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

IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data

IMPRINTS-CETSA (Integrated Modulation of Protein Interaction States-Cellular Thermal Shift Assay) provides a highly resolved means to systematically study the interactions of proteins with other cellular components, including metabolites, nucleic acids and other proteins, at the proteome level, but no freely available and user-friendly data analysis software has been reported. Here, we report IMPRINTS.CETSA, an R package that provides the basic data processing framework for robust analysis of the IMPRINTS-CETSA data format, from preprocessing and normalization to visualization. We also report an accompanying R package, IMPRINTS.CETSA.app, which offers a user-friendly Shiny interface for analysis and interpretation of IMPRINTS-CETSA results, with seamless features such as functional enrichment and mapping to other databases at a single site. For the hit generation part, the diverse behaviors of protein modulations have been typically segregated with a two-measure scoring method, i.e. the abundance and thermal stability changes. We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app, respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134.

Arteannuin B, a sesquiterpene lactone from Artemisia annua, attenuates inflammatory response by inhibiting the ubiquitin-conjugating enzyme UBE2D3-mediated NF-κB activation

BACKGROUND

Anomalous activation of NF-κB signaling is associated with many inflammatory disorders, such as ulcerative colitis (UC) and acute lung injury (ALI). NF-κB activation requires the ubiquitination of receptor-interacting protein 1 (RIP1) and NF-κB essential modulator (NEMO). Therefore, inhibition of ubiquitation of RIP1 and NEMO may serve as a potential approach for inhibiting NF-κB activation and alleviating inflammatory disorders.

PURPOSE

Here, we identified arteannuin B (ATB), a sesquiterpene lactone found in the traditional Chinese medicine Artemisia annua that is used to treat malaria and inflammatory diseases, as a potent anti-inflammatory compound, and then characterized the putative mechanisms of its anti-inflammatory action.

METHODS

Detections of inflammatory mediators and cytokines in LPS- or TNF-α-stimulated murine macrophages using RT-qPCR, ELISA, and western blotting, respectively. Western blotting, CETSA, DARTS, MST, gene knockdown, LC-MS/MS, and molecular docking were used to determine the potential target and molecular mechanism of ATB. The pharmacological effects of ATB were further evaluated in DSS-induced colitis and LPS-induced ALI in vivo.

RESULTS

ATB effectively diminished the generation of NO and PGE2 by down-regulating iNOS and COX2 expression, and decreased the mRNA expression and release of IL-1β, IL-6, and TNF-α in LPS-exposed RAW264.7 macrophages. The anti-inflammatory effect of ATB was further demonstrated in LPS-treated BMDMs and TNF-α-activated RAW264.7 cells. We further found that ATB obviously inhibited NF-κB activation induced by LPS or TNF-α in vitro. Moreover, compared with ATB, dihydroarteannuin B (DATB) which lost the unsaturated double bond, completely failed to repress LPS-induced NO release and NF-κB activation in vitro. Furthermore, UBE2D3, a ubiquitin-conjugating enzyme, was identified as the functional target of ATB, but not DATB. UBE2D3 knockdown significantly abolished ATB-mediated inhibition on LPS-induced NO production. Mechanistically, ATB could covalently bind to the catalytic cysteine 85 of UBE2D3, thereby inhibiting the function of UBE2D3 and preventing ubiquitination of RIP1 and NEMO. In vivo, ATB treatment exhibited robust protective effects against DSS-induced UC and LPS-induced ALI.

CONCLUSION

Our findings first demonstrated that ATB exerted anti-inflammatory functions by repression of NF-κB pathway via covalently binding to UBE2D3, and raised the possibility that ATB could be effective in the treatment of inflammatory diseases and other diseases associated with abnormal NF-κB activation.

Target discovery of bioactive natural products with native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB): Applications, mechanisms and outlooks

Natural products (NPs) represent a treasure trove for drug discovery and development due to their chemical structural diversity and a broad spectrum of biological activities. Uncovering the biological targets and understanding their molecular mechanism of actions are crucial steps in the development of clinical therapeutics. However, the structural complexity of NPs and intricate nature of biological system present formidable challenges in target identification of NPs. Although significant advances have been made in the development of new chemical tools, these methods often require high levels of synthetic skills for preparing chemical probes. This can be costly and time-consuming relaying on operationally complicated procedures and instruments. In recent efforts, we and others have successfully developed an operationally simple and practical chemical tool known as native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB) for NP target identification. In this approach, a native compound readily reacts with commercial CNBr-activated Sepharose 4B beads with a process that is easily performed in any biology laboratory. Based on NCCB, our group has identified the direct targets of more than 60 NPs. In this review, we will elucidate the application scopes, including flavonoids, quinones, terpenoids and others, characteristics, chemical mechanisms, procedures, advantages, disadvantages, and future directions of NCCB in specific target discovery.

