Revealing promiscuous drug-target interactions by chemical proteomics

Chemical Proteomics Reveals That Camptothecin Weakens Insect Immunity against Bacteria by Suppressing Antimicrobial Peptide Expression

Plant natural products are crucial in defending against herbivorous insects and are widely used in pest control, yet their mechanisms remain complex and insufficiently studied. This study employed a reverse strategy to investigate the mechanism of camptothecin (CPT), a botanical pesticide. By using a CPT-based chemical probe coupled with proteomic analysis, immune-related proteins, including those involved in prophenoloxidase (PPO) activation and antimicrobial peptide (AMP) synthesis, were identified in the Asian corn borer, Ostrinia furnacalis. The findings demonstrated that CPT weakens insect immunity, primarily by inhibiting AMP synthesis rather than affecting PPO activation. Specifically, CPT downregulated the expression of genes in the IMD pathway and those encoding AMPs (attacin and gloverin). Additionally, CPT-fed insects exhibited reduced antibacterial activity. This research uncovers a novel mechanism of CPT as an insect immunosuppressant, offering new insights that may enhance the application of CPT in pest control.

Revealing novel protein interaction partners of glyphosate in Escherichia coli

Despite all debates about its safe use, glyphosate remains the most widely applied active ingredient in herbicide products, with renewed approval in the European Union until 2033. Non-target organisms are commonly exposed to glyphosate as a matter of its mode of application, with its broader environmental and biological impacts remaining under investigation. Glyphosate displays structural similarity to phosphoenolpyruvate (PEP), thereby competitively inhibiting the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), crucial for the synthesis of aromatic amino acids in plants, fungi, bacteria, and archaea. Most microbes, including the gut bacterium Escherichia coli (E. coli), possess a glyphosate-sensitive class I EPSPS, making them vulnerable to glyphosate's effects. Yet, little is known about glyphosate's interactions with other bacterial proteins or its broader modes of action at the proteome level. Here, we employed a quantitative proteomics and thermal proteome profiling (TPP) approach to identify novel protein binding partners of glyphosate in the E. coli proteome. Glyphosate exposure significantly altered amino acid synthesizing pathways. The abundance of shikimate pathway proteins was increased, suggesting a compensatory mechanism. Extracellular riboflavin concentrations were elevated upon glyphosate exposure, while intracellular levels remained stable. Beyond the target enzyme EPSPS, thermal proteome profiling indicated an effect of glyphosate on the thermal stability of certain proteins, including AroH and ProA, indicating interactions. Similar to the competitive binding between PEP and glyphosate at EPSPS, one reason for the interaction of AroH and ProA with the herbicide could be a high structural similarity between their substrates and glyphosate. Overall, glyphosate induced metabolic disturbances in E. coli, extending beyond its primary target, thereby providing new insights into glyphosate's broader impact on microbial systems.

Improved deconvolution of natural products' protein targets using diagnostic ions from chemical proteomics linkers

Identification of interactions between proteins and natural products or similar active small molecules is crucial for understanding of their mechanism of action on a molecular level. To search elusive, often labile, and low-abundant conjugates between proteins and active compounds, chemical proteomics introduces a feasible strategy that allows to enrich and detect these conjugates. Recent advances in mass spectrometry techniques and search algorithms provide unprecedented depth of proteome coverage and the possibility to detect desired modified peptides with high sensitivity. The chemical 'linker' connecting an active compound-protein conjugate with a detection tag is the critical component of all chemical proteomic workflows. In this review, we discuss the properties and applications of different chemical proteomics linkers with special focus on their fragmentation releasing diagnostic ions and how these may improve the confidence in identified active compound-peptide conjugates. The application of advanced search options improves the identification rates and may help to identify otherwise difficult to find interactions between active compounds and proteins, which may result from unperturbed conditions, and thus are of high physiological relevance.

Exposure to the Natural Compound Climacostol Induces Cell Damage and Oxidative Stress in the Fruit Fly Drosophila melanogaster

The ciliate Climacostomum virens produces the metabolite climacostol that displays antimicrobial activity and cytotoxicity on human and rodent tumor cells. Given its potential as a backbone in pharmacological studies, we used the fruit fly Drosophila melanogaster to evaluate how the xenobiotic climacostol affects biological systems in vivo at the organismal level. Food administration with climacostol demonstrated its harmful role during larvae developmental stages but not pupation. The midgut of eclosed larvae showed apoptosis and increased generation of reactive oxygen species (ROS), thus demonstrating gastrointestinal toxicity. Climacostol did not affect enteroendocrine cell proliferation, suggesting moderate damage that does not initiate the repairing program. The fact that climacostol increased brain ROS and inhibited the proliferation of neural cells revealed a systemic (neurotoxic) role of this harmful substance. In this line, we found lower expression of relevant antioxidant enzymes in the larvae and impaired mitochondrial activity. Adult offsprings presented no major alterations in survival and mobility, as well the absence of abnormal phenotypes. However, mitochondrial activity and oviposition behavior was somewhat affected, indicating the chronic toxicity of climacostol, which continues moderately until adult stages. These results revealed for the first time the detrimental role of ingested climacostol in a non-target multicellular organism.

Different chemical proteomic approaches to identify the targets of lapatinib

The process of identifying the protein targets and off-targets of a biologically active compound is of great importance in modern drug discovery. Various chemical proteomics approaches have been established for this purpose. To compare the different approaches, and to understand which method would provide the best results, we have chosen the EGFR inhibitor lapatinib as an example molecule. Lapatinib derivatives were designed using linkers with motifs, including amino (amidation), alkyne (click chemistry) and the diazirine group (photo-affinity). These modified lapatinib analogues were validated for their ability to inhibit EGFR activity in vitro and were shown to pull down purified recombinant EGFR protein. In all of the approaches evaluated here, we identified EGFR as the main protein target from the lysate of immortalised cell line expressing EGFR, thus validating its potential use to identify unknown protein targets. Taken together, the results reported here give insight into the cellular activities of lapatinib.

