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 learning can be complemented with MS to yield more accurate structural models on a small and large scale.

Keywords: integrative modeling; machine learning; protein structure prediction; structural proteomics.

FIGURE 1

(a) The structural mass spectrometry (MS) toolbox offers information that is directly complementary to machine learning‐based structure prediction. MS can inform about proteoforms (MS sequencing), structural dynamics (HDX‐MS), the spatial arrangements of proteins in a complex (ion mobility and crosslinking MS), and oligomeric states (native MS). (b) Left: Experimental and predicted structures for holo‐ (left) and apo‐DHODH show near‐identical three‐dimensional folds. Middle: Native MS reveals the presence of a small population of apo protein. Right: IM‐MS of the 13+ charge states of apo‐ and holo‐DHODH shows that the protein with co‐factor has a native‐like CCS, whereas the protein without co‐factor is unfolded. (c) Left: Crystal structures for the HSP 17.7 and 18.1 homodimers are virtually indistinguishable from the AF2‐predicted heterodimer. Native MS of a mixture of HSP 17.7 and 18.1 under denaturing conditions (middle) and after refolding (right) reveals that no heterodimer formation takes place. (d) Left: AF2 predicts that the D40N mutant of MaSp1 NT forms a homodimer that closely resembles the dimeric structure of wt MaSp1 NT, despite showing partial loss of the D39/D40/K65 salt bridge. Middle: pLDDT plots indicate that the D40N mutation does not affect the prediction confidence for the subunits in the NT dimer. Right: Native MS analysis of both NT variants at pH 6.0 shows that the D40N mutation abolishes NT dimerization. All AF predictions were carried out using ColabFold V1.5, using AF2 Multimer 2.2. Predictions were run with the AMBER refinement step but without templates. The MS data for all three proteins were taken from each respective reference publications.