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

Barcoded screening identifies nanocarriers for protein delivery to kidney

Targeted protein delivery with nanocarriers holds significant potential to enhance therapeutic outcomes by precisely directing proteins to specific organs or tissues. However, the complex interactions between nanocarriers and the biological environment pose considerable challenges in designing effective targeted delivery vehicles. In this study, we address this challenge by leveraging DNA-barcoded high-throughput screening. We construct a nanocapsule library via in-situ polymerization, incorporating various monomers to create nanocapsules with unique surface properties. In vitro and in vivo screening, using female mice, identify nanocapsules with high cell association and different biodistribution. Our investigation into kidney-enriched nanocapsules highlights the crucial role of polymer composition in biodistribution, demonstrating the potential of surface engineering for precise control over nanoparticle distribution. The kidney-enriched nanocapsule successfully delivers catalase, showcasing its therapeutic potential in mitigating cisplatin-induced acute kidney injury. Overall, our study presents an approach for identifying protein delivery vehicles, with the capacity to broaden the application of proteins as therapeutic agents or research tools.

Fluorescent d-amino Acid-Based Approach Enabling Fast and Reliable Measure of Antibiotic Susceptibility in Bacterial Cells

The threat of multidrug-resistant bacteria has been increasing steadily in the past century, posing a major health risk (Organización Mundial de la Salud. Directrices Sobre Componentes Básicos Para Los Programas de Prevención y Control de Infecciones a Nivel Nacional y de Establecimientos de Atención de Salud Para Pacientes Agudos; Organización Mundial de la Salud: Ginebra, 2017). Even though every year, 226 million antibiotics are prescribed in the United States alone, 50% of these prescriptions are inappropriate for the patient's condition (CDC. Get Smart about Antibiotics Week; Centers for Disease Control and Prevention. 2016,https://www.cdc.gov/media/dpk/antibiotic-resistance/antibiotics-week-2016/dpk-antibiotics-week-2016.html). The increasing abuse of antibiotics in healthcare as well as agriculture has resulted in the rise of antibiotic resistance at an alarming rate. In a clinical setting, timely and accurate recognition of the pathogen allows for the most effective choice of treatment, highlighting the need for novel, fast, and reliable antibiotic susceptibility testing. Traditional susceptibility testing techniques require costly and complex experimental setups or extended cell incubation periods, delaying a timely treatment response to the infection. Herein, we report that a short-pulse fluorescent d-amino acid (FDAA)-based approach provides insight not only into bacterial antibiotic susceptibility but also into the mechanism of action of the antibiotic. Using the FDAA-labeling signal as a reflection of peptidoglycan (PG) integrity after antibiotic treatment, we observed that drugs targeting PG biosynthesis resulted in a significant decrease in fluorescence, while antimicrobials affecting other cellular targets resulted in no fluorescence changes. Our method was validated and optimized via fluorescence microscopy and spectrofluorometry, shortening the required procedure time to 15 min and providing reliably reproducible results. Significantly, we demonstrate that our protocol can be used to identify β-lactam-resistant bacterial strains, further demonstrating the utility of these valuable molecular tools.

Chemical proteomic profiling reveals prostaglandin termination enzyme PTGR2 as a key molecular target of natural coumarin fraxetin

Natural coumarins represent a diverse group of secondary metabolites with a wide range of biological activities. However, their specific molecular targets have remained largely unexplored. Employing chemical proteomics, a comprehensive analysis of the protein targets of the natural coumarin fraxetin has been conducted. Prostaglandin reductase 2 (PTGR2), a key enzyme involved in the final inactivation of prostaglandins, was identified as a primary target of fraxetin. Inhibition of PTGR2 can lead to the accumulation of 15-keto-PGE2, which subsequently activates the Nrf2 signaling pathway and suppresses NF-κB, resulting in notable anti-inflammatory effects. These findings provide novel insights into the molecular targets of fraxetin and other coumarins, which are crucial for fully exploring their therapeutic potential.

