Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications

An approach to characterize mechanisms of action of anti-amyloidogenic compounds in vitro and in situ

Amyloid aggregation is associated with neurodegenerative disease and its modulation is a focus of drug development. We developed a chemical proteomics pipeline to probe the mechanism of action of anti-amyloidogenic compounds. Our approach identifies putative interaction sites with high resolution, can probe compound interactions with specific target conformations and directly in cell and brain extracts, and identifies off-targets. We analysed interactions of six anti-amyloidogenic compounds and the amyloid binder Thioflavin T with different conformations of the Parkinson's disease protein α-Synuclein and tested specific compounds in cell or brain lysates. AC Immune compound 2 interacted with α-Synuclein in vitro, in intact neurons and in neuronal lysates, reduced neuronal α-Synuclein levels in a seeded model, and had protective effects. EGCG, Baicalein, ThT and doxycycline interacted with α-Synuclein in vitro but not substantially in cell lysates, with many additional putative targets, underscoring the importance of testing compounds in situ. Our pipeline will enable screening of compounds against any amyloidogenic proteins in cell and patient brain extracts and mechanistic studies of compound action.

A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?

Interpreting variants of uncertain significance (VUS) in mismatch repair (MMR) genes remains a major challenge in managing Lynch syndrome and other hereditary cancer syndromes. This review outlines recommended VUS classification procedures, encompassing foundational and specialized methodologies tailored for MMR genes by expert organizations, including InSiGHT and ClinGen's Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP). Key approaches include: (1) functional data, encompassing direct assays measuring MMR proficiency such as in vitro MMR assays, deep mutational scanning, and MMR cell-based assays, as well as techniques like methylation-tolerant assays, proteomic-based approaches, and RNA sequencing, all of which provide critical functional evidence supporting variant pathogenicity; (2) computational data/tools, including in silico meta-predictors and models, which contribute to robust VUS classification when integrated with experimental evidence; and (3) enhanced variant detection to identify the actual causal variant through whole-genome sequencing and long-read sequencing to detect pathogenic variants missed by traditional methods. These strategies improve diagnostic precision, support clinical decision-making for Lynch syndrome, and establish a flexible framework that can be applied to other OMIM-listed genes.

Discovery of RNA-Protein Molecular Clamps Using Proteome-Wide Stability Assays

Uncompetitive inhibition is an effective strategy for suppressing dysregulated enzymes and their substrates, but discovery of suitable ligands depends on often-unavailable structural knowledge and serendipity. Hence, despite surging interest in mass spectrometry-based target identification, proteomic studies of substrate-dependent target engagement remain sparse. Herein, we describe a strategy for the discovery of substrate-dependent ligand binding. Using proteome integral solubility alteration (PISA) assays, we show that simple biochemical additives can enable detection of RNA-protein-small molecule complexes in native cell lysates. We apply our approach to rocaglates, molecules that specifically clamp RNA to eukaryotic translation initiation factor 4A (eIF4A), DEAD-box helicase 3X (DDX3X), and potentially other members of the DEAD-box (DDX) helicase family. To identify unexpected interactions, we used a target class-specific thermal window and compared ATP analog and RNA base dependencies for key rocaglate-DDX interactions. We report novel DDX targets of high-profile rocaglates-including the clinical candidate Zotatifin-and validate our findings using limited proteolysis-mass spectrometry and fluorescence polarization (FP) experiments. We also provide structural insight into divergent DDX3X affinities between synthetic rocaglates. Taken together, our study provides a model for screening uncompetitive inhibitors using a chemical proteomics approach and uncovers actionable DDX clamping targets, clearing a path toward characterization of novel molecular clamps and associated RNA helicases.

Analysis of Limited Proteolysis-Coupled Mass Spectrometry Data

Limited proteolysis combined with mass spectrometry (LiP-MS) facilitates probing structural changes on a proteome-wide scale. This method leverages differences in the proteinase K accessibility of native protein structures to concurrently assess structural alterations for thousands of proteins in situ. Distinguishing different contributions to the LiP-MS signal, such as changes in protein abundance or chemical modifications, from structural protein alterations remains challenging. Here, we present the first comprehensive computational pipeline to infer structural alterations for LiP-MS data using a two-step approach. 1) We remove unwanted variations from the LiP signal that are not caused by protein structural effects and 2) infer the effects of variables of interest on the remaining signal. Using LiP-MS data from three species, we demonstrate that this approach outperforms previously employed approaches. Our framework provides a uniquely powerful approach for deconvolving LiP-MS signals and separating protein structural changes from changes in protein abundance, posttranslational modifications, and alternative splicing. Our approach may also be applied to analyze other types of peptide-centric structural proteomics data, such as FPOP or molecular painting data.

