Machine learning guided aptamer refinement and discovery

Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without significantly compromising binding affinity.

Bibliographic Details
Main Authors:	Ali Bashir, Qin Yang, Jinpeng Wang, Stephan Hoyer, Wenchuan Chou, Cory McLean, Geoff Davis, Qiang Gong, Zan Armstrong, Junghoon Jang, Hui Kang, Annalisa Pawlosky, Alexander Scott, George E. Dahl, Marc Berndl, Michelle Dimon, B. Scott Ferguson
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-04-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-22555-9

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spelling	doaj-9508da0079cf405d9f6f644c1119f62f2021-04-25T11:13:53ZengNature Publishing GroupNature Communications2041-17232021-04-0112111110.1038/s41467-021-22555-9Machine learning guided aptamer refinement and discoveryAli Bashir0Qin Yang1Jinpeng Wang2Stephan Hoyer3Wenchuan Chou4Cory McLean5Geoff Davis6Qiang Gong7Zan Armstrong8Junghoon Jang9Hui Kang10Annalisa Pawlosky11Alexander Scott12George E. Dahl13Marc Berndl14Michelle Dimon15B. Scott Ferguson16Google ResearchAptitude Medical Systems Inc.Aptitude Medical Systems Inc.Google ResearchAptitude Medical Systems Inc.Google ResearchGoogle ResearchAptitude Medical Systems Inc.Google ResearchAptitude Medical Systems Inc.Aptitude Medical Systems Inc.Google ResearchAptitude Medical Systems Inc.Google ResearchGoogle ResearchGoogle ResearchAptitude Medical Systems Inc.Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without significantly compromising binding affinity.https://doi.org/10.1038/s41467-021-22555-9
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Ali Bashir Qin Yang Jinpeng Wang Stephan Hoyer Wenchuan Chou Cory McLean Geoff Davis Qiang Gong Zan Armstrong Junghoon Jang Hui Kang Annalisa Pawlosky Alexander Scott George E. Dahl Marc Berndl Michelle Dimon B. Scott Ferguson
spellingShingle	Ali Bashir Qin Yang Jinpeng Wang Stephan Hoyer Wenchuan Chou Cory McLean Geoff Davis Qiang Gong Zan Armstrong Junghoon Jang Hui Kang Annalisa Pawlosky Alexander Scott George E. Dahl Marc Berndl Michelle Dimon B. Scott Ferguson Machine learning guided aptamer refinement and discovery Nature Communications
author_facet	Ali Bashir Qin Yang Jinpeng Wang Stephan Hoyer Wenchuan Chou Cory McLean Geoff Davis Qiang Gong Zan Armstrong Junghoon Jang Hui Kang Annalisa Pawlosky Alexander Scott George E. Dahl Marc Berndl Michelle Dimon B. Scott Ferguson
author_sort	Ali Bashir
title	Machine learning guided aptamer refinement and discovery
title_short	Machine learning guided aptamer refinement and discovery
title_full	Machine learning guided aptamer refinement and discovery
title_fullStr	Machine learning guided aptamer refinement and discovery
title_full_unstemmed	Machine learning guided aptamer refinement and discovery
title_sort	machine learning guided aptamer refinement and discovery
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-04-01
description	Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without significantly compromising binding affinity.
url	https://doi.org/10.1038/s41467-021-22555-9
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Machine learning guided aptamer refinement and discovery

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