Protein design and variant prediction using autoregressive generative models

The ability to design functional sequences is central to protein engineering and biotherapeutics. Here the authors introduce a deep generative alignment-free model for sequence design applied to highly variable regions and design and test a diverse nanobody library with improved properties for selec...

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Auteurs principaux:	Jung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew C. Kruse, Debora S. Marks
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Science Q
Accès en ligne:	https://doaj.org/article/9f71edbc554b43bcb6739269e59cc88c
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id	oai:doaj.org-article:9f71edbc554b43bcb6739269e59cc88c
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spelling	oai:doaj.org-article:9f71edbc554b43bcb6739269e59cc88c2021-12-02T17:33:34ZProtein design and variant prediction using autoregressive generative models10.1038/s41467-021-22732-w2041-1723https://doaj.org/article/9f71edbc554b43bcb6739269e59cc88c2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22732-whttps://doaj.org/toc/2041-1723The ability to design functional sequences is central to protein engineering and biotherapeutics. Here the authors introduce a deep generative alignment-free model for sequence design applied to highly variable regions and design and test a diverse nanobody library with improved properties for selection experiments.Jung-Eun ShinAdam J. RiesselmanAaron W. KollaschConor McMahonElana SimonChris SanderAashish ManglikAndrew C. KruseDebora S. MarksNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jung-Eun Shin Adam J. Riesselman Aaron W. Kollasch Conor McMahon Elana Simon Chris Sander Aashish Manglik Andrew C. Kruse Debora S. Marks Protein design and variant prediction using autoregressive generative models
description	The ability to design functional sequences is central to protein engineering and biotherapeutics. Here the authors introduce a deep generative alignment-free model for sequence design applied to highly variable regions and design and test a diverse nanobody library with improved properties for selection experiments.
format	article
author	Jung-Eun Shin Adam J. Riesselman Aaron W. Kollasch Conor McMahon Elana Simon Chris Sander Aashish Manglik Andrew C. Kruse Debora S. Marks
author_facet	Jung-Eun Shin Adam J. Riesselman Aaron W. Kollasch Conor McMahon Elana Simon Chris Sander Aashish Manglik Andrew C. Kruse Debora S. Marks
author_sort	Jung-Eun Shin
title	Protein design and variant prediction using autoregressive generative models
title_short	Protein design and variant prediction using autoregressive generative models
title_full	Protein design and variant prediction using autoregressive generative models
title_fullStr	Protein design and variant prediction using autoregressive generative models
title_full_unstemmed	Protein design and variant prediction using autoregressive generative models
title_sort	protein design and variant prediction using autoregressive generative models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9f71edbc554b43bcb6739269e59cc88c
work_keys_str_mv	AT jungeunshin proteindesignandvariantpredictionusingautoregressivegenerativemodels AT adamjriesselman proteindesignandvariantpredictionusingautoregressivegenerativemodels AT aaronwkollasch proteindesignandvariantpredictionusingautoregressivegenerativemodels AT conormcmahon proteindesignandvariantpredictionusingautoregressivegenerativemodels AT elanasimon proteindesignandvariantpredictionusingautoregressivegenerativemodels AT chrissander proteindesignandvariantpredictionusingautoregressivegenerativemodels AT aashishmanglik proteindesignandvariantpredictionusingautoregressivegenerativemodels AT andrewckruse proteindesignandvariantpredictionusingautoregressivegenerativemodels AT deborasmarks proteindesignandvariantpredictionusingautoregressivegenerativemodels
_version_	1718379957048049664

Protein design and variant prediction using autoregressive generative models

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