Time-lapse tryptic digestion: a proteomic approach to improve sequence coverage of extracellular matrix proteins

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes, including proliferation, adhesion, and migration. Alterations of ECM structure and/or composition have been linked to many pathological processes, including cancer and fibrosis. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundances in the ECM. They also do not permit to achieve the high coverage needed to gain finer biochemical on ECM proteoforms (e.g., isoforms, post-translational modifications) and topographical information critical to better understand ECM protein functions. Here, we present the development of a time-lapsed proteomic pipeline using limited tryptic proteolysis and sequential release of peptides over time. This experimental pipeline was combined with data-independent acquisition mass spectrometry and the assembly of a custom matrisome spectral library to enhance peptide-to-spectrum matching. This pipeline shows superior protein identification, peptide-to-spectrum matching, and significantly increased sequence coverage against standard ECM proteomic pipelines. Exploiting the spatio-temporal resolution of this method, we further demonstrate how time-resolved 3-dimensional peptide mapping can identify protein regions differentially susceptible to trypsin, which may aid in identifying protein-protein interaction sites.

Post-translational modifications orchestrate the intrinsic signaling bias of GPR52

Despite recent advances in G-protein-coupled receptor (GPCR) biology, the regulation of GPCR activation, signaling and function by post-translational modifications (PTMs) remains largely unexplored. In this study of GPR52, an orphan GPCR with exceedingly high constitutive G-protein activity that is emerging as a neurotherapeutic target, we discovered its disproportionately low arrestin recruitment activity. After profiling the N-glycosylation and phosphorylation patterns, we found that these two types of PTMs differentially shape the intrinsic signaling bias of GPR52. While N-terminal N-glycosylation promotes constitutive Gs signaling possibly through favoring the self-activating conformation, phosphorylation in helix 8, to our great surprise, suppresses arrestin recruitment and attenuates receptor internalization. In addition, we uncovered the counteracting roles of N-glycosylation and phosphorylation in modulating GPR52-dependent accumulation of the huntingtin protein in brain striatal cells. Our study provides new insights into the regulation of intrinsic signaling bias and cellular function of an orphan GPCR through distinct PTMs in different motifs.

Extreme Tolerance of Nanoparticle-Protein Corona to Ultra-High Abundance Proteins Enhances the Depth of Serum Proteomics

The serum nanoparticle-protein corona (NPC) provides specific disease information, thus opening a new avenue for high-throughput in-depth proteomics to facilitate biomarker discovery. Yet, little is known about the interactions between NPs and proteins, and its role in enhanced depth of serum proteomics. Herein, a series of protein spike-in experiments are conducted to systematically investigate protein depletion and enrichment during NPC formation. Proteomic depth is serum concentration-dependent, and NPC exhibits powerful tolerance to ultra-high abundant proteins. In addition, protein-protein interactions (PPI), especially those involving albumin, play a pivotal role in promoting proteomic depth. Furthermore, a triple-protein assay is established to interrogate the relationship between protein binding affinity and concentration. NPC formation is a product of balancing binding affinity, concentration, and PPI. Overall, this study elucidates how NPs achieve protein depletion and enrichment for enhanced serum proteomic depth to gain a better understanding of NPC as an essential tool of proteome profiling.

Deciphering Polymer Interactions in Bioconjugates with Different Architectures by Structural Analysis via Time-Resolved Limited Proteolysis Mass Spectrometry

