ProTargetMiner as a proteome signature library of anticancer molecules for functional discovery

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

Multifaceted Proteome Analysis at Solubility, Redox, and Expression Dimensions for Target Identification

Multifaceted interrogation of the proteome deepens the system-wide understanding of biological systems; however, mapping the redox changes in the proteome has so far been significantly more challenging than expression and solubility/stability analyses. Here, the first high-throughput redox proteomics approach integrated with expression analysis (REX) is devised and combined with the Proteome Integral Solubility Alteration (PISA) assay. The whole PISA-REX experiment with up to four biological replicates can be multiplexed into a single tandem mass tag TMTpro set. For benchmarking this compact tool, HCT116 cells treated with auranofin are analyzed, showing great improvement compared with previous studies. PISA-REX is then applied to study proteome remodeling upon stimulation of human monocytes by interferon α (IFN-α). Applying this tool to study the proteome changes in plasmacytoid dendritic cells (pDCs) isolated from wild-type versus Ncf1-mutant mice treated with interferon α, shows that NCF1 deficiency enhances the STAT1 pathway and modulates the expression, solubility, and redox state of interferon-induced proteins. Providing comprehensive multifaceted information on the proteome, the compact PISA-REX has the potential to become an industry standard in proteomics and to open new windows into the biology of health and disease.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules

The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients (namely, glucose, triglyceride, diglycerol, phosphatidylcholine, phosphatidylethanolamine, L-α-phosphatidylinositol, inosine 5'-monophosphate, and B complex), into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 μg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in a single plasma sample. Our molecular dynamic results revealed that phosphatidylcholine interacts with albumin via hydrophobic interactions, h-bonds, and water-bridges. Addition of phosphatidylcholine also enabled the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. These significant achievements are made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in plasma proteome profiling. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.

Proteomics Meets Dose Response: A New Paradigm for Deciphering Drug Effects

In a groundbreaking study, Eckert and colleagues introduce DecryptE, an innovative approach to dose-resolved proteomics that significantly advances our understanding of drug effects at the proteomic level. This method integrates cutting edge sample preparation and mass spectrometry technologies, establishing a robust platform for high-throughput proteome analysis. DecryptE enables the quantification of more than 7,000 proteins per hour and was employed to study 144 clinical drugs and research compounds, generating more than 1 million dose-response curves using Jurkat acute T-cell leukemia cells as a model system. The platform demonstrates outstanding reproducibility, ensuring reliable and consistent results across multiple experiments. By providing detailed information on drug potency and efficacy, DecryptE allows the identification of subtle changes in protein expression and facilitates the clustering of drugs based on their proteomic profiles. This study not only reveals novel drug mechanisms but also creates a comprehensive resource that can be utilized by the broader research community. Furthermore, it highlights the potential of integrating proteomics-and potentially other omics modalities in the future-with dose-response analysis to advance pharmacological research and improve therapeutic strategies.

Therapy-induced secretion of spliceosomal components mediates pro-survival crosstalk between ovarian cancer cells

Ovarian cancer often develops resistance to conventional therapies, hampering their effectiveness. Here, using ex vivo paired ovarian cancer ascites obtained before and after chemotherapy and in vitro therapy-induced secretomes, we show that molecules secreted by ovarian cancer cells upon therapy promote cisplatin resistance and enhance DNA damage repair in recipient cancer cells. Even a short-term incubation of chemonaive ovarian cancer cells with therapy-induced secretomes induces changes resembling those that are observed in chemoresistant patient-derived tumor cells after long-term therapy. Using integrative omics techniques, we find that both ex vivo and in vitro therapy-induced secretomes are enriched with spliceosomal components, which relocalize from the nucleus to the cytoplasm and subsequently into the extracellular vesicles upon treatment. We demonstrate that these molecules substantially contribute to the phenotypic effects of therapy-induced secretomes. Thus, SNU13 and SYNCRIP spliceosomal proteins promote therapy resistance, while the exogenous U12 and U6atac snRNAs stimulate tumor growth. These findings demonstrate the significance of spliceosomal network perturbation during therapy and further highlight that extracellular signaling might be a key factor contributing to the emergence of ovarian cancer therapy resistance.