Analgesic Activity of 5-Acetamido-2-Hydroxy Benzoic Acid Derivatives and an In-Vivo and In-Silico Analysis of Their Target Interactions

The design, synthesis, and evaluation of novel non-steroidal anti-inflammatory drugs (NSAIDs) with better activity and lower side effects are big challenges today. In this work, two 5-acetamido-2-hydroxy benzoic acid derivatives were proposed, increasing the alkyl position (methyl) in an acetamide moiety, and synthesized, and their structural elucidation was performed using 1H NMR and 13C NMR. The changes in methyl in larger groups such as phenyl and benzyl aim to increase their selectivity over cyclooxygenase 2 (COX-2). These 5-acetamido-2-hydroxy benzoic acid derivatives were prepared using classic methods of acylation reactions with anhydride or acyl chloride. Pharmacokinetics and toxicological properties were predicted using computational tools, and their binding affinity (kcal/mol) with COX-2 receptors (Mus musculus and Homo sapiens) was analyzed using docking studies (PDB ID 4PH9, 5KIR, 1PXX and 5F1A). An in-silico study showed that 5-acetamido-2-hydroxy benzoic acid derivates have a better bioavailability and binding affinity with the COX-2 receptor, and in-vivo anti-nociceptive activity was investigated by means of a writhing test induced by acetic acid and a hot plate. PS3, at doses of 20 and 50 mg/kg, reduced painful activity by 74% and 75%, respectively, when compared to the control group (20 mg/kg). Regarding the anti-nociceptive activity, the benzyl showed reductions in painful activity when compared to acetaminophen and 5-acetamido-2-hydroxy benzoic acid. However, the proposed derivatives are potentially more active than 5-acetamido-2-hydroxy benzoic acid and they support the design of novel and safer derivative candidates. Consequently, more studies need to be conducted to evaluate the different pharmacological actions, the toxicity of possible metabolites that can be generated, and their potential use in inflammation and pain therapy.

Design, synthesis, and evaluation of a mitoxantrone probe (MXP) for biological studies

Mitoxantrone (MX) is a robust chemotherapeutic with well-characterized applications in treating certain leukemias and advanced breast and prostate cancers. The canonical mechanism of action associated with MX is its ability to intercalate DNA and inhibit topoisomerase II, giving it the designation of a topoisomerase II poison. Years after FDA approval, investigations have unveiled novel protein-binding partners, such as methyl-CpG-binding domain protein (MBD2), PIM1 serine/threonine kinase, RAD52, and others that may contribute to the therapeutic profile of MX. Moreover, recent proteomic studies have revealed MX's ability to modulate protein expression, illuminating the complex cellular interactions of MX. Although mechanistically relevant, the differential expression across the proteome does not address the direct interaction with potential binding partners. Identification and characterization of these MX-binding cellular partners will provide the molecular basis for the alternate mechanisms that influence MX's cytotoxicity. Here, we describe the design and synthesis of a MX-biotin probe (MXP) and negative control (MXP-NC) that can be used to define MX's cellular targets and expand our understanding of the proteome-wide profile for MX. In proof of concept studies, we used MXP to successfully isolate a recently identified protein-binding partner of MX, RAD52, in a cell lysate pulldown with streptavidin beads and western blotting.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Gene Modulation with CRISPR-based Tools in Human iPSC-Cardiomyocytes

Precise control of gene expression (knock-out, knock-in, knockdown or overexpression) is at the heart of functional genomics - an approach to dissect the contribution of a gene/protein to the system's function. The development of a human in vitro system that can be patient-specific, induced pluripotent stem cells, iPSC, and the ability to obtain various cell types of interest, have empowered human disease modeling and therapeutic development. Scalable tools have been deployed for gene modulation in these cells and derivatives, including pharmacological means, DNA-based RNA interference and standard RNA interference (shRNA/siRNA). The CRISPR/Cas9 gene editing system, borrowed from bacteria and adopted for use in mammalian cells a decade ago, offers cell-specific genetic targeting and versatility. Outside genome editing, more subtle, time-resolved gene modulation is possible by using a catalytically "dead" Cas9 enzyme linked to an effector of gene transcription in combination with a guide RNA. The CRISPRi / CRISPRa (interference/activation) system evolved over the last decade as a scalable technology for performing functional genomics with libraries of gRNAs. Here, we review key developments of these approaches and their deployment in cardiovascular research. We discuss specific use with iPSC-cardiomyocytes and the challenges in further translation of these techniques.

Design, synthesis, and evaluation of a mitoxantrone probe (MXP) for biological studies

Mitoxantrone (MX) is a robust chemotherapeutic with well-characterized applications in treating certain leukemias and advanced breast and prostate cancers. The canonical mechanism of action associated with MX is its ability to intercalate DNA and inhibit topoisomerase II, giving it the designation of a topoisomerase II poison. Years after FDA approval, investigations have unveiled novel protein-binding partners, such as methyl-CpG-binding domain protein (MBD2), PIM1 serine/threonine kinase, RAD52, and others that may contribute to the therapeutic profile of MX. Moreover, recent proteomic studies have revealed MX's ability to modulate protein expression, illuminating the complex cellular interactions of MX. Although mechanistically relevant, the differential expression across the proteome does not address the direct interaction with potential binding partners. Identification and characterization of these MX-binding cellular partners will provide the molecular basis for the alternate mechanisms that influence MX's cytotoxicity. Here, we describe the design and synthesis of a MX-biotin probe (MXP) and negative control (MXP-NC) that can be used to define MX's cellular targets and expand our understanding of the proteome-wide profile for MX. In proof of concept studies, we used MXP to successfully isolate a recently identified protein-binding partner of MX, RAD52, in a cell lysate pulldown with streptavidin beads and western blotting.

