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

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AIMS: Nonalcoholic steatohepatitis (NASH) is the severe subtype of nonalcoholic fatty diseases (NAFLD) with few options for treatment. Patients with NASH exhibit partial responses to the current therapeutics and adverse effects. Identification of the binding proteins for the drugs is essential to understanding the mechanism and adverse effects of the drugs and fuels the discovery of potent and safe drugs. This paper aims to critically discuss recent advances in covalent and noncovalent approaches for identifying binding proteins that mediate NASH progression, along with an in-depth analysis of the mechanisms by which these targets regulate NASH. MATERIALS AND METHODS: A literature search was conducted to identify the relevant studies in the database of PubMed and the American Chemical Society. The search covered articles published from January 1990 to July 2024, using the search terms with keywords such as NASH, benzophenone, diazirine, photo-affinity labeling, thermal protein profiling, CETSA, target identification. KEY FINDINGS: The covalent approaches utilize drugs modified with diazirine and benzophenone to covalently crosslink with the target proteins, which facilitates the purification and identification of target proteins. In addition, they map the binding sites in the target proteins. By contrast, noncovalent approaches identify the binding targets of unmodified drugs in the intact cell proteome. The advantages and limitations of both approaches have been compared, along with a comprehensive analysis of recent innovations that further enhance the efficiency and specificity. SIGNIFICANCE: The analyses of the applicability of these approaches provide novel tools to delineate NASH pathogenesis and promote drug discovery.

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Targeting the PD-1/PD-L1 axis with small-molecular inhibitors is a promising approach for immunotherapy. Here, we identify a natural pentacyclic triterpenoid, Pygenic Acid A (PA), as a PD-1 signaling inhibitor. PA exerts anti-tumor activity in hPD-1 knock-in C57BL/6 mice and enhances effector functions of T cells to promote immune responses by disrupting the PD-1 signaling transduction. Furthermore, we identify SHP-2 as the direct molecular target of PA for inhibiting the PD-1 signaling transduction. Subsequently, mechanistic studies suggest that PA binds to a new druggable site in the phosphorylated PD-1 ITSM recognition site of SHP-2, inhibiting the recruitment of SHP-2 by PD-1. Taken together, our findings demonstrate that PA has a potential application in cancer immunotherapy and occupying the phosphorylated ITSM recognition site of SHP-2 may serve as an alternative strategy to develop PD-1 signaling inhibitors. In addition, our success in target recognition provides a paradigm of target identification and confirmation for natural products.

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Liquid crystal monomers (LCMs) are identified as emerging organic contaminations with largely unexplored health impacts. To elucidate their toxic mechanisms, support the establishment of environmental discharge and management standards, and promote effective LCMs control, this study constructs a database covering 20,545 potential targets of 1431 LCMs, highlighting 9 key toxic target proteins that disrupt the nervous system and metabolic functions. GO and KEGG pathway analysis suggests LCMs severely affect nervous system, linked to neurodegenerative diseases and mental health disorders, with toxicity variations driven by electronegativity and structural complexity of LCM terminal groups. To achieve tiered control of LCMs, construct toxicity risk control lists for 9 key toxic target proteins, suitable for the graded control of LCMs, management recommendations are provided based on toxicity levels. These lists were validated for reliability and offer reliable toxicity predictions for LCMs. SHAP analysis points to electronic properties, molecular shape, and structural characteristics of LCMs as primary health impact factors. As the first study integrating machine learning with computational toxicology to outline LCMs health impacts, it aims to enhance public understanding of LCM toxicity risks and support the development of environmental standards, effective management of LCM production and emissions, and reduction of public exposure risks.

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Purpose: Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Methods: Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. Results: In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Conclusion: Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

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Throughout the human lifespan, from conception to the end of life, small molecules have an intrinsic relationship with numerous physiological processes. The investigation into small-molecule targets holds significant implications for pharmacological discovery. The determination of the action sites of small molecules provide clarity into the pharmacodynamics and toxicological mechanisms of small-molecule drugs, assisting in the elucidation of drug off-target effects and resistance mechanisms. Consequently, innovative methods to study small-molecule targets have proliferated in recent years, with chemical proteomics standing out as a vanguard development in chemical biology in the post-genomic age. Chemical proteomics can non-selectively identify unknown targets of compounds within complex biological matrices, with both probe and non-probe modalities enabling effective target identification. This review attempts to summarize methods and illustrative examples of small-molecule target identification via chemical proteomics. It delves deeply into the interactions between small molecules and human biology to provide pivotal directions and strategies for the discovery and comprehension of novel pharmaceuticals, as well as to improve the evaluation of drug safety.

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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.

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MicroRNA482 (miR482) is a conserved microRNA family in plants, playing critical regulatory roles in different biological activities. Though the members of the miR482 gene family have been identified in plants, a systematic study has not been reported yet. In the present research, 140 mature sequences generated by 106 precursors were used for molecular characterization, phylogenetic analysis, and target gene prediction, and the competing endogenous RNA (ceRNA) network mediated by miR482 was summarized. The length of mature sequences ranged from 17 nt to 25 nt, with 22 nt being the most abundant, and the start and end of the mature sequences had a preference for uracil (U). By sequence multiplex comparison, it was found that the mature sequences of 5p were clustered into one group, and others were clustered into the other group. Phylogenetic analysis revealed that the 140 mature sequences were categorized into six groups. Meanwhile, all the precursor sequences formed a stable hairpin structure, and the 106 precursors were divided into five groups. However, the expression of miR482 varied significantly between different species and tissues. In total, 149 target genes were predicted and their functions focused on single-organism process, cellular process, and cell and cell part. The ceRNA network of miR482 in tomato, cotton, and peanut was summarized based on related publications. In conclusion, this research will provide a foundation for further understanding of the miR482 gene family.

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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.

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Metabolic disorders, including obesity, dyslipidemia, diabetes, nonalcoholic fatty liver disease, and metabolic syndrome, are characterized by insulin resistance, abnormalities in circulating cholesterol and lipid profiles, and hypertension. The most common pathophysiologies of metabolic disorders are glucose/lipid metabolism dysregulation, insulin resistance, inflammatory response, and oxidative stress. Although several agents have been approved for the treatment of metabolic disorders, there is still a strong demand for more efficacious drugs with less side effects. Natural products have been critical sources of drug research and discovery for decades. However, the usefulness of bioactive natural products is often limited by incomplete understanding of their direct cellular targets. In this review, we highlight the current understanding of the established and emerging molecular mechanisms of metabolic disorders. We further summarize the therapeutic effects and underlying mechanisms of natural products on metabolic disorders, with highlights on their direct cellular targets, which are mainly implicated in the regulation of glucose/lipid metabolism, insulin resistance, metabolic inflammation, and oxidative stress. Finally, this review also covers the clinical studies of natural products in metabolic disorders. These progresses are expected to facilitate the application of these natural products and their derivatives in the development of novel drugs against metabolic disorders.

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Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

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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.

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Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.

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Chemical proteomics using biotin probes of natural products have significantly advanced our understanding of molecular targets and therapeutic potential. This review highlights recent progress in the application of biotin probes of homoisoflavonoids for identifying binding proteins and elucidating mechanisms of action. Notably, homoisoflavonoids exhibit antiangiogenic, anti-inflammatory, and antidiabetic effects. A combination of biotin probes, pull-down assays, mass spectrometry, and molecular modeling has revealed how natural products and their derivatives interact with several proteins such as ferrochelatase (FECH), soluble epoxide hydrolase (sEH), inosine monophosphate dehydrogenase 2 (IMPDH2), phosphodiesterase 4 (PDE4), and deoxyhypusine hydroxylase (DOHH). These target identification approaches pave the way for new therapeutic avenues, especially in the fields of oncology and ophthalmology. Future research aimed at expanding the repertoire of target identification using biotin probes of homoisoflavonoids promises to further elucidate the complex mechanisms and develop new drug candidates.

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Immune-mediated inflammatory disease (IMID) prevalence is estimated at 3-7% for Westernised populations, with annual incidence reported at almost 1 in 100 people globally. More recently, drug discovery approaches have been evolving towards more targeted therapies with an improved long-term safety profile, while the requirement for individualisation of medicine in complex conditions such as IMIDs, is acknowledged. However, existing preclinical models-such as cellular and in vivo mammalian models-are not ideal for modern drug discovery model requirements, such as real-time in vivo visualisation of drug effects, logistically feasible safety assessment over the course of a lifetime, or dynamic assessment of physiological changes during disease development. Zebrafish share high homology with humans in terms of proteins and disease-causing genes, with high conservation of physiological processes at organ, tissue, cellular and molecular level. These and other unique attributes, such as high fecundity, relative transparency and ease of genetic manipulation, positions zebrafish as the next major role player in IMID drug discovery. This review provides a brief overview of the suitability of this organism as model for human inflammatory disease and summarises the range of approaches used in zebrafish-based drug discovery research. Strengths and limitations of zebrafish as model organism, as well as important considerations in research study design, are discussed. Finally, under-utilised avenues for investigation in the IMID context are highlighted.

