Target identification and mechanism of action in chemical biology and drug discovery

Opportunities and challenges of protein-based targeted protein degradation

In the 20 years since the first report of a proteolysis targeting chimeric (PROTAC) molecule, targeted protein degradation (TPD) technologies have attempted to revolutionize the fields of chemical biology and biomedicine by providing exciting research opportunities and potential therapeutics. However, they primarily focus on the use of small molecules to recruit the ubiquitin proteasome system to mediate target protein degradation. This then limits protein targets to cytosolic domains with accessible and suitable small molecule binding pockets. In recent years, biologics such as proteins and nucleic acids have instead been used as binders for targeting proteins, thereby expanding the scope of TPD platforms to include secreted proteins, transmembrane proteins, and soluble but highly disordered intracellular proteins. This perspective summarizes the recent TPD platforms that utilize nanobodies, antibodies, and other proteins as binding moieties to deplete challenging targets, either through the ubiquitin proteasome system or the lysosomal degradation pathway. Importantly, the perspective also highlights opportunities and remaining challenges of current protein-based TPD technologies.

FDW028, a novel FUT8 inhibitor, impels lysosomal proteolysis of B7-H3 via chaperone-mediated autophagy pathway and exhibits potent efficacy against metastatic colorectal cancer

Metastatic colorectal cancer (mCRC) is a major cause of cancer-related mortality due to the absence of effective therapeutics. Thus, it is urgent to discover new drugs for mCRC. Fucosyltransferase 8 (FUT8) is a potential therapeutic target with high level in most malignant cancers including CRC. FUT8 mediates the core fucosylation of CD276 (B7-H3), a key immune checkpoint molecule (ICM), in CRC. FUT8-silence-induced defucosylation at N104 on B7-H3 attracts heat shock protein family A member 8 (HSPA8, also known as HSC70) to bind with 106-110 SLRLQ motif and consequently propels lysosomal proteolysis of B7-H3 through the chaperone-mediated autophagy (CMA) pathway. Then we report the development and characterization of a potent and highly selective small-molecule inhibitor of FUT8, named FDW028, which evidently prolongs the survival of mice with CRC pulmonary metastases (CRPM). FDW028 exhibits potent anti-tumor activity by defucosylation and impelling lysosomal degradation of B7-H3 through the CMA pathway. Taken together, FUT8 inhibition destabilizes B7-H3 through CMA-mediated lysosomal proteolysis, and FDW028 acts as a potent therapeutic candidate against mCRC by targeting FUT8. FDW028, an inhibitor specifically targeted FUT8, promotes defucosylation and consequent HSC70/LAMP2A-mediated lysosomal degradation of B7-H3, and exhibits potent anti-mCRC activities.

μMap Photoproximity Labeling Enables Small Molecule Binding Site Mapping

The characterization of ligand binding modes is a crucial step in the drug discovery process and is especially important in campaigns arising from phenotypic screening, where the protein target and binding mode are unknown at the outset. Elucidation of target binding regions is typically achieved by X-ray crystallography or photoaffinity labeling (PAL) approaches; yet, these methods present significant challenges. X-ray crystallography is a mainstay technique that has revolutionized drug discovery, but in many cases structural characterization is challenging or impossible. PAL has also enabled binding site mapping with peptide- and amino-acid-level resolution; however, the stoichiometric activation mode can lead to poor signal and coverage of the resident binding pocket. Additionally, each PAL probe can have its own fragmentation pattern, complicating the analysis by mass spectrometry. Here, we establish a robust and general photocatalytic approach toward the mapping of protein binding sites, which we define as identification of residues proximal to the ligand binding pocket. By utilizing a catalytic mode of activation, we obtain sets of labeled amino acids in the proximity of the target protein binding site. We use this methodology to map, in vitro, the binding sites of six protein targets, including several kinases and molecular glue targets, and furthermore to investigate the binding site of the STAT3 inhibitor MM-206, a ligand with no known crystal structure. Finally, we demonstrate the successful mapping of drug binding sites in live cells. These results establish μMap as a powerful method for the generation of amino-acid- and peptide-level target engagement data.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

Navigating drug repurposing for Chagas disease: advances, challenges, and opportunities

Chagas disease is a vector-borne illness caused by the protozoan parasite Trypanosoma cruzi (T. cruzi). It poses a significant public health burden, particularly in the poorest regions of Latin America. Currently, there is no available vaccine, and chemotherapy has been the traditional treatment for Chagas disease. However, the treatment options are limited to just two outdated medicines, nifurtimox and benznidazole, which have serious side effects and low efficacy, especially during the chronic phase of the disease. Collectively, this has led the World Health Organization to classify it as a neglected disease. To address this problem, new drug regimens are urgently needed. Drug repurposing, which involves the use of existing drugs already approved for the treatment of other diseases, represents an increasingly important option. This approach offers potential cost reduction in new drug discovery processes and can address pharmaceutical bottlenecks in the development of drugs for Chagas disease. In this review, we discuss the state-of-the-art of drug repurposing approaches, including combination therapy with existing drugs, to overcome the formidable challenges associated with treating Chagas disease. Organized by original therapeutic area, we describe significant recent advances, as well as the challenges in this field. In particular, we identify candidates that exhibit potential for heightened efficacy and reduced toxicity profiles with the ultimate objective of accelerating the development of new, safe, and effective treatments for Chagas disease.

