ArticleAnalytical chemistry2022
Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation.
Article in Analytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.Mass spectrometry reviews · 2026Review
- Incorporating Surfaced-Induced Dissociation Mass Spectrometry Data into an AlphaFold-derived deep learning network improves protein structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Deep-learning structure elucidation from single-mutant deep mutational scanning.Nature communications · 2025Article
- Energy Resolved Mass Spectrometry Data from Surfaced Induced Dissociation Improves Prediction of Protein Complex Structure.Analytical chemistry · 2025Article
- Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems and optimizing the use of sparse experimental data for structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety of methods to collect structural data for proteins. One example is surface-induced dissociation (SID), which is used to break apart protein complexes (via a surface collision) into intact subcomplexes and can be performed at multiple laboratory frame SID collision energies. These energy-resolved MS/MS experiments have shown that the profile of the breakages depends on the acceleration energy of the collision. It is possible to extract an appearance energy (AE) from energy-resolved mass spectrometry (ERMS) data, which shows the relative intensity of each type of subcomplex as a function of SID acceleration energy. We previously determined that these AE values for specific interfaces correlated with structural features related to interface strength. In this study, we further examined the structural relationships by developing a method to predict the full ERMS plot from the structure, rather than extracting a single value. First, we noted that for proteins with multiple interface types, we could reproduce the correct shapes of breakdown curves, further confirming previous structural hypotheses. Next, we demonstrated that interface size and energy density (measured using Rosetta) correlated with data derived from the ERMS plot (
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