Target identification of small molecules: an overview of the current applications in drug discovery

Target identification is an essential part of the drug discovery and development process, and its efficacy plays a crucial role in the success of any given therapy. Although protein target identification research can be challenging, two main approaches can help researchers make significant discoveries: affinity-based pull-down and label-free methods. Affinity-based pull-down methods use small molecules conjugated with tags to selectively isolate target proteins, while label-free methods utilize small molecules in their natural state to identify targets. Target identification strategy selection is essential to the success of any drug discovery process and must be carefully considered when determining how to best pursue a specific project. This paper provides an overview of the current target identification approaches in drug discovery related to experimental biological assays, focusing primarily on affinity-based pull-down and label-free approaches, and discusses their main limitations and advantages.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Applications of machine learning in microbial natural product drug discovery

INTRODUCTION

Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development.

AREAS COVERED

This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery.

EXPERT OPINION

Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.

CETSA and thermal proteome profiling strategies for target identification and drug discovery of natural products

BACKGROUND

Monitoring target engagement at various stages of drug development is essential for natural product (NP)-based drug discovery and development. The cellular thermal shift assay (CETSA) developed in 2013 is a novel, broadly applicable, label-free biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins, which enables direct assessment of drug-target engagement in physiologically relevant contexts, including intact cells, cell lysates and tissues. This review aims to provide an overview of the work principles of CETSA and its derivative strategies and their recent progress in protein target validation, target identification and drug lead discovery of NPs.

METHODS

A literature-based survey was conducted using the Web of Science and PubMed databases. The required information was reviewed and discussed to highlight the important role of CETSA-derived strategies in NP studies.

RESULTS

After nearly ten years of upgrading and evolution, CETSA has been mainly developed into three formats: classic Western blotting (WB)-CETSA for target validation, thermal proteome profiling (TPP, also known as MS-CETSA) for unbiased proteome-wide target identification, and high-throughput (HT)-CETSA for drug hit discovery and lead optimization. Importantly, the application possibilities of a variety of TPP approaches for the target discovery of bioactive NPs are highlighted and discussed, including TPP-temperature range (TPP-TR), TPP-compound concentration range (TPP-CCR), two-dimensional TPP (2D-TPP), cell surface-TPP (CS-TPP), simplified TPP (STPP), thermal stability shift-based fluorescence difference in 2D gel electrophoresis (TS-FITGE) and precipitate supported TPP (PSTPP). In addition, the key advantages, limitations and future outlook of CETSA strategies for NP studies are discussed.

CONCLUSION

The accumulation of CETSA-based data can significantly accelerate the elucidation of the mechanism of action and drug lead discovery of NPs, and provide strong evidence for NP treatment against certain diseases. The CETSA strategy will certainly bring a great return far beyond the initial investment and open up more possibilities for future NP-based drug research and development.

The toxic natural product tutin causes epileptic seizures in mice by activating calcineurin

Tutin, an established toxic natural product that causes epilepsy in rodents, is often used as a tool to develop animal model of acute epileptic seizures. However, the molecular target and toxic mechanism of tutin were unclear. In this study, for the first time, we conducted experiments to clarify the targets in tutin-induced epilepsy using thermal proteome profiling. Our studies showed that calcineurin (CN) was a target of tutin, and that tutin activated CN, leading to seizures. Binding site studies further established that tutin bound within the active site of CN catalytic subunit. CN inhibitor and calcineurin A (CNA) knockdown experiments in vivo proved that tutin induced epilepsy by activating CN, and produced obvious nerve damage. Together, these findings revealed that tutin caused epileptic seizures by activating CN. Moreover, further mechanism studies found that N-methyl-D-aspartate (NMDA) receptors, gamma-aminobutyric acid (GABA) receptors and voltage- and Ca²⁺- activated K⁺ (BK) channels might be involved in related signaling pathways. Our study fully explains the convulsive mechanism of tutin, which provides new ideas for epilepsy treatment and drug development.

