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PyUUL provides an interface between biological structures and deep learning algorithms

Structural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottlen...

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Autores principales: Orlando, Gabriele, Raimondi, Daniele, Duran-Romaña, Ramon, Moreau, Yves, Schymkowitz, Joost, Rousseau, Frederic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857184/
https://www.ncbi.nlm.nih.gov/pubmed/35181656
http://dx.doi.org/10.1038/s41467-022-28327-3
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author Orlando, Gabriele
Raimondi, Daniele
Duran-Romaña, Ramon
Moreau, Yves
Schymkowitz, Joost
Rousseau, Frederic
author_facet Orlando, Gabriele
Raimondi, Daniele
Duran-Romaña, Ramon
Moreau, Yves
Schymkowitz, Joost
Rousseau, Frederic
author_sort Orlando, Gabriele
collection PubMed
description Structural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottleneck in biological research. Here we present PyUUL (https://pyuul.readthedocs.io/), a library to translate biological structures into 3D tensors, allowing an out-of-the-box application of state-of-the-art deep learning algorithms. The library converts biological macromolecules to data structures typical of computer vision, such as voxels and point clouds, for which extensive machine learning research has been performed. Moreover, PyUUL allows an out-of-the box GPU and sparse calculation. Finally, we demonstrate how PyUUL can be used by researchers to address some typical bioinformatics problems, such as structure recognition and docking.
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spelling pubmed-88571842022-03-04 PyUUL provides an interface between biological structures and deep learning algorithms Orlando, Gabriele Raimondi, Daniele Duran-Romaña, Ramon Moreau, Yves Schymkowitz, Joost Rousseau, Frederic Nat Commun Article Structural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottleneck in biological research. Here we present PyUUL (https://pyuul.readthedocs.io/), a library to translate biological structures into 3D tensors, allowing an out-of-the-box application of state-of-the-art deep learning algorithms. The library converts biological macromolecules to data structures typical of computer vision, such as voxels and point clouds, for which extensive machine learning research has been performed. Moreover, PyUUL allows an out-of-the box GPU and sparse calculation. Finally, we demonstrate how PyUUL can be used by researchers to address some typical bioinformatics problems, such as structure recognition and docking. Nature Publishing Group UK 2022-02-18 /pmc/articles/PMC8857184/ /pubmed/35181656 http://dx.doi.org/10.1038/s41467-022-28327-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Orlando, Gabriele
Raimondi, Daniele
Duran-Romaña, Ramon
Moreau, Yves
Schymkowitz, Joost
Rousseau, Frederic
PyUUL provides an interface between biological structures and deep learning algorithms
title PyUUL provides an interface between biological structures and deep learning algorithms
title_full PyUUL provides an interface between biological structures and deep learning algorithms
title_fullStr PyUUL provides an interface between biological structures and deep learning algorithms
title_full_unstemmed PyUUL provides an interface between biological structures and deep learning algorithms
title_short PyUUL provides an interface between biological structures and deep learning algorithms
title_sort pyuul provides an interface between biological structures and deep learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857184/
https://www.ncbi.nlm.nih.gov/pubmed/35181656
http://dx.doi.org/10.1038/s41467-022-28327-3
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