Chemo-Selection Strategy for Limited Proteolysis Experiments on the Proteomic Scale

Thermostability-assisted limited proteolysis-coupled mass spectrometry for capturing drug target proteins and sites

BACKGROUND

Identifying drug-binding targets and their corresponding sites is crucial for drug discovery and mechanism studies. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a sophisticated method used for the detection of compound and protein interactions. However, in some cases, LiP-MS cannot identify the target proteins due to the small structure changes or the lack of enrichment of low-abundant protein. To overcome this drawback, we developed a thermostability-assisted limited proteolysis-coupled mass spectrometry (TALiP-MS) approach for efficient drug target discovery.

RESULTS

We proved that the novel strategy, TALiP-MS, could efficiently identify target proteins of various ligands, including cyclosporin A (a calcineurin inhibitor), geldanamycin (an HSP90 inhibitor), and staurosporine (a kinase inhibitor), with accurately recognizing drug-binding domains. The TALiP protocol increased the number of target peptides detected in LiP-MS experiments by 2- to 8-fold. Meanwhile, the TALiP-MS approach can not only identify both ligand-binding stability and destabilization proteins but also shows high complementarity with the thermal proteome profiling (TPP) and machine learning-based limited proteolysis (LiP-Quant) methods. The developed TALiP-MS approach was applied to identify the target proteins of celastrol (CEL), a natural product known for its strong antioxidant and anti-cancer angiogenesis effect. Among them, four proteins, MTHFD1, UBA1, ACLY, and SND1 were further validated for their strong affinity to CEL by using cellular thermal shift assay. Additionally, the destabilized proteins induced by CEL such as TAGLN2 and CFL1 were also validated.

SIGNIFICANCE

Collectively, these findings underscore the efficacy of the TALiP-MS method for identifying drug targets, elucidating binding sites, and even detecting drug-induced conformational changes in target proteins in complex proteomes.

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.

Stability-Based Proteomics for Investigation of Structured RNA-Protein Interactions

RNA-protein interactions are essential to RNA function throughout biology. Identifying the protein interactions associated with a specific RNA, however, is currently hindered by the need for RNA labeling or costly tiling-based approaches. Conventional strategies, which commonly rely on affinity pull-down approaches, are also skewed to the detection of high affinity interactions and frequently miss weaker interactions that may be biologically important. Reported here is the first adaptation of stability-based mass spectrometry methods for the global analysis of RNA-protein interactions. The stability of proteins from rates of oxidation (SPROX) and thermal protein profiling (TPP) methods are used to identify the protein targets of three RNA ligands, the MALAT1 triple helix (TH), a viral stem loop (SL), and an unstructured RNA (PolyU), in LNCaP nuclear lysate. The 315 protein hits with RNA-induced conformational and stability changes detected by TPP and/or SPROX were enriched in previously annotated RNA-binding proteins and included new proteins for hypothesis generation. Also demonstrated are the orthogonality of the SPROX and TPP approaches and the utility of the domain-specific information available with SPROX. This work establishes a novel platform for the global discovery and interrogation of RNA-protein interactions that is generalizable to numerous biological contexts and RNA targets.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Datasets 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 the fast, sensitive, and accurate search and label-free quantification algorithms in 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 new data merging scheme (from ions to cut-sites) and a protein-centric multiple hypothesis correction scheme, collectively enabling processed LiP-MS datasets to be more robust and less redundant. These improvements substantially strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. As a final feature, FLiPPR facilitates the collection of structural metadata to identify correlations between experiments and structural features. 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.

Radical-Mediated Covalent Azidylation of Hydrophobic Microdomains in Water-Soluble Proteins

Hydrophobic microdomains, also known as hydrophobic patches, are essential for many important biological functions of water-soluble proteins. These include ligand or substrate binding, protein-protein interactions, proper folding after translation, and aggregation during denaturation. Unlike transmembrane domains, which are easily recognized from stretches of contiguous hydrophobic sidechains in amino acids via primary protein sequence, these three-dimensional hydrophobic patches cannot be easily predicted. The lack of experimental strategies for directly determining their locations hinders further understanding of their structure and function. Here, we posit that the small triatomic anion N3- (azide) is attracted to these patches and, in the presence of an oxidant, forms a radical that covalently modifies C-H bonds of nearby amino acids. Using two model proteins (BSA and lysozyme) and a cell-free lysate from the model higher plant Arabidopsis thaliana, we find that radical-mediated covalent azidylation occurs within buried catalytic active sites and ligand binding sites and exhibits similar behavior to established hydrophobic probes. The results herein suggest a model in which the azido radical is acting as an "affinity reagent" for nonaqueous three-dimensional protein microenvironments and is consistent with both the nonlocalized electron density of the azide moiety and the known high reactivity of azido radicals widely used in organic chemistry syntheses. We propose that the azido radical is a facile means of identifying hydrophobic microenvironments in soluble proteins and, in addition, provides a simple new method for attaching chemical handles to proteins without the need for genetic manipulation or specialized reagents.

