Protein interaction landscapes revealed by advanced in vivo cross-linking-mass spectrometry

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

SEC-MX: an approach to systematically study the interplay between protein assembly states and phosphorylation

A protein's molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Conducting SEC-MX on HEK293 and HCT116 cells, we generate a proof-of-concept dataset, mapping thousands of phosphopeptides and their assembly states. Our analysis reveals intricate relationships between phosphorylation events and assembly states and generates testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

Quantitative Glycan-Protein Cross-Linking Mass Spectrometry Using Enrichable Linkers Reveals Extensive Glycan-Mediated Protein Interaction Networks

Protein-protein interactions in the cell membrane are typically mediated by glycans, with terminal sialic acid often involved in these interactions. To probe the nature of the interactions, we developed quantitative cross-linking methods involving the glycans of the glycoproteins and the polypeptide moieties of proteins. We designed and synthesized biotinylated enrichable cross-linkers that were click-tagged to metabolically incorporate azido-sialic acid on cell surface glycans to allow cross-linking of the azido-glycans with lysine residues on proximal polypeptides. The glycopeptide-peptide cross-links (GPx) were enriched using biotin groups through affinity purification with streptavidin resin beads. Workflows using two linkers, one photocleavable and the other disulfide, were developed and applied to reveal the sialic acid-mediated cell-surface protein networks of PNT2 (prostate) cells. Glycopeptide-peptide pairs were identified, with 12000 GPx linked by sialylated glycoforms revealing interactions between source glycoproteins and nearly 700 target proteins. Protein-protein interactions were characterized by as many as 40 peptide pairs, and the extent of the interactions between proteins was prioritized by the number of GPx. Quantitation was performed by counting the number of GPx that identify the protein pairs. Abundant membrane proteins such as ITGB1 yielded an interactome consisting of around 400 other proteins, which were ranked from the most extensive interaction, having the largest number of GPx, to at least one. The interactome was further confirmed separately by published databases. This tool will enhance our understanding of glycosylation on protein-protein interactions and provide new potential targets for therapeutics.

Covalent Modification of Protein by Chemical Probe in Living Cells for Structural and Interaction Studies

Cellular activities are predominantly carried out by proteins that can dynamically adopt different structural conformations and differentially interact with other biomolecules according to cellular needs. Chemical probes are small molecules used to selectively interact and modulate the activities of specific proteins to study their functions such as the validation of potential drug targets. The remarkable performance of AlphaFold algorithms in the prediction of protein structures has pivoted interest toward elucidating the intracellular dynamics of protein structural conformation where covalent modification of proteins by chemical probes could be used to shed light upon. However, due to the barrier to entry by cell membrane and the general unfavorable reactive conditions of the intracellular environment, most studies using reactive chemical probes are still conducted on purified proteins and cell lysates. Nevertheless, recent progresses have been made in designing chemical probes with improved membrane permeability, stability and reactivity. This paper surveys the literature on recent advancements in membrane-permeable chemical probes and their applications with protein mass spectrometry for the intracellular studies of protein structural conformations and biomolecular interactions.

Recent Advances in Mass Spectrometry-Based Protein Interactome Studies

The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.

Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies

Cross-linking mass spectrometry (XL-MS) allows characterizing protein-protein interactions (PPIs) in native biological systems by capturing cross-links between different proteins (inter-links). However, inter-link identification remains challenging, requiring dedicated data filtering schemes and thorough error control. Here, we benchmark existing data filtering schemes combined with error rate estimation strategies utilizing concatenated target-decoy protein sequence databases. These workflows show shortcomings either in sensitivity (many false negatives) or specificity (many false positives). To ameliorate the limited sensitivity without compromising specificity, we develop an alternative target-decoy search strategy using fused target-decoy databases. Furthermore, we devise a different data filtering scheme that takes the inter-link context of the XL-MS dataset into account. Combining both approaches maintains low error rates and minimizes false negatives, as we show by mathematical simulations, analysis of experimental ground-truth data, and application to various biological datasets. In human cells, inter-link identifications increase by 75% and we confirm their structural accuracy through proteome-wide comparisons to AlphaFold2-derived models. Taken together, target-decoy fusion and context-sensitive data filtering deepen and fine-tune XL-MS-based interactomics.

Proteome-wide non-cleavable crosslink identification with MS Annika 3.0 reveals the structure of the C. elegans Box C/D complex

The field of crosslinking mass spectrometry has seen substantial advancements over the past decades, enabling the structural analysis of proteins and protein complexes and serving as a powerful tool in protein-protein interaction studies. However, data analysis of large non-cleavable crosslink studies is still a mostly unsolved problem due to its n-squared complexity. We here introduce an algorithm for the identification of non-cleavable crosslinks implemented in our crosslinking search engine MS Annika that is based on sparse matrix multiplication and allows for proteome-wide searches on commodity hardware. We compare our algorithm to other state-of-the-art crosslinking search engines commonly used in the field and conclude that MS Annika unifies high sensitivity, accurate FDR estimation and computational performance, outperforming competing tools. Application of this algorithm enabled us to employ a proteome-wide search of C. elegans nuclei samples, where we were able to uncover previously unknown protein interactions and conclude a comprehensive structural analysis that provides a detailed view of the Box C/D complex. Moreover, our algorithm will enable researchers to conduct similar studies that were previously unfeasible.