A proteomic landscape of pharmacologic perturbations for functional relevance

Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds. The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells. Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol, as well as the noncanonical targets of clinically approved tamoxifen, lovastatin, and their derivatives. Furthermore, the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints. This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.

Deep Proteomic Compound Profiling with the Orbitrap Ascend Tribrid Mass Spectrometer Using Tandem Mass Tags and Real-Time Search

Tandem mass tags (TMT) and tribrid mass spectrometers are a powerful combination for high-throughput proteomics with high quantitative accuracy. Increasingly, this technology is being used to map the effects of drugs on the proteome. However, the depth of proteomic profiling is still limited by sensitivity and speed. The new Orbitrap Ascend mass spectrometer was designed to address these limitations with a combination of hardware and software improvements. We evaluated the performance of the Ascend in multiple contexts including deep proteomic profiling. We found that the Ascend exhibited increased sensitivity, yielding higher signal-to-noise ratios than the Orbitrap Eclipse with shorter injection times. As a result, higher numbers of peptides and proteins were identified and quantified, especially with low sample input. TMT measurements had significantly improved signal-to-noise ratios, improving quantitative precision. In a fractionated 16plex sample that profiled proteomic differences across four human cell lines, the Ascend was able to quantify hundreds more proteins than the Eclipse, many of them low-abundant proteins, and the Ascend was able to quantify >8000 proteins in 30% less instrument time. We used the Ascend to analyze 8881 proteins in HCT116 cancer cells treated with covalent sulfolane/sulfolene inhibitors of peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1), a phosphorylation-specific peptidyl-prolyl cis-trans isomerase implicated in several cancers. We characterized these PIN1 inhibitors' effects on the proteome and identified discrepancies among the different compounds, which will facilitate a better understanding of the structure-activity relationship of this class of compounds. The Ascend was able to quantify statistically significant, potentially therapeutically relevant changes in proteins that the Eclipse could not detect.

Massive Solubility Changes in Neuronal Proteins upon Simulated Traumatic Brain Injury Reveal the Role of Shockwaves in Irreversible Damage

We investigated the immediate molecular consequences of traumatic brain injuries (TBIs) using a novel proteomics approach. We simulated TBIs using an innovative laboratory apparatus that employed a 5.1 kg dummy head that held neuronal cells and generated a ≤4000 g-force acceleration upon impact. A Proteome Integral Solubility Alteration (PISA) assay was then employed to monitor protein solubility changes in a system-wide manner. Dynamic impacts led to both a reduction in neuron viability and massive solubility changes in the proteome. The affected proteins mapped not only to the expected pathways, such as those of cell adhesion, collagen, and laminin structures, as well as the response to stress, but also to other dense protein networks, such as immune response, complement, and coagulation cascades. The cellular effects were found to be mainly due to the shockwave rather than the g-force acceleration. Soft materials could reduce the impact's severity only until they were fully compressed. This study shows a way of developing a proteome-based meter for measuring irreversible shockwave-induced cell damage and provides a resource for identifying protein biomarkers of TBIs and potential drug targets for the development of products aimed at primary prevention and intervention.

A proteome-wide atlas of drug mechanism of action

Defining the cellular response to pharmacological agents is critical for understanding the mechanism of action of small molecule perturbagens. Here, we developed a 96-well-plate-based high-throughput screening infrastructure for quantitative proteomics and profiled 875 compounds in a human cancer cell line with near-comprehensive proteome coverage. Examining the 24-h proteome changes revealed ligand-induced changes in protein expression and uncovered rules by which compounds regulate their protein targets while identifying putative dihydrofolate reductase and tankyrase inhibitors. We used protein-protein and compound-compound correlation networks to uncover mechanisms of action for several compounds, including the adrenergic receptor antagonist JP1302, which we show disrupts the FACT complex and degrades histone H1. By profiling many compounds with overlapping targets covering a broad chemical space, we linked compound structure to mechanisms of action and highlighted off-target polypharmacology for molecules within the library.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Multi-omics analysis of magnetically levitated plasma biomolecules

