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Quantifying Biomolecular Interactions Using Slow Mixing Mode (SLOMO) Nanoflow ESI-MS.

Duong T Bui1, Zhixiong Li1, Pavel I Kitov1

  • 1Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2, Canada.

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|August 1, 2022
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Summary
This summary is machine-generated.

A new slow mixing mode (SLOMO) nanoESI-MS technique accurately quantifies biomolecular interactions by simultaneously determining response factors and binding affinities from a single measurement.

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Area of Science:

  • Biophysical Chemistry
  • Analytical Chemistry
  • Mass Spectrometry

Background:

  • Electrospray ionization mass spectrometry (ESI-MS) is a label-free method for studying biomolecular interactions.
  • Accurate quantification of binding affinities requires accounting for differing ESI-MS response factors (RFs) between free and bound species.
  • Existing RF correction methods are often incompatible with nanoflow ESI (nanoESI) sources used in interaction studies.

Purpose of the Study:

  • To develop a direct technique for determining both RFs and binding affinities (Kd) using static nanoESI.
  • To overcome limitations of current ESI-MS methods for quantifying biomolecular interactions.

Main Methods:

  • Introduction of slow mixing mode (SLOMO) nanoESI-MS.
  • Continuous monitoring of interacting species and complexes under nonhomogeneous solution conditions.
  • Determining relative RFs from changes in ion signals approaching or leaving steady-state.

Main Results:

  • SLOMO allows simultaneous determination of RFs and Kd from a single static nanoESI measurement.
  • Demonstrated reliability across diverse systems: peptide-antibiotic, protease-inhibitor, and protein oligomerization.
  • Accurate affinity measurements for lectin-nanobody, lectin-glycoprotein, and lectin-glycolipid interactions.

Conclusions:

  • SLOMO nanoESI-MS is a versatile and accurate method for quantifying biomolecular interactions.
  • The technique overcomes previous limitations in RF correction for nanoESI.
  • SLOMO has broad applicability for studying interactions relevant to human health and disease.