Cellular Exposure to Chloroacetanilide Herbicides Induces Distinct Protein Destabilization Profiles

Herbicides in the widely used chloroacetanilide class harbor a potent electrophilic moiety, which can damage proteins through nucleophilic substitution. In general, damaged proteins are subject to misfolding. Accumulation of misfolded proteins compromises cellular integrity by disrupting cellular proteostasis networks, which can further destabilize the cellular proteome. While direct conjugation targets can be discovered through affinity-based protein profiling, there are few approaches to probe how cellular exposure to toxicants impacts the stability of the proteome. We apply a quantitative proteomics methodology to identify chloroacetanilide-destabilized proteins in HEK293T cells based on their binding to the H31Q mutant of the human Hsp40 chaperone DNAJB8. We find that a brief cellular exposure to the chloroacetanilides acetochlor, alachlor, and propachlor induces misfolding of dozens of cellular proteins. These herbicides feature distinct but overlapping profiles of protein destabilization, highly concentrated in proteins with reactive cysteine residues. Consistent with the recent literature from the pharmacology field, reactivity is driven by neither inherent nucleophilic nor electrophilic reactivity but is idiosyncratic. We discover that propachlor induces a general increase in protein aggregation and selectively targets GAPDH and PARK7, leading to a decrease in their cellular activities. Hsp40 affinity profiling identifies a majority of propachlor targets identified by competitive activity-based protein profiling (ABPP), but ABPP can only identify about 10% of protein targets identified by Hsp40 affinity profiling. GAPDH is primarily modified by the direct conjugation of propachlor at a catalytic cysteine residue, leading to global destabilization of the protein. The Hsp40 affinity strategy is an effective technique to profile cellular proteins that are destabilized by cellular toxin exposure. Raw proteomics data is available through the PRIDE Archive at PXD030635.

Protein Folding Stability Profiling of Colorectal Cancer Chemoresistance Identifies Functionally Relevant Biomarkers

Reported here is the application of three protein folding stability profiling techniques (including the stability of proteins from rates of oxidation, thermal protein profiling, and limited proteolysis approaches) to identify differentially stabilized proteins in six patient-derived colorectal cancer (CRC) cell lines with different oxaliplatin sensitivities and eight CRC patient-derived xenografts (PDXs) derived from two of the patient derived cell lines with different oxaliplatin sensitivities. Compared to conventional protein expression level analyses, which were also performed here, the stability profiling techniques identified both unique and novel proteins and cellular components that differentiated the sensitive and resistant samples including 36 proteins that were differentially stabilized in at least two techniques in both the cell line and PDX studies of oxaliplatin resistance. These 36 differentially stabilized proteins included 10 proteins previously connected to cancer chemoresistance. Two differentially stabilized proteins, fatty acid synthase and elongation factor 2, were functionally validated in vitro and found to be druggable protein targets with biological functions that can be modulated to improve the efficacy of CRC chemotherapy. These results add to our understanding of CRC oxaliplatin resistance, suggest biomarker candidates for predicting oxaliplatin sensitivity in CRC, and inform new strategies for overcoming chemoresistance in CRC.

Comparative Analysis of Protein Folding Stability-Based Profiling Methods for Characterization of Biological Phenotypes

Recently, a new suite of mass spectrometry-based proteomic methods has been developed that enables evaluation of protein folding stability on the proteomic scale. These methods utilize chemical and thermal denaturation approaches (SPROX and TPP, respectively) as well as proteolysis strategies (DARTS, LiP, and PP) to assess protein folding stability. The analytical capabilities of these technique have been well-established for protein target discovery applications. However, less is known about the relative advantages and disadvantages of using these different strategies to characterize biological phenotypes. Reported here is a comparative study of SPROX, TPP, LiP, and conventional protein expression level measurements using both a mouse model of aging and a mammalian cell culture model of breast cancer. Analyses on proteins in brain tissue cell lysates derived from 1- and 18-month-old mice (n = 4-5 at each time point) and on proteins in cell lysates derived from the MCF-7 and MCF-10A cell lines revealed a majority of the differentially stabilized protein hits in each phenotype analysis had unchanged expression levels. In both phenotype analyses, TPP generated the largest number and fraction of differentially stabilized protein hits. Only a quarter of all the protein hits identified in each phenotype analysis had a differential stability that was detected using multiple techniques. This work also reports the first peptide-level analysis of TPP data, which was required for the correct interpretation of the phenotype analyses performed here. Studies on selected protein stability hits also uncovered phenotype-related functional changes.