We recently discovered that superparamagnetic iron oxide nanoparticles (SPIONs) can levitate plasma biomolecules in the magnetic levitation (MagLev) system and cause formation of ellipsoidal biomolecular bands. To better understand the composition of the levitated biomolecules in various bands, we comprehensively characterized them by multi-omics analyses. To probe whether the biomolecular composition of the levitated ellipsoidal bands correlates with the health of plasma donors, we used plasma from individuals who had various types of multiple sclerosis (MS), as a model disease with significant clinical importance. Our findings reveal that, while the composition of proteins does not show much variability, there are significant differences in the lipidome and metabolome profiles of each magnetically levitated ellipsoidal band. By comparing the lipidome and metabolome compositions of various plasma samples, we found that the levitated biomolecular ellipsoidal bands do contain information on the health status of the plasma donors. More specifically, we demonstrate that there are particular lipids and metabolites in various layers of each specific plasma pattern that significantly contribute to the discrimination of different MS subtypes, i.e., relapsing-remitting MS (RRMS), secondary-progressive MS (SPMS), and primary-progressive MS (PPMS). These findings will pave the way for utilization of MagLev of biomolecules in biomarker discovery for identification of diseases and discrimination of their subtypes.

Proteome Integral Solubility Alteration (PISA) for High-Throughput Ligand Target Deconvolution with Increased Statistical Significance and Reduced Sample Amount

Proteome Integral Solubility Alteration (PISA) is a recently developed mass spectrometry-based, deep proteomics method for unbiased, proteome-wide target deconvolution of ligands, requiring no chemical ligand modification. PISA can be applied to living cells for studying target engagement in vivo or alternatively to protein extracts to identify in vitro ligand-interacting proteins. Here we describe the PISA workflow optimized in our lab. PISA improves the target discovery throughput 10-100 folds compared to the previously used proteomics methods and provides higher statistical significance for target candidates by enabling several biological replicates. Sample multiplexing makes all-in-one analysis of multiple ligands simultaneously possible. PISA dramatically reduces analysis costs, allowing many research questions in need of target deconvolution to be addressed, and unlocks the potential of miniaturizing biological models, including primary cells.

Increased levels of cell wall degrading enzymes and peptidases are associated with aggressiveness in a virulent isolate of Pyrenophora teres f. maculata

Pyrenophora teres f. maculata (Ptm) is a fungal pathogen that causes the spot form of net blotch on barley and leads to economic losses in many of the world's barley-growing regions. Isolates of Ptm exhibit varying levels of aggressiveness that result in quantifiable changes in the severity of the disease. Previous research on plant-pathogen interactions has shown that such divergence is reflected in the proteome and secretome of the pathogen, with certain classes of proteins more prominent in aggressive isolates. Here we have made a detailed comparative analysis of the secretomes of two Ptm isolates, GPS79 and E35 (highly and mildly aggressive, respectively) using a proteomics-based approach. The secretomes were obtained in vitro using media amended with barley leaf sections. Secreted proteins therein were harvested, digested with trypsin, and fractionated offline by HPLC prior to LC-MS in a high-resolution instrument to obtain deep coverage of the proteome. The subsequent analysis used a label-free quantitative proteomics approach with relative quantification of proteins based on precursor ion intensities. A total of 1175 proteins were identified, 931 from Ptm and 244 from barley. Further analysis revealed 160 differentially abundant proteins with at least a two-fold abundance difference between the isolates, with the most enriched in the aggressive GPS79 secretome. These proteins were mainly cell-wall (carbohydrate) degrading enzymes and peptidases, with some oxidoreductases and other pathogenesis-related proteins also identified, suggesting that aggressiveness is associated with an improved ability of GPS79 to overcome cell wall barriers and neutralize host defense responses.

Proteome Integral Solubility Alteration (PISA) Assay in Mammalian Cells for Deep, High-Confidence, and High-Throughput Target Deconvolution

