Structure-based protein function prediction using graph convolutional networks View Full Text


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Article Info

DATE

2021-05-26

AUTHORS

Vladimir Gligorijević, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, Ian M. Fisk, Hera Vlamakis, Ramnik J. Xavier, Rob Knight, Kyunghyun Cho, Richard Bonneau

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/. More... »

PAGES

3168

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-021-23303-9

    DOI

    http://dx.doi.org/10.1038/s41467-021-23303-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1138356830

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34039967


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    358 grid-institutes:grid.9654.e schema:alternateName The Liggins Institute, University of Auckland, Auckland, New Zealand
    359 schema:name Broad Institute of MIT and Harvard, Cambridge, MA, USA
    360 The Liggins Institute, University of Auckland, Auckland, New Zealand
    361 rdf:type schema:Organization
     




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