Therapeutic proteins are commonly conjugated with polymers to modulate their pharmacokinetics but often lack a description of the polymer-protein interaction. We deployed limited proteolysis mass spectrometry (LiP-MS) to reveal the interaction of polyethylene glycol (PEG) and PEG alternative polymers with interferon-α2a (IFN). Target conjugates were digested with the specific protease trypsin and a "heavy" 15N-IFN wild type (IFN-WT) for time-resolved quantification of the cleavage dynamics. Interactions between IFN-α2a and its high-affinity receptor were detailed by LiP-MS. Then, 10 kDa polymers of PEG, linear polyglycerol (LPG), and poly(2-oxazoline) (POX) with two different cyclooctyne linkers (BCN/DBCO) were used for site-specific bioconjugation to azide functionalized IFN-α2a. Tryptic events at each cleavage site and in different structural environments (loops/helices) were compared. PEG and LPG were similar, and POX showed a reduced interaction profile with the IFN-α2a surface. All-atom molecular dynamics simulations of IFN-DBCO-polymer conjugates revealed distinct and transient (below 50 ns) protein-interaction profiles for PEG, LPG, and POX. Cleavage dynamics of IFN-polymer conjugates from the BCN handle were homogeneous, pointing to a more conserved IFN structure than DBCO-polymer conjugates. In summary, time-resolved LiP-MS for quantification of cleavage events enhances the structural understanding of transient IFN-polymer interactions, which may be extended to other bioconjugate types.

Exploring protein conformations with limited proteolysis coupled to mass spectrometry

Limited proteolysis coupled to mass spectrometry (LiP-MS) has emerged as a powerful proteomic tool for studying protein conformations. Since its introduction in 2014, LiP-MS has expanded its scope to explore complex biological systems and shed light on disease mechanisms, and has been used for protein drug research. This review discusses the evolution of the technique, recent technical advances, including enhanced protocols and integration of machine learning, and diverse applications across various experimental models. Despite its achievements, challenges in protein extraction and conformotypic peptide identification remain. Ongoing methodological refinements will be crucial to overcome these challenges and enhance the capabilities of the technique. However, LiP-MS offers significant potential for future discoveries in structural proteomics and medical research.

Global analysis of endogenous protein disorder in cells

Disorder and flexibility in protein structures are essential for biological function but can also contribute to diseases, such as neurodegenerative disorders. However, characterizing protein folding on a proteome-wide scale within biological matrices remains challenging. Here we present a method using a bifunctional chemical probe, named TME, to capture in situ, enrich and quantify endogenous protein disorder in cells. TME exhibits a fluorescence turn-on effect upon selective conjugation with proteins with free cysteines in surface-exposed and flexible environments-a distinctive signature of protein disorder. Using an affinity-based proteomic approach, we identify both basal disordered proteins and those whose folding status changes under stress, with coverage to proteins even of low abundance. In lymphoblastoid cells from individuals with Parkinson's disease and healthy controls, our TME-based strategy distinguishes the two groups more effectively than lysate profiling methods. High-throughput TME fluorescence and proteomics further reveal a universal cellular quality-control mechanism in which cells adapt to proteostatic stress by adopting aggregation-prone distributions and sequestering disordered proteins, as illustrated in Huntington's disease cell models.

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.

Discovery of RNA-Protein Molecular Clamps Using Proteome-Wide Stability Assays

Uncompetitive inhibition is an effective strategy for suppressing dysregulated enzymes and their substrates, but discovery of suitable ligands depends on often-unavailable structural knowledge and serendipity. Hence, despite surging interest in mass spectrometry-based target identification, proteomic studies of substrate-dependent target engagement remain sparse. Herein, we describe a strategy for the discovery of substrate-dependent ligand binding. Using proteome integral solubility alteration (PISA) assays, we show that simple biochemical additives can enable detection of RNA-protein-small molecule complexes in native cell lysates. We apply our approach to rocaglates, molecules that specifically clamp RNA to eukaryotic translation initiation factor 4A (eIF4A), DEAD-box helicase 3X (DDX3X), and potentially other members of the DEAD-box (DDX) helicase family. To identify unexpected interactions, we used a target class-specific thermal window and compared ATP analog and RNA base dependencies for key rocaglate-DDX interactions. We report and validate novel DDX targets of high-profile rocaglates - including the clinical candidate Zotatifin - using limited proteolysis-mass spectrometry and fluorescence polarization (FP) experiments. We also provide structural insight into divergent DDX3X affinities between synthetic rocaglates. Taken together, our study provides a model for screening uncompetitive inhibitors using a chemical proteomics approach and uncovers actionable DDX clamping targets, clearing a path towards characterization of novel molecular clamps and associated RNA helicases.