Chemical Probes for Investigating the Endocannabinoid System

Cannabis sativa has been used therapeutically since early civilizations, with key cannabinoids Δ9-tetrahydrocannabinol (THC) 3.1 and cannabidiol characterized in the 1960s, leading to the discovery of cannabinoid receptors type 1 (CB1R) and type 2 (CB2R) and the endocannabinoid system (ECS) in the 1990s. The ECS, involving endogenous ligands like 2-arachidonoylglycerol (2-AG) 1.1, anandamide (N-arachidonoylethanolamine (AEA)) 1.2, and various proteins, regulates vital processes such as sleep, appetite, and memory, and holds significant therapeutic potential, especially for neurological disorders. Small molecule-derived pharmacological tools, or chemical probes, target key components of the ECS and are crucial for target validation, mechanistic studies, pathway elucidation, phenotypic screening, and drug discovery. These probes selectively interact with specific proteins or pathways, enabling researchers to modulate target activity and observe biological effects. When they carry an additional reporter group, they are referred to as labeled chemical probes. Developed through medicinal chemistry, structural biology, and high-throughput screening, effective chemical probes must be selective, potent, and depending on their purpose meet additional criteria such as cell permeability and metabolic stability.This chapter describes high-quality labeled and unlabeled chemical probes targeting ECS constituents that have been successfully applied for various research purposes. CB1R and CB2R, class A G protein-coupled receptors, are activated by 2-AG 1.1, AEA 1.2, and THC 3.1, with numerous ligands developed for these receptors. Imaging techniques like single-photon emission computed tomography, positron emission tomography, and fluorescently labeled CB1R and CB2R probes have enhanced CB receptor studies. CB2R activation generally results in immunosuppressive effects, limiting tissue injury. AEA 1.2 is mainly degraded by fatty acid amide hydrolase (FAAH) or N-acylethanolamine acid amidase (NAAA) into ethanolamine and arachidonic acid (AA) 1.3. FAAH inhibitors increase endogenous fatty acid amides, providing analgesic effects without adverse effects. NAAA inhibitors reduce inflammation and pain in animal models. Diacylglycerol lipase (DAGL) is essential for 2-AG 1.1 biosynthesis, while monoacylglycerol lipase (MAGL) degrades 2-AG 1.1 into AA 1.3, thus regulating cannabinoid signaling. Multiple inhibitors targeting FAAH and MAGL have been generated, though NAAA and DAGL probe development lags behind. Similarly, advancements in inhibitors targeting endocannabinoid (eCB) cellular uptake or trafficking proteins like fatty acid-binding proteins have been slower. The endocannabinoidome (eCBome) includes the ECS and related molecules and receptors, offering therapeutic opportunities from non-THC cannabinoids and eCBome mediators. Ongoing research aims to refine chemical tools for ECS and eCBome study, addressing unmet medical needs in central nervous system disorders and beyond.

LIN28-Targeting Chromenopyrazoles and Tetrahydroquinolines Induced Cellular Morphological Changes and Showed High Biosimilarity with BRD PROTACs

The probing of small molecules with heterocyclic scaffolds covering unexplored chemical space and the evaluation of their biological relevance are essential parts of forward chemical genetics approaches and for the development of potential small-molecule therapeutics. In this study, we profiled sets of chromenopyrazoles (CMPs) and tetrahydroquinolines (THQs), originally developed to target the protein-RNA interaction of LIN28-let-7, in a cell painting assay (CPA) measuring cellular morphological changes. Selected LIN28-inactive CMPs and THQs induced cellular morphological changes to different extents. The most CPA-active CMPs 2 and 3 exhibited high bio-similarity with the LCH and BET clusters, while the most CPA-active THQs 13 and 20 indicated a mechanism of action beyond the currently established biosimilarity clusters. Overall, this work demonstrated that CPA is useful in revealing "hidden" biological targets and mechanisms of action for biologically inactive small molecules, which are CMPs and THQs targeting the RNA-binding protein LIN28 in this case, evaluated in target-based strategies. When compared with annotated reference compounds, CMP 3 exhibited a high biosimilarity with the dual BRD7/9 degrading PROTAC VZ185, suggesting that CPA could potentially function as a new phenotypic approach to identify degrader molecules.