Chemical proteomics focuses on the drug-target-phenotype relationship for target deconvolution and elucidation of the mechanism of action-key and bottleneck in drug development and repurposing. Majorly due to the limits of using chemically modified ligands in affinity-based methods, new, unbiased, proteome-wide, and MS-based chemical proteomics approaches have been developed to perform drug target deconvolution, using full proteome profiling and no chemical modification of the studied ligand. Of note among them, thermal proteome profiling (TPP) aims to identify the target(s) by measuring the difference in melting temperatures between each identified protein in drug-treated versus vehicle-treated samples, with the thermodynamic interpretation of "protein melting" and curve fitting of all quantified proteins, at all temperatures, in each biological replicate. Including TPP, all the other chemical proteomics approaches often fail to provide target deconvolution with sufficient proteome depth, statistical power, throughput, and sustainability, which could hardly fulfill the final purpose of drug development. The proteome integral solubility alteration (PISA) assay provides no thermodynamic interpretation, but a throughput 10-100-fold compared to the other proteomics methods, high sustainability, much lower time of analysis and sample amount requirements, high confidence in results, maximal proteome coverage (~10,000 protein IDs), and up to five drugs / test molecules in one assay, with at least biological triplicates of each treatment. Each drug-treated or vehicle-treated sample is split into many fractions and exposed to a gradient of heat as solubility perturbing agent before being recomposed into one sample; each soluble fraction is isolated, then deep and quantitative proteomics is applied across all samples. The proteins interacting with the tested molecules (targets and off-targets), the activated mechanistic factors, or proteins modified during the treatment show reproducible changes in their soluble amount compared to vehicle-treated controls. As of today, the maximal multiplexing capability is 18 biological samples per PISA assay, which enables statistical robustness and flexible experimental design accommodation for fuller target deconvolution, including integration of orthogonal chemical proteomics methods in one PISA assay. Living cells for studying target engagement in vivo or, alternatively, protein extracts to identify in vitro ligand-interacting proteins can be studied, and the minimal need in sample amount unlocks target deconvolution using primary cells and their derived cultures. This protocol was validated in: J Biol Chem (2021), DOI: 10.1016/j.jbc.2021.10153 Graphical abstract.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Speciation Analysis Highlights the Interactions of Auranofin with the Cytoskeleton Proteins of Lung Cancer Cells

Two types of lung cells (epithelial cancer lung cells, A-549 and lung fibroblasts MRC-5) were exposed to the clinically established gold drug auranofin at concentrations close to the half-maximal inhibitory drug concentrations (IC₅₀). Collected cells were subjected to speciation analysis using inductively coupled plasma mass spectrometry (ICP-MS). Auranofin showed better affinity toward proteins than DNA, RNA, and hydrophilic small molecular weight compounds. It can bind to proteins that vary in size (~20 kDa, ~75 kDa, and ≥200 kDa) and pI. However, the possibility of dimerization and protein–protein complex formation should also be taken into account. µRPLC/CZE-ESI-MS/MS studies on trypsinized proteins allowed the indication of 76 peptides for which signal intensity was influenced by auranofin presence in cells. Based on it, identity was proposed for 20 proteins. Except for thioredoxin reductase (TrxR), which is directly targeted by gold complex, the proteins were found to be transformed. Five indicated proteins: myosin, plectin, talin, two annexins, and kinase M3K5, are responsible for cell–cell, cell–protein interactions, and cell motility. A wound healing test confirmed their regulation by auranofin as cell migration decreased by 40% while the cell cycle was not interrupted.

Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells

Glioblastoma (GBM) is characterized by exceptionally high intratumoral heterogeneity. However, the molecular mechanisms underlying the origin of different GBM cell populations remain unclear. Here, we found that the compositions of ribosomes of GBM cells in the tumour core and edge differ due to alternative RNA splicing. The acidic pH in the core switches before messenger RNA splicing of the ribosomal gene RPL22L1 towards the RPL22L1b isoform. This allows cells to survive acidosis, increases stemness and correlates with worse patient outcome. Mechanistically, RPL22L1b promotes RNA splicing by interacting with lncMALAT1 in the nucleus and inducing its degradation. Contrarily, in the tumour edge region, RPL22L1a interacts with ribosomes in the cytoplasm and upregulates the translation of multiple messenger RNAs including TP53. We found that the RPL22L1 isoform switch is regulated by SRSF4 and identified a compound that inhibits this process and decreases tumour growth. These findings demonstrate how distinct GBM cell populations arise during tumour growth. Targeting this mechanism may decrease GBM heterogeneity and facilitate therapy.

Mass Spectrometry-Based Chemical Proteomics for Drug Target Discoveries

Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.

