In situ identification of cellular drug targets in mammalian tissue.

The lack of tools to observe drug-target interactions at cellular resolution in intact tissue has been a major barrier to understanding in vivo drug actions. Here, we develop clearing-assisted tissue click chemistry (CATCH) to optically image covalent drug targets in intact mammalian tissues. CATCH permits specific and robust in situ fluorescence imaging of target-bound drug molecules at subcellular resolution and enables the identification of target cell types. Using well-established inhibitors of endocannabinoid hydrolases and monoamine oxidases, direct or competitive CATCH not only reveals distinct anatomical distributions and predominant cell targets of different drug compounds in the mouse brain but also uncovers unexpected differences in drug engagement across and within brain regions, reflecting rare cell types, as well as dose-dependent target shifts across tissue, cellular, and subcellular compartments that are not accessible by conventional methods. CATCH represents a valuable platform for visualizing in vivo interactions of small molecules in tissue.

Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer.

Diversity in the genetic lesions that cause cancer is extreme. In consequence, a pressing challenge is the development of drugs that target patient-specific disease mechanisms. To address this challenge, we employed a chemistry-first discovery paradigm for de novo identification of druggable targets linked to robust patient selection hypotheses. In particular, a 200,000 compound diversity-oriented chemical library was profiled across a heavily annotated test-bed of >100 cellular models representative of the diverse and characteristic somatic lesions for lung cancer. This approach led to the delineation of 171 chemical-genetic associations, shedding light on the targetability of mechanistic vulnerabilities corresponding to a range of oncogenotypes present in patient populations lacking effective therapy. Chemically addressable addictions to ciliogenesis in TTC21B mutants and GLUT8-dependent serine biosynthesis in KRAS/KEAP1 double mutants are prominent examples. These observations indicate a wealth of actionable opportunities within the complex molecular etiology of cancer.

Creative approaches using proximity labeling to gain new biological insights.

At its most fundamental level, life is a collection of synchronized cellular processes driven by interactions among biomolecules. Proximity labeling has emerged as a powerful technique to capture these interactions in native settings, revealing previously unexplored elements of biology. This review highlights recent developments in proximity labeling, focusing on methods that push the fundamental technologies beyond the classic bait-prey paradigm, such as RNA-protein interactions, ligand/small-molecule-protein interactions, cell surface protein interactions, and subcellular protein trafficking. The advancement of proximity labeling methods to address different biological problems will accelerate our understanding of the complex biological systems that make up life.

Pharmaceutical-Grade Rigosertib Is a Microtubule-Destabilizing Agent.

We recently used CRISPRi/a-based chemical-genetic screens and cell biological, biochemical, and structural assays to determine that rigosertib, an anti-cancer agent in phase III clinical trials, kills cancer cells by destabilizing microtubules. Reddy and co-workers (Baker et al., 2020, this issue of Molecular Cell) suggest that a contaminating degradation product in commercial formulations of rigosertib is responsible for the microtubule-destabilizing activity. Here, we demonstrate that cells treated with pharmaceutical-grade rigosertib (>99.9% purity) or commercially obtained rigosertib have qualitatively indistinguishable phenotypes across multiple assays. The two formulations have indistinguishable chemical-genetic interactions with genes that modulate microtubule stability, both destabilize microtubules in cells and in vitro, and expression of a rationally designed tubulin mutant with a mutation in the rigosertib binding site (L240F TUBB) allows cells to proliferate in the presence of either formulation. Importantly, the specificity of the L240F TUBB mutant for microtubule-destabilizing agents has been confirmed independently. Thus, rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

An allelic-series rare-variant association test for candidate-gene discovery.

Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n = 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis.

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFß treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.

Identification and structural validation of purine nucleoside phosphorylase from Plasmodium falciparum as a target of MMV000848.

