Complementing machine learning-based structure predictions with native mass spectrometry

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.

Automated summary

The development of machine learning-based structure prediction algorithms has made it possible for the scientific community to generate accurate structural models of cellular protein machinery. However, predicting the structure of protein complexes may still require user input and experimental validation. Mass spectrometry can be used in conjunction with machine learning to uncover structural features of ligand interactions, homology models, and point mutations that may not be detectable by machine learning algorithms alone.