SimPLIT: Simplified Sample Preparation for Large-Scale Isobaric Tagging Proteomics

Large scale proteomic profiling of cell lines can reveal molecular signatures attributed to variable genotypes or induced perturbations, enabling proteogenomic associations and elucidation of pharmacological mechanisms of action. Although isobaric labeling has increased the throughput of proteomic analysis, the commonly used sample preparation workflows often require time-consuming steps and costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost-effective one-pot reaction workflow in a 96-well plate format (SimPLIT) that minimizes processing steps and demonstrates improved reproducibility compared to alternative approaches. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent cleanup step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. We showcase the applicability of the workflow in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in any proteomics laboratory for medium-to-large scale TMT-based studies for deep profiling of cell lines.

Single-Cell Chemical Proteomics (SCCP) Interrogates the Timing and Heterogeneity of Cancer Cell Commitment to Death

Chemical proteomics studies the effects of drugs upon a cellular proteome. Due to the complexity and diversity of tumors, the response of cancer cells to drugs is also heterogeneous, and thus, proteome analysis at the single-cell level is needed. Here, we demonstrate that single-cell proteomics techniques have become quantitative enough to tackle the drug effects on target proteins, enabling single-cell chemical proteomics (SCCP). Using SCCP, we studied here the time-resolved response of individual adenocarcinoma A549 cells to anticancer drugs methotrexate, camptothecin, and tomudex, revealing the early emergence of cellular subpopulations committed and uncommitted to death. As a novel and useful approach to exploring the heterogeneous response to drugs of cancer cells, SCCP may prove to be a breakthrough application for single-cell proteomics.

Targeting Ribosome Biogenesis in Cancer: Lessons Learned and Way Forward

Simple Summary

Cells need to produce ribosomes to sustain continuous proliferation and expand in numbers, a feature that is even more prominent in uncontrollably proliferating cancer cells. Certain cancer cell types are expected to depend more on ribosome biogenesis based on their genetic background, and this potential vulnerability can be exploited in designing effective, targeted cancer therapies. This review provides information on anti-cancer molecules that target the ribosome biogenesis machinery and indicates avenues for future research.

Abstract

Rapid growth and unrestrained proliferation is a hallmark of many cancers. To accomplish this, cancer cells re-wire and increase their biosynthetic and metabolic activities, including ribosome biogenesis (RiBi), a complex, highly energy-consuming process. Several chemotherapeutic agents used in the clinic impair this process by interfering with the transcription of ribosomal RNA (rRNA) in the nucleolus through the blockade of RNA polymerase I or by limiting the nucleotide building blocks of RNA, thereby ultimately preventing the synthesis of new ribosomes. Perturbations in RiBi activate nucleolar stress response pathways, including those controlled by p53. While compounds such as actinomycin D and oxaliplatin effectively disrupt RiBi, there is an ongoing effort to improve the specificity further and find new potent RiBi-targeting compounds with improved pharmacological characteristics. A few recently identified inhibitors have also become popular as research tools, facilitating our advances in understanding RiBi. Here we provide a comprehensive overview of the various compounds targeting RiBi, their mechanism of action, and potential use in cancer therapy. We discuss screening strategies, drug repurposing, and common problems with compound specificity and mechanisms of action. Finally, emerging paths to discovery and avenues for the development of potential biomarkers predictive of therapeutic outcomes across cancer subtypes are also presented.

Comprehensive Target Screening and Cellular Profiling of the Cancer-Active Compound b-AP15 Indicate Abrogation of Protein Homeostasis and Organelle Dysfunction as the Primary Mechanism of Action