PhoXplex: Combining Phospho-enrichable Cross-Linking with Isobaric Labeling for Quantitative Proteome-Wide Mapping of Protein Interfaces

Integrating cross-linking mass spectrometry (XL-MS) into structural biology workflows provides valuable information about the spatial arrangement of amino acid stretches, which can guide elucidation of protein assembly architecture. Additionally, the combination of XL-MS with peptide quantitation techniques is a powerful approach to delineate protein interface dynamics across diverse conditions. While XL-MS is increasingly effective with isolated proteins or small complexes, its application to whole-cell samples poses technical challenges related to analysis depth and throughput. The use of enrichable cross-linkers has greatly improved the detectability of protein interfaces in a proteome-wide scale, facilitating global protein-protein interaction mapping. Therefore, bringing together enrichable cross-linking and multiplexed peptide quantification is an appealing approach to enable comparative characterization of structural attributes of proteins and protein interactions. Here, we combined phospho-enrichable cross-linking with TMT labeling to develop a streamline workflow (PhoXplex) for the detection of differential structural features across a panel of cell lines in a global scale. We achieved deep coverage with quantification of over 9000 cross-links and long loop-links in total including potentially novel interactions. Overlaying AlphaFold predictions and disorder protein annotations enables exploration of the quantitative cross-linking data set, to reveal possible associations between mutations and protein structures. Lastly, we discuss current shortcomings and perspectives for deep whole-cell profiling of protein interfaces at large-scale.

Global profiling of protein complex dynamics with an experimental library of protein interaction markers

Methods to systematically monitor protein complex dynamics are needed. We introduce serial ultrafiltration combined with limited proteolysis-coupled mass spectrometry (FLiP-MS), a structural proteomics workflow that generates a library of peptide markers specific to changes in PPIs by probing differences in protease susceptibility between complex-bound and monomeric forms of proteins. The library includes markers mapping to protein-binding interfaces and markers reporting on structural changes that accompany PPI changes. Integrating the marker library with LiP-MS data allows for global profiling of protein-protein interactions (PPIs) from unfractionated lysates. We apply FLiP-MS to Saccharomyces cerevisiae and probe changes in protein complex dynamics after DNA replication stress, identifying links between Spt-Ada-Gcn5 acetyltransferase activity and the assembly state of several complexes. FLiP-MS enables protein complex dynamics to be probed on any perturbation, proteome-wide, at high throughput, with peptide-level structural resolution and informing on occupancy of binding interfaces, thus providing both global and molecular views of a system under study.

Cell fixation improves performance of in situ crosslinking mass spectrometry while preserving cellular ultrastructure

Crosslinking mass spectrometry (XL-MS) has the potential to map the interactome of the cell with high resolution and depth of coverage. However, current in vivo XL-MS methods are hampered by crosslinkers that demonstrate low cell permeability and require long reaction times. Consequently, interactome sampling is not high and long incubation times can distort the cell, bringing into question the validity any protein interactions identified by the method. We address these issues with a fast formaldehyde-based fixation method applied prior to the introduction of secondary crosslinkers. Using human A549 cells and a range of reagents, we show that 4% formaldehyde fixation with membrane permeabilization preserves cellular ultrastructure and simultaneously improves reaction conditions for in situ XL-MS. Protein labeling yields can be increased even for nominally membrane-permeable reagents, and surprisingly, high-concentration formaldehyde does not compete with conventional amine-reactive crosslinking reagents. Prefixation with permeabilization uncouples cellular dynamics from crosslinker dynamics, enhancing control over crosslinking yield and permitting the use of any chemical crosslinker.

Spatially resolved profiling of protein conformation and interactions by biocompatible chemical cross-linking in living cells

Unlocking the intricacies of protein structures and interactions within the dynamic landscape of subcellular organelles presents a significant challenge. To address this, we introduce SPACX, a method for spatially resolved protein complex profiling via biocompatible chemical cross(x)-linking with subcellular isolation, designed to monitor protein conformation, interactions, and translocation in living cells. By rapidly capturing protein complexes in their native physiological state and efficiently enriching cross-linked peptides, SPACX allows comprehensive analysis of the protein interactome within living cells. Leveraging structure refinement with cross-linking restraints, we identify subcellular-specific conformation heterogeneity of PTEN, revealing dynamic differences in its dual specificity domains between the nucleus and cytoplasm. Furthermore, by discerning conformational disparities, we identify 83 cytoplasm-exclusive and 109 nucleus-exclusive PTEN-interacting proteins, each associated with distinct biological functions. Upon induction of ubiquitin-proteasome system stress, we observe dynamic alterations in PTEN assembly and its interacting partners during translocation. These changes, including the identification of components and interaction sites, are characterized using the SPACX approach. Notably, SPACX enables identification of unique interacting proteins specific to PTEN isoforms, including PTEN and PTEN-Long, through the determination of sequence-specific cross-linking interfaces. These findings underscore the potential of SPACX to elucidate the functional diversity of proteins within distinct subcellular sociology.

Advances in crosslinking chemistry and proximity-enabled strategies: deciphering protein complexes and interactions

Mass spectrometry, coupled with innovative crosslinking techniques to decode protein conformations and interactions through uninterrupted signal connections, has undergone remarkable progress in recent years. It is crucial to develop selective crosslinking reagents that minimally disrupt protein structure and dynamics, providing insights into protein network regulation and biological functions. Compared to traditional crosslinkers, new bifunctional chemical crosslinkers exhibit high selectivity and specificity in connecting proximal amino acid residues, resulting in stable molecular crosslinked products. The conjugation with specific amino acid residues like lysine, cysteine, arginine and tyrosine expands the XL-MS toolbox, enabling more precise modeling of target substrates and leading to improved data quality and reliability. Another emerging crosslinking method utilizes unnatural amino acids (UAAs) derived from proximity-enabled reactivity with specific amino acids or sulfur-fluoride exchange (SuFEx) reactions with nucleophilic residues. These UAAs are genetically encoded into proteins for the formation of specific covalent bonds. This technique combines the benefits of genetic encoding for live cell compatibility with chemical crosslinking, providing a valuable method for capturing transient and weak protein-protein interactions (PPIs) for mapping PPI coordinates and improving the pharmacological properties of proteins. With continued advancements in technology and applications, crosslinking mass spectrometry is poised to play an increasingly significant role in guiding our understanding of protein dynamics and function in the future.

Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking

The exploration of protein structure and function stands at the forefront of life science and represents an ever-expanding focus in the development of proteomics. As mass spectrometry (MS) offers readout of protein conformational changes at both the protein and peptide levels, MS-based structural proteomics is making significant strides in the realms of structural and molecular biology, complementing traditional structural biology techniques. This review focuses on two powerful MS-based techniques for peptide-level readout, namely limited proteolysis-mass spectrometry (LiP-MS) and cross-linking mass spectrometry (XL-MS). First, we discuss the principles, features, and different workflows of these two methods. Subsequently, we delve into the bioinformatics strategies and software tools used for interpreting data associated with these protein conformation readouts and how the data can be integrated with other computational tools. Furthermore, we provide a comprehensive summary of the noteworthy applications of LiP-MS and XL-MS in diverse areas including neurodegenerative diseases, interactome studies, membrane proteins, and artificial intelligence-based structural analysis. Finally, we discuss the factors that modulate protein conformational changes. We also highlight the remaining challenges in understanding the intricacies of protein conformational changes by LiP-MS and XL-MS technologies.

Conjugation of primary amine groups in targeted proteomics

Primary amines, in the form of unmodified N-terminus of peptide/protein and unmodified lysine residue, are perhaps the most important functional groups that can serve as the starting points in proteomic analysis, especially via mass spectrometry-based approaches. A variety of multifunctional probes that conjugate primary amine groups through covalent bonds have been developed and employed to facilitate protein/protein complex characterization, including identification, quantification, structure and localization elucidation, protein-protein interaction investigation, and so forth. As an integral part of more accurate peptide quantification in targeted proteomics, isobaric stable isotope-coded primary amine labeling approaches eventually facilitated protein/peptide characterization at the single-cell level, paving the way for single-cell proteomics. The development and advances in the field can be reviewed in terms of key components of a multifunctional probe: functional groups and chemistry for primary amine conjugation; hetero-bifunctional moiety for separation/enrichment of conjugated protein/protein complex; and functionalized linker/spacer. Perspectives are primarily focused on optimizing primary amine conjugation under physiological conditions to improve characterization of native proteins, especially those associated with the surface of living cells/microorganisms.

Trioxane-based MS-cleavable Cross-linking Mass Spectrometry for Profiling Multimeric Interactions of Cellular Networks

Cross-linking mass spectrometry (XL-MS) is a powerful technology for mapping protein-protein interactions (PPIs) at the systems-level. By covalently connecting pairs of proximal residues, cross-linking reagents provide distance restraints to infer protein conformations and interaction interfaces. While binary cross-links have been remarkably informative, multimeric cross-links can offer enhanced spatial resolution to facilitate the characterization of dynamic and heterogeneous protein complexes. However, the identification of multimeric cross-links remains extremely challenging due to fragmentation complexity and the vast expansion of database search space. Here, we present a novel trioxane-based MS-cleavable homotrifunctional cross-linker TSTO, which can target three proximal lysine residues simultaneously. Owing to its unique structure and MS-cleavability, TSTO enables fast and unambiguous identification of cross-linked peptides using LC-MS n analysis. Importantly, we have demonstrated that the TSTO-based XL-MS platform is effective for mapping PPIs of protein complexes and cellular networks. The trimeric interactions captured by TSTO have uncovered new structural details that cannot be easily revealed by existing reagents, allowing in-depth description of PPIs to facilitate structural modeling. This development not only advances XL-MS technologies for global PPI profiling from living cells, but also offers a new direction for creating multifunctional MS-cleavable cross-linkers to further push structural systems biology forward in the future.

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Structural biology in cellulo: Minding the gap between conceptualization and realization

Recent technological advances have deepened our perception of cellular structure. However, most structural data doesn't originate from intact cells, limiting our understanding of cellular processes. Here, we discuss current and future developments that will bring us towards a structural picture of the cell. Electron cryotomography is the standard bearer, with its ability to provide in cellulo snapshots. Single-particle electron microscopy (of purified biomolecules and of complex mixtures) and covalent crosslinking combined with mass spectrometry also have significant roles to play, as do artificial intelligence algorithms in their many guises. To integrate these multiple approaches, data curation and standardisation will be critical - as is the need to expand efforts beyond our current protein-centric view to the other (macro)molecules that sustain life.

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

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.

Microscale measurements of protein complexes from single cells

Proteins execute numerous cell functions in concert with one another in protein-protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

A Multiplexed SEC-MS Approach to Systematically Study the Interplay Between Protein Assembly-States and Phosphorylation Events

Protein molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly-states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly-states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Applying SEC-MX to HEK293 and HCT116 cells, we generated a proof-of-concept dataset mapping thousands of phosphopeptides and their assembly-states. Our analysis revealed intricate relationships between phosphorylation events and assembly-states and generated testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

An Optimized Miniaturized Filter-Aided Sample Preparation Method for Sensitive Cross-Linking Mass Spectrometry Analysis of Microscale Samples

