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Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads
Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusi...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287906/ https://www.ncbi.nlm.nih.gov/pubmed/37014016 http://dx.doi.org/10.1093/nar/gkad235 |
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author | Levin, Itay Štrajbl, Marek Fastman, Yair Baran, Dror Twito, Shir Mioduser, Jessica Keren, Adi Fischman, Sharon Zhenin, Michael Nimrod, Guy Levitin, Natalie Mayor, May Ben Gadrich, Meital Ofran, Yanay |
author_facet | Levin, Itay Štrajbl, Marek Fastman, Yair Baran, Dror Twito, Shir Mioduser, Jessica Keren, Adi Fischman, Sharon Zhenin, Michael Nimrod, Guy Levitin, Natalie Mayor, May Ben Gadrich, Meital Ofran, Yanay |
author_sort | Levin, Itay |
collection | PubMed |
description | Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data. |
format | Online Article Text |
id | pubmed-10287906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102879062023-06-24 Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads Levin, Itay Štrajbl, Marek Fastman, Yair Baran, Dror Twito, Shir Mioduser, Jessica Keren, Adi Fischman, Sharon Zhenin, Michael Nimrod, Guy Levitin, Natalie Mayor, May Ben Gadrich, Meital Ofran, Yanay Nucleic Acids Res Methods Online Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data. Oxford University Press 2023-04-04 /pmc/articles/PMC10287906/ /pubmed/37014016 http://dx.doi.org/10.1093/nar/gkad235 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Online Levin, Itay Štrajbl, Marek Fastman, Yair Baran, Dror Twito, Shir Mioduser, Jessica Keren, Adi Fischman, Sharon Zhenin, Michael Nimrod, Guy Levitin, Natalie Mayor, May Ben Gadrich, Meital Ofran, Yanay Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads |
title | Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads |
title_full | Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads |
title_fullStr | Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads |
title_full_unstemmed | Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads |
title_short | Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads |
title_sort | accurate profiling of full-length fv in highly homologous antibody libraries using umi tagged short reads |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287906/ https://www.ncbi.nlm.nih.gov/pubmed/37014016 http://dx.doi.org/10.1093/nar/gkad235 |
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