Graphical abstract Draft

Highlights

An 8-step synthesis was used to generate a biotinylated-mitoxantrone probe (MXP).A pulldown of MXP demonstrated selectivity for RAD52, but not Replication Protein A.Western blot confirmed the identity of the isolated protein, RAD52.

Evaluation of a Pooling Chemoproteomics Strategy with an FDA-Approved Drug Library

Chemoproteomics is a key platform for characterizing the mode of action for compounds, especially for targeted protein degraders such as proteolysis targeting chimeras (PROTACs) and molecular glues. With deep proteome coverage, multiplexed tandem mass tag-mass spectrometry (TMT-MS) can tackle up to 18 samples in a single experiment. Here, we present a pooling strategy for further enhancing the throughput and apply the strategy to an FDA-approved drug library (95 best-in-class compounds). The TMT-MS-based pooling strategy was evaluated in the following steps. First, we demonstrated the capability of TMT-MS by analyzing more than 15 000 unique proteins (> 12 000 gene products) in HEK293 cells treated with five PROTACs (two BRD/BET degraders and three degraders for FAK, ALK, and BTK kinases). We then introduced a rationalized pooling strategy to separate structurally similar compounds in different pools and identified the proteomic response to 14 pools from the drug library. Finally, we validated the proteomic response from one pool by reprofiling the cells via treatment with individual drugs with sufficient replicates. Interestingly, numerous proteins were found to change upon drug treatment, including AMD1, ODC1, PRKX, PRKY, EXO1, AEN, and LRRC58 with 7-hydroxystaurosporine; C6orf64, HMGCR, and RRM2 with Sorafenib; SYS1 and ALAS1 with Venetoclax; and ATF3, CLK1, and CLK4 with Palbocilib. Thus, pooling chemoproteomics screening provides an efficient method for dissecting the molecular targets of compound libraries.

Curcumin suppresses cell proliferation and reduces cholesterol absorption in Caco-2 cells by activating the TRPA1 channel

BACKGROUND

Curcumin (Cur) is a bioactive dietary polyphenol of turmeric with various biological activities against several cancers. Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths. Intestinal cholesterol homeostasis is associated with CRC. Chemotherapy for CRC is related to varied adverse effects. Therefore, natural products with anti-cancer properties represent a potential strategy for primary prevention of CRC.

METHODS

The present study used Cur as a therapeutic approach against CRC using the Caco-2 cell line. The cells were treated with different concentrations of Cur for different duration of time and then the proliferation ability of cells was assessed using Cell Counting Kit-8 and 5-Ethynyl-2'-deoxyuridine assays. Oil red O staining and cholesterol assay kit were used to evaluate cellular lipid content and cholesterol outward transportation. Finally, the protein expressions of cholesterol transport-related protein and signal transduction molecules were assessed using Western blot assay.

RESULTS

Cur inhibited cell proliferation in Caco-2 cells in a dose- and time-dependent manner by activating the transient receptor potential cation channel subfamily A member 1 (TRPA1) channel. Activation of the TRPA1 channel led to increased intracellular calcium, peroxisome proliferator-activated receptor gamma (PPARγ) upregulation, and the subsequent downregulation of the specificity protein-1 (SP-1)/sterol regulatory element-binding protein-2 (SREBP-2)/Niemann-Pick C1-like 1 (NPC1L1) signaling pathway-related proteins, and finally reduced cholesterol absorption in Caco-2 cells.

CONCLUSIONS

Cur inhibits cell proliferation and reduces cholesterol absorption in Caco-2 cells through the Ca²⁺/PPARγ/SP-1/SREBP-2/NPC1L1 signaling by activating the TRPA1 channel, suggesting that Cur can be used as a dietary supplement for the primary prevention of CRC. In Caco-2 cells, Cur first stimulates calcium influx by activating the TRPA1 channel, further upregulates PPARγ and downregulates SP-1/SREBP-2/NPC1L1 signaling pathway, and finally inhibits the absorption of cholesterol. TRPA1, transient receptor potential cation channel subfamily A member 1; NPC1L1, Niemann-Pick C1-like 1; PPARγ, peroxisome proliferator-activated receptor gamma; SP-1, specificity protein-1; SREBP-2, sterol regulatory element-binding protein-2; Cur, curcumin.

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.

Baicalin Targets HSP70/90 to Regulate PKR/PI3K/AKT/eNOS Signaling Pathways

Baicalin is a major active ingredient of traditional Chinese medicine Scutellaria baicalensis, and has been shown to have antiviral, anti-inflammatory, and antitumor activities. However, the protein targets of baicalin have remained unclear. Herein, a chemical proteomics strategy was developed by combining baicalin-functionalized magnetic nanoparticles (BCL-N3@MNPs) and quantitative mass spectrometry to identify the target proteins of baicalin. Bioinformatics analysis with the use of Gene Ontology, STRING and Ingenuity Pathway Analysis, was performed to annotate the biological functions and the associated signaling pathways of the baicalin targeting proteins. Fourteen proteins in human embryonic kidney cells were identified to interact with baicalin with various binding affinities. Bioinformatics analysis revealed these proteins are mainly ATP-binding and/or ATPase activity proteins, such as CKB, HSP86, HSP70-1, HSP90, ATPSF1β and ACTG1, and highly associated with the regulation of the role of PKR in interferon induction and the antiviral response signaling pathway (P = 10-6), PI3K/AKT signaling pathway (P = 10-5) and eNOS signaling pathway (P = 10-4). The results show that baicalin exerts multiply pharmacological functions, such as antiviral, anti-inflammatory, antitumor, and antioxidant functions, through regulating the PKR and PI3K/AKT/eNOS signaling pathways by targeting ATP-binding and ATPase activity proteins. These findings provide a fundamental insight into further studies on the mechanism of action of baicalin.

Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins

Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed after thermal treatment, while the precipitate fractions that also contain abundant information of drug-induced stability shifts are discarded; the sigmoid melting curve fitting strategy used for data processing suffers from discriminations for a part of human proteome with multiple transitions. In this study, a precipitate-supported TPP (PSTPP) assay was presented for unbiased and comprehensive analysis of protein-drug interactions at the proteome level. In PSTPP, only these temperatures where significant precipitation is observed were applied to induce protein denaturation and the complementary information contained in both supernatant fractions and precipitate fractions was used to improve the screening specificity and sensitivity. In addition, a novel image recognition algorithm based on deep learning was developed to recognize the target proteins, which circumvented the problems that exist in the sigmoid curve fitting strategy. PSTPP assay was validated by identifying the known targets of methotrexate, raltitrexed, and SNS-032 with good performance. Using a promiscuous kinase inhibitor, staurosporine, we delineated 99 kinase targets with a specificity up to 83% in K562 cell lysates, which represented a significant improvement over the existing thermal shift methods. Furthermore, the PSTPP strategy was successfully applied to analyze the binding targets of rapamycin, identifying the well-known targets, FKBP1A, as well as revealing a few other potential targets.

Using common genetic variants to find drugs for common epilepsies

Better drugs are needed for common epilepsies. Drug repurposing offers the potential of significant savings in the time and cost of developing new treatments. In order to select the best candidate drug(s) to repurpose for a disease, it is desirable to predict the relative clinical efficacy that drugs will have against the disease. Common epilepsy can be divided into different types and syndromes. Different antiseizure medications are most effective for different types and syndromes of common epilepsy. For predictions of antiepileptic efficacy to be clinically translatable, it is essential that the predictions are specific to each form of common epilepsy, and reflect the patterns of drug efficacy observed in clinical studies and practice. These requirements are not fulfilled by previously published drug predictions for epilepsy. We developed a novel method for predicting the relative efficacy of drugs against any common epilepsy, by using its Genome-Wide Association Study summary statistics and drugs' activity data. The methodological advancement in our technique is that the drug predictions for a disease are based upon drugs' effects on the function and abundance of proteins, and the magnitude and direction of those effects, relative to the importance, degree and direction of the proteins' dysregulation in the disease. We used this method to predict the relative efficacy of all drugs, licensed for any condition, against each of the major types and syndromes of common epilepsy. Our predictions are concordant with findings from real-world experience and randomized clinical trials. Our method predicts the efficacy of existing antiseizure medications against common epilepsies; in this prediction, our method outperforms the best alternative existing method: area under receiver operating characteristic curve (mean ± standard deviation) 0.83 ± 0.03 and 0.63 ± 0.04, respectively. Importantly, our method predicts which antiseizure medications are amongst the more efficacious in clinical practice, and which antiseizure medications are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual antiseizure medications in clinical trials of different common epilepsies. We identify promising candidate drugs for each of the major syndromes of common epilepsy. We screen five promising predicted drugs in an animal model: each exerts a significant dose-dependent effect upon seizures. Our predictions are a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major syndromes of common epilepsy. Our method is potentially generalizable to other complex diseases.

The GFP thermal shift assay for screening ligand and lipid interactions to solute carrier transporters

Solute carrier (SLC) transporters represent the second-largest fraction of the membrane proteome after G-protein-coupled receptors, but have been underutilized as drug targets and the function of many members of this family is still unknown. They are technically challenging to work with as they are difficult to express and highly dynamic, making them unstable in detergent solution. Many SLCs lack known inhibitors that could be utilized for stabilization. Furthermore, as they bind their physiological substrates with high micromolar to low millimolar affinities, binding and transport assays have proven to be particularly challenging to implement. Previously, we reported a GFP-based method for the overexpression and purification of membrane proteins in Saccharomyces cerevisiae. Here, we extend this expression platform with the GFP thermal shift (GFP-TS) assay, which is a simplified version of fluorescence-detection size-exclusion chromatography that combines the sample versatility of fluorescence-detection size-exclusion chromatography with the high-throughput capability of dye-based thermal shift assays. We demonstrate how GFP-TS can be used for detecting specific ligand interactions of SLC transporter fusions and measuring their affinities in crude detergent-solubilized membranes. We further show how GFP-TS can be employed on purified SLC transporter fusions to screen for specific lipid-protein interactions, which is an important complement to native mass spectrometry approaches that cannot cope easily with crude lipid-mixture preparations. This protocol is simple to perform and can be followed by researchers with a basic background in protein chemistry. Starting with an SLC transporter construct that can be expressed and purified from S. cerevisiae in a well-folded state, this protocol extension can be completed in ~4-5 d.

Unraveling the Neuroprotective Effect of Tinospora cordifolia in a Parkinsonian Mouse Model through the Proteomics Approach

Stress-induced dopaminergic (DAergic) neuronal death in the midbrain region is the primary cause of Parkinson's disease (PD). Following the discovery of l-dopa, multiple drugs have been developed to improve the lifestyle of PD patients; however, none have been suitable for clinical use due to their multiple side effects. Tinospora cordifolia has been used in traditional medicines to treat neurodegenerative diseases. Previously, we reported the neuroprotective role of Tc via inhibition of NF-κB-associated proinflammatory cytokines against MPTP-intoxicated Parkinsonian mice. In the present study, we investigated the neuroprotective molecular mechanism of Tc in a rotenone (ROT)-intoxicated mouse model, using a proteomics approach. Mice were pretreated with Tc extract by oral administration, followed by ROT intoxication. Behavioral tests were performed to check motor functions of mice. Protein was isolated, and label-free quantification (LFQ) was carried out to identify differentially expressed protein (DEP) in control vs PD and PD vs treatment groups. Results were validated by qRT-PCR with the expression of target genes correlating with the proteomics data. In this study, we report 800 DEPs in control vs PD and 133 in PD vs treatment groups. In silico tools demonstrate significant enrichment of biochemical and molecular pathways with DEPs, which are known to be important for PD progression including mitochondrial gene expression, PD pathways, TGF-β signaling, and Alzheimer's disease. This study provides novel insights into the PD progression as well as new therapeutic targets. More importantly, it demonstrates that Tc can exert therapeutic effects by regulating multiple pathways, resulting in neuroprotection.