Cross-linking mass spectrometry (XL-MS) is a powerful tool for elucidating protein structures and protein-protein interactions (PPIs) at the global scale. However, sensitive XL-MS analysis of mass-limited samples remains challenging, due to serious sample loss during sample preparation of the low-abundance cross-linked peptides. Herein, an optimized miniaturized filter-aided sample preparation (O-MICROFASP) method was presented for sensitive XL-MS analysis of microscale samples. By systematically investigating and optimizing crucial experimental factors, this approach dramatically improves the XL identification of low and submicrogram samples. Compared with the conventional FASP method, more than 7.4 times cross-linked peptides were identified from single-shot analysis of 1 μg DSS cross-linked HeLa cell lysates (440 vs 59). The number of cross-linked peptides identified from 0.5 μg HeLa cell lysates was increased by 58% when further reducing the surface area of the filter to 0.058 mm2 in the microreactor. To deepen the identification coverage of XL-proteome, five different types of cross-linkers were used and each μg of cross-linked HeLa cell lysates was processed by O-MICROFASP integrated with tip-based strong cation exchange (SCX) fractionation. Up to 2741 unique cross-linked peptides were identified from the 5 μg HeLa cell lysates, representing 2579 unique K-K linkages on 1092 proteins. About 96% of intraprotein cross-links were within the maximal distance restraints of 26 Å, and 75% of the identified PPIs reported by the STRING database were with high confidence (scores ≥0.9), confirming the high validity of the identified cross-links for protein structural mapping and PPI analysis. This study demonstrates that O-MICROFASP is a universal and efficient method for proteome-wide XL-MS analysis of microscale samples with high sensitivity and reliability.

Systematic identification of structure-specific protein-protein interactions

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

Structural proteomics of a bacterial mega membrane protein complex: FtsH-HflK-HflC

Recent advances in mass spectrometry (MS) yielding sensitive and accurate measurements along with developments in software tools have enabled the characterization of complex systems routinely. Thus, structural proteomics and cross-linking mass spectrometry (XL-MS) have become a useful method for structural modeling of protein complexes. Here, we utilized commonly used XL-MS software tools to elucidate the protein interactions within a membrane protein complex containing FtsH, HflK, and HflC, over-expressed in E. coli. The MS data were processed using MaxLynx, MeroX, MS Annika, xiSEARCH, and XlinkX software tools. The number of identified inter- and intra-protein cross-links varied among software. Each interaction was manually checked using the raw MS and MS/MS data and distance restraints to verify inter- and intra-protein cross-links. A total of 37 inter-protein and 148 intra-protein cross-links were determined in the FtsH-HflK-HflC complex. The 59 of them were new interactions on the lacking region of recently published structures. These newly identified interactions, when combined with molecular docking and structural modeling, present opportunities for further investigation. The results provide valuable information regarding the complex structure and function to decipher the intricate molecular mechanisms underlying the FtsH-HflK-HflC complex.

Probing Protein Structural Changes in Alzheimer's Disease via Quantitative Cross-linking Mass Spectrometry

Alzheimer's disease (AD) is a progressive neurological disorder featuring abnormal protein aggregation in the brain, including the pathological hallmarks of amyloid plaques and hyperphosphorylated tau. Despite extensive research efforts, understanding the molecular intricacies driving AD development remains a formidable challenge. This study focuses on identifying key protein conformational changes associated with the progression of AD. To achieve this, we employed quantitative cross-linking mass spectrometry (XL-MS) to elucidate conformational changes in the protein networks in cerebrospinal fluid (CSF). By using isotopically labeled cross-linkers BS3d0 and BS3d4, we reveal a dynamic shift in protein interaction networks during AD progression. Our comprehensive analysis highlights distinct alterations in protein-protein interactions within mild cognitive impairment (MCI) states. This study accentuates the potential of cross-linked peptides as indicators of AD-related conformational changes, including previously unreported site-specific binding between α-1-antitrypsin (A1AT) and complement component 3 (CO3). Furthermore, this work enables detailed structural characterization of apolipoprotein E (ApoE) and reveals modifications within its helical domains, suggesting their involvement in MCI pathogenesis. The quantitative approach provides insights into site-specific interactions and changes in the abundance of cross-linked peptides, offering an improved understanding of the intricate protein-protein interactions underlying AD progression. These findings lay a foundation for the development of potential diagnostic or therapeutic strategies aimed at mitigating the negative impact of AD.

Cross-link assisted spatial proteomics to map sub-organelle proteomes and membrane protein topologies

The functions of cellular organelles and sub-compartments depend on their protein content, which can be characterized by spatial proteomics approaches. However, many spatial proteomics methods are limited in their ability to resolve organellar sub-compartments, profile multiple sub-compartments in parallel, and/or characterize membrane-associated proteomes. Here, we develop a cross-link assisted spatial proteomics (CLASP) strategy that addresses these shortcomings. Using human mitochondria as a model system, we show that CLASP can elucidate spatial proteomes of all mitochondrial sub-compartments and provide topological insight into the mitochondrial membrane proteome. Biochemical and imaging-based follow-up studies confirm that CLASP allows discovering mitochondria-associated proteins and revising previous protein sub-compartment localization and membrane topology data. We also validate the CLASP concept in synaptic vesicles, demonstrating its applicability to different sub-cellular compartments. This study extends the scope of cross-linking mass spectrometry beyond protein structure and interaction analysis towards spatial proteomics, and establishes a method for concomitant profiling of sub-organelle and membrane proteomes.

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology

The validity of protein structures and interactions, whether determined under ideal laboratory conditions or predicted by AI tools such as Alphafold2, to precisely reflect those found in living cells remains to be examined. Moreover, understanding the changes in protein structures and interactions in response to stimuli within living cells, under both normal and disease conditions, is key to grasping proteins' functionality and cellular processes. Nevertheless, achieving high-resolution identification of these protein structures and interactions within living cells presents a technical challenge. In this Perspective, we summarize the recent advancements in in-cell nuclear magnetic resonance (NMR) and in vivo cross-linking mass spectrometry (XL-MS) for studying protein structures and interactions within a cellular context. Additionally, we discuss the challenges, opportunities, and potential benefits of integrating in-cell NMR and in vivo XL-MS in future research to offer an exhaustive approach to studying proteins in their natural habitat.