Fused oxazepine-naphthoquinones as novel cytotoxic agents with diverse modes of action in yeast

The naphthoquinone moiety is commonly found in numerous natural cytotoxic compounds with diverse and pleiotropic modes of action (MOAs). The moiety can exist as a standalone pharmacophore or combined with other pharmacophores to enrich their MOAs. Here, we report that the synthetic fusion of naphthoquinones and oxazepines provides potent cytotoxic compounds with diverse MOAs. Fused oxazepine-naphthoquinones were identified through a cytotoxic screen in Saccharomyces cerevisiae. The two most active compounds, CM-568 and CM-728, contained the same 3-pyridyl appendage in the oxazepine ring and were further evaluated along with close chemical derivatives. Both were highly cytotoxic, killing yeast cells in the low micromolar range; however, the role of reactive oxygen species in their MOA was significantly different. Investigations with yeast strains specifically designed to assess cell cycle, chromatin compaction, and nucleolar activity suggest that at lethal concentrations, cells die shortly after drug exposure through programmed death. Conversely, at sublethal concentrations, cell cycle progression is severely impaired. Interestingly, CM-568 labels cells with highly refractive non-fluorescent parallel rods. We conclude that the oxazepine moiety confers novel cytotoxic MOAs to naphthoquinones, which may be potentially useful in pharmacology.

Chemical tools to expand the ligandable proteome: Diversity-oriented synthesis-based photoreactive stereoprobes

Chemical proteomics enables the global analysis of small molecule-protein interactions in native biological systems and has emerged as a versatile approach for ligand discovery. The range of small molecules explored by chemical proteomics has, however, remained limited. Here, we describe a diversity-oriented synthesis (DOS)-inspired library of stereochemically defined compounds bearing diazirine and alkyne units for UV light-induced covalent modification and click chemistry enrichment of interacting proteins, respectively. We find that these "photo-stereoprobes" interact in a stereoselective manner with hundreds of proteins from various structural and functional classes in human cells and demonstrate that these interactions can form the basis for high-throughput screening-compatible NanoBRET assays. Integrated phenotypic screening and chemical proteomics identified photo-stereoprobes that modulate autophagy by engaging the mitochondrial serine protease CLPP. Our findings show the utility of DOS-inspired photo-stereoprobes for expanding the ligandable proteome, furnishing target engagement assays, and facilitating the discovery and characterization of bioactive compounds in phenotypic screens.

Synthesis and Preliminary Evaluation of Tanshinone Mimic Conjugates for Mechanism of Action Studies

Human antigen R (HuR) is an RNA binding protein (RBP) belonging to the ELAV (Embryonic Lethal Abnormal Vision) family, which stabilizes mRNAs and regulates the expression of multiple genes. Its altered expression or localization is related to pathological features such as cancer or inflammation. Dihydrotanshinone I (DHTS I) is a naturally occurring, tetracyclic ortho-quinone inhibitor of the HuR-mRNA interaction. Our earlier efforts led to the identification of a synthetic Tanshinone Mimic (TM) 2 with improved affinity for HuR. Here we report five new TM probes 3-5 bearing a detection-promoting moiety (either photo affinity probe - PAP or biotin) as a para-substituent on the phenyl-sulphonamide for mechanism of action (MoA) studies. Biological and biochemical assays were used to characterize the novel TM conjugates 3-5. They showed similar toxic activity in HuR-expressing triple-negative breast cancer MDA-MB-231 cells, with micromolar CC50s. REMSAs revealed that photoactivatable groups (4 a and 4 b), but not biotin (5 a and 5 b), prevented conjugates' ability to disrupt rHuR-RNA complexes. Further biochemical studies confirmed that biotinylated probes, in particular 5 a, can be used to isolate rM1 M2 from solutions, taking advantage of streptavidin-coated magnetic beads, thus being the most promising HuR inhibitor to be used for further MoA studies in cell lysates.

Mechanisms of H2pmen-Induced cell death: Necroptosis and apoptosis in MDA cells, necrosis in MCF7 cells

Breast cancer is the second leading cause of cancer-related deaths in women around the world. Several cancer therapeutics have already been discovered and are being used to treat breast cancer. However, most of them cause severe side effects. H2pmen, a tetradentate ligand, was used in this study to investigate its cytotoxic effects on growth, viability, and induction of cell death in MCF7 and MDA cells. The cell viability was determined by treating cells with different concentrations of H2pmen. MTT assay was used to obtain an IC50, and the cells were then assayed for membrane damage, apoptotic induction, and metabolism. Protein expression of Bax, p53, Bcl2, and xIAP was identified using Western blot analysis. The gene expression of RIPK1, RIPK3, and MKLK was determined using qPCR. In MDA cells, H2pmen increases cytotoxicity, as evidenced by upregulated LDH and JC-10, and enhances apoptosis, indicated by upregulated caspase-3/7 and Bax. In contrast, MCF7 cells exhibit a more stable profile with downregulated LDH and Annexin V Activity. MCF7 cells also show reduced necroptosis and increased necrosis. These findings highlight that H2pmen induces varied cytotoxic effects across MDA and MCF7 cells, with MDA cells exhibiting more pronounced apoptosis and necroptosis alongside complex anti-apoptotic responses.

Investigation of antibacterial mode of action of ω-aminoalkoxylxanthones by NMR-based metabolomics and molecular docking

INTRODUCTION

The knowledge of the mode of action of an antimicrobial is essential for drug development and helps to fight against bacterial resistance. Thus, it is crucial to use analytical techniques to study the mechanism of action of substances that have potential to act as antibacterial agents OBJECTIVE: To use NMR-based metabolomics combined with chemometrics and molecular docking to identify the metabolic responses of Staphylococcus aureus following exposure to commercial antibiotics and some synthesized ω-aminoalkoxylxanthones.

METHODS

Intracellular metabolites of S. aureus were extracted after treatment with four commercial antibiotics and three synthesized ω-aminoalkoxylxanthones. NMR spectra were obtained and 1H NMR data was analyzed using both unsupervised and supervised algorithms (PCA and PLS-DA, respectively). Docking simulations on DNA topoisomerase IV protein were also performed for the ω-aminoalkoxylxanthones.

RESULTS

Through chemometric analysis, we distinguished between the control group and antibiotics with extracellular (ampicillin) and intracellular targets (kanamycin, tetracycline, and ciprofloxacin). We identified 21 metabolites, including important metabolites that differentiate the groups, such as betaine, acetamide, glutamate, lysine, alanine, isoleucine/leucine, acetate, threonine, proline, and ethanol. Regarding the xanthone-type derivatives (S6, S7 and S8), we observed a greater similarity between S7 and ciprofloxacin, which targets bacterial DNA replication. The molecular docking analysis showed high affinity of the ω-aminoalkoxylxanthones with the topoisomerase IV enzyme, as well as ciprofloxacin.

CONCLUSION

NMR-based metabolomics has shown to be an effective technique to assess the metabolic profile of S. aureus after treatment with certain antimicrobial compounds, helping the investigation of their mechanism of action.

Chemical proteomics accelerates the target discovery of natural products

More than half of the global novel drugs are directly or indirectly derived from natural products (NPs) because of their better selectivity towards proteins. Traditional medicines perform multiple bioactivities through various NPs binding to drug targets, which highlights the opportunities of target discovery for drug development. However, detecting the binding relationship between NPs and targets remains challenging. Chemical proteomics, an interdisciplinary field of chemistry, proteomics, biology, and bioinformatics, has emerged as a potential approach for uncovering drug-target interactions. This review summarizes the principles and characteristics of the current widely applied chemical proteomic technologies, while delving into their latest applications in the target discovery of natural medicine. These endeavours demonstrate the potential of chemical proteomics for target discovery to supply dependable methodologies for the target elucidation of NPs.

Drug-target interaction prediction by integrating heterogeneous information with mutual attention network

BACKGROUND

Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction.

METHODS

Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction.

RESULTS

DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing.

Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure-activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer.

