https://orcid.org/0000-0002-5630-5167
- given-names: Rob
family-names: Knight
affiliation: Department of Pediatrics, University of California San Diego,
La Jolla, CA, USA
orcid: https://orcid.org/0000-0002-0975-9019
- given-names: Kyunghyun
family-names: Cho
affiliation: Center for Data Science, New York University, New York, NY, USA
- given-names: Richard
family-names: Bonneau
affiliation: Center for Computational Biology, Flatiron Institute, New York, NY, USA
orcid: https://orcid.org/0000-0003-4354-7906
version: 1.0.0
date-released: 2021-03-31
repository-code: https://github.com/flatironinstitute/DeepFRI
license: BSD-3-Clause
keywords:
- "Graph Neural Networks"
- "Protein function"
- "Function prediction"
preferred-citation:
type: article
authors:
- given-names: Vladimir
family-names: Gligorijević
affiliation: Center for Computational Biology, Flatiron
Institute, New York, NY, USA
orcid: https://orcid.org/0000-0002-5165-0973
- given-names: P. Douglas
family-names: Renfrew
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
- given-names: Tomasz
family-names: Kosciolek
affiliation: Malopolska Centre of Biotechnology, Jagiellonian University,
Krakow, Poland
orcid: https://orcid.org/0000-0002-5693-3593
- given-names: Julia Koehler
family-names: Leman
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
orcid: https://orcid.org/0000-0002-5693-3593
- given-names: Daniel
family-names: Berenberg
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
- given-names: Tommi
family-names: Vatanen
affiliation: Broad Institute of MIT and Harvard, Cambridge, MA, USA
orcid: https://orcid.org/0000-0003-0949-1291
- given-names: Chris
family-names: Chandler
affiliation: Center for Computational Biology, Flatiron Institute,
New York, NY, USA
- given-names: Bryn C.
family-names: Taylor
affiliation: Biomedical Sciences Graduate Program,
University of California San Diego, La Jolla, CA, USA
- given-names: Ian M.
family-names: Fisk
affiliation: Scientific Computing Core, Flatiron Institute,
Simons Foundation, New York, NY, USA
- given-names: Hera
family-names: Vlamakis
affiliation: Broad Institute of MIT and Harvard, Cambridge, MA, USA
orcid: https://orcid.org/0000-0003-1086-9191
- given-names: Ramnik J.
family-names: Xavier
affiliation: Broad Institute of MIT and Harvard, Cambridge, MA, USA
orcid: https://orcid.org/0000-0002-5630-5167
- given-names: Rob
family-names: Knight
affiliation: Department of Pediatrics, University of California San Diego,
La Jolla, CA, USA
orcid: https://orcid.org/0000-0002-0975-9019
- given-names: Kyunghyun
family-names: Cho
affiliation: Center for Data Science, New York University, New York, NY, USA
- given-names: Richard
family-names: Bonneau
affiliation: Center for Computational Biology, Flatiron Institute, New York, NY, USA
orcid: https://orcid.org/0000-0003-4354-7906
doi: "10.1038/s41467-021-23303-9"
journal: "Nature Communications"
month: 5
title: "Structure-based protein function prediction using
graph convolutional networks"
abstract: '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/.'
year: 2021