Chemical cross-linking and mass spectrometry enabled systems-level structural biology

Abstract

Structural information on protein-protein interactions (PPIs) is essential for improved understanding of regulatory interactome networks that confer various physiological and pathological responses. Additionally, maladaptive PPIs constitute desirable therapeutic targets due to inherently high disease state specificity. Recent advances in chemical cross-linking strategies coupled with mass spectrometry (XL-MS) have positioned XL-MS as a promising technology to not only elucidate the molecular architecture of individual protein assemblies, but also to characterize proteome-wide PPI networks. Moreover, quantitative in vivo XL-MS provides a new capability for the visualization of cellular interactome dynamics elicited by drug treatments, disease states, or aging effects. The emerging field of XL-MS based complexomics enables unique insights on protein moonlighting and protein complex remodeling. These techniques provide complimentary information necessary for in-depth structural interactome studies to better comprehend how PPIs mediate function in living systems.

Keywords: Chemical cross-linking and mass spectrometry (XL-MS); Complexomics; Native separation; Protein–protein interactions (PPIs).

Figure 1.. In vivo qualitative and quantitative cross-linking.

In vivo cross-linking experiments utilize membrane permeable cross-linking reagents to capture native protein-protein interactions and conformations in intact cells, tissues and organelles. After the in vivo reaction is complete, proteins are extracted and digested, subjected to various methods to enrich cross-links, and analyzed on a high-resolution mass spectrometer. Mass spectrometry data is used to identify cross-links and obtain information on intra-protein (conformations) and inter-protein (PPIs) structural networks that exist in cells, tissues, or organelles. Quantitative in vivo XL can be used to compare phenotypes, disease states, age effects, drug treatments and other perturbations. Quantitation can be done on the MS1 level (LFQ, SILAC, isotope labeled light/heavy cross-linker) or on the MS2 level (PRM, DIA, TMT, isobaric cross-linker). Quantitative XL-MS allows detection of changes in PPI networks and protein conformations and complexes.

Figure 2.. Novel Cross-linkers and Integration of Cross-linking with Other Structural Biology Methods.

A) Novel cross-linkers enable capture of higher order structural information (Bisby), enrichment of cross-linked peptides (BSP and tBu-PhoX), enhanced detection with cleavability (Bisby and TDS), faster cross-link formation (DOPAn), and enhanced membrane permeability (tBu-PhoX). B) Proteome cross-linking is performed to provide distance restraints of proximate residues within a protein or between subunits for protein complexes. Cross-linked peptides can be enriched by SEC, SCX, or affinity purification to facilitate identification. Identified cross-links, measured protein relative quantitation, and PPI network database can be used to predict the composition of protein complexes. XL-MS data can be integrated with other structural information either experimentally determined (HDX-MS, Cryo-EM, X-Ray crystallography, NMR, SAXS/WAXS) or computationally predicted (e.g. AlphaFold) to provide novel structural insights using tools such as AlphaLink, HADDOCK, ROSETTA, or UCSF IMP.

Figure 3.. The Future Workflow of Complexomics Integrates Complexome Profiling with Cross-linking MS and AI-enabled Informatics Pipelines.

Upstream native separations such as size exclusion chromatography (SEC), native IEF liquid fractionation, or BN-PAGE can reduce sample complexity and facilitate cross-link identification. Orthogonal cross-linking with cross-linkers of various length, polarity, and reactivity could be used to broaden structural information sampling. Complex-specific cross-links derived from BN-PAGE in-gel cross-linking experiments or cross-linked SEC fractions combined with relative protein quantitation can be used to computationally predict protein complex composition and connectivity. Integration of XL-MS data with other structural information leads to novel structural insights as described in Figure 2B. Structure analysis tools, such as DProQA, could be used to automatically evaluate the quality of the predicted structures.