Chemical reagents for the enrichment of modified peptides in MS-based identification

Chemical reagents with special groups as enrichable handles have empowered the ability to label and enrich modified peptides. Here is an overview of different chemical reagents with affinity tags to isolate labeled peptides and the latest developments of enrichment strategies. Biotin is the most used affinity tag due to its high interaction with avidin. To decrease the unfavorable influence of biotin for its poor efficiency in ionization and fragmentation in downstream MS analysis, cleavable moieties were installed between the reactive groups and biotin to release labeled peptides from the biotin. To minimize the steric hindrance of biotin, a two-step method was developed, for which alkyne- or azide-tagged linkers were firstly used to label peptides and then biotin was installed through click chemistry. Recently, new linkers using a small phosphonic acid as the affinity tag for IMAC or TiO2 enrichment have been developed and successfully used to isolate chemically labeled peptides in XL-MS. A stable P-C instead of P-O bond was introduced to linkers to differentiate labeled and endogenous phosphopeptides. Furthermore, a membrane-permeable phosphonate-containing reagent was reported, which facilitated the study of living systems. Taking a cue from classic chemical reactions, stable metal-complex intermediates, including cobalt and palladium complexes, have been developed as peptide purification systems. Advanced enrichment strategies have also been proposed, such as the two-stage IMAC enrichment method and biotin-based two-step reaction strategy, allowing the reduction of unwanted peptides and improvements for the analysis of specific labeled peptides. Finally, future trends in the area are briefly discussed.

Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

A protein-protein interaction analysis tool for targeted cross-linking mass spectrometry

Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein-protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand. Although the cross-linking mass spectrometry (XL-MS) method is a representative approach to identify physical interactions between proteins, calculating theoretical mass values for application to targeted mass spectrometry can be difficult. To address this challenge, our research team developed PPIAT, a web application that integrates information on reviewed human proteins, protein-protein interactions, cross-linkers, enzymes, and modifications. PPIAT leverages publicly accessible databases such as STRING to identify interactomes associated with target proteins. Moreover, it autonomously computes the theoretical mass value, accounting for all potential cross-linking scenarios pertinent to the application of XL-MS in SRM analysis. The outputs generated by PPIAT can be concisely represented in terms of protein interaction probabilities, complemented by findings from alternative analytical tools like Prego. These comprehensive summaries enable researchers to customize the results according to specific experimental conditions. All functions of PPIAT are available for free on the web application, making it a valuable tool for researchers studying protein-protein interactions.

In-Cell Dynamics: The Next Focus of All-Atom Simulations

The cell is a crowded space where large biomolecules and metabolites are in continuous motion. Great strides have been made in in vitro studies of protein dynamics, folding, and protein-protein interactions, and much new data are emerging of how they differ in the cell. In this Perspective, we highlight the current progress in atomistic modeling of in-cell environments, both bacteria and mammals, with emphasis on classical all-atom molecular dynamics simulations. These simulations have been recently used to capture and characterize functional and non-functional protein-protein interactions, protein folding dynamics of small proteins with varied topologies, and dynamics of metabolites. We further discuss the challenges and efforts for updating modern force fields critical to the progress of cellular environment simulations. We also briefly summarize developments in relevant state-of-the-art experimental techniques. As computational and experimental methodologies continue to progress and produce more directly comparable data, we are poised to capture the complex atomistic picture of the cell.

All-in-One Pseudo-MS3 Method for the Analysis of Gas-Phase Cleavable Protein Crosslinking Reactions

Crosslinking mass spectrometry (XL-MS) supports structure analysis of individual proteins and highly complex whole-cell interactomes. The identification of crosslinked peptides from enzymatic digests remains challenging, especially at the cell level. Empirical methods that use gas-phase cleavable crosslinkers can simplify the identification process by enabling an MS3-based strategy that turns crosslink identification into a simpler problem of detecting two separable peptides. However, the method is limited to select instrument platforms and is challenged by duty cycle constraints. Here, we revisit a pseudo-MS3 concept that incorporates in-source fragmentation, where a fast switch between gentle high-transmission source conditions and harsher in-source fragmentation settings liberates peptides for standard MS2-based peptide identification. We present an all-in-one method where retention time matches between the crosslink precursor and the liberated peptides establish linkage, and MS2 sequencing identifies the source-liberated peptides. We demonstrate that DC4, a very labile cleavable crosslinker, generates high-intensity peptides in-source. Crosslinks can be identified from these liberated peptides, as they are chromatographically well-resolved from monolinks. Using bovine serum albumin (BSA) as a crosslinking test case, we detect 27% more crosslinks with pseudo-MS3 over a best-in-class MS3 method. While performance is slightly lower for whole-cell lysates (generating two-thirds of the identifications of a standard method), we find that 60% of these hits are unique, highlighting the complementarity of the method.

4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry

Cross-linking mass spectrometry (XL-MS) is a powerful tool for examining protein structures and interactions. Nevertheless, analysis of low-abundance cross-linked peptides is often limited in the data-dependent acquisition (DDA) mode due to its semistochastic nature. To address this issue, we introduced a workflow called 4D-diaXLMS, representing the first-ever application of four-dimensional data-independent acquisition for proteome-wide cross-linking analysis. Cross-linking studies of the HeLa cell proteome were evaluated using the classical cross-linker disuccinimidyl suberate as an example. Compared with the DDA analysis, 4D-diaXLMS exhibited marked improvement in the identification coverage of cross-linked peptides, with a total increase of 36% in single-shot analysis across all 16 SCX fractions. This advantage was further amplified when reducing the fraction number to 8 and 4, resulting in 125 and 149% improvements, respectively. Using 4D-diaXLMS, up to 83% of the cross-linked peptides were repeatedly identified in three replicates, more than twice the 38% in the DDA mode. Furthermore, 4D-diaXLMS showed good performance in the quantitative analysis of yeast cross-linked peptides even in a 15-fold excess amount of HeLa cell matrix, with a low coefficient of variation and high quantitative accuracies in all concentrations. Overall, 4D-diaXLMS was proven to have high coverage, good reproducibility, and accurate quantification for in-depth XL-MS analysis in complex samples, demonstrating its immense potential for advances in the field.

