ECNet is an evolutionary context-integrated deep learning framework for protein engineering

Protein engineering is an active area of research in which machine learning has proven quite powerful. Here, the authors present a deep learning method that integrates both general and protein-specific sequence representations to improve the engineering of one’s protein of interest.

Bibliographic Details
Main Authors:	Yunan Luo, Guangde Jiang, Tianhao Yu, Yang Liu, Lam Vo, Hantian Ding, Yufeng Su, Wesley Wei Qian, Huimin Zhao, Jian Peng
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-09-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-25976-8

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spelling	doaj-0bb0a79962b04f8bba4f09b68761698c2021-10-03T11:51:06ZengNature Publishing GroupNature Communications2041-17232021-09-0112111410.1038/s41467-021-25976-8ECNet is an evolutionary context-integrated deep learning framework for protein engineeringYunan Luo0Guangde Jiang1Tianhao Yu2Yang Liu3Lam Vo4Hantian Ding5Yufeng Su6Wesley Wei Qian7Huimin Zhao8Jian Peng9Department of Computer Science, University of Illinois at Urbana-ChampaignDepartment of Chemical and Biomolecular Engineering, University of Illinois at Urbana-ChampaignDepartment of Chemical and Biomolecular Engineering, University of Illinois at Urbana-ChampaignDepartment of Computer Science, University of Illinois at Urbana-ChampaignDepartment of Chemical and Biomolecular Engineering, University of Illinois at Urbana-ChampaignDepartment of Computer Science, University of Illinois at Urbana-ChampaignDepartment of Computer Science, University of Illinois at Urbana-ChampaignDepartment of Computer Science, University of Illinois at Urbana-ChampaignDepartment of Chemical and Biomolecular Engineering, University of Illinois at Urbana-ChampaignDepartment of Computer Science, University of Illinois at Urbana-ChampaignProtein engineering is an active area of research in which machine learning has proven quite powerful. Here, the authors present a deep learning method that integrates both general and protein-specific sequence representations to improve the engineering of one’s protein of interest.https://doi.org/10.1038/s41467-021-25976-8
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yunan Luo Guangde Jiang Tianhao Yu Yang Liu Lam Vo Hantian Ding Yufeng Su Wesley Wei Qian Huimin Zhao Jian Peng
spellingShingle	Yunan Luo Guangde Jiang Tianhao Yu Yang Liu Lam Vo Hantian Ding Yufeng Su Wesley Wei Qian Huimin Zhao Jian Peng ECNet is an evolutionary context-integrated deep learning framework for protein engineering Nature Communications
author_facet	Yunan Luo Guangde Jiang Tianhao Yu Yang Liu Lam Vo Hantian Ding Yufeng Su Wesley Wei Qian Huimin Zhao Jian Peng
author_sort	Yunan Luo
title	ECNet is an evolutionary context-integrated deep learning framework for protein engineering
title_short	ECNet is an evolutionary context-integrated deep learning framework for protein engineering
title_full	ECNet is an evolutionary context-integrated deep learning framework for protein engineering
title_fullStr	ECNet is an evolutionary context-integrated deep learning framework for protein engineering
title_full_unstemmed	ECNet is an evolutionary context-integrated deep learning framework for protein engineering
title_sort	ecnet is an evolutionary context-integrated deep learning framework for protein engineering
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-09-01
description	Protein engineering is an active area of research in which machine learning has proven quite powerful. Here, the authors present a deep learning method that integrates both general and protein-specific sequence representations to improve the engineering of one’s protein of interest.
url	https://doi.org/10.1038/s41467-021-25976-8
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ECNet is an evolutionary context-integrated deep learning framework for protein engineering

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