Mass spectrometry-complemented molecular modeling predicts the interaction interface for a camelid single-domain antibody targeting the Plasmodium falciparum circumsporozoite protein's C-terminal domain

Background

Bioanalytical methods that enable rapid and high-detail characterization of binding specificities and strengths of protein complexes with low sample consumption are highly desired. The interaction between a camelid single domain antibody (sdAbCSP1) and its target antigen (PfCSP-Cext) was selected as a model system to provide proof-of-principle for the here described methodology.

Research design and methods

The structure of the sdAbCSP1 - PfCSP-Cext complex was modeled using AlphaFold2. The recombinantly expressed proteins, sdAbCSP1, PfCSP-Cext, and the sdAbCSP1 - PfCSP-Cext complex, were subjected to limited proteolysis and mass spectrometric peptide analysis. ITEM MS (Intact Transition Epitope Mapping Mass Spectrometry) and ITC (Isothermal Titration Calorimetry) were applied to determine stoichiometry and binding strength.

Results

The paratope of sdAbCSP1 mainly consists of its CDR3 (aa100-118). PfCSP-Cext's epitope is assembled from its α-helix (aa40-52) and opposing loop (aa83-90). PfCSP-Cext's GluC cleavage sites E46 and E58 were shielded by complex formation, confirming the predicted epitope. Likewise, sdAbCSP1's tryptic cleavage sites R105 and R108 were shielded by complex formation, confirming the predicted paratope. ITEM MS determined the 1:1 stoichiometry and the high complex binding strength, exemplified by the gas phase dissociation reaction enthalpy of 50.2 kJ/mol. The in-solution complex dissociation constant is 5 × 10-10 M.

Conclusions

Combining AlphaFold2 modeling with mass spectrometry/limited proteolysis generated a trustworthy model for the sdAbCSP1 - PfCSP-Cext complex interaction interface.

Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases

Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Proteomic insights into the extracellular matrix: a focus on proteoforms and their implications in health and disease

INTRODUCTION

The extracellular matrix (ECM) is a highly organized and dynamic network of proteins and glycosaminoglycans that provides critical structural, mechanical, and biochemical support to cells. The functions of the ECM are directly influenced by the conformation of the proteins that compose it. ECM proteoforms, which can result from genetic, transcriptional, and/or post-translational modifications, adopt different conformations and, consequently, confer different structural properties and functionalities to the ECM in both physiological and pathological contexts.

AREAS COVERED

In this review, we discuss how bottom-up proteomics has been applied to identify, map, and quantify post-translational modifications (e.g. additions of chemical groups, proteolytic cleavage, or cross-links) and ECM proteoforms arising from alternative splicing or genetic variants. We further illustrate how proteoform-level information can be leveraged to gain novel insights into ECM protein structure and ECM functions in health and disease.

EXPERT OPINION

In the Expert opinion section, we discuss remaining challenges and opportunities with an emphasis on the importance of devising experimental and computational methods tailored to account for the unique biochemical properties of ECM proteins with the goal of increasing sequence coverage and, hence, accurate ECM proteoform identification.

Chemical proteomic mapping of reversible small molecule binding sites in native systems

The impact of small molecules in human biology are manifold; not only are they critical regulators of physiological processes, but they also serve as probes to investigate biological pathways and leads for therapeutic development. Identifying the protein targets of small molecules, and where they bind, is critical to understanding their functional consequences and potential for pharmacological use. Over the past two decades, chemical proteomics has emerged as a go-to strategy for the comprehensive mapping of small molecule-protein interactions. Recent advancements in this field, particularly innovations of photoaffinity labeling (PAL)-based methods, have enabled the robust identification of small molecule binding sites on protein targets, often in live cells. In this opinion article, we examine these advancements as well as reflect on how their strategic integration with other emerging tools can advance therapeutic development.

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.

Development of a new paradigm model for deciphering action mechanism of Danhong injection using a combination of isothermal shift assay and database interrogation

BACKGROUND

Identification of active components of traditional Chinese Medicine (TCM) and their respective targets is important for understanding the mechanisms underlying TCM efficacy. However, there are still no effective technical methods to achieve this.

METHODS

Herein, we have established a method for rapidly identifying targets of a specific TCM and interrogating the targets with their corresponding active components based on Isothermal Shift Assay (iTSA) and database interrogation.

RESULTS

We optimized iTSA workflow and identified 110 targets for Danhong injection (DHI) which is used as an effective remedy for cardiovascular and cerebrovascular diseases. Moreover, we identified the targets of the nine major ingredients found in DHI. Database interrogation found that the potential targets for DHI, in which we verified that ADK as the target for salvianolic acid A and ALDH1B1 as the target for protocatechualdehyde in DHI, respectively.

