ArticleProtein science : a publication of the Protein Society2022
Complementing machine learning-based structure predictions with native mass spectrometry.
Article in Protein science : a publication of the Protein Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation.Analytical and bioanalytical chemistry · 2026Review
- Mass spectrometry integrates protein design into structural biology method development.QRB discovery · 2026Review
- AI-first structural identification of pathogenic protein target interfaces.PLoS computational biology · 2025Article
- Energy Resolved Mass Spectrometry Data from Surfaced Induced Dissociation Improves Prediction of Protein Complex Structure.Analytical chemistry · 2025Article
- Exploring protein functions from structural flexibility using CABS-flex modeling.Protein science : a publication of the Protein Society · 2024Review
- Systematic analysis of the relationship between fold-dependent flexibility and artificial intelligence protein structure prediction.PloS one · 2024Article
- Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy.Micromachines · 2023Review
- AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.Drug discovery today · 2023Review
- AlphaFold2 and its applications in the fields of biology and medicine.Signal transduction and targeted therapy · 2023Review
- Navigating with chemometrics and machine learning in chemistry.Artificial intelligence review · 2023Article
- Complementing machine learning-based structure predictions with native mass spectrometry.Protein science : a publication of the Protein Society · 2022Article
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Authors and funding
7 authors.
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Abstract
The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user-provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.
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