Identification of the target protein and molecular mechanism of honokiol in anti-inflammatory action

BACKGROUND

Searching the targets of natural products is very important for drug discovery and elucidating the mechanism of drug action and disease. Honokiol (HK), as the major active component of Magnolia officinalis Rehder & E.H.Wilson, has been widely used in medicine and cosmetics. Among its bioactivities, its anti-inflammatory activity is particularly impressive. However, the target protein of HK in anti-inflammatory action and its regulatory mechanism are unclear.

PURPOSE

Here, we identified the target protein and molecular mechanism of the anti- inflammatory action of HK.

METHODS

First, an LPS-induced septic shock model and DSS-induced ulcerative colitis model were used to assess the anti-inflammatory efficacy of HK. Second, the drug affinity responsive target stability, proteomics analysis, thermal shift assays and cellular thermal shift assays were used to identify and validate the target of HK. Finally, western blot, ELISA, LDH immunofluorescence staining, shRNA and LC/MS for L-leucine analysis were performed to determine the mechanism of the anti-inflammatory action of HK.

RESULTS

This study revealed that HK significantly alleviated LPS-induced septic shock and DSS-induced ulcerative colitis in vivo, suggesting that HK has significant anti-inflammatory activity. HK treatment dramatically reduced IL-1β release and caspase-1 activation at different time points, showing that HK could inhibit both NLRP3 inflammasome priming and activation processes in cells. HK also suppressed adaptor apoptosis speck-like protein oligomerization. Mechanistically, SLC3A2 was identified as a direct target of HK in THP-1 cells. HK downregulated SLC3A2 expression by promoting its degradation via proteasome-mediated proteolysis. Further study demonstrated that HK triggered SLC3A2 to suppress NLRP3 inflammasome activation by significantly reducing the content of L-leucine transported into cells and lysosomes to block the mTORC1 pathway.

CONCLUSIONS

Our work identified HK as a promising anti-inflammatory drug candidate through the SLC3A2/L-leucine/mTORC1/NLRP3 pathways.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Identification of anthelmintic parbendazole as a therapeutic molecule for HNSCC through connectivity map-based drug repositioning

Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers; however, its outcome of pharmacotherapy is always very limited. Herein, we performed a batch query in the connectivity map (cMap) based on bioinformatics, queried out 35 compounds with therapeutic potential, and screened out parbendazole as a most promising compound, which had an excellent inhibitory effect on the proliferation of HNSCC cell lines. In addition, tubulin was identified as a primary target of parbendazole, and the direct binding between them was further verified. Parbendazole was further proved as an effective tubulin polymerization inhibitor, which can block the cell cycle, cause apoptosis and prevent cell migration, and it exhibited reasonable therapeutic effect and low toxicity in the in vivo and in vitro anti-tumor evaluation. Our study repositioned an anthelmintic parbendazole to treat HNSCC, which revealed a therapeutic utility and provided a new treatment option for human cancers.

Induction of Genes Implicated in Stress Response and Autophagy by a Novel Quinolin-8-yl-nicotinamide QN523 in Pancreatic Cancer

Using a cytotoxicity-based phenotypic screen of a highly diverse library of 20,000 small-molecule compounds, we identified a quinolin-8-yl-nicotinamide, QN519, as a promising lead. QN519 represents a novel scaffold with drug-like properties, showing potent in vitro cytotoxicity in a panel of 12 cancer cell lines. Subsequently, lead optimization campaign generated compounds with IC50 values < 1 μM. An optimized compound, QN523, shows significant in vivo efficacy in a pancreatic cancer xenograft model. QN523 treatment significantly increased the expression of HSPA5, DDIT3, TRIB3, and ATF3 genes, suggesting activation of the stress response pathway. We also observed a significant increase in the expression of WIPI1, HERPUD1, GABARAPL1, and MAP1LC3B, implicating autophagy as a major mechanism of action. Due to the lack of effective treatments for pancreatic cancer, discovery of novel agents such as the QN series of compounds with unique mechanism of action has the potential to fulfill a clear unmet medical need.

Target Characterization of Kaempferol against Myocardial Infarction Using Novel In Silico Docking and DARTS Prediction Strategy

Target identification is a crucial process for advancing natural products and drug leads development, which is often the most challenging and time-consuming step. However, the putative biological targets of natural products obtained from traditional prediction studies are also informatively redundant. Thus, how to precisely identify the target of natural products is still one of the major challenges. Given the shortcomings of current target identification methodologies, herein, a novel in silico docking and DARTS prediction strategy was proposed. Concretely, the possible molecular weight was detected by DARTS method through examining the protected band in SDS-PAGE. Then, the potential targets were obtained from screening and identification through the PharmMapper Server and TargetHunter method. In addition, the candidate target Src was further validated by surface plasmon resonance assay, and the anti-apoptosis effects of kaempferol against myocardial infarction were further confirmed by in vitro and in vivo assays. Collectively, these results demonstrated that the integrated strategy could efficiently characterize the targets, which may shed a new light on target identification of natural products.