APDB: a database on air pollutant characterization and similarity prediction

The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood-brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it.

Identification of inhibitors for the transmembrane Trypanosoma cruzi eIF2α kinase relevant for parasite proliferation

The TcK2 protein kinase of Trypanosoma cruzi, the causative agent of Chagas disease, is structurally similar to the human kinase PERK, which phosphorylates the initiation factor eIF2α and, in turn, inhibits translation initiation. We have previously shown that absence of TcK2 kinase impairs parasite proliferation within mammalian cells, positioning it as a potential target for treatment of Chagas disease. To better understand its role in the parasite, here we initially confirmed the importance of TcK2 in parasite proliferation by generating CRISPR/Cas9 TcK2-null cells, albeit they more efficiently differentiate into infective forms. Proteomics indicates that the TcK2 knockout of proliferative forms expresses proteins including trans-sialidases, normally restricted to infective and nonproliferative trypomastigotes explaining decreased proliferation and better differentiation. TcK2 knockout cells lost phosphorylation of eukaryotic initiation factor 3 and cyclic AMP responsive-like element, recognized to promote growth, likely explaining both decreased proliferation and augmented differentiation. To identify specific inhibitors, a library of 379 kinase inhibitors was screened by differential scanning fluorimetry using a recombinant TcK2 encompassing the kinase domain and selected molecules were tested for kinase inhibition. Only Dasatinib and PF-477736, inhibitors of Src/Abl and ChK1 kinases, showed inhibitory activity with IC50 of 0.2 ± 0.02 mM and 0.8 ± 0.1, respectively. In infected cells Dasatinib inhibited growth of parental amastigotes (IC50 = 0.6 ± 0.2 mM) but not TcK2 of depleted parasites (IC50 > 34 mM) identifying Dasatinib as a potential lead for development of therapeutics for Chagas disease targeting TcK2.

In Silico Discovery of 5'-Modified 7-Deoxy-7-ethynyl-4'-thioadenosine as a HASPIN Inhibitor and Its Synergistic Anticancer Effect with the PLK1 Inhibitor

Despite genetic perturbations resulting in embryo lethality for most mitotic kinases, loss of the histone H3 mitotic kinase HASPIN reveals no adverse effect in mice models, establishing HASPIN as a promising target for anticancer therapy. However, developing a HASPIN inhibitor from conventional pharmacophores poses a technical challenge as this atypical kinase shares slight similarities with eukaryotic protein kinases. Chemically modifying a cytotoxic 4'-thioadenosine analogue through high genotoxicity yielded several novel nongenotoxic kinase inhibitors. In silico apporoaches utilizing transcriptomic and chemical similarities with known compounds and KINOMEscan profiles unveiled the HASPIN inhibitor LJ4827. LJ4827's specificity and potency as a HASPIN inhibitor were verified through in vitro kinase assay and X-ray crystallography. HASPIN inhibition by LJ4827 reduced histone H3 phosphorylation and impeded Aurora B recruitment in cancer cell centromeres but not in noncancer cells. Through transcriptome analysis of lung cancer patients, PLK1 was determined as a druggable synergistic partner to complement HASPIN inhibition. Chemical or genetic PLK1 perturbation with LJ4827 effectuated pronounced lung cancer cytotoxicity in vitro and in vivo. Therefore, LJ4827 is a novel anticancer therapeutic for selectively impeding cancer mitosis through potent HASPIN inhibition, and simultaneous HASPIN and PLK1 interference is a promising therapeutic strategy for lung cancer.

SERS Endoscopy for Monitoring Intracellular Drug Dynamics

Understanding the dynamics and distribution of medicinal drugs in living cells is essential for the design and discovery of treatments. The tools available for revealing this information are, however, extremely limited. Here, we report the application of surface-enhanced Raman scattering (SERS) endoscopy, using plasmonic nanowires as SERS probes, to monitor the intracellular fate and dynamics of a common chemo-drug, doxorubicin, in A549 cancer cells. The unique spatio-temporal resolution of this technique reveals unprecedented information on the mode of action of doxorubicin: its localization in the nucleus, its complexation with medium components, and its intercalation with DNA as a function of time. Notably, we were able to discriminate these factors for the direct administration of doxorubicin or the use of a doxorubicin delivery system. The results reported here show that SERS endoscopy may have an important future role in medicinal chemistry for studying the dynamics and mechanism of action of drugs in cells.

The Introduction of Detergents in Thermal Proteome Profiling Requires Lowering the Applied Temperatures for Efficient Target Protein Identification

Although the use of detergents in thermal proteome profiling (TPP) has become a common practice to identify membrane protein targets in complex biological samples, surprisingly, there is no proteome-wide investigation into the impacts of detergent introduction on the target identification performance of TPP. In this study, we assessed the target identification performance of TPP in the presence of a commonly used non-ionic detergent or a zwitterionic detergent using a pan-kinase inhibitor staurosporine, our results showed that the addition of either of these detergents significantly impaired the identification performance of TPP at the optimal temperature for soluble target protein identification. Further investigation showed that detergents destabilized the proteome and increased protein precipitation. By lowering the applied temperature point, the target identification performance of TPP with detergents is significantly improved and is comparable to that in the absence of detergents. Our findings provide valuable insight into how to select the appropriate temperature range when detergents are used in TPP. In addition, our results also suggest that the combination of detergent and heat may serve as a novel precipitation-inducing force that can be applied for target protein identification.