Profiling protein targets of cellular toxicant exposure

Environmental agents of exposure can damage proteins, affecting protein function and cellular protein homeostasis. Specific residues are inherently chemically susceptible to damage from individual types of exposure. Amino acid content is not completely predictive of protein susceptibility, as secondary, tertiary, and quaternary structures of proteins strongly influence the reactivity of the proteome to individual exposures. Because we cannot readily predict which proteins will be affected by which chemical exposures, mass spectrometry-based proteomic strategies are necessary to determine the protein targets of environmental toxins and toxicants. This review describes the mechanisms by which environmental exposure to toxins and toxicants can damage proteins and affect their function, and emerging omic methodologies that can be used to identify the protein targets of a given agent. These methods include target identification strategies that have recently revolutionized the drug discovery field, such as activity-based protein profiling, protein footprinting, and protein stability profiling technologies. In particular, we highlight the necessity of multiple, complementary approaches to fully interrogate how protein integrity is challenged by individual exposures.

Mass spectrometry-based technologies for probing the 3D world of plant proteins

Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions-which are more difficult to study-dynamically regulate the proteome's 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based "structural" proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function.

Discovery of the Xenon-Protein Interactome Using Large-Scale Measurements of Protein Folding and Stability

The intermolecular interactions of noble gases in biological systems are associated with numerous biochemical responses, including apoptosis, inflammation, anesthesia, analgesia, and neuroprotection. The molecular modes of action underlying these responses are largely unknown. This is in large part due to the limited experimental techniques to study protein-gas interactions. The few techniques that are amenable to such studies are relatively low-throughput and require large amounts of purified proteins. Thus, they do not enable the large-scale analyses that are useful for protein target discovery. Here, we report the application of stability of proteins from rates of oxidation (SPROX) and limited proteolysis (LiP) methodologies to detect protein-xenon interactions on the proteomic scale using protein folding stability measurements. Over 5000 methionine-containing peptides and over 5000 semi-tryptic peptides, mapping to ∼1500 and ∼950 proteins, respectively, in the yeast proteome, were assayed for Xe-interacting activity using the SPROX and LiP techniques. The SPROX and LiP analyses identified 31 and 60 Xe-interacting proteins, respectively, none of which were previously known to bind Xe. A bioinformatics analysis of the proteomic results revealed that these Xe-interacting proteins were enriched in those involved in ATP-driven processes. A fraction of the protein targets that were identified are tied to previously established modes of action related to xenon's anesthetic and organoprotective properties. These results enrich our knowledge and understanding of biologically relevant xenon interactions. The sample preparation protocols and analytical methodologies developed here for xenon are also generally applicable to the discovery of a wide range of other protein-gas interactions in complex biological mixtures, such as cell lysates.

Recent advances in proteome-wide label-free target deconvolution for bioactive small molecules

Small-molecule drugs modulate biological processes and disease states through engagement of target proteins in cells. Assessing drug-target engagement on a proteome-wide scale is of utmost importance in better understanding the molecular mechanisms of action of observed beneficial and adverse effects, as well as in developing next generation tool compounds and drugs with better efficacies and specificities. However, systematic assessment of drug-target engagement has been an arduous task. With the continuous development of mass spectrometry-based proteomics instruments and techniques, various chemical proteomics approaches for drug target deconvolution (i.e., the identification of molecular target for drugs) have emerged. Among these, the label-free target deconvolution approaches that do not involve the chemical modification of compounds of interest, have gained increased attention in the community. Here we provide an overview of the basic principles and recent biological applications of the most important label-free methods including the cellular thermal shift assay, pulse proteolysis, chemical denaturant and protein precipitation, stability of proteins from rates of oxidation, drug affinity responsive target stability, limited proteolysis, and solvent-induced protein precipitation. The state-of-the-art technical implications and future outlook for the label-free approaches are also discussed.