A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data

Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows at High Sensitivity and Specificity

Cross-linking mass spectrometry has become a powerful tool for the identification of protein-protein interactions and for gaining insight into the structures of proteins. We previously published MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable cross-linkers. In this publication, we present MS Annika 2.0, an updated version implementing a new search algorithm that, in addition to MS2 level, only supports the processing of data from MS2-MS3-based approaches for the identification of peptides from MS3 spectra, and introduces a novel scoring function for peptides identified across multiple MS stages. Detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika 2.0 on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines. We show that MS Annika detects up to 4 times more true unique cross-links while simultaneously yielding less false positive hits and therefore a more accurate FDR estimation than the other two search engines. All mass spectrometry proteomics data along with result files have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD041955.

Comparative Analysis of Chemical Cross-Linking Mass Spectrometry Data Indicates That Protein STY Residues Rarely React with N-Hydroxysuccinimide Ester Cross-Linkers

When it comes to mass spectrometry data analysis for identification of peptide pairs linked by N-hydroxysuccinimide (NHS) ester cross-linkers, search engines bifurcate in their setting of cross-linkable sites. Some restrict NHS ester cross-linkable sites to lysine (K) and protein N-terminus, referred to as K only for short, whereas others additionally include serine (S), threonine (T), and tyrosine (Y) by default. Here, by setting amino acids with chemically inert side chains such as glycine (G), valine (V), and leucine (L) as cross-linkable sites, which serves as a negative control, we show that software-identified STY-cross-links are only as reliable as GVL-cross-links. This is true across different NHS ester cross-linkers including DSS, DSSO, and DSBU, and across different search engines including MeroX, xiSearch, and pLink. Using a published data set originated from synthetic peptides, we demonstrate that STY-cross-links indeed have a high false discovery rate. Further analysis revealed that depending on the data and the search engine used to analyze the data, up to 65% of the STY-cross-links identified are actually K-K cross-links of the same peptide pairs, up to 61% are actually K-mono-links, and the rest tend to contain short peptides at high risk of false identification.

Cross-Linking Mass Spectrometry Uncovers Interactions Between High-Density Lipoproteins and the SARS-CoV-2 Spike Glycoprotein

High-density lipoprotein (HDL) levels are reduced in patients with coronavirus disease 2019 (COVID-19), and the extent of this reduction is associated with poor clinical outcomes. While lipoproteins are known to play a key role during the life cycle of the hepatitis C virus, their influence on coronavirus (CoV) infections is poorly understood. In this study, we utilize cross-linking mass spectrometry (XL-MS) to determine circulating protein interactors of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoprotein. XL-MS of plasma isolated from patients with COVID-19 uncovered HDL protein interaction networks, dominated by acute-phase serum amyloid proteins, whereby serum amyloid A2 was shown to bind to apolipoprotein (Apo) D. XL-MS on isolated HDL confirmed ApoD to interact with SARS-CoV-2 spike but not SARS-CoV-1 spike. Other direct interactions of SARS-CoV-2 spike upon HDL included ApoA1 and ApoC3. The interaction between ApoD and spike was further validated in cells using immunoprecipitation-MS, which uncovered a novel interaction between both ApoD and spike with membrane-associated progesterone receptor component 1. Mechanistically, XL-MS coupled with data-driven structural modeling determined that ApoD may interact within the receptor-binding domain of the spike. However, ApoD overexpression in multiple cell-based assays had no effect upon viral replication or infectivity. Thus, SARS-CoV-2 spike can bind to apolipoproteins on HDL, but these interactions do not appear to alter infectivity.

In vivo cross-linking-based affinity purification and mass spectrometry for targeting intracellular protein-protein interactions

Comprehensive interactome analysis of targeted proteins is important to understand how proteins work together in regulating functions. Commonly, affinity purification followed by mass spectrometry (AP-MS) has been recognized as the most often used technique for studying protein-protein interactions (PPIs). However, some proteins with weak interactions, which are responsible for key roles in regulation, are easily broken during cell lysis and purification through an AP approach. Herein, we have developed an approach termed in vivo cross-linking-based affinity purification and mass spectrometry (ICAP-MS). By this method, in vivo cross-linking was introduced to covalently fix intracellular PPIs in their functional states to assure all PPIs could be integrally maintained during cell disruption. In addition, the chemically cleavable crosslinkers which were employed enabled unbinding of PPIs for in-depth identification of components within the interactome and biological analysis, while allowing binding of PPIs for cross-linking-mass spectrometry (CXMS)-based direct interaction determination. Multi-level information on targeted PPIs network can be obtained by ICAP-MS, including composition of interacting proteins, as well as direct interacting partners and binding sites. As a proof of concept, the interactome of MAPK3 from 293A cells was profiled with 6.15-fold improvement in identification than by conventional AP-MS. Meanwhile, 184 cross-link site pairs of these PPIs were experimentally identified by CXMS. Furthermore, ICAP-MS was applied in the temporal profiling of MAPK3 interactions under activation by cAMP-mediated pathway. The regulatory manner of MAPK pathways was presented through the quantitative changes of MAPK3 and its interacting proteins at different time points after activation. Therefore, all reported results demonstrated that the ICAP-MS approach may provide comprehensive information on interactome of targeted protein for functional exploration.

