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Complementing machine learning-based structure predictions with native mass spectrometry
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Number of Authors: 72022 (English)In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 31, no 6, article id e4333Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2022. Vol. 31, no 6, article id e4333
Keywords [en]
integrative modeling, machine learning, protein structure prediction, structural proteomics
National Category
Bioinformatics (Computational Biology) Bioinformatics and Systems Biology
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URN: urn:nbn:se:su:diva-205220DOI: 10.1002/pro.4333ISI: 000798697700001OAI: oai:DiVA.org:su-205220DiVA, id: diva2:1664382
Available from: 2022-06-03 Created: 2022-06-03 Last updated: 2022-06-03Bibliographically approved

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Elofsson, Arne

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Science for Life Laboratory (SciLifeLab)Department of Biochemistry and Biophysics
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