Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry.

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

FAIMS Enhances the Detection of PTM Crosstalk Sites.

Protein post-translational modifications (PTMs) enable cells to rapidly change in response to biological stimuli. With hundreds of different PTMs, understanding these control mechanisms is complex. To date, efforts have focused on investigating the effect of a single PTM on protein function. Yet, many proteins contain multiple PTMs. Moreover, one PTM can alter the prevalence of another, a phenomenon termed PTM crosstalk. Understanding PTM crosstalk is critical; however, its detection is challenging since PTMs occur substoichiometrically. Here, we develop an enrichment-free, label-free proteomics method that utilizes high-field asymmetric ion mobility spectrometry (FAIMS) to enhance the detection of PTM crosstalk. We show that by searching for multiple combinations of dynamic PTMs on peptide sequences, a 6-fold increase in candidate PTM crosstalk sites is identified compared with that of standard liquid chromatography-tandem mass spectrometry (LC-MS/MS) workflows. Additionally, by cycling through FAIMS compensation voltages within a single LC-FAIMS-MS/MS run, we show that our LC-FAIMS-MS/MS workflow can increase multi-PTM-containing peptide identifications without additional increases in run times. With 159 novel candidate crosstalk sites identified, we envisage LC-FAIMS-MS/MS to play an important role in expanding the repertoire of multi-PTM identifications. Moreover, it is only by detecting PTM crosstalk that we can "see" the full picture of how proteins are regulated.