About 247 million cases of malaria occurred in 2021 with Plasmodium falciparum accounting for the majority of 619,000 deaths. In the absence of a widely available vaccine, chemotherapy remains crucial to prevent, treat, and contain the disease. The efficacy of several drugs currently used in the clinic is likely to suffer from the emergence of resistant parasites. A global effort to identify lead compounds led to several initiatives such as the Medicine for Malaria Ventures (MMV), a repository of compounds showing promising efficacy in killing the parasite in cell-based assays. Here, we used mass spectrometry coupled with cellular thermal shift assay to identify putative protein targets of MMV000848, a compound with an in vitro EC50 of 0.5 µM against the parasite. Thermal shift assays showed a strong increase of P. falciparum purine nucleoside phosphorylase (PfPNP) melting temperature by up to 15 °C upon incubation with MMV000848. Binding and enzymatic assays returned a KD of 1.52 ± 0.495 µM and an IC50 value of 21.5 ± 2.36 µM. The inhibition is competitive with respect to the substrate, as confirmed by a cocrystal structure of PfPNP bound with MMV000848 at the active site, determined at 1.85 Å resolution. In contrast to transition states inhibitors, MMV000848 specifically inhibits the parasite enzyme but not the human ortholog. An isobologram analysis shows subadditivity with immucillin H and with quinine respectively, suggesting overlapping modes of action between these compounds. These results point to PfPNP as a promising antimalarial target and suggest avenues to improve inhibitor potency.

Rethinking rare disease: longevity-enhancing drug targets through X-linked aneuploidy.

Complex diseases, including ageing, often exhibit sexual dimorphism. These sex differences can obfuscate attribution to causal genes within a target ID campaign. Mendelian randomisation (MR)-inspired analysis provides a natural setting to incorporate X-linked aneuploid populations, resulting in prioritisation of longevity-enhancing drug targets and motivating greater inclusion of said populations in future profiling studies.

Combined CRISPRi/a-Based Chemical Genetic Screens Reveal that Rigosertib Is a Microtubule-Destabilizing Agent.

Chemical libraries paired with phenotypic screens can now readily identify compounds with therapeutic potential. A central limitation to exploiting these compounds, however, has been in identifying their relevant cellular targets. Here, we present a two-tiered CRISPR-mediated chemical-genetic strategy for target identification: combined genome-wide knockdown and overexpression screening as well as focused, comparative chemical-genetic profiling. Application of these strategies to rigosertib, a drug in phase 3 clinical trials for high-risk myelodysplastic syndrome whose molecular target had remained controversial, pointed singularly to microtubules as rigosertib's target. We showed that rigosertib indeed directly binds to and destabilizes microtubules using cell biological, in vitro, and structural approaches. Finally, expression of tubulin with a structure-guided mutation in the rigosertib-binding pocket conferred resistance to rigosertib, establishing that rigosertib kills cancer cells by destabilizing microtubules. These results demonstrate the power of our chemical-genetic screening strategies for pinpointing the physiologically relevant targets of chemical agents.

Small molecule photocatalysis enables drug target identification via energy transfer.

Over half of new therapeutic approaches fail in clinical trials due to a lack of target validation. As such, the development of new methods to improve and accelerate the identification of cellular targets, broadly known as target ID, remains a fundamental goal in drug discovery. While advances in sequencing and mass spectrometry technologies have revolutionized drug target ID in recent decades, the corresponding chemical-based approaches have not changed in over 50 y. Consigned to outdated stoichiometric activation modes, modern target ID campaigns are regularly confounded by poor signal-to-noise resulting from limited receptor occupancy and low crosslinking yields, especially when targeting low abundance membrane proteins or multiple protein target engagement. Here, we describe a broadly general platform for photocatalytic small molecule target ID, which is founded upon the catalytic amplification of target-tag crosslinking through the continuous generation of high-energy carbene intermediates via visible light-mediated Dexter energy transfer. By decoupling the reactive warhead tag from the small molecule ligand, catalytic signal amplification results in unprecedented levels of target enrichment, enabling the quantitative target and off target ID of several drugs including (+)-JQ1, paclitaxel (Taxol), dasatinib (Sprycel), as well as two G-protein-coupled receptors-ADORA2A and GPR40.

NDUFA12 as a Functional Target of the Anticancer Compound Ertredin in Human Hepatoma Cells As Revealed by Label-Free Chemical Proteomics.