A proteome-wide atlas of drug mechanism of action

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

NFX1-123: A potential therapeutic target in cervical cancer

NFX1-123 is a splice variant isoform of the NFX1 gene. It is highly expressed in cervical cancers caused by HPV, and NFX1-123 is a protein partner with the HPV oncoprotein E6. Together, NFX1-123 and E6 affect cellular growth, longevity, and differentiation. The expression status of NFX1-123 in cancers beyond cervical and head and neck cancers, and its potential as therapeutic target, have not been investigated. TSVdb of TCGA was used to quantify NFX1-123 expression in 24 cancers compared with normal tissues. The NFX1-123 protein structure was predicted and then submitted to retrieve suitable drug molecules. The top four compounds, found to bind in silico to NFX1-123, were tested experimentally to determine their effects on NFX1-123-related cellular growth, survival, and migration. 46% of cancers (11 of 24 had significant differences in NFX1-123 expression, with nine having had greater NFX1-123 expression, when compared with adjacent normal tissues. Bioinformatics and proteomic predictive analysis modeled the three-dimensional structure of NFX1-123, and drug libraries were screened for high-binding affinity compounds using this modeled structure. Seventeen drugs with binding energies ranging from -1.3 to -10 Kcal/mol were identified. The top four compounds were used to treat HPV- and HPV+ cervical cancer cell lines, three of which (Ropitoin, R428 and Ketoconazole) reduced NFX1-123 protein levels, inhibited cellular growth, survival, and migration, and enhanced the cytotoxicity of Cisplatin. These findings highlight cancers expressing high levels of NFX1-123, and drugs that target it, may reduce cellular growth, survival, and migration, making NFX1-123 a potential novel therapeutic target.

Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models

Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.

Next Generation Gold Drugs and Probes: Chemistry and Biomedical Applications

The gold drugs, gold sodium thiomalate (Myocrisin), aurothioglucose (Solganal), and the orally administered auranofin (Ridaura), are utilized in modern medicine for the treatment of inflammatory arthritis including rheumatoid and juvenile arthritis; however, new gold agents have been slow to enter the clinic. Repurposing of auranofin in different disease indications such as cancer, parasitic, and microbial infections in the clinic has provided impetus for the development of new gold complexes for biomedical applications based on unique mechanistic insights differentiated from auranofin. Various chemical methods for the preparation of physiologically stable gold complexes and associated mechanisms have been explored in biomedicine such as therapeutics or chemical probes. In this Review, we discuss the chemistry of next generation gold drugs, which encompasses oxidation states, geometry, ligands, coordination, and organometallic compounds for infectious diseases, cancer, inflammation, and as tools for chemical biology via gold-protein interactions. We will focus on the development of gold agents in biomedicine within the past decade. The Review provides readers with an accessible overview of the utility, development, and mechanism of action of gold-based small molecules to establish context and basis for the thriving resurgence of gold in medicine.

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 critical role of mode of action studies in kinetoplastid drug discovery

Understanding the target and mode of action of compounds identified by phenotypic screening can greatly facilitate the process of drug discovery and development. Here, we outline the tools currently available for target identification against the neglected tropical diseases, human African trypanosomiasis, visceral leishmaniasis and Chagas' disease. We provide examples how these tools can be used to identify and triage undesirable mechanisms, to identify potential toxic liabilities in patients and to manage a balanced portfolio of target-based campaigns. We review the primary targets of drugs that are currently in clinical development that were initially identified via phenotypic screening, and whose modes of action affect protein turnover, RNA trans-splicing or signalling in these protozoan parasites.

A multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN

Giant axonal neuropathy (GAN) is a fatal neurodegenerative disorder for which there is currently no treatment. Affecting the nervous system, GAN starts in infancy with motor deficits that rapidly evolve toward total loss of ambulation. Using the gan zebrafish model that reproduces the loss of motility as seen in patients, we conducted the first pharmacological screening for the GAN pathology. Here, we established a multilevel pipeline to identify small molecules restoring both the physiological and the cellular deficits in GAN. We combined behavioral, in silico, and high-content imaging analyses to refine our Hits to five drugs restoring locomotion, axonal outgrowth, and stabilizing neuromuscular junctions in the gan zebrafish. The postsynaptic nature of the drug's cellular targets provides direct evidence for the pivotal role the neuromuscular junction holds in the restoration of motility. Our results identify the first drug candidates that can now be integrated in a repositioning approach to fasten therapy for the GAN disease. Moreover, we anticipate both our methodological development and the identified hits to be of benefit to other neuromuscular diseases.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Targeting the PEDV 3CL protease for identification of small molecule inhibitors: an insight from virtual screening, ADMET prediction, molecular dynamics, free energy landscape, and binding energy calculations

BACKGROUND

The porcine epidemic diarrhea virus (PEDV) represents a major health issue for piglets worldwide and does significant damage to the pork industry. Thus, new therapeutic approaches are urgently needed to manage PEDV infections. Due to the current lack of a reliable remedy, this present study aims to identify novel compounds that inhibit the 3CL protease of the virus involved in replication and pathogenesis.

