Protein Design with Deep Learning

Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of p...

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Autores principales:	Marianne Defresne, Sophie Barbe, Thomas Schiex
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	computational protein design artificial neural network protein structure inverse folding problem language models deep learning Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ec6909658fea4b868fdc62fff2334617
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id	oai:doaj.org-article:ec6909658fea4b868fdc62fff2334617
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spelling	oai:doaj.org-article:ec6909658fea4b868fdc62fff23346172021-11-11T17:12:00ZProtein Design with Deep Learning10.3390/ijms2221117411422-00671661-6596https://doaj.org/article/ec6909658fea4b868fdc62fff23346172021-10-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/21/11741https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.Marianne DefresneSophie BarbeThomas SchiexMDPI AGarticlecomputational protein designartificial neural networkprotein structureinverse folding problemlanguage modelsdeep learningBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 11741, p 11741 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	computational protein design artificial neural network protein structure inverse folding problem language models deep learning Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	computational protein design artificial neural network protein structure inverse folding problem language models deep learning Biology (General) QH301-705.5 Chemistry QD1-999 Marianne Defresne Sophie Barbe Thomas Schiex Protein Design with Deep Learning
description	Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.
format	article
author	Marianne Defresne Sophie Barbe Thomas Schiex
author_facet	Marianne Defresne Sophie Barbe Thomas Schiex
author_sort	Marianne Defresne
title	Protein Design with Deep Learning
title_short	Protein Design with Deep Learning
title_full	Protein Design with Deep Learning
title_fullStr	Protein Design with Deep Learning
title_full_unstemmed	Protein Design with Deep Learning
title_sort	protein design with deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ec6909658fea4b868fdc62fff2334617
work_keys_str_mv	AT mariannedefresne proteindesignwithdeeplearning AT sophiebarbe proteindesignwithdeeplearning AT thomasschiex proteindesignwithdeeplearning
_version_	1718432152965611520

Protein Design with Deep Learning

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