CONCLUSION

Overall, we established a novel paradigm model for the identification of targets and their respective ingredients in DHI, which facilitates the discovery of drug candidates and targets for improving disease management and contributes to revealing the underlying mechanisms of TCM and fostering TCM development and modernization.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Microbial hoolIgAn dismantles gut defenses

Immunoglobulin degradation by a gut bacterium causes immunodeficiency in mice.

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.

Advances in spatial proteomics: Mapping proteome architecture from protein complexes to subcellular localizations

Proteins are responsible for most intracellular functions, which they perform as part of higher-order molecular complexes, located within defined subcellular niches. Localization is both dynamic and context specific and mislocalization underlies a multitude of diseases. It is thus vital to be able to measure the components of higher-order protein complexes and their subcellular location dynamically in order to fully understand cell biological processes. Here, we review the current range of highly complementary approaches that determine the subcellular organization of the proteome. We discuss the scale and resolution at which these approaches are best employed and the caveats that should be taken into consideration when applying them. We also look to the future and emerging technologies that are paving the way for a more comprehensive understanding of the functional roles of protein isoforms, which is essential for unraveling the complexities of cell biology and the development of disease treatments.

Progress in mass spectrometry approaches to profiling protein-protein interactions in the studies of the innate immune system

Understanding protein-protein interactions (PPIs) is pivotal for deciphering the intricacies of biological processes. Dysregulation of PPIs underlies a spectrum of diseases, including cancer, neurodegenerative disorders, and autoimmune conditions, highlighting the imperative of investigating these interactions for therapeutic advancements. This review delves into the realm of mass spectrometry-based techniques for elucidating PPIs and their profound implications in biological research. Mass spectrometry in the PPI research field not only facilitates the evaluation of protein-protein interaction modulators but also discovers unclear molecular mechanisms and sheds light on both on- and off-target effects, thus aiding in drug development. Our discussion navigates through six pivotal techniques: affinity purification mass spectrometry (AP-MS), proximity labeling mass spectrometry (PL-MS), cross-linking mass spectrometry (XL-MS), size exclusion chromatography coupled with mass spectrometry (SEC-MS), limited proteolysis-coupled mass spectrometry (LiP-MS), and thermal proteome profiling (TPP).

On the pH-dependence of α-synuclein amyloid polymorphism and the role of secondary nucleation in seed-based amyloid propagation

The aggregation of the protein α-synuclein is closely associated with several neurodegenerative disorders and as such the structures of the amyloid fibril aggregates have high scientific and medical significance. However, there are dozens of unique atomic-resolution structures of these aggregates, and such a highly polymorphic nature of the α-synuclein fibrils hampers efforts in disease-relevant in vitro studies on α-synuclein amyloid aggregation. In order to better understand the factors that affect polymorph selection, we studied the structures of α-synuclein fibrils in vitro as a function of pH and buffer using cryo-EM helical reconstruction. We find that in the physiological range of pH 5.8-7.4, a pH-dependent selection between Type 1, 2, and 3 polymorphs occurs. Our results indicate that even in the presence of seeds, the polymorph selection during aggregation is highly dependent on the buffer conditions, attributed to the non-polymorph-specific nature of secondary nucleation. We also uncovered two new polymorphs that occur at pH 7.0 in phosphate-buffered saline. The first is a monofilament Type 1 fibril that highly resembles the structure of the juvenile-onset synucleinopathy polymorph found in patient-derived material. The second is a new Type 5 polymorph that resembles a polymorph that has been recently reported in a study that used diseased tissues to seed aggregation. Taken together, our results highlight the shallow amyloid energy hypersurface that can be altered by subtle changes in the environment, including the pH which is shown to play a major role in polymorph selection and in many cases appears to be the determining factor in seeded aggregation. The results also suggest the possibility of producing disease-relevant structure in vitro.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects

Kynurenic acid (KYNA) is an antioxidant degradation product of tryptophan that has been shown to have a variety of cytoprotective, neuroprotective and neuronal signalling properties. However, mammalian transporters and receptors display micromolar binding constants; these are consistent with its typically micromolar tissue concentrations but far above its serum/plasma concentration (normally tens of nanomolar), suggesting large gaps in our knowledge of its transport and mechanisms of action, in that the main influx transporters characterized to date are equilibrative, not concentrative. In addition, it is a substrate of a known anion efflux pump (ABCC4), whose in vivo activity is largely unknown. Exogeneous addition of L-tryptophan or L-kynurenine leads to the production of KYNA but also to that of many other co-metabolites (including some such as 3-hydroxy-L-kynurenine and quinolinic acid that may be toxic). With the exception of chestnut honey, KYNA exists at relatively low levels in natural foodstuffs. However, its bioavailability is reasonable, and as the terminal element of an irreversible reaction of most tryptophan degradation pathways, it might be added exogenously without disturbing upstream metabolism significantly. Many examples, which we review, show that it has valuable bioactivity. Given the above, we review its potential utility as a nutraceutical, finding it significantly worthy of further study and development.

Impact of Charge State on Characterization of Large Middle-Down Sized Peptides by Tandem Mass Spectrometry

Fragmentation trends of large peptides were characterized by five activation methods, including HCD, ETD, EThcD, 213 nm UVPD, and 193 nm UVPD. Sequence coverages and scores were assessed based on charge site, peptide sequence, and peptide size. The effect of charge state and peptide size on sequence coverage was explored for a Glu-C digest of E. coli ribosomal proteins, and linear regression analysis of the collection of peptides indicated that HCD, ETD, and EThcD have a higher dependence charge state than 193 and 213 nm UV. Four model peptides, neuromedin, glucagon, galanin, and amyloid β, were characterized in greater detail based on charge site analysis and showed a charge state dependence on sequence coverage for collision and electron-based activation methods.

In situ analysis of osmolyte mechanisms of proteome thermal stabilization

Organisms use organic molecules called osmolytes to adapt to environmental conditions. In vitro studies indicate that osmolytes thermally stabilize proteins, but mechanisms are controversial, and systematic studies within the cellular milieu are lacking. We analyzed Escherichia coli and human protein thermal stabilization by osmolytes in situ and across the proteome. Using structural proteomics, we probed osmolyte effects on protein thermal stability, structure and aggregation, revealing common mechanisms but also osmolyte- and protein-specific effects. All tested osmolytes (trimethylamine N-oxide, betaine, glycerol, proline, trehalose and glucose) stabilized many proteins, predominantly via a preferential exclusion mechanism, and caused an upward shift in temperatures at which most proteins aggregated. Thermal profiling of the human proteome provided evidence for intrinsic disorder in situ but also identified potential structure in predicted disordered regions. Our analysis provides mechanistic insight into osmolyte function within a complex biological matrix and sheds light on the in situ prevalence of intrinsically disordered regions.

Structural Mass Spectrometry Captures Residue-Resolved Comprehensive Conformational Rearrangements of a G Protein-Coupled Receptor

G protein-coupled receptor (GPCR) structural studies with in-solution spectroscopic approaches have offered distinctive insights into GPCR activation and signaling that highly complement those yielded from structural snapshots by crystallography or cryo-EM. While most current spectroscopic approaches allow for probing structural changes at selected residues or loop regions, they are not suitable for capturing a holistic view of GPCR conformational rearrangements across multiple domains. Herein, we develop an approach based on limited proteolysis mass spectrometry (LiP-MS) to simultaneously monitor conformational alterations of a large number of residues spanning both flexible loops and structured transmembrane domains for a given GPCR. To benchmark LiP-MS for GPCR conformational profiling, we studied the adenosine 2A receptor (A2AR) in response to different ligand binding (agonist/antagonist/allosteric modulators) and G protein coupling. Systematic and residue-resolved profiling of A2AR conformational rearrangements by LiP-MS precisely captures structural mechanisms in multiple domains underlying ligand engagement, receptor activation, and allostery, and may also reflect local conformational flexibility. Furthermore, these residue-resolution structural fingerprints of the A2AR protein allow us to readily classify ligands of different pharmacology and distinguish the G protein-coupled state. Thus, our study provides a new structural MS approach that would be generalizable to characterizing conformational transition and plasticity for challenging integral membrane proteins.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Stability-based approaches in chemoproteomics

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in need to be structurally modified by the chaperone. Here, we measured the protein disaggregation and refolding activities of the main yeast cytosolic Hsp70, Ssa1, in the presence of its most abundant JDPs, Sis1 and Ydj1, and two swap mutants, in which the J-domains have been interchanged. The observed differences by which the four constructs differently cooperate with Ssa1 and cooperate with each other, as well as their observed intrinsic ability to bind misfolded substrates and trigger Ssa1's ATPase, indicate the presence of yet uncharacterized intramolecular dynamic interactions between the J-domains and the remaining C-terminal segments of these proteins. Taken together, the data suggest an autoregulatory role to these intramolecular interactions within both type A and B JDPs, which might have evolved to reduce energy-costly ATPase cycles by the Ssa1-4 chaperones that are the most abundant Hsp70s in the yeast cytosol.

