Complementing machine learning-based structure predictions with native mass spectrometry.

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 le

SUBMITTER: Allison TM 

PROVIDER: S-EPMC9123603 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature