XL-MS Data Research Articles

Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair

Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC’s assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD’s DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC’s dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5′ ssDNA, exposing XPA’s crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.

Chemical crosslinking extends and complements UV crosslinking in analysis of RNA/DNA nucleic acid-protein interaction sites by mass spectrometry.

UV (ultra-violet) crosslinking with mass spectrometry (XL-MS) has been established for identifying RNA-and DNA-binding proteins along with their domains and amino acids involved. Here, we explore chemical XL-MS for RNA-protein, DNA-protein, and nucleotide-protein complexes in vitro and in vivo . We introduce a specialized nucleotide-protein-crosslink search engine, NuXL, for robust and fast identification of such crosslinks at amino acid resolution. Chemical XL-MS complements UV XL-MS by generating different crosslink species, increasing crosslinked protein yields in vivo almost four-fold and thus it expands the structural information accessible via XL-MS. Our workflow facilitates integrative structural modelling of nucleic acid-protein complexes and adds spatial information to the described RNA-binding properties of enzymes, for which crosslinking sites are often observed close to their cofactor-binding domains. In vivo UV and chemical XL-MS data from E. coli cells analysed by NuXL establish a comprehensive nucleic acid-protein crosslink inventory with crosslink sites at amino acid level for more than 1500 proteins. Our new workflow combined with the dedicated NuXL search engine identified RNA crosslinks that cover most RNA-binding proteins, with DNA and RNA crosslinks detected in transcriptional repressors and activators.

A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline.

Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.

Leveraging Cross-Linking Mass Spectrometry for Modeling Antibody-Antigen Complexes.

Elucidating antibody-antigen complexes at the atomic level is of utmost interest for understanding immune responses and designing better therapies. Cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for mapping protein-protein interactions, suggesting valuable structural insights. However, the use of XL-MS studies to enable epitope/paratope mapping of antibody-antigen complexes is still limited up to now. XL-MS data can be used to drive integrative modeling of antibody-antigen complexes, where cross-links information serves as distance restraints for the precise determination of binding interfaces. In this approach, XL-MS data are employed to identify connections between binding interfaces of the antibody and the antigen, thus informing molecular modeling. Current literature provides minimal input about the impact of XL-MS data on the integrative modeling of antibody-antigen complexes. Here, we applied XL-MS to retrieve information about binding interfaces of three antibody-antigen complexes. We leveraged XL-MS data to perform integrative modeling using HADDOCK (active-passive residues and distance restraints strategies) and AlphaLink2. We then compared these three approaches with initial predictions of investigated antibody-antigen complexes by AlphaFold Multimer. This work emphasizes the importance of cross-linking data in resolving conformational dynamics of antibody-antigen complexes, ultimately enhancing the design of better protein therapeutics and vaccines.

RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control.

We present RawVegetable 2.0, a software tailored for assessing mass spectrometry data quality and fine-tuned for cross-linking mass spectrometry (XL-MS) applications. Building upon the capabilities of its predecessor, RawVegetable 2.0 introduces four main modules, each providing distinct and new functionalities: 1) Pair Finder, which identifies ion doublets characteristic of cleavable cross-linking experiments; 2) Diagnostic Peak Finder, which locates potential reporter ions associated with a specific cross-linker; 3) Precursor Signal Ratio, which computes the ratio between precursor intensity and the total signal in an MS/MS scan; and 4) Xrea, which evaluates spectral quality by analyzing the heterogeneity of peak intensities within a spectrum. These modules collectively streamline the process of optimizing mass spectrometry data acquisition for both Proteomics and XL-MS experiments. RawVegetable 2.0, along with a comprehensive tutorial is freely accessible for academic use at: http://patternlabforproteomics.org/rawvegetable2.

Do Not Waste Time─Ensure Success in Your Cross-Linking Mass Spectrometry Experiments before You Begin.

