Using singular value decomposition to characterize protein-protein interactions by in-cell NMR spectroscopy

Subhabrata Majumder, Christopher M. Demott, David S. Burz, Alexander Shekhtman

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

Distinct differences between how model proteins interact in-cell and in vitro suggest that the cytosol might have a profound effect in modulating protein-protein and/or protein-ligand interactions that are not observed in vitro. Analyses of in-cell NMR spectra of target proteins interacting with physiological partners are further complicated by low signal-to-noise ratios, and the long overexpression times used in protein-protein interaction studies may lead to changes in the in-cell spectra over the course of the experiment. To unambiguously resolve the principal binding mode between two interacting species against the dynamic cellular background, we analyzed in-cell spectral data of a target protein over the time course of overexpression of its interacting partner by using single-value decomposition (SVD). SVD differentiates between concentration-dependent and concentration-independent events and identifies the principal binding mode between the two species. The analysis implicates a set of amino acids involved in the specific interaction that differs from previous NMR analyses but is in good agreement with crystallographic data. Peering inside a cell: Single-value decomposition (SVD) analysis of in-cell NMR spectra differentiates between specific binding and random events and identifies the principal binding mode between two interacting species. The analysis implicates a set of amino acids involved in specific in-cell protein-protein interactions that differs from previous NMR analyses but is in good agreement with crystallographic data.

Original languageEnglish (US)
Pages (from-to)929-933
Number of pages5
JournalChemBioChem
Volume15
Issue number7
DOIs
StatePublished - May 5 2014

Keywords

  • NMR spectroscopy
  • protein-protein interactions
  • single-cell measurements
  • single-value decomposition
  • statistical analysis

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