Innovative strategies for measuring kinase activity to accelerate the next wave of novel kinase inhibitors

The development of protein kinase inhibitors (PKIs) has gained significance owing to their therapeutic potential for diseases like cancer. In addition, there has been a rise in refining kinase activity assays, each possessing unique biological and analytical characteristics crucial for PKI development. However, the PKI development pipeline experiences high attrition rates and approved PKIs exhibit unexploited potential because of variable patient responses. Enhancing PKI development efficiency involves addressing challenges related to understanding the PKI mechanism of action and employing biomarkers for precision medicine. Selecting appropriate kinase activity assays for these challenges can overcome these attrition rate issues. This review delves into the current obstacles in kinase inhibitor development and elucidates kinase activity assays that can provide solutions.

Monitoring host-pathogen interactions using chemical proteomics

With the rapid emergence and the dissemination of microbial resistance to conventional chemotherapy, the shortage of novel antimicrobial drugs has raised a global health threat. As molecular interactions between microbial pathogens and their mammalian hosts are crucial to establish virulence, pathogenicity, and infectivity, a detailed understanding of these interactions has the potential to reveal novel therapeutic targets and treatment strategies. Bidirectional molecular communication between microbes and eukaryotes is essential for both pathogenic and commensal organisms to colonise their host. In particular, several devastating pathogens exploit host signalling to adjust the expression of energetically costly virulent behaviours. Chemical proteomics has emerged as a powerful tool to interrogate the protein interaction partners of small molecules and has been successfully applied to advance host-pathogen communication studies. Here, we present recent significant progress made by this approach and provide a perspective for future studies.

Special Issue "Deployment of Proteomics Approaches in Biomedical Research"

Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].

Advanced Techniques for Detecting Protein Misfolding and Aggregation in Cellular Environments

Protein misfolding and aggregation, a key contributor to the progression of numerous neurodegenerative diseases, results in functional deficiencies and the creation of harmful intermediates. Detailed visualization of this misfolding process is of paramount importance for improving our understanding of disease mechanisms and for the development of potential therapeutic strategies. While in vitro studies using purified proteins have been instrumental in delivering significant insights into protein misfolding, the behavior of these proteins in the complex milieu of living cells often diverges significantly from such simplified environments. Biomedical imaging performed in cell provides cellular-level information with high physiological and pathological relevance, often surpassing the depth of information attainable through in vitro methods. This review highlights a variety of methodologies used to scrutinize protein misfolding within biological systems. This includes optical-based methods, strategies leaning on mass spectrometry, in-cell nuclear magnetic resonance, and cryo-electron microscopy. Recent advancements in these techniques have notably deepened our understanding of protein misfolding processes and the features of the resulting misfolded species within living cells. The progression in these fields promises to catalyze further breakthroughs in our comprehension of neurodegenerative disease mechanisms and potential therapeutic interventions.

Proteomic approaches advancing targeted protein degradation

Targeted protein degradation (TPD) is an emerging modality for research and therapeutics. Most TPD approaches harness cellular ubiquitin-dependent proteolytic pathways. Proteolysis-targeting chimeras (PROTACs) and molecular glue (MG) degraders (MGDs) represent the most advanced TPD approaches, with some already used in clinical settings. Despite these advances, TPD still faces many challenges, pertaining to both the development of effective, selective, and tissue-penetrant degraders and understanding their mode of action. In this review, we focus on progress made in addressing these challenges. In particular, we discuss the utility and application of recent proteomic approaches as indispensable tools to enable insights into degrader development, including target engagement, degradation selectivity, efficacy, safety, and mode of action.