RESULTS

To identify potent antiviral compounds against the 3CL protease, a virtual screening of natural compounds (n = 97,999) was conducted. The top 10 compounds were selected based on the lowest binding energy and the protein-ligand interaction analyzed. Further, the top five compounds that demonstrated a strong binding affinity were subjected to drug-likeness analysis using the ADMET prediction, which was followed by molecular dynamics simulations (500 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method. Based on these parameters, four putative lead (ZINC38167083, ZINC09517223, ZINC04339983, and ZINC09517238) compounds were identified that represent potentially effective inhibitors of the 3CL protease.

CONCLUSION

Therefore, these can be utilized for the development of novel antiviral drugs against PEDV. However, this requires further validation through in vitro and in vivo studies.

Molecular basis and mechanism of action of Albizia julibrissin in depression treatment and clinical application of its formulae

Albizzia julibrissin is empirically used as an antidepressant in clinical practice. Preclinical studies have indicated that its total extracts or bioactive constituents exerted antidepressant-like responses in animal models, providing the molecular basis to reveal its underlying mechanism of action. While attempts have been made to understand the antidepressant effect of A. julibrissin, many fundamental questions regarding its mechanism of action remain to be addressed at the molecular and systems levels. In this review, we conclusively discussed the mechanism of action of A. julibrissin and A. julibrissin formulae by reviewing recent preclinical and clinical studies conducted by using depressive animal models and depressive patients. Several representative bioactive constituents and formulae were highlighted as examples, and their mechanisms of action were discussed. In addition, some representative A. julibrissin formulae that have been shown to be compatible with conventional antidepressants in clinical practice were also reviewed. Furthermore, we discussed the future research directions to reveal the underlying mechanism of A. julibrissin at the molecular and systems levels in depression treatment. The integrated study using both the molecular and systematic approaches is required not only for improving our understanding of its molecular basis and mechanisms of action, but also for providing a way to discover novel agents or approaches for the effective and systematic treatment of depression.

Proteome-Wide Fragment-Based Ligand and Target Discovery

Chemical probes are invaluable tools to investigate biological processes and can serve as lead molecules for the development of new therapies. However, despite their utility, only a fraction of human proteins have selective chemical probes, and more generally, our knowledge of the "chemically-tractable" proteome is limited, leaving many potential therapeutic targets unexploited. To help address these challenges, powerful chemical proteomic approaches have recently been developed to globally survey the ability of proteins to bind small molecules (i. e., ligandability) directly in native systems. In this review, we discuss the utility of such approaches, with a focus on the integration of chemoproteomic methods with fragment-based ligand discovery (FBLD), to facilitate the broad mapping of the ligandable proteome while also providing starting points for progression into lead chemical probes.

Pipeline for Characterizing Alternative Mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity

The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).

Base-Exchange Enabling the Visualization of SARM1 Activities in Sciatic Nerve-Injured Mice

Enzymes are important in homeostasis in living organisms. Since abnormal enzyme activities are highly associated with many human diseases, detection of in vivo activities of a specific enzyme is important to study the pathology of the related diseases. In this work, we have designed and synthesized a series of new small-molecule-activatable fluorescent probes for the imaging of Sterile Alpha and TIR Motif-containing 1 (SARM1) activities based on its transglycosidase activities (base-exchange reactions of NAD⁺). Probe 1a was found to undergo base-exchange reactions with NAD⁺ in the presence of activated SARM1 but not CD38 nor NADase and formed a highly emissive product AD-1a [about a 100-fold fluorescence enhancement in 20 min with a 150 nm (5665 cm^-1) Stokes shift and a 100 nm (3812 cm^-1) red shift]. This probe exhibited a higher reactivity and sensitivity than those commonly used for SARM1 imaging. The utilities of 1a have also been demonstrated in live-cell imaging and detection of in vivo activities of SARM1 in a sciatic nerve injury mouse model.

Chemical Biology Approaches Confirm MCT4 as the Therapeutic Target of a Cellular Optimized Hit

Lactic acid transport is a key process maintaining glycolytic flux in tumors. Inhibition of this process will result in glycolytic shutdown, impacting on cell growth and survival and thus has been pursued as a therapeutic approach for cancers. Using a cell-based screen in a MCT4-dependent cell line, we identified and optimized compounds for their ability to inhibit the efflux of intracellular lactic acid with good physical and pharmacokinetic properties. To deconvolute the mechanism of lactic acid efflux inhibition, we have developed three assays to measure cellular target engagement. Specifically, we synthesized a biologically active photoaffinity probe (IC₅₀ < 10 nM), and using this probe, we demonstrated selective engagement of MCT4 of our parent molecule through a combination of confocal microscopy and in-cell chemoproteomics. As an orthogonal assay, the cellular thermal shift assay (CETSA) confirmed binding to MCT4 in the cellular system. Comparisons of lactic acid efflux potencies in cells with differential expression of MCT family members further confirmed that the optimized compounds inhibit the efflux of lactic acid through the inhibition of MCT4. Taken together, these data demonstrate the power of orthogonal chemical biology methods to determine cellular target engagement, particularly for proteins not readily amenable to traditional biophysical methods.

Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis

In silico machine learning applications for phenotype-based screening have primarily been limited due to the lack of machine-readable data related to disease phenotypes. Adiponectin, a nuclear receptor (NR)-regulated adipocytokine, is relatively downregulated in human metabolic diseases. Here, we present a machine-learning model to predict the adiponectin-secretion-promoting activity of flavonoid-associated phytochemicals (FAPs). We modeled a structure-activity relationship between the chemical similarity of FAPs and their bioactivities using a random forest-based classifier, which provided the NR activity of each FAP as a probability. To link the classifier-predicted NR activity to the phenotype, we next designed a single-cell transcriptomics-based multiple linear regression model to generate the relative adiponectin score (RAS) of FAPs. In experimental validation, estimated RAS values of FAPs isolated from Scutellaria baicalensis exhibited a significant correlation with their adiponectin-secretion-promoting activity. The combined cheminformatics and bioinformatics approach enables the computational reconstruction of phenotype-based screening systems.

Activity-based CRISPR scanning uncovers allostery in DNA methylation maintenance machinery

Allostery enables dynamic control of protein function. A paradigmatic example is the tightly orchestrated process of DNA methylation maintenance. Despite the fundamental importance of allosteric sites, their identification remains highly challenging. Here, we perform CRISPR scanning on the essential maintenance methylation machinery-DNMT1 and its partner UHRF1-with the activity-based inhibitor decitabine to uncover allosteric mechanisms regulating DNMT1. In contrast to non-covalent DNMT1 inhibition, activity-based selection implicates numerous regions outside the catalytic domain in DNMT1 function. Through computational analyses, we identify putative mutational hotspots in DNMT1 distal from the active site that encompass mutations spanning a multi-domain autoinhibitory interface and the uncharacterized BAH2 domain. We biochemically characterize these mutations as gain-of-function, exhibiting increased DNMT1 activity. Extrapolating our analysis to UHRF1, we discern putative gain-of-function mutations in multiple domains, including key residues across the autoinhibitory TTD-PBR interface. Collectively, our study highlights the utility of activity-based CRISPR scanning for nominating candidate allosteric sites, and more broadly, introduces new analytical tools that further refine the CRISPR scanning framework.

Stereodivergent Desymmetrization of Phenols En Route to Modular Access to Densely Functionalized Quinazoline and Oxazine Scaffolds

The de novo assembly of stereochemically and skeletally diverse scaffolds is a powerful tool for the discovery of novel chemotypes. Hence, the development of modular, step- and atom-economic synthetic methods to access stereochemically and skeletally diverse compound collection is particularly important. Herein, we show a metal-free, stereodivergent build/couple/pair strategy that allows access to a unique collection of benzo[5,6][1,4]oxazino[4,3-a]quinazoline, quinolino[1,2-a]quinazoline and benzo[b]benzo [4,5]imidazo[1,2-d][1,4]oxazine scaffolds with complete diastereocontrol and wide distribution of molecular architectures. This metal-free process proceeds via desymmetrization of phenol derivatives. The cascade unites Mannich with aza-Michael addition reactions, providing expeditious entries to diverse classes of molecular shapes in a single operation.

System-wide health risk prediction for 4-methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene(MBP), a major active metabolite of environmental pollutant and food contaminant - Bisphenol A

Human exposure to plastic contaminated foods and environmental micro/nano plastic derived chemicals necessitates system-wide health risk assessment. Hence, current study intend to explore the mode of action (MoA) based adverse outcome pathways of 4-methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene (MBP), the major active metabolite of bisphenol A (BPA). The computational study employed broad range of target prediction, systems biology tools and molecular docking protocols. Further, validation of MBP targets was done using protein-ligand fluorescence quenching assay, endothelial cell culture and chicken embryo vascular angiogenesis models. Interestingly, the current results illustrate that various physiological signaling pathways (MAPK and VEGF related angiogenesis signaling) and disease progression pathways (hypertension, cancer and endocrine disorders) were enriched as potential targets of MBP. Further, docking studies highlights the possible binding mechanism of MBP with important targets including endothelial nitric oxide synthase (eNOS) and serum albumin (BSA). In addition, the validation studies on MBP-BSA interaction (fluorescence quenching), eNOS derived nitric oxide (NOx) generation in endothelial cells and chicken embryo angiogenesis support the system-wide impacts of MBP with highlights on cardiovascular pathogenesis. Thus, the current observation provides novel insights into the system wide impacts of MBP for the futuristic health risk assessment of plastic derived chemicals.

Nanotechnology Involved in Treating Urinary Tract Infections: An Overview

Considered as the most frequent contaminations that do not require hospitalization, urinary tract infections (UTIs) are largely known to cause significant personal burdens on patients. Although UTIs overall are highly preventable health issues, the recourse to antibiotics as drug treatments for these infections is a worryingly spread approach that should be addressed and gradually overcome in a contemporary, modernized healthcare system. With a virtually alarming global rise of antibiotic resistance overall, nanotechnologies may prove to be the much-needed 'lifebuoy' that will eventually suppress this prejudicial phenomenon. This review aims to present the most promising, currently known nano-solutions, with glimpses on clinical and epidemiological aspects of the UTIs, prospective diagnostic instruments, and non-antibiotic treatments, all of these engulfed in a comprehensive overview.

DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms

Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activating/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug-target (DT) mechanisms and data scarcity, modelling this problem based on deep learning methods to accurately predict DT activating/inhibiting mechanisms remains a considerable challenge. Here, by considering network pharmacology, we propose a multi-view deep learning model, DrugAI, which combines four modules, i.e. a graph neural network for drugs, a convolutional neural network for targets, a network embedding module for drugs and targets and a deep neural network for predicting activating/inhibiting mechanisms between drugs and targets. Computational experiments show that DrugAI performs better than state-of-the-art methods and has good robustness and generalization. To demonstrate the reliability of the predictive results of DrugAI, bioassay experiments are conducted to validate two drugs (notopterol and alpha-asarone) predicted to activate TRPV1. Moreover, external validation bears out 61 pairs of mechanism relationships between natural products and their targets predicted by DrugAI based on independent literatures and PubChem bioassays. DrugAI, for the first time, provides a powerful multi-view deep learning framework for robust prediction of DT activating/inhibiting mechanisms.

Deep learning in image-based phenotypic drug discovery

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).

How to Design Peptides

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

Roles of anoikis in colorectal cancer therapy and the assessment of anoikis-regulatory molecules as therapeutic targets

Colorectal cancer (CRC) is a deadly malignancy and therapeutic approaches for CRC are evolving every day. Anoikis is a key mechanism for programmed cell death of cancer cells that undergo anchorage-independent growth at a different matrix than the one which is expected. Yet, anoikis is a less studied mechanism of cell death in comparison to other mechanisms such as apoptosis. Relating to this, resistance to anoikis among cancer cells remains critical for improved metastasis and survival in a new environment evading anoikis. Since CRC cells have the ability to metastasize from proximal sites to secondary organs such as liver and promote cancer in those distant sites, a clear knowledge of the mechanisms essential for anchorage-independent growth and subsequent metastasis is necessary to counteract CRC progression and spread. Therefore, the identification of novel drug candidates and studying the roles of anoikis in assisting CRC therapy using such drugs can prevent anchorage-independent cancer cell growth. Additionally, the identification of novel biomarkers or therapeutic targets seems essential for implementing superior therapy, impeding relapse among malignant cells and improving the survival rate of clinical patients. As there are no reviews published on this topic till date, anoikis as a mechanism of cell death and its therapeutic roles in CRC are discussed in this review. In addition, several molecules were identified as therapeutic targets for CRC.

Ebselen suitably interacts with the potential SARS-CoV-2 targets: an in-silico approach

Ebselen (SPI-1005) is an active selenoorganic compound that can be found potential inhibitory activity against different types of viral infections such as zika virus, influenza A virus, HCV, and HIV-1; and also be found to exhibit promising antiviral activity against SARS-CoV-2 in cell-based assays but its particular target action against specific non-structural and structural proteins of SARS-CoV-2 is unclear to date. The purpose of this study is to evaluate the anti-SARS-CoV-2 efficacy of Ebselen along with the determination of the specific target among the 12 most common target proteins of SARS-CoV-2. AutoDock Vina in PyRx platform was used for docking analysis against the 12 selected SARS-CoV-2 encoded drug targets. ADME profiling was examined by using SwissADME online server. The stability of binding mode in the target active sites was evaluated using molecular dynamics (MD) simulation studies through NAMD and Desmond package software application. In this docking study, we recognized that Ebselen possesses the highest affinity to N protein (C domain) (PDB ID: 6YUN) and PLpro (PDB ID: 6WUU) among the selected SARS-CoV-2 targets showing -7.4 kcal/mol binding energy. The stability of Ebselen-6YUN and Ebselen-6WUU was determined by a 100 ns trajectory of all-atom molecular dynamics simulation. Structural conformation of these two complexes displayed stable root mean square deviation (RMSD), while root mean square fluctuations (RMSF) were also found to be consistent. This molecular docking study may propose the efficiency of Ebselen against SARS-CoV-2 to a significant extent which makes it a candidature of COVID-19 treatment.

Discovery of Pan-peroxisome Proliferator-Activated Receptor Modulators from an Endolichenic Fungus, Daldinia childiae

Adiponectin-synthesis-promoting compounds possess therapeutic potential to treat diverse metabolic diseases, including obesity and diabetes. Phenotypic screening to find adiponectin-synthesis-promoting compounds was performed using the adipogenesis model of human bone marrow mesenchymal stem cells. The extract of the endolichenic fungus Daldinia childiae 047215 significantly promoted adiponectin production. Bioactivity-guided isolation led to 13 active polyketides (1-13), which include naphthol monomers, dimers, and trimers. To the best of our knowledge, trimers of naphthol (1-4) have not been previously isolated as either natural or synthetic products. The novel naphthol trimer 3,1',3',3″-ternaphthalene-5,5',5″-trimethoxy-4,4',4″-triol (2) and a dimer, nodulisporin A (12), exhibited concentration-dependent adiponectin-synthesis-promoting activity (EC₅₀ 30.8 and 15.2 μM, respectively). Compounds 2 and 12 bound to all three peroxisome proliferator-activated receptor (PPAR) subtypes, PPARα, PPARγ, and PPARδ. In addition, compound 2 transactivated retinoid X receptor α, whereas 12 did not. Naphthol oligomers 2 and 12 represent novel pan-PPAR modulators and are potential pharmacophores for designing new therapeutic agents against hypoadiponectinemia-associated metabolic diseases.

