Complementing machine learning‐based structure predictions with native mass spectrometry
Open accessPublished version of a preprint
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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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1× · see citing works
“Machine learning (ML)-based algorithms have been hailed as the solution to the protein structure prediction problem and are already being used to predict structures across entire proteomes.(1,2) For example, using protein sequence data as the only user input, AF2(3) can generate models of ordered, monomeric proteins t…”
MethodImproved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Self-citationA structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
Self-citationAlphaFold and Implications for Intrinsically Disordered Proteins
Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635 · see citing works
<scp>NMR</scp> hawk‐eyed view of <scp>AlphaFold2</scp> structures
Zweckstetter, M. (2021) · Protein Science · doi:10.1002/pro.4175 · Cited by 47 · see citing works
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- Authors
- Allison, T. M., Degiacomi, M. T., Marklund, E. G., Jovine, L., Elofsson, A., Benesch, J. L. P., & Landreh, M.
- Affiliations
- University of Canterbury (NZ)
- Durham University (GB)
- Uppsala University (SE)
- Karolinska Institutet (SE)
- Stockholm University (SE)
- University of Oxford (GB)
- Corresponding author
- Landreh, Michael
- ORCID
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2022-05-21
- Volume
- 31
- Issue
- 6
- Pages
- e4333-e4333
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC9123603
- OpenAlex
- W4281252394
- Cited by
- 20
- References
- 7
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 7 · 2022: 3
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Durham Research Online (Durham University) (acceptedVersion, other-oa)
- Durham Research Online (Durham University) (submittedVersion, cc-by-nc-nd)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
- Funders
- Cancerfonden (19 0480)
- Vetenskapsrådet (2019‐01961)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- native mass spectrometry, AlphaFold2, RoseTTAFold, protein complexes, ligand interactions, homology model, point mutations, oligomerization, post-translational modification, structural validation
- MeSH terms
- Machine Learning, Furylfuramide, Ligands, Proteins, Mass Spectrometry
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