An unconventional gatekeeper mutation sensitizes inositol hexakisphosphate kinases to an allosteric inhibitor

Inositol hexakisphosphate kinases (IP6Ks) are emerging as relevant pharmacological targets because a multitude of disease-related phenotypes has been associated with their function. While the development of potent IP6K inhibitors is gaining momentum, a pharmacological tool to distinguish the mammalian isozymes is still lacking. Here, we implemented an analog-sensitive approach for IP6Ks and performed a high-throughput screen to identify suitable lead compounds. The most promising hit, FMP-201300, exhibited high potency and selectivity toward the unique valine gatekeeper mutants of IP6K1 and IP6K2, compared to the respective wild-type (WT) kinases. Biochemical validation experiments revealed an allosteric mechanism of action that was corroborated by hydrogen deuterium exchange mass spectrometry measurements. The latter analysis suggested that displacement of the αC helix, caused by the gatekeeper mutation, facilitates the binding of FMP-201300 to an allosteric pocket adjacent to the ATP-binding site. FMP-201300 therefore serves as a valuable springboard for the further development of compounds that can selectively target the three mammalian IP6Ks; either as analog-sensitive kinase inhibitors or as an allosteric lead compound for the WT kinases.

The potential of cross-linking mass spectrometry in the development of protein-protein interaction modulators

Cross-linking mass spectrometry (XL-MS) can provide a wealth of information on endogenous protein-protein interaction (PPI) networks and protein binding interfaces. These features make XL-MS an attractive tool to support the development of PPI-targeting drugs. Though not yet widely used, applications of XL-MS to drug characterization are beginning to emerge. Here, we compare XL-MS to established structural proteomics methods in drug research, discuss the current state and remaining challenges of XL-MS technology, and provide a perspective on the future role XL-MS can play in drug development, with a particular emphasis on PPI modulators.

Artificial intelligence-coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases

Diagnosis of neurodegenerative disorders (NDDs) including Parkinson's disease and Alzheimer's disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker-based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies.

DiLeu Isobaric Labeling Coupled with Limited Proteolysis Mass Spectrometry for High-Throughput Profiling of Protein Structural Changes in Alzheimer's Disease

High-throughput quantitative analysis of protein conformational changes has a profound impact on our understanding of the pathological mechanisms of Alzheimer's disease (AD). To establish an effective workflow enabling quantitative analysis of changes in protein conformation within multiple samples simultaneously, here we report the combination of N,N-dimethyl leucine (DiLeu) isobaric tag labeling with limited proteolysis mass spectrometry (DiLeu-LiP-MS) for high-throughput structural protein quantitation in serum samples collected from AD patients and control donors. Twenty-three proteins were discovered to undergo structural changes, mapping to 35 unique conformotypic peptides with significant changes between the AD group and the control group. Seven out of 23 proteins, including CO3, CO9, C4BPA, APOA1, APOA4, C1R, and APOA, exhibited a potential correlation with AD. Moreover, we found that complement proteins (e.g., CO3, CO9, and C4BPA) related to AD exhibited elevated levels in the AD group compared to those in the control group. These results provide evidence that the established DiLeu-LiP-MS method can be used for high-throughput structural protein quantitation, which also showed great potential in achieving large-scale and in-depth quantitative analysis of protein conformational changes in other biological systems.

Toward the analysis of functional proteoforms using mass spectrometry-based stability proteomics

Functional proteomics aims to elucidate biological functions, mechanisms, and pathways of proteins and proteoforms at the molecular level to examine complex cellular systems and disease states. A series of stability proteomics methods have been developed to examine protein functionality by measuring the resistance of a protein to chemical or thermal denaturation or proteolysis. These methods can be applied to measure the thermal stability of thousands of proteins in complex biological samples such as cell lysate, intact cells, tissues, and other biological fluids to measure proteome stability. Stability proteomics methods have been popularly applied to observe stability shifts upon ligand binding for drug target identification. More recently, these methods have been applied to characterize the effect of structural changes in proteins such as those caused by post-translational modifications (PTMs) and mutations, which can affect protein structures or interactions and diversify protein functions. Here, we discussed the current application of a suite of stability proteomics methods, including thermal proteome profiling (TPP), stability of proteomics from rates of oxidation (SPROX), and limited proteolysis (LiP) methods, to observe PTM-induced structural changes on protein stability. We also discuss future perspectives highlighting the integration of top-down mass spectrometry and stability proteomics methods to characterize intact proteoform stability and understand the function of variable protein modifications.