Radotinib attenuates TGFβ -mediated pulmonary fibrosis in vitro and in vivo: exploring the potential of drug repurposing

BACKGROUND

Tyrosine kinase (TK) plays a crucial role in the pathogenesis of idiopathic pulmonary fibrosis. Here, we aimed to investigate whether radotinib (Rb) could inhibit pulmonary fibrosis by inhibiting TK in vitro and in vivo.

METHODS

The antifibrotic effects of Rb in transforming growth factor-β (TGF-β)1-stimulated A549 cells were determined using real-time polymerase chain reaction, western blotting, and immunocytochemistry assays. Rb inhibition of bleomycin-induced lung fibrosis in Sprague Dawley (SD) rats was determined by histopathological and​ immunohistochemical analyses. Rb-interfering metabolites were analyzed using LC-MS/MS.

RESULTS

Rb concentrations of up to 1000 nM did not affect the viability of A549 cells, but Rb (30 nM) significantly reduced expression of TGF-β1 (10 ng/mL)-induced ECM factors, such as Snail, Twist, and F-actin. Rb also regulated TGF-β1-overexpressed signal cascades, such as fibronectin and α-smooth muscle actin. Furthermore, Rb attenuated the phosphorylation of Smad2 and phosphorylation of kinases, such as, extracellular signal-regulated kinase, and protein kinase B. In the inhibitory test against bleomycin (5 mg/kg)-induced lung fibrosis, the Rb (30 mg/kg/daily)-treated group showed a half-pulmonary fibrosis region compared to the positive control group. In addition, Rb significantly reduced collagen type I and fibronectin expression in the bleomycin-induced fibrotic region of SD rats. Further, the identified metabolite pantothenic acid was not altered by Rb.

CONCLUSION

Taken together, these results indicate that Rb inhibits TGF-β1-induced pulmonary fibrosis both in vitro and in vivo. These findings suggest that Rb may be an effective treatment for pulmonary fibrosis-related disorders and idiopathic pulmonary fibrosis.

High-throughput functional annotation of natural products by integrated activity profiling

Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform Functional Signature Ontology, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov-Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could find target classes not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery.

CRISPR-Suppressor Scanning for Systematic Discovery of Drug-Resistance Mutations

CRISPR-Cas9 genome editing technologies have enabled complex genetic manipulations in situ, including large-scale, pooled screening approaches to probe and uncover mechanistic insights across various biological processes. The RNA-programmable nature of CRISPR-Cas9 greatly empowers tiling mutagenesis approaches to elucidate molecular details of protein function, in particular the interrogation of mechanisms of resistance to small molecules, an approach termed CRISPR-suppressor scanning. In a typical CRISPR-suppressor scanning experiment, a pooled library of single-guide RNAs is designed to target across the coding sequence(s) of one or more genes, enabling the Cas9 nuclease to systematically mutate the targeted proteins and generate large numbers of diverse protein variants in situ. This cellular pool of protein variants is then challenged with drug treatment to identify mutations conferring a fitness advantage. Drug-resistance mutations identified with this approach can not only elucidate drug mechanism of action but also reveal deeper mechanistic insights into protein structure-function relationships. In this article, we outline the framework for a standard CRISPR-suppressor scanning experiment. Specifically, we provide instructions for the design and construction of a pooled sgRNA library, execution of a CRISPR-suppressor scanning screen, and basic computational analysis of the resulting data. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Design and generation of a pooled sgRNA library Support Protocol 1: sgRNA library design using command-line CRISPOR Support Protocol 2: Production and titering of pooled sgRNA library lentivirus Basic Protocol 2: Execution and analysis of a CRISPR-suppressor scanning experiment.

Efforts in Bioprospecting Research: A Survey of Novel Anticancer Phytochemicals Reported in the Last Decade

Bioprospecting natural products to find prominent agents for medical application is an area of scientific endeavor that has produced many clinically used bioactive compounds, including anticancer agents. These compounds come from plants, microorganisms, and marine life. They are so-called secondary metabolites that are important for a species to survive in the hostile environment of its respective ecosystem. The kingdom of Plantae has been an important source of traditional medicine in the past and is also enormously used today as an exquisite reservoir for detecting novel bioactive compounds that are potent against hard-to-treat maladies such as cancer. Cancer therapies, especially chemotherapies, are fraught with many factors that are difficult to manage, such as drug resistance, adverse side effects, less selectivity, complexity, etc. Here, we report the results of an exploration of the databases of PubMed, Science Direct, and Google Scholar for bioactive anticancer phytochemicals published between 2010 and 2020. Our report is restricted to new compounds with strong-to-moderate bioactivity potential for which mass spectroscopic structural data are available. Each of the phytochemicals reported in this review was assigned to chemical classes with peculiar anticancer properties. In our survey, we found anticancer phytochemicals that are reported to have selective toxicity against cancer cells, to sensitize MDR cancer cells, and to have multitarget effects in several signaling pathways. Surprisingly, many of these compounds have limited follow-up studies. Detailed investigations into the synthesis of more functional derivatives, chemical genetics, and the clinical relevance of these compounds are required to achieve safer chemotherapy.

