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  Protein interaction perturbation profiling at amino-acid resolution

Woodsmith, J., Apelt, L., Casado-Medrano, V., Özkan, Z., Timmermann, B., & Stelzl, U. (2017). Protein interaction perturbation profiling at amino-acid resolution. Nature methods, 14(12), 1213-1221. doi:10.1038/nmeth.4464.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-1EEF-D Version Permalink: http://hdl.handle.net/21.11116/0000-0001-4309-A
Genre: Journal Article

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 Creators:
Woodsmith, Jonathan 1, Author
Apelt, Luise, Author
Casado-Medrano, Victoria, Author
Özkan, Ziya , Author
Timmermann, Bernd2, Author              
Stelzl, Ulrich1, Author
Affiliations:
1Institute of Pharmaceutical Sciences, University of Graz and BioTechMed-Graz, Graz, Austria, ou_persistent22              
2Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479670              

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Free keywords: Genetic techniques High-throughput screening Mechanisms of disease Protein–protein interaction networks Systems biology
 Abstract: The identification of genomic variants in healthy and diseased individuals continues to rapidly outpace our ability to functionally annotate these variants. Techniques that both systematically assay the functional consequences of nucleotide-resolution variation and can scale to hundreds of genes are urgently required. We designed a sensitive yeast two-hybrid-based 'off switch' for positive selection of interaction-disruptive variants from complex genetic libraries. Combined with massively parallel programmed mutagenesis and a sequencing readout, this method enables systematic profiling of protein-interaction determinants at amino-acid resolution. We defined >1,000 interaction-disrupting amino acid mutations across eight subunits of the BBSome, the major human cilia protein complex associated with the pleiotropic genetic disorder Bardet–Biedl syndrome. These high-resolution interaction-perturbation profiles provide a framework for interpreting patient-derived mutations across the entire protein complex and thus highlight how the impact of disease variation on interactome networks can be systematically assessed.

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Language(s): eng - English
 Dates: 2017-10-16
 Publication Status: Published online
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1038/nmeth.4464
ISSN: 1548-7105 (online) 1548-7091 (print)
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Title: Nature methods
  Other : Nature methods
Source Genre: Journal
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Publ. Info: New York, NY : Nature Pub. Group
Pages: - Volume / Issue: 14 (12) Sequence Number: - Start / End Page: 1213 - 1221 Identifier: ISSN: 1548-7091
CoNE: /journals/resource/111088195279556