Application of omics- and multi-omics-based techniques for natural product target discovery

Natural products continue to be an unparalleled source of pharmacologically active lead compounds because of their unprecedented structures and unique biological activities. Natural product target discovery is a vital component of natural product-based medicine translation and development and is required to understand and potentially reduce mechanisms that may be associated with off-target side effects and toxicity. Omics-based techniques, including genomics, transcriptomics, proteomics, metabolomics, and bioinformatics, have become recognized as effective tools needed to construct innovative strategies to discover natural product targets. Although considerable progress has been made, the successful discovery of natural product targets remains a challenging time-consuming process that has come to increasingly rely on the effective integration of multi-omics-based technologies to create emerging panomics (a.k.a., integrative omics, pan-omics, multiomics)-based strategies. This review summarizes a series of successful studies regarding the application of integrative omics-based methods in natural product target discovery. The advantages and disadvantages of each technique are discussed, with a particular focus on the systematic integration of multi-omics strategies. Further, emerging micro-scale single-cell-based techniques are introduced, especially to deal with minute natural product samples.

Quantitative Chemical Proteomics Reveals Resveratrol Inhibition of A549 Cell Migration Through Binding Multiple Targets to Regulate Cytoskeletal Remodeling and Suppress EMT

Resveratrol (RSV), a health-promoting natural product, has been shown to affect various cellular processes in tumor cells. However, the specific protein targets of RSV and the mechanism of action (MOA) of its anticancer effect remain elusive. In this study, the pharmacological activity of RSV was first evaluated in A549 cells, and the results showed that RSV significantly inhibited A549 cell migration but did not affect cell viability. To elucidate the underlying mechanism, a quantitative chemical proteomics approach was employed to identify the protein targets of RSV. A total of 38 target proteins were identified, and proteomic analysis showed that the targets were mainly involved in cytoskeletal remodeling and EMT, which were verified by subsequent in vitro and in vivo assays. In conclusion, RSV inhibits A549 cell migration by binding to multiple targets to regulate cytoskeletal remodeling and suppress EMT.

Probing the methotrexate-protein interactions by proteomics and thermostability assay for drug resistance study

Screening of drug targets is critical to understand the mechanism of action of the drug. The aim of this study is to screen the drug-resistant target proteins of the anticancer drug methotrexate (MTX) by using chemical proteomics and to further study the interaction between MTX and its target protein in vitro and in vivo according to the principle of the increasing thermal stability of the target protein after binding with the drug molecule. The results showed that 21 drug resistance related proteins of MTX including phosphoglycerate kinase 1 (PGK1) were detected by quantitative proteomics. The expression of PGK1 increased with the prolongation of incubation time of MTX, indicating PGK1 protein is affected by MTX time dependently in cells. Further the results of the study on the interaction between MTX and PGK1 in vitro and in vivo using cellular thermal shift assay (CETSA) showed that the level of PGK1 in MTX-treated groups was higher than that in the control group under the stimulation of higher temperature conditions, indicating that PGK1 has direct interactions with MTX. The present study provided the data and theoretical support for the study of the resistant target proteins of MTX and a novel point for the extension application of MTX.

Target Profiling of an Anticancer Drug Curcumin by an In Situ Chemical Proteomics Approach

Interdisciplinary chemical proteomics approaches have been widely applied to the identification of specific targets of bioactive small molecules or drugs. In this chapter, we describe the application of a cell-permeable activity-based curcumin probe (Cur-P) with an alkyne moiety to detect and identify specific binding targets of curcumin in HCT116 colon cancer cells. Through click chemistry, a fluorescent tag or a biotin tag is attached to the probe-modified curcumin targets for visualization or affinity purification followed by mass spectrometric identification. A quantitative proteomics approach of isobaric tags for relative and absolute quantification (iTRAQ)™ is applied to distinguish specific curcumin targets from nonspecific binding proteins.

Broad-Spectrum Flavivirus Inhibitors: a Medicinal Chemistry Point of View

Infections by flaviviruses, such as Dengue, West Nile, Yellow Fever and Zika viruses, represent a growing risk for global health. There are vaccines only for few flaviviruses while no effective treatments are available. Flaviviruses share epidemiological, structural, and ecologic features and often different viruses can co-infect the same host. Therefore, the identification of broad-spectrum inhibitors is highly desirable either for known flaviviruses or for viruses that likely will emerge in the future. Strategies targeting both virus and host factors have been pursued to identify broad-spectrum antiflaviviral agents. In this review, we describe the most promising and best characterized targets and their relative broad-spectrum inhibitors, identified by drug repurposing/libraries screenings and by focused medicinal chemistry campaigns. Finally, we discuss about future strategies to identify new broad-spectrum antiflavivirus agents.

Recent progress on cheminformatics approaches to epigenetic drug discovery

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.

Augmented base pairing networks encode RNA-small molecule binding preferences

RNA-small molecule binding is a key regulatory mechanism which can stabilize 3D structures and activate molecular functions. The discovery of RNA-targeting compounds is thus a current topic of interest for novel therapies. Our work is a first attempt at bringing the scalability and generalization abilities of machine learning methods to the problem of RNA drug discovery, as well as a step towards understanding the interactions which drive binding specificity. Our tool, RNAmigos, builds and encodes a network representation of RNA structures to predict likely ligands for novel binding sites. We subject ligand predictions to virtual screening and show that we are able to place the true ligand in the 71st-73rd percentile in two decoy libraries, showing a significant improvement over several baselines, and a state of the art method. Furthermore, we observe that augmenting structural networks with non-canonical base pairing data is the only representation able to uncover a significant signal, suggesting that such interactions are a necessary source of binding specificity. We also find that pre-training with an auxiliary graph representation learning task significantly boosts performance of ligand prediction. This finding can serve as a general principle for RNA structure-function prediction when data is scarce. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights for possible applications to other structure-function learning tasks. The source code, data and a Web server are freely available at http://rnamigos.cs.mcgill.ca.

