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Analyzing Large Protein Complexes by Structural Mass Spectrometry
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Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data.

Justin T Seffernick1, Sophie R Harvey1, Vicki H Wysocki1

  • 1Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, Ohio 43210, United States.

ACS Central Science
|September 5, 2019
PubMed
Summary
This summary is machine-generated.

Surface-induced dissociation (SID) coupled with mass spectrometry (MS) provides interface strength data. This study integrates SID appearance energy with RosettaDock to improve protein complex structure prediction, enhancing accuracy and model confidence.

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Published on: March 1, 2020

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Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • Mass spectrometry (MS) is increasingly used for protein structure elucidation.
  • Surface-induced dissociation (SID) paired with native MS offers insights into protein complex connectivity, topology, and interface strengths.
  • Computational methods like protein-protein docking can predict quaternary structures but benefit from experimental data integration.

Purpose of the Study:

  • To integrate surface-induced dissociation (SID) appearance energy (AE) data with computational structure prediction for improved protein complex analysis.
  • To develop and validate a computational model that combines SID AE with RosettaDock to evaluate protein-protein docking poses.
  • To assess the impact of incorporating experimental SID data on the accuracy of predicted protein quaternary structures.

Main Methods:

  • Developed an improved model to predict SID appearance energies (AEs).
  • Integrated a novel SID scoring term into the RosettaDock scoring function to quantify agreement between experimental data and predicted structures.
  • Tested the method on 57 systems using ideal SID AE data and on 9 systems using experimental SID data.

Main Results:

  • Incorporating SID AE data improved or maintained the RMSD of selected structures in 95% of cases when using theoretical AEs.
  • Using experimental SID data, the method predicted near-native structures ( < 2 Å RMSD) for 6 out of 9 systems, outperforming unrestrained RosettaDock (3 out of 9 cases).
  • Score versus RMSD funnel profiles were enhanced with SID data, and a confidence measure for model quality was developed.

Conclusions:

  • Surface-induced dissociation appearance energy is a valuable experimental constraint for refining protein-protein docking predictions.
  • Combining SID data with computational tools like RosettaDock significantly enhances the accuracy of quaternary structure prediction.
  • The developed method and confidence measure offer a powerful approach for structural biologists to predict and assess protein complex structures, even without high-resolution experimental data.