Total glucosides of Picrorhizae Rhizome alleviate non-alcoholic steatohepatitis (NASH) by specifically targeting acyl-CoA oxidase 1

Nonalcoholic steatohepatitis (NASH), a chronic liver disease characterized by the accumulation of fat in the liver, is highly prevalent on a global scale. In this study, we investigated the effects of total glucosides of Picrorhizae Rhizome (TGPR), the primary active ingredients in traditional Chinese herbal medicine derived from Picrorhiza scrophulariiflora Pennell. TGPR is known for its efficiency in attenuating NASH, in mouse models induced by methionine-choline deficient (MCD) diet or high-fat diet (HFD). Our findings indicated that TGPR exhibited efficacy in reducing hepatic steatosis and lowering serum lipid levels, specifically triglyceride and total cholesterol in the NASH model. Meanwhile, TGPR exhibited a suppressive effect on the production of pro-inflammatory cytokines. Mechanistically, we identified acyl-CoA oxidase 1 (Acox1) as a crucial cellular target of TGPR, influencing lipid metabolism and ATP production to treat NASH. Additionally, we found that the major components of TGPR, including Picroside I, Picroside II, and Picroside IV, exhibit significant binding abilities to the target Acox1 at its catalytic C-terminal α-domain, stabilizing its protein expression. TGPR binding to Acox1 facilitated the degradation of fatty acids via the Acox1-mediated MAPK signaling pathways, and consequently plays a role in regulating energy metabolism and reducing liver inflammation. In summary, our study demonstrates that TGPR effectively counteracts NASH by specifically targeting Acox1, thereby providing a significant clinical solution for the treatment of NASH.

Nrf2 pathways in neuroprotection: Alleviating mitochondrial dysfunction and cognitive impairment in aging

Mitochondrial dysfunction and cognitive impairment are widespread phenomena among the elderly, being crucial factors that contribute to neurodegenerative diseases. Nuclear factor erythroid 2-related factor 2 (Nrf2) is an important regulator of cellular defense systems, including that against oxidative stress. As such, increased Nrf2 activity may serve as a strategy to avert mitochondrial dysfunction and cognitive decline. Scientific data on Nrf2-mediated neuroprotection was collected from PubMed, Google Scholar, and Science Direct, specifically addressing mitochondrial dysfunction and cognitive impairment in older people. Search terms included "Nrf2", "mitochondrial dysfunction," "cognitive impairment," and "neuroprotection." Studies focusing on in vitro and in vivo models and clinical investigations were included to review Nrf2's therapeutic potential comprehensively. The relative studies have demonstrated that increased Nrf2 activity could improve mitochondrial performance, decrease oxidative pressure, and mitigate cognitive impairment. To a large extent, this is achieved through the modulation of critical cellular signalling pathways such as the Keap1/Nrf2 pathway, mitochondrial biogenesis, and neuroinflammatory responses. The present review summarizes the recent progress in comprehending the molecular mechanisms regarding the neuroprotective benefits mediated by Nrf2 through its substantial role against mitochondrial dysfunction and cognitive impairment. This review also emphasizes Nrf2-target pathways and their contribution to cognitive function improvement and rescue from mitochondria-related abnormalities as treatment strategies for neurodegenerative diseases that often affect elderly individuals.

AI-empowered perturbation proteomics for complex biological systems

The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.

Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB . SCIENTIFIC CONTRIBUTION: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions.

Structure-guided drug repurposing identifies aristospan as a potential inhibitor of β-lactamase: insights from virtual screening and molecular dynamics simulations

The rise of β-Lactamase mediated antibiotic resistance is a major concern for public health; hence, there is an urgent need to find new treatment approaches. Structure-guided drug repurposing offers a promising approach to swiftly deliver essential therapeutics in the fight against escalating antibiotic resistance. Here, a structure-guided virtual screening approach was used involving drug profiling, molecular docking, and molecular dynamics (MD) simulation to identify existing drugs against β-Lactamase-associated drug resistance. We exploited a large panel of FDA-approved drugs to an extensive in silico analysis to ascertain their ability to inhibit β-Lactamase. First, molecular docking investigations were performed to assess the binding affinities and interactions of screened molecules with the active site of β-Lactamase enzymes. Out of all the screened candidates, Aristospan was identified to possess promising characteristics, which include appropriate drug profiles, high binding specificity, and efficiency towards the binding pocket of β-Lactamase. Further analysis showed that Aristospan possesses several desirable biological characteristics and tends to bind to the β-Lactamase binding site. To explore the interactions further, the best docking pose of Aristospan was selected for MD simulations to assess the thermodynamic stability of the drug-enzyme complex and its conformational changes over 500 ns. The MD simulations in independent replica runs demonstrated that the β-Lactamase-Aristospan complex was stable in the 500 ns trajectory. These enlightening results suggest that Aristospan may harbor the potential for further evolution into a possible β-Lactamase inhibitor, with potential applications in overcoming antibiotic resistance in both Gram-positive and Gram-negative bacteria.