Target identification of natural medicine with chemical proteomics approach: probe synthesis, target fishing and protein identification

Natural products are an important source of new drugs for the treatment of various diseases. However, developing natural product-based new medicines through random moiety modification is a lengthy and costly process, due in part to the difficulties associated with comprehensively understanding the mechanism of action and the side effects. Identifying the protein targets of natural products is an effective strategy, but most medicines interact with multiple protein targets, which complicate this process. In recent years, an increasing number of researchers have begun to screen the target proteins of natural products with chemical proteomics approaches, which can provide a more comprehensive array of the protein targets of active small molecules in an unbiased manner. Typically, chemical proteomics experiments for target identification consist of two key steps: (1) chemical probe design and synthesis and (2) target fishing and identification. In recent decades, five different types of chemical proteomic probes and their respective target fishing methods have been developed to screen targets of molecules with different structures, and a variety of protein identification approaches have been invented. Presently, we will classify these chemical proteomics approaches, the application scopes and characteristics of the different types of chemical probes, the different protein identification methods, and the advantages and disadvantages of these strategies.

Synthesis of imatinib, a tyrosine kinase inhibitor, labeled with carbon-14

Imatinib (Gleevec) is a multiple tyrosine kinase inhibitor that decreases the activity of the fusion oncogene called BCR-ABL (breakpoint cluster region protein-Abelson murine leukemia viral oncogene homolog) and is clinically used for the treatment of chronic myelogenous leukemia and acute lymphocytic leukemia. Small molecule drugs, such as imatinib, can bind to several cellular proteins including the target proteins in the cells, inducing undesirable effects along with the effects against the disease. In this study, we report the synthetic optimization for 14 C-labeling and radiosynthesis of [14 C]imatinib to analyze binding with cellular proteins using accelerator mass spectroscopy. 14 C-labeling of imatinib was performed by the synthesis of 14 C-labeld 2-aminopyrimidine intermediate using [14 C]guanidine·HCl, which includes an in situ reduction of an inseparable byproduct for easy purification by HPLC, followed by a cross-coupling reaction with aryl bromide precursor. The radiosynthesis of [14 C]imatinib (specific activity, 631 MBq/mmol; radiochemical purity, 99.6%) was achieved in six steps with a total chemical yield of 29.2%.

Crellastatin A, a PARP-1 Inhibitor Discovered by Complementary Proteomic Approaches

Crellastatin A, a cytotoxic sulfated bis-steroid isolated from the Vanuatu Island marine sponge Crella sp., was selected as an interesting probe for a comprehensive proteomic analysis directed at the characterization of its protein interactors. Given its peculiar structural features, A was submitted to a mass spectrometry-based drug affinity responsive target stability (DARTS) assay combined with (targeted-limited proteolysis-multiple reaction monitoring (t-LiP MRM), rather than a classical affinity purification strategy. Poly-ADP-ribose-polymerase-1 (PARP-1) emerged as the main crellastatin A cellular partner. This result was confirmed by both biochemical and in silico analyses. Further in vitro biological assays highlighted an interesting crellastatin A inhibitory activity on PARP-1.

Epigenetic drug target deconvolution by mass spectrometry-based technologies

The identification of the full target spectrum of active molecules, known as target deconvolution, has become an indispensable step during the drug discovery process. It is now achievable thanks to mass spectrometry-based technologies. Here we discuss these approaches in the context of epigenetic drug discovery.

Novel Computational Approach to Predict Off-Target Interactions for Small Molecules

Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.

Chemoproteomic identification of molecular targets of antifungal prototypes, thiosemicarbazide and a camphene derivative of thiosemicarbazide, in Paracoccidioides brasiliensis

Paracoccidioidomycosis (PCM) is a neglected human systemic disease caused by species of the genus Paracoccidioides. The disease attacks the host’s lungs and may disseminate to many other organs. Treatment involves amphotericin B, sulfadiazine, trimethoprim-sulfamethoxazole, itraconazole, ketoconazole, or fluconazole. The treatment duration is usually long, from 6 months to 2 years, and many adverse effects may occur in relation to the treatment; co-morbidities and poor treatment adherence have been noted. Therefore, the discovery of more effective and less toxic drugs is needed. Thiosemicarbazide (TSC) and a camphene derivative of thiosemicarbazide (TSC-C) were able to inhibit P. brasiliensis growth at a low dosage and were not toxic to fibroblast cells. In order to investigate the mode of action of those compounds, we used a chemoproteomic approach to determine which fungal proteins were bound to each of these compounds. The compounds were able to inhibit the activities of the enzyme formamidase and interfered in P. brasiliensis dimorphism. In comparison with the transcriptomic and proteomic data previously obtained by our group, we determined that TSC and TSC-C were multitarget compounds that exerted effects on the electron-transport chain and cell cycle regulation, increased ROS formation, inhibited proteasomes and peptidases, modulated glycolysis, lipid, protein and carbohydrate metabolisms, and caused suppressed the mycelium to yeast transition.

Target discovery focused approaches to overcome bottlenecks in the exploitation of antimycobacterial natural products

Tuberculosis is a major global health hazard. The search for new antimycobacterials has focused on such as screening combinational chemistry libraries or designing chemicals to target predefined pockets of essential bacterial proteins. The relative ineffectiveness of these has led to a reappraisal of natural products for new antimycobacterial drug leads. However, progress has been limited, we suggest through a failure in many cases to define the drug target and optimize the hits using this information. We highlight methods of target discovery needed to develop a drug into a candidate for clinical trials. We incorporate these into suggested analysis pipelines which could inform the research strategies to accelerate the development of new drug leads from natural products.