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BACKGROUND: Microbial communities are important drivers of global biogeochemical cycles, xenobiotic detoxification, as well as organic matter decomposition. Their major metabolic role in ecosystem functioning is ensured by a unique set of enzymes, providing a tremendous yet mostly hidden enzymatic potential. Exploring this enzymatic repertoire is therefore not only relevant for a better understanding of how microorganisms function in their natural environment, and thus for ecological research, but further turns microbial communities, in particular from extreme habitats, into a valuable resource for the discovery of novel enzymes with potential applications in biotechnology. Different strategies for their uncovering such as bioprospecting, which relies mainly on metagenomic approaches in combination with sequence-based bioinformatic analyses, have emerged; yet accurate function prediction of their proteomes and deciphering the in vivo activity of an enzyme remains challenging. RESULTS: Here, we present environmental activity-based protein profiling (eABPP), a multi-omics approach that extends genome-resolved metagenomics with mass spectrometry-based ABPP. This combination allows direct profiling of environmental community samples in their native habitat and the identification of active enzymes based on their function, even without sequence or structural homologies to annotated enzyme families. eABPP thus bridges the gap between environmental genomics, correct function annotation, and in vivo enzyme activity. As a showcase, we report the successful identification of active thermostable serine hydrolases from eABPP of natural microbial communities from two independent hot springs in Kamchatka, Russia. CONCLUSIONS: By reporting enzyme activities within an ecosystem in their native state, we anticipate that eABPP will not only advance current methodological approaches to sequence homology-guided enzyme discovery from environmental ecosystems for subsequent biocatalyst development but also contributes to the ecological investigation of microbial community interactions by dissecting their underlying molecular mechanisms.

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Centromere protein A (CENP-A), a centromere-specific histone H3 variant, is crucial for kinetochore positioning and chromosome segregation. However, its regulatory mechanism in human cells remains incompletely understood. A structure-activity relationship (SAR) study of the cell-cycle-arresting indole terpenoid mimic JP18 leads to the discovery of two more potent analogs, (+)-6-Br-JP18 and (+)-6-Cl-JP18. Tubulin is identified as a potential cellular target of these halogenated analogs by using the drug affinity responsive target stability (DARTS) based method. X-ray crystallography analysis reveals that both molecules bind to the colchicine-binding site of ß-tubulin. Treatment of human cells with microtubule-targeting agents (MTAs), including these two compounds, results in CENP-A accumulation by destabilizing Cdh1, a co-activator of the anaphase-promoting complex/cyclosome (APC/C) E3 ubiquitin ligase. This study establishes a link between microtubule dynamics and CENP-A accumulation using small-molecule tools and highlights the role of Cdh1 in CENP-A proteolysis.

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Molecular glues can induce proximity between a target protein and ubiquitin ligases to induce target degradation, but strategies for their discovery remain limited. We screened 3,200 bioactive small molecules and identified that C646 requires neddylation-dependent protein degradation to induce cytotoxicity. Although the histone acetyltransferase p300 is the canonical target of C646, we provide extensive evidence that C646 directly targets and degrades Exportin-1 (XPO1). Multiple cellular phenotypes induced by C646 were abrogated in cells expressing the known XPO1C528S drug-resistance allele. While XPO1 catalyzes nuclear-to-cytoplasmic transport of many cargo proteins, it also directly binds chromatin. We demonstrate that p300 and XPO1 co-occupy hundreds of chromatin loci. Degrading XPO1 using C646 or the known XPO1 modulator S109 diminishes the chromatin occupancy of both XPO1 and p300, enabling direct targeting of XPO1 to phenocopy p300 inhibition. This work highlights the utility of drug-resistant alleles and further validates XPO1 as a targetable regulator of chromatin state.

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Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

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