Cross-linking mass spectrometry (XL-MS) has become a very useful tool for studying protein complexes and interactions in living systems. It enables the investigation of many large and dynamic assemblies in their native state, providing an unbiased view of their protein interactions and restraints for integrative modeling. More researchers are turning toward trying XL-MS to probe their complexes of interest, especially in their native environments. However, due to the presence of other potentially higher abundant proteins, sufficient cross-links on a system of interest may not be reached to achieve satisfactory structural and interaction information. There are currently no rules for predicting whether XL-MS experiments are likely to work or not; in other words, if a protein complex of interest will lead to useful XL-MS data. Here, we show that a simple iBAQ (intensity-based absolute quantification) analysis performed from trypsin digest data can provide a good understanding of whether proteins of interest are abundant enough to achieve successful cross-linking data. Comparing our findings to large-scale data on diverse systems from several other groups, we show that proteins of interest should be at least in the top 20% abundance range to expect more than one cross-link found per protein. We foresee that this guideline is a good starting point for researchers who would like to use XL-MS to study their protein of interest and help ensure a successful cross-linking experiment from the beginning. Data are available via ProteomeXchange with identifier PXD045792.

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 1Å 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.

Characterization of β-Barrel Outer Membrane Proteins and Their Interactions with Chaperones by Chemical-Crosslinking Mass Spectrometry.

Chemical crosslinking-mass spectrometry (XL-MS) is an established tool that can be used to study the architecture and dynamics of proteins and protein assemblies. Here the application of XL-MS to study outer membrane proteins (OMPs) and their interactions with periplasmic chaperones is described, to inform on the molecular mechanisms underpinning OMP assembly. XL-MS data are especially powerful when used to complement high-resolution structural data, data from structural prediction or to drive molecular modeling of proteins and protein assemblies. The approach described here could be applied to the study of any protein assembly (including other membrane proteins).

RNxQuest: An Extension to the xQuest Pipeline Enabling Analysis of Protein-RNA Cross-Linking/Mass Spectrometry Data.

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.

Cross-linking mass spectrometry discovers, evaluates, and corroborates structures and protein–protein interactions in the human cell

Significant recent advances in structural biology, particularly in the field of cryoelectron microscopy, have dramatically expanded our ability to create structural models of proteins and protein complexes. However, many proteins remain refractory to these approaches because of their low abundance, low stability, or-in the case of complexes-simply not having yet been analyzed. Here, we demonstrate the power of using cross-linking mass spectrometry (XL-MS) for the high-throughput experimental assessment of the structures of proteins and protein complexes. This included those produced by high-resolution but invitro experimental data, as well as in silico predictions based on amino acid sequence alone. We present the largest XL-MS dataset to date, describing 28,910 unique residue pairs captured across 4,084 unique human proteins and 2,110 unique protein-protein interactions. We show that models of proteins and their complexes predicted by AlphaFold2, and inspired and corroborated by the XL-MS data, offer opportunities to deeply mine the structural proteome and interactome and reveal mechanisms underlying protein structure and function.

Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data.

Cross-linking mass spectrometry (XL-MS) has become an indispensable tool for the emerging field of systems structural biology over the recent years. However, the confidence in individual protein-protein interactions (PPIs) depends on the correct assessment of individual inter-protein cross-links. In this article, we describe a mono- and intralink filter (mi-filter) that is applicable to any kind of cross-linking data and workflow. It stipulates that only proteins for which at least one monolink or intra-protein cross-link has been identified within a given data set are considered for an inter-protein cross-link and therefore participate in a PPI. We show that this simple and intuitive filter has a dramatic effect on different types of cross-linking data ranging from individual protein complexes over medium-complexity affinity enrichments to proteome-wide cell lysates and significantly reduces the number of false-positive identifications for inter-protein links in all these types of XL-MS data.

Exhaustive Cross-Linking Search with Protein Feedback

Improving the sensitivity of protein-protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/β mass detection scheme and establishes protein-peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links.

Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons.

Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.

Structural Probing of Hsp26 Activation and Client Binding by Quantitative Cross-Linking Mass Spectrometry.