Protein Folding Stability Changes Across the Proteome Reveal Targets of Cu Toxicity in E. coli

The ability of metal ionophores to induce cellular metal hyperaccumulation endows them with potent antimicrobial activity; however, the targets and mechanisms behind these outcomes are not well understood. This work describes the first utilization of proteome-wide measurements of protein folding stability in combination with protein expression level analysis to identify protein targets of copper, thereby providing new insight into ionophore-induced copper toxicity in E. coli. The protein folding stability analysis employed a one-pot protocol for pulse proteolysis (PP) in combination with a semi-tryptic peptide enrichment strategy for proteolysis procedures (STEPP) to generate stability profiles for proteins in cell lysates derived from E. coli exposed to copper with and without two ionophores, the antimicrobial agent pyrithione and its β-lactamase-activated prodrug, PcephPT. As part of this work, the above cell lysates were also subject to protein expression level analysis using conventional quantitative bottom-up proteomic methods. The protein folding stability and expression level profiles generated here enabled the effects of ionophore vs copper to be distinguished and revealed copper-driven stability changes in proteins involved in processes spanning metabolism, translation, and cell redox homeostasis. The 159 differentially stabilized proteins identified in this analysis were significantly more numerous (∼3×) than the 53 proteins identified with differential expression levels. These results illustrate the unique information that protein stability measurements can provide to decipher metal-dependent processes in drug mode of action studies.

Target identification and validation of natural products with label-free methodology: A critical review from 2005 to 2020

Natural products (NPs) have been an important source of therapeutic drugs in clinic use and contributed many chemical probes for research. The usefulness of NPs is however often marred by the incomplete understanding of their direct cellular targets. A number of experimental methods for drug target identification have been developed over the years. One class of methods, termed "label-free" methodology, exploits the energetic and biophysical features accompanying the association of macromolecules with drugs and other compounds in their native forms. Herein we review the working principles, assay implementations, and key applications of the most important approaches, and also give examples where they have been applied to NPs. We also assess the key advantages and limitations of each method. Furthermore, we address when and how the label-free methodology can be particularly useful considering some of the unique features of NP chemistry and bioactivation.

A Simplified Thermal Proteome Profiling Approach to Screen Protein Targets of a Ligand

Thermal proteome profiling is a powerful energetic-based chemical proteomics method to reveal the ligand-protein interaction. However, the costly multiplexed isotopic labeling reagent, mainly Multiplexed isobaric tandem mass tag (TMT), and the long mass spectrometric time limits the wide application of this method. Here a simple and cost-effective strategy by using dimethyl labeling technique instead of TMT labeling is reported to quantify proteins and by using the peptides derived from the same protein to determine significantly changed proteins in one LC-MS run. This method is validated by identifying the known targets of methotrexate and geldanamycin. In addition, several potential off-targets involved in detoxification of reactive oxygen species pathway are also discovered for geldanamycin. This method is further applied to map the interactome of adenosine triphosphate (ATP) in the 293T cell lysate by using ATP analogue, adenylyl imidodiphosphate (AMP-PNP), as the ligand. As a result, a total of 123 AMP-PNP-sensitive proteins are found, of which 59 proteins are stabilized by AMP-PNP. Approximately 53% and 20% of these stabilized candidate protein targets are known as ATP and RNA binding proteins. Overall, above results demonstrated that this approach could be a valuable platform for the unbiased target proteins identification with reduced reagent cost and mass spectrometric time.

Comparative Analysis of Mass-Spectrometry-Based Proteomic Methods for Protein Target Discovery Using a One-Pot Approach

Recently, several mass-spectrometry- and protein-denaturation-based proteomic methods have been developed to facilitate protein target discovery efforts in drug mode-of-action studies. These methods, which include the stability of proteins from rates of oxidation (SPROX), pulse proteolysis (PP), chemical denaturation and protein precipitation (CPP), and thermal proteome profiling (TPP) techniques, have been used in an increasing number of applications in recent years. However, while the advantages and disadvantages to using these different techniques have been reviewed, the analytical characteristics of these methods have not been directly compared. Reported here is such a direct comparison using the well-studied immunosuppressive drug, cyclosporine A (CsA), and the proteins in a yeast cell lysate. Also described is a one-pot strategy that can be utilized with each technique to streamline data acquisition and analysis. We find that there are benefits to utilizing all four strategies for protein target discovery including increased proteomic coverage and reduced false positive rates that approach 0%. Moreover, the one-pot strategy described here makes such an experiment feasible, because of the 10-fold reduction in reagent costs and instrument time it affords.