A comprehensive survey of drug-target interaction analysis in allopathy and siddha medicine

Effective drug delivery is the cornerstone of modern healthcare, ensuring therapeutic compounds reach their intended targets efficiently. This paper explores the potential of personalized and holistic healthcare, driven by the synergy between traditional and allopathic medicine systems, with a specific focus on the vast reservoir of medicinal compounds found in plants rooted in the historical legacy of traditional medicine. Motivated by the desire to unlock the therapeutic potential of medicinal plants and bridge the gap between traditional and allopathic medicine, this survey delves into in-silico computational approaches for studying Drug-Target Interactions (DTI) within the contexts of allopathy and siddha medicine. The contributions of this survey are multifaceted: it offers a comprehensive overview of in-silico methods for DTI analysis in both systems, identifies common challenges in DTI studies, provides insights into future directions to advance DTI analysis, and includes a comparative analysis of DTI in allopathy and siddha medicine. The findings of this survey highlight the pivotal role of in-silico computational approaches in advancing drug research and development in both allopathy and siddha medicine, emphasizing the importance of integrating these methods to drive the future of personalized healthcare.

Activation of the Melatonin Receptor MT1 by the Natural Product Gastrodin to Promote Sleep

The activation of melatonin receptors, belonging to the G-protein coupled receptors (GPCRs) superfamily, has been recognized as a vital approach in the clinical management of sleep disorders. Although the natural agonist melatonin and synthetic agonists (e.g., ramelteon) targeting these receptors have been extensively studied, the identification of natural compounds acting as ligands remains elusive. We applied a combination of methods including GPCR-induced ERK1/2 MAP kinase phosphorylation assay, inhibition of forskolin-stimulated cAMP production, drug affinity responsive target stability (DARTS), cellular thermal shift assay (CETSA), solvent-induced protein precipitation (SIP), 2-[125I]-iodomelatonin binding assay, fluorescence resonance energy transfer (FRET), and molecular docking to investigate MT1 activation by gastrodin and the gastrodin-MT1 interaction. The in vivo study was performed with mice whose MT1 receptors were knocked down in the suprachiasmatic nucleus (SCN) of the brain. The sleep behavior and sleep-related hypothalamic neurotransmitters were evaluated. The results identified that the gastrodin acted as an agonist of MT1 through direct binding to the receptor. The interaction of gastrodin-MT1 was similar to that of melatonin-MT1. The in vivo sleep-promoting effect of the gastrodin depended on the presence of MT1 in the SCN and was associated with the hypothalamic neurotransmitters, similarly to melatonin.

A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale

Biomedical network learning offers fresh prospects for expediting drug repositioning. However, traditional network architectures struggle to quantify the relationship between micro-scale drug spatial structures and corresponding macro-scale biomedical networks, limiting their ability to capture key pharmacological properties and complex biomedical information crucial for drug screening and therapeutic discovery. Moreover, challenges such as difficulty in capturing long-range dependencies hinder current network-based approaches. To address these limitations, we introduce the Spatial Hierarchical Network, modeling molecular 3D structures and biological associations into a unified network. We propose an end-to-end framework, SpHN-VDA, integrating spatial hierarchical information through triple attention mechanisms to enhance machine understanding of molecular functionality and improve the accuracy of virus-drug association identification. SpHN-VDA outperforms leading models across three datasets, particularly excelling in out-of-distribution and cold-start scenarios. It also exhibits enhanced robustness against data perturbation, ranging from 20% to 40%. It accurately identifies critical motifs for binding sites, even without protein residue annotations. Leveraging reliability of SpHN-VDA, we have identified 25 potential candidate drugs through gene expression analysis and CMap. Molecular docking experiments with the SARS-CoV-2 spike protein further corroborate the predictions. This research highlights the broad potential of SpHN-VDA to enhance drug repositioning and identify effective treatments for various diseases.