Many attempts have been made to develop new agents that target EGFR mutants or regulate downstream factors in various cancers. Cell-based screening showed that a natural small molecule, Ertredin, inhibited the growth of EGFRvIII mutant cancer cells. Previous studies have shown that Ertredin effectively inhibits anchorage-independent 3D growth of sphere-forming cells transfected with EGFRvIII mutant cDNA. However, the underlying mechanism remains unclear. In this study, we investigated the target protein of Ertredin by combining drug affinity-responsive target stability (DARTS) assays with liquid chromatography-mass spectrometry using label-free Ertredin as a bait and HepG2 cell lysates as a proteome pool. NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 12 (NDUFA12) was identified as an Ertredin-binding protein that was responsible for its biological activity. The interaction between NDUFA12 and Ertredin was validated by DARTS and cellular thermal shift assays. In addition, the genetic knockdown of the identified target, NDUFA12, was shown to suppress cell proliferation. NDUFA12 was identified as a biologically relevant target protein of Ertredin that is responsible for its antitumor activity, and these results provide insights into the role of NDUFA12 as a downstream factor in EGFRvIII mutants.

Development of uniform ribosome display technology enabling easy and efficient identification of full-length proteins that interact with bioactive small and large molecules.

Identifying target proteins that interact with bioactive molecules is indispensable for understanding their mechanisms of action. In this study, we developed a uniform ribosome display technology using equal-length DNAs and mRNAs to improve molecular display principle for target identification. The equal-length DNAs were designed to contain various coding sequences for full-length proteins with molecular weights of up to 130 kDa and were used to synthesize equal-length mRNAs, which allowed the formation of full-length protein-ribosome-equal-length mRNA complexes. Uniform ribosome display selections of dihydrofolate reductase and haloalkane dehalogenase mutant were performed against methotrexate and chlorohexane, respectively. Quantitative changes of proteins after each selection indicated that the target protein-displaying ribosomal complexes were specifically selected through non-covalent or covalent interactions with the corresponding bioactive molecules. Furthermore, selection of full-length proteins interacting with methotrexate or anti-DDX46 antibody from protein pools showed that only the target proteins could be precisely identified even though the molar amounts of equal-length mRNAs encoding them were adjusted to 1/20,000 of the total equal-length mRNAs. Thus, the uniform ribosome display technology enabled efficient identification of target proteins that interact with bioactive small and large molecules through simplified operations without deep sequencing.

Botanical drugs: a new strategy for structure-based target prediction.

Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand-protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.

Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary.

High-throughput genomic technologies are increasingly used in personalized cancer medicine. However, computational tools to maximize the use of scarce tissues combining distinct molecular layers are needed. Here we present a refined strategy, based on the R-package 'conumee', to better predict somatic copy number alterations (SCNA) from deoxyribonucleic acid (DNA) methylation arrays. Our approach, termed hereafter as 'conumee-KCN', improves SCNA prediction by incorporating tumor purity and dynamic thresholding. We trained our algorithm using paired DNA methylation and SNP Array 6.0 data from The Cancer Genome Atlas samples and confirmed its performance in cancer cell lines. Most importantly, the application of our approach in cancers of unknown primary identified amplified potentially actionable targets that were experimentally validated by Fluorescence in situ hybridization and immunostaining, reaching 100% specificity and 93.3% sensitivity.

Screening of promising molecules against potential drug targets in Yersinia pestis by integrative pan and subtractive genomics, docking and simulation approach.

This study focuses on Yersinia pestis, the bacterium responsible for plague, which posed a severe threat to public health in history. Despite the availability of antibiotics treatment, the emergence of antibiotic resistance in this pathogen has increased challenges of controlling the infections and plague outbreaks. The development of new drug targets and therapies is urgently needed. This research aims to identify novel protein targets from 28 Y. pestis strains by the integrative pan-genomic and subtractive genomics approach. Additionally, it seeks to screen out potential safe and effective alternative therapies against these targets via high-throughput virtual screening. Targets should lack homology to human, gut microbiota, and known human 'anti-targets', while should exhibit essentiality for pathogen's survival and virulence, druggability, antibiotic resistance, and broad spectrum across multiple pathogenic bacteria. We identified two promising targets: the aminotransferase class I/class II domain-containing protein and 3-oxoacyl-[acyl-carrier-protein] synthase 2. These proteins were modeled using AlphaFold2, validated through several structural analyses, and were subjected to molecular docking and ADMET analysis. Molecular dynamics simulations determined the stability of the ligand-target complexes, providing potential therapeutic options against Y. pestis.