Progress toward Proteome-Wide Photo-Cross-Linking to Enable Residue-Level Visualization of Protein Structures and Networks In Vivo

Cross-linking mass spectrometry (XL-MS) is emerging as a method at the crossroads of structural and cellular biology, uniquely capable of identifying protein-protein interactions with residue-level resolution and on the proteome-wide scale. With the development of cross-linkers that can form linkages inside cells and easily cleave during fragmentation on the mass spectrometer (MS-cleavable cross-links), it has become increasingly facile to identify contacts between any two proteins in complex samples, including in live cells or tissues. Photo-cross-linkers possess the advantages of high temporal resolution and high reactivity, thereby engaging all residue-types (rather than just lysine); nevertheless, photo-cross-linkers have not enjoyed widespread use and are yet to be employed for proteome-wide studies because their products are challenging to identify. Here, we demonstrate the synthesis and application of two heterobifunctional photo-cross-linkers that feature diazirines and N-hydroxy-succinimidyl carbamate groups, the latter of which unveil doubly fissile MS-cleavable linkages upon acyl transfer to protein targets. Moreover, these cross-linkers demonstrate high water-solubility and cell-permeability. Using these compounds, we demonstrate the feasibility of proteome-wide photo-cross-linking in cellulo. These studies elucidate a small portion of Escherichia coli's interaction network, albeit with residue-level resolution. With further optimization, these methods will enable the detection of protein quinary interaction networks in their native environment at residue-level resolution, and we expect that they will prove useful toward the effort to explore the molecular sociology of the cell.

Targeted cross-linker delivery for the in situ mapping of protein conformations and interactions in mitochondria

Current methods for intracellular protein analysis mostly require the separation of specific organelles or changes to the intracellular environment. However, the functions of proteins are determined by their native microenvironment as they usually form complexes with ions, nucleic acids, and other proteins. Here, we show a method for in situ cross-linking and analysis of mitochondrial proteins in living cells. By using the poly(lactic-co-glycolic acid) (PLGA) nanoparticles functionalized with dimethyldioctadecylammonium bromide (DDAB) to deliver protein cross-linkers into mitochondria, we subsequently analyze the cross-linked proteins using mass spectrometry. With this method, we identify a total of 74 pairs of protein-protein interactions that do not exist in the STRING database. Interestingly, our data on mitochondrial respiratory chain proteins ( ~ 94%) are also consistent with the experimental or predicted structural analysis of these proteins. Thus, we provide a promising technology platform for in situ defining protein analysis in cellular organelles under their native microenvironment.

Ynamide Coupling Reagent for the Chemical Cross-Linking of Proteins in Live Cells

Chemical cross-linking of proteins coupled with mass spectrometry analysis (CXMS) is a powerful method for the study of protein structure and protein-protein interactions (PPIs). However, the chemical probes used in the CXMS are limited to bidentate reactive warheads, and the available zero-length cross-linkers are restricted to 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) and 4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium chloride (DMTMM). To alleviate this issue, an efficient coupling reagent, sulfonyl ynamide, was developed as a new zero-length cross-linker that can connect high-abundance carboxyl residues (D/E) with lysine (K) to form amide bonds in the absence of any catalyst. Significant improvement in the cross-linking efficiency and specificity in comparison with traditional EDC/NHS was achieved with model proteins, which includes inter- and intramolecular conjugations. The cross-linked structures were validated by X-ray crystallography. Importantly, this coupling reagent can be successfully used to capture interacting proteins in the whole proteome and can be a useful reagent for probing potential protein-protein interactions in situ.

Improved Cross-Linking Coverage for Protein Complexes Containing Low Levels of Lysine by Using an Enrichable Photo-Cross-Linker

Chemical cross-linking coupled with mass spectrometry (XL-MS) is an important technique for the structural analysis of protein complexes where the coverage of amino acids and the identification of cross-linked sites are crucial. Photo-cross-linking has multisite reactivity and is valuable for the structural analysis of chemical cross-linking. However, a high degree of heterogeneity results from this multisite reactivity, which results in samples with higher complexity and lower abundance. Additionally, the applicability of photo-cross-linking is limited to purified protein complexes. In this work, we demonstrate a photo-cross-linker, alkynyl-succinimidyl-diazirine (ASD) with the reactive groups of N-hydroxysuccinimide ester and diazirine, as well as the click-enrichable alkyne group. Photo-cross-linkers can provide higher site reactivity for proteins that contain only a small number of lysine residues, thereby complementing the more commonly used lysine-targeting cross-linkers. By systematically analyzing proteins with differing lysine contents and differing flexibilities, we demonstrated clear enhancement in structure elucidation for proteins containing less lysine and with high flexibility. In addition, enrichment approaches of alkynyl-azide click chemistry conjugated with biotin-streptavidin purification (coinciding with parallel orthogonal digestion) improved the identification coverage of cross-links. We show that this photo-cross-linking approach can be used for membrane proteome-wide complex analysis. This method led to the identification of a total of 14066 lysine-X cross-linked site pairs from a total of 2784 proteins. Thus, this cross-linker is a valuable addition to a photo-cross-linking toolkit and improves the identification coverage of XL-MS in functional structure analysis.

