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, the authors introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence fea...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/38a935b25ce6438ca4cd1a800c3d5727
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spelling	oai:doaj.org-article:38a935b25ce6438ca4cd1a800c3d57272021-12-02T14:47:28ZStructure-based protein function prediction using graph convolutional networks10.1038/s41467-021-23303-92041-1723https://doaj.org/article/38a935b25ce6438ca4cd1a800c3d57272021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23303-9https://doaj.org/toc/2041-1723The 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.Vladimir GligorijevićP. Douglas RenfrewTomasz KosciolekJulia Koehler LemanDaniel BerenbergTommi VatanenChris ChandlerBryn C. TaylorIan M. FiskHera VlamakisRamnik J. XavierRob KnightKyunghyun ChoRichard BonneauNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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 Structure-based protein function prediction using graph convolutional networks
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, 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.
format	article
author	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
author_facet	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
author_sort	Vladimir Gligorijević
title	Structure-based protein function prediction using graph convolutional networks
title_short	Structure-based protein function prediction using graph convolutional networks
title_full	Structure-based protein function prediction using graph convolutional networks
title_fullStr	Structure-based protein function prediction using graph convolutional networks
title_full_unstemmed	Structure-based protein function prediction using graph convolutional networks
title_sort	structure-based protein function prediction using graph convolutional networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/38a935b25ce6438ca4cd1a800c3d5727
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Structure-based protein function prediction using graph convolutional networks

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