Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.

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Autores principales: Michael Heyne, Niv Papo, Julia M. Shifman
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Q
Acceso en línea:https://doaj.org/article/30d06668541b438abf2226ef9ab7a880
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spelling oai:doaj.org-article:30d06668541b438abf2226ef9ab7a8802021-12-02T17:33:15ZGenerating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization10.1038/s41467-019-13895-82041-1723https://doaj.org/article/30d06668541b438abf2226ef9ab7a8802020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13895-8https://doaj.org/toc/2041-1723Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.Michael HeyneNiv PapoJulia M. ShifmanNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-7 (2020)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Michael Heyne
Niv Papo
Julia M. Shifman
Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization
description Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.
format article
author Michael Heyne
Niv Papo
Julia M. Shifman
author_facet Michael Heyne
Niv Papo
Julia M. Shifman
author_sort Michael Heyne
title Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization
title_short Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization
title_full Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization
title_fullStr Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization
title_full_unstemmed Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization
title_sort generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/30d06668541b438abf2226ef9ab7a880
work_keys_str_mv AT michaelheyne generatingquantitativebindinglandscapesthroughfractionalbindingselectionscombinedwithdeepsequencinganddatanormalization
AT nivpapo generatingquantitativebindinglandscapesthroughfractionalbindingselectionscombinedwithdeepsequencinganddatanormalization
AT juliamshifman generatingquantitativebindinglandscapesthroughfractionalbindingselectionscombinedwithdeepsequencinganddatanormalization
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