Dataset Information

Native holdup (nHU) to measure binding affinities from cell extracts

ABSTRACT: Characterizing macromolecular interactions is essential for understanding cellular processes, yet nearly all methods used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to quantify equilibrium binding constants and explore binding mechanisms of protein interactions from cell extracts. Compared to other pulldown-based assays, nHU requires less sample preparation and can be coupled to any analytical methods, such as western blotting (nHU-WB) or mass spectrometry (nHU-MS) as readouts. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. This challenges the unwritten paradigm stating that biophysical parameters like binding constants cannot be accurately determined from cells or cellular extracts. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.

INSTRUMENT(S): Orbitrap Exploris 480, LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell

DISEASE(S): Brain Cancer

SUBMITTER: Luc Negroni

LAB HEAD: Gergo Gogl

PROVIDER: PXD036024 | Pride | 2023-03-11

REPOSITORIES: Pride

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Publications

Native holdup (nHU) to measure binding affinities from cell extracts.

Zambo Boglarka B Morlet Bastien B Negroni Luc L Trave Gilles G Gogl Gergo G

Science advances 20221221 51

Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mas ...[more]

PMID: 36542723

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Project description:Over the past few decades cross-linking mass spectrometry (XLMS) has become a powerful tool for identification of protein-protein interactions and for gaining insight into the structures of proteins in living cells, tissues, and organelles. The development of new crosslinkers, enrichment strategies and data acquisition methods led to the establishment of numerous new software tools specifically for the analysis and interpretation of cross-linking data. We previously published one of these tools called MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable crosslinkers. In this publication we present an updated MS Annika and a new search algorithm that additionally supports processing of data from MS2-MS3-based approaches and identification of peptides from MS3 spectra. In the new MS2-MS3 search algorithm, MS3 spectra are matched to their corresponding precursor doublet peak in the MS2 scan to identify the crosslink modification and the monoisotopic peptide mass. This information is then used to adjust the MS3 spectra for search with MS Amanda, our in-house developed peptide search engine, to identify the cross-linked peptides. Peptides that are identified in the MS2 scan and one or more of the associated product MS3 scans are re-scored with a novel scoring function to reflect the increased confidence. Finally, the detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines that support MS3 crosslink identification. Three of the datasets were benchmark datasets of synthetic peptides that allow calculation of an experimentally validated FDR, and we show that MS Annika detects up to 4 times more true unique crosslinks than MaXLinker and up to 35% more than XlinkX while simultaneously yielding less false positive hits and therefore a more accurate FDR than the other two search engines. Additionally, for the other two datasets we could show that MS Annika finds between 74% to 2.5 times more crosslinks at 1% estimated FDR and reveals protein-protein interactions that are not detected by either XlinkX or MaXLinker.

Dataset Information

Native holdup (nHU) to measure binding affinities from cell extracts

Publications

Native holdup (nHU) to measure binding affinities from cell extracts.

Similar Datasets

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