Dienone compounds have been demonstrated to display tumor-selective anti-cancer activity independently of the mutational status of TP53. Previous studies have shown that cell death elicited by this class of compounds is associated with inhibition of the ubiquitin-proteasome system (UPS). Here we extend previous findings by showing that the dienone compound b-AP15 inhibits proteasomal degradation of long-lived proteins. We show that exposure to b-AP15 results in increased association of the chaperones VCP/p97/Cdc48 and BAG6 with proteasomes. Comparisons between the gene expression profile generated by b-AP15 to those elicited by siRNA showed that knock-down of the proteasome-associated deubiquitinase (DUB) USP14 is the closest related to drug response. USP14 is a validated target for b-AP15 and we show that b-AP15 binds covalently to two cysteines, Cys203 and Cys257, in the ubiquitin-binding pocket of the enzyme. Consistent with this, deletion of USP14 resulted in decreased sensitivity to b-AP15. Targeting of USP14 was, however, found to not fully account for the observed proteasome inhibition. In search for additional targets, we utilized genome-wide CRISPR/Cas9 library screening and Proteome Integral Solubility Alteration (PISA) to identify mechanistically essential genes and b-AP15 interacting proteins respectively. Deletion of genes encoding mitochondrial proteins decreased the sensitivity to b-AP15, suggesting that mitochondrial dysfunction is coupled to cell death induced by b-AP15. Enzymes known to be involved in Phase II detoxification such as aldo-ketoreductases and glutathione-S-transferases were identified as b-AP15-targets using PISA. The finding that different exploratory approaches yielded different results may be explained in terms of a “target” not necessarily connected to the “mechanism of action” thus highlighting the importance of a holistic approach in the identification of drug targets. We conclude that b-AP15, and likely also other dienone compounds of the same class, affect protein degradation and proteasome function at more than one level.

Cancer Conformational Landscape Shapes Tumorigenesis

During tumorigenesis, DNA mutations in protein coding sequences can alter amino acid sequences which can change the structures of proteins. While the 3D structure of mutated proteins has been studied with atomic resolution, the precise impact of somatic mutations on the 3D proteome during malignant transformation remains unknown because methods to reveal in vivo protein structures in high throughput are limited. Here, we measured the accessibility of the lysine ε-amine for chemical modification across proteomes using covalent protein painting (CPP) to indirectly determine alterations in the 3D proteome. CPP is a novel, high-throughput quantitative mass spectrometric method that surveyed a total of 8052 lysine sites across the 60 cell lines of the well-studied anticancer cell line panel (NCI60). Overall, 5.2 structural alterations differentiated any cancer cell line from the other 59. Structural aberrations in 98 effector proteins correlated with the selected presence of 90 commonly mutated proteins in the NCI60 cell line panel, suggesting that different tumor genotypes reshape a limited set of effector proteins. We searched our dataset for druggable conformational aberrations and identified 49 changes in the cancer conformational landscape that correlated with the growth inhibition profiles of 300 drug candidates out of 50,000 small molecules. We found that alterations in heat shock proteins are key predictors of anticancer drug efficacy, which implies that the proteostasis network may have a general but hitherto unrecognized role in maintaining malignancy. Individual lysine sites may serve as biomarkers to guide drug selection or may be directly targeted for anticancer drug development.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

An Integrative Proteome-Based Pharmacologic Characterization and Therapeutic Strategy Exploration of SAHA in Solid Malignancies

Targeting histone epigenetic modification is an important strategy for anticancer therapy. Histone deacetylase inhibitors (HDACis) have been clinically approved in the treatment of diverse hematological cancers, but mechanisms of drug resistance and poor therapeutic efficacy in solid malignancies remain largely unknown. In this study, we applied a mass spectrometry-based quantitative proteomic strategy to investigate the molecular differences in HDACi vorinostat (SAHA) sensitive and resistant cell lines. The proteomic results revealed that the glycolysis pathway was highly enriched after vorinostat treatment in the resistant cell line, leading to the prediction of a new drug combination, SAHA and hexokinase inhibitor (2-deoxyglucose). The efficacy of this combination was further verified in several solid tumor cell lines. Quantitative proteomics revealed that alterations in the transcription process and protein homeostasis could play roles in the synergetic utilization of these two compounds. Our study showed the application of proteomics in elucidating the drug mechanism and predicting drug combination and the potential of expanding the utilization of HDACi.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

The p97 Inhibitor UPCDC-30245 Blocks Endo-Lysosomal Degradation

The diverse modes of action of small molecule inhibitors provide versatile tools to investigate basic biology and develop therapeutics. However, it remains a challenging task to evaluate their exact mechanisms of action. We identified two classes of inhibitors for the p97 ATPase: ATP competitive and allosteric. We showed that the allosteric p97 inhibitor, UPCDC-30245, does not affect two well-known cellular functions of p97, endoplasmic-reticulum-associated protein degradation and the unfolded protein response pathway; instead, it strongly increases the lipidated form of microtubule-associated proteins 1A/1B light chain 3B (LC3-II), suggesting an alteration of autophagic pathways. To evaluate the molecular mechanism, we performed proteomic analysis of UPCDC-30245 treated cells. Our results revealed that UPCDC-30245 blocks endo-lysosomal degradation by inhibiting the formation of early endosome and reducing the acidity of the lysosome, an effect not observed with the potent p97 inhibitor CB-5083. This unique effect allows us to demonstrate UPCDC-30245 exhibits antiviral effects against coronavirus by blocking viral entry.