Proteomic discovery of chemical probes that perturb protein complexes in human cells

Most human proteins lack chemical probes, and several large-scale and generalizable small-molecule binding assays have been introduced to address this problem. How compounds discovered in such "binding-first" assays affect protein function, nonetheless, often remains unclear. Here, we describe a "function-first" proteomic strategy that uses size exclusion chromatography (SEC) to assess the global impact of electrophilic compounds on protein complexes in human cells. Integrating the SEC data with cysteine-directed activity-based protein profiling identifies changes in protein-protein interactions that are caused by site-specific liganding events, including the stereoselective engagement of cysteines in PSME1 and SF3B1 that disrupt the PA28 proteasome regulatory complex and stabilize a dynamic state of the spliceosome, respectively. Our findings thus show how multidimensional proteomic analysis of focused libraries of electrophilic compounds can expedite the discovery of chemical probes with site-specific functional effects on protein complexes in human cells.

Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Capturing the hierarchically assorted modules of protein-protein interactions in the organized nucleome

Nuclear proteins are major constituents and key regulators of nucleome topological organization and manipulators of nuclear events. To decipher the global connectivity of nuclear proteins and the hierarchically organized modules of their interactions, we conducted two rounds of cross-linking mass spectrometry (XL-MS) analysis, one of which followed a quantitative double chemical cross-linking mass spectrometry (in vivoqXL-MS) workflow, and identified 24,140 unique crosslinks in total from the nuclei of soybean seedlings. This in vivo quantitative interactomics enabled the identification of 5340 crosslinks that can be converted into 1297 nuclear protein-protein interactions (PPIs), 1220 (94%) of which were non-confirmative (or novel) nuclear PPIs compared with those in repositories. There were 250 and 26 novel interactors of histones and the nucleolar box C/D small nucleolar ribonucleoprotein complex, respectively. Modulomic analysis of orthologous Arabidopsis PPIs produced 27 and 24 master nuclear PPI modules (NPIMs) that contain the condensate-forming protein(s) and the intrinsically disordered region-containing proteins, respectively. These NPIMs successfully captured previously reported nuclear protein complexes and nuclear bodies in the nucleus. Surprisingly, these NPIMs were hierarchically assorted into four higher-order communities in a nucleomic graph, including genome and nucleolus communities. This combinatorial pipeline of 4C quantitative interactomics and PPI network modularization revealed 17 ethylene-specific module variants that participate in a broad range of nuclear events. The pipeline was able to capture both nuclear protein complexes and nuclear bodies, construct the topological architectures of PPI modules and module variants in the nucleome, and probably map the protein compositions of biomolecular condensates.

Mapping protein direct interactome of oxidoreductases with small molecular chemical cross-linkers in live cells

Identifying direct substrates of enzymes has been a long-term challenge. Here, we present a strategy using live cell chemical cross-linking and mass spectrometry to identify the putative substrates of enzymes for further biochemical validation. Compared with other methods, our strategy is based on the identification of cross-linked peptides supported by high-quality MS/MS spectra, which eliminates false-positive discoveries of indirect binders. Additionally, cross-linking sites allow the analysis of interaction interfaces, providing further information for substrate validation. We demonstrated this strategy by identifying direct substrates of thioredoxin in both E. coli and HEK293T cells using two bis-vinyl sulfone chemical cross-linkers BVSB and PDES. We confirmed that BVSB and PDES have high specificity in cross-linking the active site of thioredoxin with its substrates both in vitro and in live cells. Applying live cell cross-linking, we identified 212 putative substrates of thioredoxin in E. coli and 299 putative S-nitrosylation (SNO) substrates of thioredoxin in HEK293T cells. In addition to thioredoxin, we have shown that this strategy can be applied to other proteins in the thioredoxin superfamily. Based on these results, we believe future development of cross-linking techniques will further advance cross-linking mass spectrometry in identifying substrates of other classes of enzymes.

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 in vitro 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.

Dissecting the structural heterogeneity of proteins by native mass spectrometry

A single gene yields many forms of proteins via combinations of posttranscriptional/posttranslational modifications. Proteins also fold into higher-order structures and interact with other molecules. The combined molecular diversity leads to the heterogeneity of proteins that manifests as distinct phenotypes. Structural biology has generated vast amounts of data, effectively enabling accurate structural prediction by computational methods. However, structures are often obtained heterologously under homogeneous states in vitro. The lack of native heterogeneity under cellular context creates challenges in precisely connecting the structural data to phenotypes. Mass spectrometry (MS) based proteomics methods can profile proteome composition of complex biological samples. Most MS methods follow the "bottom-up" approach, which denatures and digests proteins into short peptide fragments for ease of detection. Coupled with chemical biology approaches, higher-order structures can be probed via incorporation of covalent labels on native proteins that are maintained at the peptide level. Alternatively, native MS follows the "top-down" approach and directly analyzes intact proteins under nondenaturing conditions. Various tandem MS activation methods can dissect the intact proteins for in-depth structural elucidation. Herein, we review recent native MS applications for characterizing heterogeneous samples, including proteins binding to mixtures of ligands, homo/hetero-complexes with varying stoichiometry, intrinsically disordered proteins with dynamic conformations, glycoprotein complexes with mixed modification states, and active membrane protein complexes in near-native membrane environments. We summarize the benefits, challenges, and ongoing developments in native MS, with the hope to demonstrate an emerging technology that complements other tools by filling the knowledge gaps in understanding the molecular heterogeneity of proteins.

Cross-linking mass spectrometry for mapping protein complex topologies in situ

Cross-linking mass spectrometry has become an established technology to provide structural information on the topology and dynamics of protein complexes. Readily accessible workflows can provide detailed data on simplified systems, such as purified complexes. However, using this technology to study the structure of protein complexes in situ, such as in organelles, cells, and even tissues, is still a technological frontier. The complexity of these systems remains a considerable challenge, but there have been dramatic improvements in sample handling, data acquisition, and data processing. Here, we summarise these developments and describe the paths towards comprehensive and comparative structural interactomes by cross-linking mass spectrometry.