Structure activity relationship and target prediction for ABX464 analogues in Caenorhabditis elegans.

Global challenges with treatment failures and/or widespread resistance in parasitic worms against commercially available anthelmintics lend impetus to the development of new anthelmintics with novel mechanism(s) of action. The free-living nematode Caenorhabditis elegans is an important model organism used for drug discovery, including the screening and structure-activity investigation of new compounds, and target deconvolution. Previously, we conducted a whole-organism phenotypic screen of the 'Pandemic Response Box' (from Medicines for Malaria Venture, MMV) and identified a hit compound, called ABX464, with activity against C. elegans and a related, parasitic nematode, Haemonchus contortus. Here, we tested a series of 44 synthesized analogues to explore the pharmacophore of activity on C. elegans and revealed five compounds whose potency was similar or greater than that of ABX464, but which were not toxic to human hepatoma (HepG2) cells. Subsequently, we employed thermal proteome profiling (TPP), protein structure prediction and an in silico-docking algorithm to predict ABX464-target candidates. Taken together, the findings from this study contribute significantly to the early-stage drug discovery of a new nematocide based on ABX464. Future work is aimed at validating the ABX464-protein interactions identified here, and at assessing ABX464 and associated analogues against a panel of parasitic nematodes, towards developing a new anthelmintic with a mechanism of action that is distinct from any of the compounds currently-available commercially.

Activity-based protein profiling in drug/pesticide discovery: Recent advances in target identification of antibacterial compounds.

Given the escalating incidence of bacterial diseases and the challenge posed by pathogenic bacterial resistance, it is imperative to identify appropriate methodologies for conducting proteomic investigations on bacteria, and thereby promoting the target-based drug/pesticide discovery. Interestingly, a novel technology termed "activity-based protein profiling" (ABPP) has been developed to identify the target proteins of active molecules. However, few studies have summarized advancements in ABPP for identifying the target proteins in antibacterial-active compounds. In order to accelerate the discovery and development of new drug/agrochemical discovery, we provide a concise overview of ABPP and its recent applications in antibacterial agent discovery. Diversiform cases were cited to demonstrate the potential of ABPP for target identification though highlighting the design strategies and summarizing the reported target protein of antibacterial compounds. Overall, this review is an excellent reference for probe design towards antibacterial compounds, and offers a new perspective of ABPP in bactericide development.

Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction.

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target's micro-motion component and the energy entropy of the time-frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data.

Drug upgrade: A complete methodology from old drug to new chemical entities using Nematic Protein Organization Technique.

Drug repurposing is used to propose new therapeutic perspectives. Here, we introduce "Drug Upgrade", that is, characterizing the mode of action of an old drug to generate new chemical entities and new therapeutics. We proposed a novel methodology covering target identification to pharmacology validation. As an old drug, we chose hydroxychloroquine (HCQ) for its well-documented clinical efficacy in lupus and its side effect, retinal toxicity. Using the Nematic Protein Organization Technique (NPOT®) followed by liquid chromatography-tandem mass spectrometry analyses, we identified myeloperoxidase (MPO) and alpha-crystallin ß chain (CRYAB) as primary and secondary targets to HCQ from lupus patients' peripheral blood mononuclear cells (PBMCs) and isolated human retinas. Surface plasmon resonance (SPR) and enzymatic assays confirmed the interaction of HCQ with MPO and CRYAB. We synthesized INS-072 a novel analog of HCQ that increased affinity for MPO and decreased binding to CRYAB compared to HCQ. INS-072 delayed cutaneous eruption significantly compared to HCQ in the murine MRL/lpr model of spontaneous lupus and prevents immune complex vasculitis in mice. In addition, long-term HCQ treatment caused retinal toxicity in mice, unlike INS-072. Our study illustrates a method of drug development, where new applications or improvements can be explored by fully characterizing the drug's mode of action.