An integrative proteomics method identifies a regulator of translation during stem cell maintenance and differentiation

Detailed characterization of cell type transitions is essential for cell biology in general and particularly for the development of stem cell-based therapies in regenerative medicine. To systematically study such transitions, we introduce a method that simultaneously measures protein expression and thermal stability changes in cells and provide the web-based visualization tool ProteoTracker. We apply our method to study differences between human pluripotent stem cells and several cell types including their parental cell line and differentiated progeny. We detect alterations of protein properties in numerous cellular pathways and components including ribosome biogenesis and demonstrate that modulation of ribosome maturation through SBDS protein can be helpful for manipulating cell stemness in vitro. Using our integrative proteomics approach and the web-based tool, we uncover a molecular basis for the uncoupling of robust transcription from parsimonious translation in stem cells and propose a method for maintaining pluripotency in vitro.

Comprehensive chemical proteomics analyses reveal that the new TRi-1 and TRi-2 compounds are more specific thioredoxin reductase 1 inhibitors than auranofin

Anticancer drugs that target cellular antioxidant systems have recently attracted much attention. Auranofin (AF) is currently evaluated in several clinical trials as an anticancer agent that targets the cytosolic and mitochondrial forms of the selenoprotein thioredoxin reductase, TXNRD1 and TXNRD2. Recently, two novel TXNRD1 inhibitors (TRi-1 and TRi-2) have been developed that showed anticancer efficacy comparable to AF, but with lower mitochondrial toxicity. However, the cellular action mechanisms of these drugs have not yet been thoroughly studied. Here we used several proteomics approaches to determine the effects of AF, TRi-1 and TRi-2 when used at IC50 concentrations with the mouse B16 melanoma and LLC lung adenocarcinoma cells, as these are often used for preclinical mouse models in evaluation of anticancer drugs. The results demonstrate that TRi-1 and TRi-2 are more specific TXNRD1 inhibitors than AF and reveal additional AF-specific effects on the cellular proteome. Interestingly, AF triggered stronger Nrf2-driven antioxidant responses than the other two compounds. Furthermore, AF affected several additional proteins, including GSK3A, GSK3B, MCMBP and EEFSEC, implicating additional effects on glycogen metabolism, cellular differentiation, inflammatory pathways, DNA replication and selenoprotein synthesis processes. Our proteomics data provide a resource for researchers interested in the multidimensional analysis of proteome changes associated with oxidative stress in general, and the effects of TXNRD1 inhibitors and AF protein targets in particular.

AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results

Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source command-line tool available at https://github.com/SimpleNumber/aa_stat. SIGNIFICANCE: Mass spectrometry-based PTM characterization has a long history, yet most of the methods rely on a priori knowledge of modifications of interest and do not provide a whole proteome modification landscape in a blind manner. The open database search is an efficient attempt to address this challenge by identifying peptides with mass shifts corresponding to possible modifications. Then, interpreting these mass shifts is required. Therefore, development of bioinformatics software for post-processing of the open search results, which is capable of detection and accurate annotation of new or unexpected modifications, from characterization of sample preparation efficiency and quality control to discovery of rare post-translational modifications, is of high importance.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

System-wide identification and prioritization of enzyme substrates by thermal analysis

Despite the immense importance of enzyme-substrate reactions, there is a lack of general and unbiased tools for identifying and prioritizing substrate proteins that are modified by the enzyme on the structural level. Here we describe a high-throughput unbiased proteomics method called System-wide Identification and prioritization of Enzyme Substrates by Thermal Analysis (SIESTA). The approach assumes that the enzymatic post-translational modification of substrate proteins is likely to change their thermal stability. In our proof-of-concept studies, SIESTA successfully identifies several known and novel substrate candidates for selenoprotein thioredoxin reductase 1, protein kinase B (AKT1) and poly-(ADP-ribose) polymerase-10 systems. Wider application of SIESTA can enhance our understanding of the role of enzymes in homeostasis and disease, opening opportunities to investigate the effect of post-translational modifications on signal transduction and facilitate drug discovery.