Small heat-shock proteins (sHSPs) are important members of the cellular stress response in all species. Their best-described function is the binding of early unfolding states and the resulting prevention of protein aggregation. Many sHSPs exist as a polydisperse composition of oligomers, which undergoes changes in subunit composition, folding status, and relative distribution upon heat activation. To date, only an incomplete picture of the mechanism of sHSP activation exists; in particular, the molecular basis of how sHSPs bind client proteins and mediate client specificity is not fully understood. In this study, we have applied cross-linking mass spectrometry (XL-MS) to obtain detailed structural information on sHSP activation and client binding for yeast Hsp26. Our cross-linking data reveals the middle domain of Hsp26 as a client-independent interface in multiple Hsp26::client complexes and indicates that client specificity is likely mediated via additional binding sites within its α-crystallin domain and C-terminal extension. Our quantitative XL-MS data underpins the middle domain as the main driver of heat-induced activation and client binding but shows that global rearrangements spanning all domains of Hsp26 take place simultaneously. We also investigated a Hsp26::client complex in the presence of Ssa1 (Hsp70) and Ydj1(Hsp40) at the initial stage of refolding and observe that the interaction between refolding chaperones is altered by the presence of a client protein, pointing to a mechanism where the interaction of Ydj1 with the HSP::client complex initiates the assembly of the active refolding machinery.

Revealing the Structural Plasticity of SARS-CoV-2 nsp7 and nsp8 Using Structural Proteomics.

Coronavirus (CoV) nonstructural proteins (nsps) assemble to form the replication–transcription complex (RTC) responsible for viral RNA synthesis. nsp7 and nsp8 are important cofactors of the RTC, as they interact and regulate the activity of RNA-dependent RNA polymerase and other nsps. To date, no structure of the full-length SARS-CoV-2 nsp7:nsp8 complex has been published. The current understanding of this complex is based on structures from truncated constructs, with missing electron densities, or from related CoV species where SARS-CoV-2 nsp7 and nsp8 share upward of 90% sequence identity. Despite available structures solved using crystallography and cryo-EM representing detailed static snapshots of the nsp7:nsp8 complex, it is evident that the complex has a high degree of structural plasticity. However, relatively little is known about the conformational dynamics of the individual proteins and how they complex to interact with other nsps. Here, the solution-based structural proteomic techniques, hydrogen–deuterium exchange mass spectrometry (HDX-MS) and cross-linking mass spectrometry (XL-MS), illuminate the dynamics of SARS-CoV-2 full-length nsp7 and nsp8 proteins and the nsp7:nsp8 protein complex. Results presented from the two techniques are complementary and validate the interaction surfaces identified from the published three-dimensional heterotetrameric crystal structure of the SARS-CoV-2 truncated nsp7:nsp8 complex. Furthermore, mapping of XL-MS data onto higher-order complexes suggests that SARS-CoV-2 nsp7 and nsp8 do not assemble into a hexadecameric structure as implied by the SARS-CoV full-length nsp7:nsp8 crystal structure. Instead, our results suggest that the nsp7:nsp8 heterotetramer can dissociate into a stable dimeric unit that might bind to nsp12 in the RTC without significantly altering nsp7–nsp8 interactions.

MS Annika: A New Cross-Linking Search Engine.

Cross-linking mass spectrometry (XL-MS) has become a powerful technique that enables insights into protein structures and protein interactions. The development of cleavable cross-linkers has further promoted XL-MS through search space reduction, thereby allowing for proteome-wide studies. These new analysis possibilities foster the development of new cross-linkers, which not every search engine can deal with out of the box. In addition, some search engines for XL-MS data also struggle with the validation of identified cross-linked peptides, that is, false discovery rate (FDR) estimation, as FDR calculation is hampered by the fact that not only one but two peptides in a single spectrum have to be correct. We here present our new search engine, MS Annika, which can identify cross-linked peptides in MS2 spectra from a wide variety of cleavable cross-linkers. We show that MS Annika provides realistic estimates of FDRs without the need of arbitrary score cutoffs, being able to provide on average 44% more identifications at a similar or better true FDR than comparable tools. In addition, MS Annika can be used on proteome-wide studies due to fast, parallelized processing and provides a way to visualize the identified cross-links in protein 3D structures.