Chemical Proteomic Approaches Targeting Cancer Stem Cells: A Review of Current Literature

Cancer stem cells (CSCs) have been proposed as central drivers of tumor initiation, progression, recurrence, and therapeutic resistance. Therefore, identifying stem-like cells within cancers and understanding their properties is crucial for the development of effective anticancer therapies. Recently, chemical proteomics has become a powerful tool to efficiently determine protein networks responsible for CSC pathophysiology and comprehensively elucidate molecular mechanisms of drug action against CSCs. This review provides an overview of major methodologies utilized in chemical proteomic approaches. In addition, recent successful chemical proteomic applications targeting CSCs are highlighted. Future direction of potential CSC research by integrating chemical genomic and proteomic data obtained from a single biological sample of CSCs are also suggested in this review.

Influence of parameter perturbations on the reachability of therapeutic target in systems with switchings

Background

Examination of physiological processes and the influences of the drugs on them can be efficiently supported by mathematical modeling. One of the biggest problems is related to the exact fitting of the parameters of a model. Conditions inside the organism change dynamically, so the rates of processes are very difficult to estimate. Perturbations in the model parameters influence the steady state so a desired therapeutic goal may not be reached. Here we investigate the effect of parameter deviation on the steady state in three simple models of the influence of a therapeutic drug on its target protein. Two types of changes in the model parameters are taken into account: small perturbations in the system parameter values, and changes in the switching time of a specific parameter. Additionally, we examine the systems response in case of a drug concentration decreasing with time.

Results

The models which we analyze are simplified, because we want to avoid influences of complex dynamics on the results. A system with a negative feedback loop is the most robust and the most rapid, so it requires the largest drug dose but the effects are observed very quickly. On the other hand a system with positive feedback is very sensitive to changes, so small drug doses are sufficient to reach a therapeutic target. In systems without feedback or with positive feedback, perturbations in the model parameters have a bigger influence on the reachability of the therapeutic target than in systems with negative feedback. Drug degradation or inactivation in biological systems enforces multiple drug applications to maintain the level of a drug’s target under the desired threshold. The frequency of drug application should be fitted to the system dynamics, because the response velocity is tightly related to the therapeutic effectiveness and the time for achieving the goal.

Conclusions

Systems with different types of regulation vary in their dynamics and characteristic features. Depending on the feedback loop, different types of therapy may be the most appropriate, and deviations in the model parameters have different influences on the reachability of the therapeutic target.

FD5180, a Novel Protein Kinase Affinity Probe, and the Effect of Bead Loading on Protein Kinase Identification

The effects of compound loading on the identification of protein kinases (PKs) was examined using two previously reported sepharose-supported PK inhibitors (PKIs): bisindolylmaleimide X (S1) and CZC8004 (S2). Compound loadings of 0.1, 0.5, 2.5, 5, 10, 25, and 50% content and an ethanolamine-blocked control bead (no compound) were investigated. A 50% bead loading gave the highest level of PK identification for both S1 and S2, extracting 34 and 55 PKs, respectively, from a single cell lysate. Control beads allowed overall identification of 23 PKs, which we term the kinase beadome, whereas sepharose-supported sunitinib (S7; 50% loading) identified 20, 11 of which were common to the control beads. The reliability of bead pull-downs was examined in duplicate experiments using two independently synthesized batches each of S1 and S2. Bead S1 showed high similarity in the absolute numbers of PKs identified across two experiments, at 40 and 35 PKs, of which 26 were common across the two batches of beads, with 14 and 9 unique PKs identified in each experiment. The S2 beads extracted 61 and 64 PKs with 55 PKs common across the two bead batches examined. We also report on the development and use of a novel promiscuous PKI analogue, 2-[(5-chloro-2{[4-(piperazin-1-yl)phenyl]amino}pyrimidin-4-yl)amino]-N-methylbenzene-sulfonamide (S15), which extracted 12 additional unique PKs over the two parent compounds from which it was designed, the combination of which identifies 160 unique PKs. S15 was based on the common pyrimidine core scaffold of S9 and S10. Thus, S15 expands the utility of kinobeads by broadening the kinome coverage for bead-based pull-down. Combining the data for all beads across 90 and 180 min liquid chromatography-mass spectrometry (LC-MS)/MS analysis identified a total of 160 unique PKs.

Technological advances and proteomic applications in drug discovery and target deconvolution: identification of the pleiotropic effects of statins

Proteomic-based techniques provide a powerful tool for identifying the full spectrum of protein targets of a drug, elucidating its mechanism(s) of action, and identifying biomarkers of its efficacy and safety. Herein, we outline the technological advancements in the field, and illustrate the contribution of proteomics to the definition of the pharmacological profile of statins, which represent the cornerstone of the prevention and treatment of cardiovascular diseases (CVDs). Statins act by inhibiting 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase, thus reducing cholesterol biosynthesis and consequently enhancing the clearance of low-density lipoproteins from the blood; however, HMG-CoA reductase inhibition can result in a multitude of additional effects beyond lipid lowering, known as 'pleiotropic effects'. The case of statins highlights the unique contribution of proteomics to the target profiling of a drug molecule.

Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research

With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians’ point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world’s major diseases.

Chemical proteomic analysis of the potential toxicological mechanisms of microcystin-RR in zebrafish (Danio rerio) liver

Microcystins (MCs) are common toxins produced by freshwater cyanobacteria, and they represent a potential health risk to aquatic organisms and animals, including humans. Specific inhibition of protein phosphatases 1 and 2A is considered the typical mechanism of MCs toxicity, but the exact mechanism has not been fully elucidated. To further our understanding of the toxicological mechanisms induced by MCs, this study is the first to use a chemical proteomic approach to screen proteins that exhibit special interactions with MC-arginine-arginine (MC-RR) from zebrafish (Danio rerio) liver. Seventeen proteins were identified via affinity blocking test. Integration of the results of previous studies and this study revealed that these proteins play a crucial role in various toxic phenomena of liver induced by MCs, such as the disruption of cytoskeleton assembly, oxidative stress, and metabolic disorder. Moreover, in addition to inhibition of protein phosphate activity, the overall toxicity of MCs was simultaneously modulated by the distribution of MCs in cells and their interactions with other target proteins. These results provide new insight into the mechanisms of hepatotoxicity induced by MCs. © 2015 Wiley Periodicals, Inc. Environ Toxicol 31: 1206-1216, 2016.