COVID-19: Nanomedicine Uncovers Blood-Clot Mystery

Further complications associated with infection by severe acute respiratory syndrome coronavirus 2 (a.k.a. SARS-CoV-2) continue to be reported. Very recent findings reveal that 20-30% of patients at high risk of mortality from COVID-19 infection experience blood clotting that leads to stroke and sudden death. Timely assessment of the severity of blood clotting will be of enormous help to clinicians in determining the right blood-thinning medications to prevent stroke or other life-threatening consequences. Therefore, rapid identification of blood-clotting-related proteins in the plasma of COVID-19 patients would save many lives. Several nanotechnology-based approaches are being developed to diagnose patients at high risk of death due to complications from COVID-19 infections, including blood clots. This Perspective outlines (i) the significant potential of nanomedicine in assessing the risk of blood clotting and its severity in SARS-CoV-2 infected patients and (ii) its synergistic roles with advanced mass-spectrometry-based proteomics approaches in identifying the important protein patterns that are involved in the occurrence and progression of this disease. The combination of such powerful tools might help us understand the clotting phenomenon and pave the way for development of new diagnostics and therapeutics in the fight against COVID-19.

A mass spectrometry-based proteome map of drug action in lung cancer cell lines

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

Comprehensive chemical proteomics for target deconvolution of the redox active drug auranofin

Chemical proteomics encompasses novel drug target deconvolution methods in which compound modification is not required. Herein we use Thermal Proteome Profiling, Functional Identification of Target by Expression Proteomics and multiplexed redox proteomics for deconvolution of auranofin targets to aid elucidation of its mechanisms of action. Auranofin (Ridaura®) was approved for treatment of rheumatoid arthritis in 1985. Because several clinical trials are currently ongoing to repurpose auranofin for cancer therapy, comprehensive characterization of its targets and effects in cancer cells is important. Together, our chemical proteomics tools confirmed thioredoxin reductase 1 (TXNRD1, EC:1.8.1.9) as a main auranofin target, with perturbation of oxidoreductase pathways as the top mechanism of drug action. Additional indirect targets included NFKB2 and CHORDC1. Our comprehensive data can be used as a proteomic signature resource for further analyses of the effects of auranofin. Here we also assessed the orthogonality and complementarity of different chemical proteomics methods that can furnish invaluable mechanistic information and thus the approach can facilitate drug discovery efforts in general.

Thermal Proteome Profiling Identifies Oxidative-Dependent Inhibition of the Transcription of Major Oncogenes as a New Therapeutic Mechanism for Select Anticancer Compounds

Identification of the molecular mechanism of action (MoA) of bioactive compounds is a crucial step for drug development but remains a challenging task despite recent advances in technology. In this study, we applied multidimensional proteomics, sensitivity correlation analysis, and transcriptomics to identify a common MoA for the anticancer compounds RITA, aminoflavone (AF), and oncrasin-1 (Onc-1). Global thermal proteome profiling revealed that the three compounds target mRNA processing and transcription, thereby attacking a cancer vulnerability, transcriptional addiction. This led to the preferential loss of expression of oncogenes involved in PDGF, EGFR, VEGF, insulin/IGF/MAPKK, FGF, Hedgehog, TGFβ, and PI3K signaling pathways. Increased reactive oxygen species level in cancer cells was a prerequisite for targeting the mRNA transcription machinery, thus conferring cancer selectivity to these compounds. Furthermore, DNA repair factors involved in homologous recombination were among the most prominently repressed proteins. In cancer patient samples, RITA, AF, and Onc-1 sensitized to poly(ADP-ribose) polymerase inhibitors both in vitro and ex vivo These findings might pave a way for new synthetic lethal combination therapies.Significance: These findings highlight agents that target transcriptional addiction in cancer cells and suggest combination treatments that target RNA processing and DNA repair pathways simultaneously as effective cancer therapies.