Identifying Protein-Drug Interactions in Cell Lysates Using Histidine Hydrogen Deuterium Exchange

Identifying the targets of a drug is critical to understand the mechanism of action and predicts possible side effects. The conventional approach is capturing interacting proteins by affinity purification. However, it requires drugs to be immobilized to a solid support or derivatized with chemical moieties used for pulling down interacting proteins. Such covalent modifications to drugs may mask a critical recognition site for or alter the binding affinity to their targets. To overcome the drawback, several methods that do not require covalent modifications to drugs have been developed. These methods identify targets by detecting proteins whose thermodynamic stability is enhanced in the presence of drugs. Although the utility of these methods has been demonstrated, the difficulty in identifying low abundant targets is the common problem of these methods. We have developed a new target identification method that increases the likelihood of identifying low abundant targets. The method uses histidine-hydrogen deuterium exchange (His-HDX) as a readout technique to probe the changes in protein stability induced by drugs. The workflow involves incubating cell lysates in various concentrations of a protein denaturant in the presence and absence of a drug in D₂O followed by digestion of the proteins, enrichment of His-containing peptides, and analysis of the enriched His-peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The developed method was successfully applied to identify the interaction between endogenously expressed MAPK14 and its inhibitor in HEK293 cell lysates. The implementation of selective enrichment of histidine-containing peptides in the workflow was a key that enabled identifying the MAPK14-inhibitor interaction.

Acacetin Inhibits the Growth of STAT3-Activated DU145 Prostate Cancer Cells by Directly Binding to Signal Transducer and Activator of Transcription 3 (STAT3)

Signal transducer and activator of transcription 3 (STAT3) plays a critical role in the formation and growth of human cancer. Therefore, STAT3 is a therapeutic target for cancer drug discovery. Acacetin, a flavone present in various plants, inhibits constitutive and inducible STAT3 activation in STAT3-activated DU145 prostate cancer cells. Acacetin inhibits STAT3 activity by directly binding to STAT3, which we confirmed by a pull-down assay with a biotinylated compound and two level-free methods, namely, a drug affinity responsive target stability (DARTS) experiment and a cellular thermal shift assay (CETSA). Acacetin inhibits STAT3 phosphorylation at the tyrosine 705 residue and nuclear translocation in DU145 cells, which leads to the downregulation of STAT3 target genes. Acacetin then induces apoptosis in a time-dependent manner. Interestingly, acacetin induces the production of reactive oxygen species (ROS) that are not involved in the acacetin-induced inhibition of STAT3 activation because the suppressed p-STAT3 level is not rescued by treatment with GSH or NAC, which are general ROS inhibitors. We also found that acacetin inhibits tumor growth in xenografted nude mice. These results suggest that acacetin, as a STAT3 inhibitor, could be a possible drug candidate for targeting STAT3 for the treatment of cancer in humans.

Proteomics-based target identification of natural products affecting cancer metabolism

The Warburg effect, a widely known characteristic of cancer cells, refers to the utilization of glycolysis under aerobic conditions for extended periods of time. Recent studies have revealed that cancer cells are capable of reprogramming their metabolic pathways to meet vigorous metabolic demands. New anticancer drugs that target the complicated metabolic systems of cancer cells are being developed. Identifying the potential targets of novel compounds that affect cancer metabolism may enable the discovery of new therapeutic targets for cancer treatment, and hasten the development of anticancer drugs. Historically, various drug screening techniques such as the analysis of a compound's antiproliferative effect on cancer cells and proteomic methods, that enable target identification have been used to obtain many useful drugs from natural products. Here, we review proteomics-based target identification methods applicable to natural products that affect cancer metabolism.