The Regulatory Network of Gastric Cancer Pathogenesis and Its Potential Therapeutic Active Ingredients of Traditional Chinese Medicine Based on Bioinformatics, Molecular Docking, and Molecular Dynamics Simulation

Objective. This study aims to investigate the functional gene network in gastric carcinogenesis by using bioinformatics; besides, the diagnostic utility of key genes and potential active ingredients of traditional Chinese medicine (TCM) for treatment in gastric cancer have been explored. Methods. The Cancer Genome Atlas and Gene Expression Omnibus databases have been applied to analyze the differentially expressed genes (DEGs) between gastric cancer and normal gastric tissues. Then, the DEGs underwent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the Metascape database. The STRING database and the Cytoscape software were utilized for the protein-protein interaction network of DEGs and hub genes screening. Furthermore, survival and expression analyses of hub genes were conducted using Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. By using the Comparative Toxicogenomics Database, the hub genes interconnected with active ingredients of TCM were analyzed to provide potential information for the treatment of gastric cancer. After the molecular docking of the active ingredients of TCM to specific hub gene receptor proteins, the molecular dynamics simulation GROMACS was applied to validate the conformation of the strongest binding ability in the molecular docking. Results. A total of 291 significant DEGs were found, from which 12 hub genes were screened out. Among these hub genes, the expressions of five hub genes including COL1A1, COL5A2, MMP12, SERPINE1, and VCAN were significantly correlated with the overall survival. Furthermore, four potential therapeutic active ingredients of TCM were acquired, including quercetin, resveratrol, emodin, and schizandrin B. In addition, the molecular docking results exhibited that the active ingredients of TCM formed stable binding with the hub gene targets. SERPINE1 (3UT3)-Emodin and COL1A1 (7DV6)-Quercetin were subjected to molecular dynamics simulations as conformations of continuing research significance, and both were found to be stably bound as a result of the interaction of van der Waals potentials, electrostatic, and hydrogen bonding. Conclusion. Our findings may provide novel insights and references for the screening of biomarkers, the prognostic evaluation, and the identification of potential active ingredients of TCM for gastric cancer treatment.

Processes in DNA damage response from a whole-cell multi-omics perspective

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

Genome-wide CRISPR Screen Reveal Targets of Chiral Gold(I) Anticancer Compound in Mammalian Cells

Metal-based drugs, such as cisplatin and auranofin, are used for the treatment of cancer and rheumatoid arthritis, respectively. Auranofin and other gold-derived compounds have been shown to possess anticancer, anti-inflammatory, antimicrobial, and antiparasitic activity in preclinical and clinical trials. Unlike platinum agents which are known to target DNA, the target of gold is not well elucidated. To better understand the targets and effects of gold agents in mammalian cells, we used a targeted CRISPR (ToxCRISPR) screen in K562 cancer cells to identify genes that modulate cellular sensitivity to gold. We synthesized a novel chiral gold(I) compound, JHK-21, with potent anticancer activity. Among the most sensitizing hits were proteins involved in mitochondrial carriers, mitochondrial metabolism, and oxidative phosphorylation. Further analysis revealed that JHK-21 induced inner mitochondria membrane dysfunction and modulated ATP-binding cassette subfamily member C (ABCC1) function in a manner distinct from auranofin. Characterizing the therapeutic effects and toxicities of metallodrugs in mammalian cells is of growing interest to guide future drug discovery, and cellular and preclinical/clinical studies.

Open access databases available for the pesticide lead discovery

Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.

Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer

There is an urgent need for exploring new actionable targets other than androgen receptor to improve outcome from lethal castration-resistant prostate cancer. Tumor metabolism has reemerged as a hallmark of cancer that drives and supports oncogenesis. In this regard, it is important to understand the relationship between distinctive metabolic features, androgen receptor signaling, genetic drivers in prostate cancer, and the tumor microenvironment (symbiotic and competitive metabolic interactions) to identify metabolic vulnerabilities. We explore the links between metabolism and gene regulation, and thus the unique metabolic signatures that define the malignant phenotypes at given stages of prostate tumor progression. We also provide an overview of current metabolism-based pharmacological strategies to be developed or repurposed for metabolism-based therapeutics for castration-resistant prostate cancer.

Cellular Target Deconvolution of Small Molecules Using a Selection-Based Genetic Screening Platform

Small-molecule drug target identification is an essential and often rate-limiting step in phenotypic drug discovery and remains a major challenge. Here, we report a novel platform for target identification of activators of signaling pathways by leveraging the power of a clustered regularly interspaced short palindromic repeats (CRISPR) knockout library. This platform links the expression of a suicide gene to the small-molecule-activated signaling pathway to create a selection system. With this system, loss-of-function screening using a CRISPR single-guide (sg) RNA library positively enriches cells in which the target has been knocked out. The identities of the drug targets and other essential genes required for the activity of small molecules of interest are then uncovered by sequencing. We tested this platform on BDW568, a newly discovered type-I interferon signaling activator, and identified stimulator of interferon genes (STING) as its target and carboxylesterase 1 (CES1) to be a key metabolizing enzyme required to activate BDW568 for target engagement. The platform we present here can be a general method applicable for target identification for a wide range of small molecules that activate different signaling pathways.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.