First 3D-Structural Data of Full-Length Guanylyl Cyclase 1 in Rod-Outer-Segment Preparations of Bovine Retina by Cross-Linking/Mass Spectrometry

The rod-outer-segment guanylyl cyclase 1 (ROS-GC1) is a key transmembrane protein for retinal phototransduction. Mutations of ROS-GC1 correlate with different retinal diseases that often lead to blindness. No structural data are available for ROS-GC1 so far. We performed a 3D-structural analysis of native ROS-GC1 from bovine retina by cross-linking/mass spectrometry (XL-MS) and computational modeling. Absolute quantification and activity measurements of native ROS-GC1 were performed by MS-based assays directly in bovine retina samples. Our data present the first 3D-structural analysis of active, full-length ROS-GC1 derived from bovine retina. We propose a novel domain organization for the intracellular domain ROS-GC1. Our XL-MS data of native ROS-GC1 from rod-outer-segment preparations of bovine retina agree with a dimeric architecture. Our integrated approach can serve as a blueprint for conducting 3D-structural studies of membrane proteins in their native environment.

IMProv: A Resource for Cross-link-Driven Structure Modeling that Accommodates Protein Dynamics

Proteomics methodology has expanded to include protein structural analysis, primarily through cross-linking mass spectrometry (XL-MS) and hydrogen–deuterium exchange mass spectrometry (HX-MS). However, while the structural proteomics community has effective tools for primary data analysis, there is a need for structure modeling pipelines that are accessible to the proteomics specialist. Integrative structural biology requires the aggregation of multiple distinct types of data to generate models that satisfy all inputs. Here, we describe IMProv, an app in the Mass Spec Studio that combines XL-MS data with other structural data, such as cryo-EM densities and crystallographic structures, for integrative structure modeling on high-performance computing platforms. The resource provides an easily deployed bundle that includes the open-source Integrative Modeling Platform program (IMP) and its dependencies. IMProv also provides functionality to adjust cross-link distance restraints according to the underlying dynamics of cross-linked sites, as characterized by HX-MS. A dynamics-driven conditioning of restraint values can improve structure modeling precision, as illustrated by an integrative structure of the five-membered Polycomb Repressive Complex 2. IMProv is extensible to additional types of data.

Expanding the Depth and Sensitivity of Cross-Link Identification by Differential Ion Mobility Using High-Field Asymmetric Waveform Ion Mobility Spectrometry.

In cross-linking mass spectrometry (XL-MS), the depth and sensitivity of cross-link detection is often limited by the low abundance of cross-links compared to non-cross-linked peptides in the digestion mixture. To improve the identification efficiency of cross-links, here, we present a gas-phase separation strategy using high-field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to the Orbitrap Tribrid mass spectrometers. By enabling an additional peptide separation step in the gas phase using the FAIMS device, we increase the number of cross-link identifications by 22% for a medium complex sample and 59% for strong cation exchange-fractionated HEK293T cell lysate in XL-MS experiments using disuccinimidyl sulfoxide (DSSO) cross-linker. When disuccinimidyl suberate (DSS) cross-linker is in use, we are able to boost cross-link identification by 89% for the medium and 100% for the high complex sample compared to the analyses without FAIMS. Furthermore, we show that, for medium complex samples, FAIMS enables the collection of single-shot XL-MS data with a comparable depth to the corresponding sample fractionated by chromatography-based approaches. Altogether, we demonstrate FAIMS is highly beneficial for XL-MS studies by expanding the proteome coverage of cross-links while improving the efficiency and confidence of cross-link identification.

The Conformational Preference of Chemical Cross-linkers Determines the Cross-linking Probability of Reactive Protein Residues.

Chemical cross-linking mass spectrometry (XLMS) is an emerging technique in structural biology. Providing the cross-linked peptides are identified by mass spectrometry with high confidence, a distance restraint can be applied between the two reactive protein residues, with the upper bound corresponding to the maximal span of the cross-linker. However, as the upper bound is typically over 20 Å, cross-link distance restraints are unrestrictive and provide a marginal improvement in protein structural refinement. Here we analyze the experimental cross-links for lysine or acidic residues and show that the distribution of Cβ-Cβ' distances can be described with two overlapping Gaussian species. In addition to the pairwise occurrence probability of the reactive protein residues, we show that the distribution profile of the cross-link distances is determined by the intrinsic conformational propensity of the cross-linker. The cross-linker prefers either a compact or extended conformation and, once attached to a reactive protein residue, predominantly an extended conformation. Consequently, the long-distance Gaussian species occurs at a much higher probability than the short-distance species in the observed cross-links. Together, the probabilistic distribution of the cross-link distance allows the construction of a more restrictive restraint for structural modeling and better use of the XLMS data.