Manalastas-Cantos, Karen;
Adoni, Kish R;
Pfeifer, Matthias;
Märtens, Birgit;
Grünewald, Kay;
Thalassinos, Konstantinos;
Topf, Maya;
(2024)
Modeling flexible protein structure with AlphaFold2 and cross-linking mass spectrometry.
Molecular & Cellular Proteomics (MCP)
, 23
(3)
, Article 100724. 10.1016/j.mcpro.2024.100724.
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Abstract
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 (QBP), 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 QBP, 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 (pTM) 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.
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