Improved Deconvolution of Protein Targets for Bioactive Compounds Using a Palladium Cleavable Chloroalkane Capture Tag

The benefits provided by phenotypic screening of compound libraries are often countered by difficulties in identifying the underlying cellular targets. We recently described a new approach utilizing a chloroalkane capture tag, which can be chemically attached to bioactive compounds to facilitate the isolation of their respective targets for subsequent identification by mass spectrometry. The tag minimally affects compound potency and membrane permeability, enabling target engagement inside cells. Effective enrichment of these targets is achieved through selectivity in both their rapid capture onto immobilized HaloTag and their subsequent release by competitive elution. Here, we describe a significant improvement to this method where selective elution was achieved through palladium-catalyzed cleavage of an allyl-carbamate linkage incorporated into the chloroalkane capture tag. Selective tag cleavage provided robust release of captured targets exhibiting different modes of binding to the bioactive compound, including prolonged residence time and covalent interactions. Using the kinase inhibitors ibrutinib and BIRB796 as model compounds, we demonstrated the capability of this new method to identify both expected targets and "off-targets" exhibiting a range of binding affinities, cellular abundances, and binding characteristics.

Proteome-wide drug screening using mass spectrometric imaging of bead-arrays

A fundamental challenge in the drug discovery process is to develop compounds with high efficacy and minimal side-effects. We describe a new approach to proteome-wide drug screening for detection of on- and off-target binding which combines the advantages of mass spectrometry with microarray technology. The method involves matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) of agarose micro-beads randomly arrayed at high-density in custom micro-well plates. Each bead carries a unique protein target and a corresponding photocleavable mass-tag for coding (PC-Mass-Tag). Compounds bound to specific protein beads and a photo-released coding PC-Mass-Tag are detected simultaneously using MALDI-MSI. As an initial demonstration of this approach, two kinase-targeted drugs, Dasatinib and Brigatinib (AP26113), were simultaneously screened against a model 50-member kinase-bead library. A MALDI-MSI scan performed at the equivalent density of 495,000 beads in the footprint of a microscope slide yielded 100% sensitivity for detecting known strong interactions with no false positives.

Human DDX3 protein is a valuable target to develop broad spectrum antiviral agents

Targeting a host factor essential for the replication of different viruses but not for the cells offers a higher genetic barrier to the development of resistance, may simplify therapy regimens for coinfections, and facilitates management of emerging viral diseases. DEAD-box polypeptide 3 (DDX3) is a human host factor required for the replication of several DNA and RNA viruses, including some of the most challenging human pathogens currently circulating, such as HIV-1, Hepatitis C virus, Dengue virus, and West Nile virus. Herein, we showed for the first time, to our knowledge, that the inhibition of DDX3 by a small molecule could be successfully exploited for the development of a broad spectrum antiviral agent. In addition to the multiple antiviral activities, hit compound 16d retained full activity against drug-resistant HIV-1 strains in the absence of cellular toxicity. Pharmacokinetics and toxicity studies in rats confirmed a good safety profile and bioavailability of 16d. Thus, DDX3 is here validated as a valuable therapeutic target.

The contribution of mass spectrometry-based proteomics to understanding epigenetics

Chromatin is a macromolecular complex composed of DNA and histones that regulate gene expression and nuclear architecture. The concerted action of DNA methylation, histone post-translational modifications and chromatin-associated proteins control the epigenetic regulation of the genome, ultimately determining cell fate and the transcriptional outputs of differentiated cells. Deregulation of this complex machinery leads to disease states, and exploiting epigenetic drugs is becoming increasingly attractive for therapeutic intervention. Mass spectrometry (MS)-based proteomics emerged as a powerful tool complementary to genomic approaches for epigenetic research, allowing the unbiased and comprehensive analysis of histone post-translational modifications and the characterization of chromatin constituents and chromatin-associated proteins. Furthermore, MS holds great promise for epigenetic biomarker discovery and represents a useful tool for deconvolution of epigenetic drug targets. Here, we will provide an overview of the applications of MS-based proteomics in various areas of chromatin biology.

Deciphering the Cellular Targets of Bioactive Compounds Using a Chloroalkane Capture Tag

Phenotypic screening of compound libraries is a significant trend in drug discovery, yet success can be hindered by difficulties in identifying the underlying cellular targets. Current approaches rely on tethering bioactive compounds to a capture tag or surface to allow selective enrichment of interacting proteins for subsequent identification by mass spectrometry. Such methods are often constrained by ineffective capture of low affinity and low abundance targets. In addition, these methods are often not compatible with living cells and therefore cannot be used to verify the pharmacological activity of the tethered compounds. We have developed a novel chloroalkane capture tag that minimally affects compound potency in cultured cells, allowing binding interactions with the targets to occur under conditions relevant to the desired cellular phenotype. Subsequent isolation of the interacting targets is achieved through rapid lysis and capture onto immobilized HaloTag protein. Exchanging the chloroalkane tag for a fluorophore, the putative targets identified by mass spectrometry can be verified for direct binding to the compound through resonance energy transfer. Using the interaction between histone deacetylases (HDACs) and the inhibitor, Vorinostat (SAHA), as a model system, we were able to identify and verify all the known HDAC targets of SAHA as well as two previously undescribed targets, ADO and CPPED1. The discovery of ADO as a target may provide mechanistic insight into a reported connection between SAHA and Huntington's disease.