TY - JOUR T1 - Complementing machine learning-based structure predictions with native mass spectrometry JF - bioRxiv DO - 10.1101/2022.03.17.484776 SP - 2022.03.17.484776 AU - Timothy M. Allison AU - Matteo T. Degiacomi AU - Erik G. Marklund AU - Luca Jovine AU - Arne Elofsson AU - Justin L. P. Benesch AU - Michael Landreh Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/03/19/2022.03.17.484776.abstract N2 - The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) has 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 therefore 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 the small and the large scale.Competing Interest StatementThe authors have declared no competing interest. ER -