Callyspongiolide kills cells by inducing mitochondrial dysfunction via cellular iron depletion

The highly cytotoxic marine natural product callyspongiolide holds great promise as a warhead of antibody-drug conjugate in cancer therapeutics; however, the mechanism underlying its cytotoxicity remains unclear. To elucidate how callyspongiolide kills cells, we employed label-free target identification with thermal stability-shift-based fluorescence difference in two-dimensional (2-D) gel electrophoresis (TS-FITGE), which allowed observation of a unique phenomenon of protein-spot separation on 2-D gels upon treatment with callyspongiolide at increasing temperatures. During our exploration of what proteins were associated with this phenomenon as well as why it happens, we found that callyspongiolide induces mitochondrial/lysosomal dysfunction and autophagy inhibition. Moreover, molecular biology studies revealed that callyspongiolide causes lysosomal dysfunction, which induces cellular iron depletion and leads to mitochondrial dysfunction and subsequent cytotoxicity. Notably, these effects were rescued through iron supplementation. Although our approach was unable to reveal the direct protein targets of callyspongiolide, unique phenomena observed only by TS-FITGE provided critical insight into the mechanism of action of callyspongiolide and specifically its cytotoxic activity via induction of mitochondrial dysfunction through cellular iron depletion caused by lysosomal deacidification, which occurred independent of known programmed cell death pathways.

In order to elucidate how callyspongiolide, a potent cytotoxic marine natural product, kills human lung cancer cells, Ha and Park employed TS-FITGE technique, a label-free target identification method with thermal stability-shift-based fluorescence difference in 2-D gel electrophoresis, allowing them to observe protein-spot separation upon treatment in increasing temperatures. They found that callyspongiolide induces lysosomal dysfunction followed by mitochondrial dysfunction as well as iron depletion, which sheds light on the mechanism of action of callyspongiolide.

A Comparison of Two Stability Proteomics Methods for Drug Target Identification in OnePot 2D Format

Stability proteomics techniques that do not require drug modifications have emerged as an attractive alternative to affinity purification methods in drug target engagement studies. Two representative techniques include the chemical-denaturation-based SPROX (Stability of Proteins from Rates of Oxidation), which utilizes peptide-level quantification and thermal-denaturation-based TPP (Thermal Proteome Profiling), which utilizes protein-level quantification. Recently, the "OnePot" strategy was adapted for both SPROX and TPP to increase the throughput. When combined with the 2D setup which measures both the denaturation and the drug dose dimensions, the OnePot 2D format offers improved analysis specificity with higher resource efficiency. However, a systematic evaluation of the OnePot 2D format and a comparison between SPROX and TPP are still lacking. Here, we performed SPROX and TPP to identify protein targets of a well-studied pan-kinase inhibitor staurosporine with K562 lysate, in curve-fitting and OnePot 2D formats. We found that the OnePot 2D format provided ∼10× throughput, achieved ∼1.6× protein coverage and involves more straightforward data analysis. We also compared SPROX with the current "gold-standard" stability proteomics technique TPP in the OnePot 2D format. The protein coverage of TPP is ∼1.5 fold of SPROX; however, SPROX offers protein domain-level information, identifies comparable numbers of kinase hits, has higher signal (R value), and requires ∼3× less MS time. Unique SPROX hits encompass higher-molecular-weight proteins, compared to the unique TPP hits, and include atypical kinases. We also discuss hit stratification and prioritization strategies to promote the efficiency of hit followup.

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.

System Biology-Guided Chemical Proteomics to Discover Protein Targets of Monoethylhexyl Phthalate in Regulating Cell Cycle

Chemical proteomics methods have been used as effective tools to identify novel protein targets for small molecules. These methods have great potential to be applied as environmental toxicants to figure out their mode of action. However, these assays usually generate dozens of possible targets, making it challenging to validate the most important one. In this study, we have integrated the cellular thermal shift assay (CETSA), quantitative proteomics, metabolomics, computer-assisted docking, and target validation methods to uncover the protein targets of monoethylhexyl phthalate (MEHP). Using the mass spectrometry implementation of CETSA (MS-CETSA), we have identified 74 possible protein targets of MEHP. The Gene Ontology (GO) enrichment integration was further conducted for the target proteins, the cellular dysregulated proteins, and the metabolites, showing that cell cycle dysregulation could be one primary change due to the MEHP-induced toxicity. Flow cytometry analysis confirmed that hepatocytes were arrested at the G1 stage due to the treatment with MEHP. Subsequently, the potential protein targets were ranked by their binding energy calculated from the computer-assisted docking with MEHP. In summary, we have demonstrated the development of interactomics workflow to simplify the redundant information from multiomics data and identified novel cell cycle regulatory protein targets (CPEB4, ANAPC5, and SPOUT1) for MEHP.

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no "one size fits all" approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.