Labeled and Label-Free Target Identifications of Natural Products

Target identification, employing chemical proteomics, constitutes a continuous challenging endeavor in the drug development of natural products (NPs). Understanding their targets is crucial for deciphering their mechanisms and developing potential probes or drugs. Identifications fall into two main categories: labeled and label-free techniques. Labeled methods use the molecules tagged with markers such as biotin or fluorescent labels to easily detect interactions with target proteins. Thorough structure-activity relationships are essential before labeling to avoid changes in the biological activity or binding specificity. In contrast, label-free technologies identify target proteins without modifying natural products, relying on changes in the stability, thermal properties, or precipitation in the presence or absence of these products. Each approach has its advantages and disadvantages, offering a comprehensive understanding of the mechanisms and therapeutic potential of the NPs. Here, we summarize target identification techniques for natural molecules, highlight case studies of notable NPs, and explore future applications and directions.

Annonaceous acetogenins mimic AA005 targets mitochondrial trifunctional enzyme alpha subunit to treat obesity in male mice

Obesity and related diseases pose a major health risk, yet current anti-obesity drugs inadequately addressing clinical needs. Here we show AA005, an annonaceous acetogenin mimic, resists obesity induced by high-fat diets and leptin mutations at non-toxic doses, with the alpha subunit of the mitochondrial trifunctional protein (HADHA) as a target identified through proteomics and in vitro validation. Pharmacokinetic analysis shows AA005 enriches in adipose tissue, prompting the creation of adipose-specific Hadha-deficient mice. These mice significantly mitigate diet-induced obesity, echoing AA005's anti-obesity effects. AA005 treatment and Hadha deletion in adipose tissues increase body temperature and energy expenditure in high-fat diet-fed mice. The beneficial impact of AA005 on obesity mitigation is ineffective without uncoupling protein 1 (UCP1), essential for thermogenesis regulation. Our investigation shows the interaction between AA005 and HADHA in mitochondria, activating the UCP1-mediated thermogenic pathway. This substantiates AA005 as a promising compound for obesity treatment, targeting HADHA specifically.

Characterization of the Conformational Hotspots of the RNA-Dependent RNA Polymerase Complex Identifies a Unique Structural Malleability of nsp8

Several antiviral therapeutic approaches have been targeted toward the RNA-dependent RNA polymerase (RdRp) complex that is involved in viral genome replication. In SARS-CoV-2, although the RdRp is a multiprotein complex, the focus has been on the ligand binding catalytic core (nonstructural protein nsp12), and not the multiprotein functional dynamics. In this study, we focus on the conformational ensembles of the RdRp complex and their modulation by the presence of RNA, performing comprehensive microsecond-scale atomistic simulations of the apo- and RNA-bound complex. We delineate the differential impact of RNA on the constituent proteins, such as conformational polymorphisms, dominant segment-specific fluctuations, and the switch in dynamical crosstalk within the complex. We distinguish dynamical signatures of nsp7, nsp8, and nsp12 in the apo-state that are reduced in the presence of the RNA and appear to "prime" the complex for activity. Importantly, we identify a unique structural malleability of the nsp8 protein with high conformational heterogeneity in the apo state, especially at three sites (Y71 for nsp8A, and D52 and A66 for nsp8B). Our work highlights the functional implications of the polymorphism of nsp8 structures and reveals possibilities for the development of allosteric inhibitors.

Multiplexed Target Profiling with Integrated Chemical Genomics and Chemical Proteomics

Target identification is crucial for elucidating the mechanisms of bioactive molecules in drug discovery. However, traditional methods assess compounds individually, making it challenging to efficiently examine multiple compounds in parallel, especially for structurally diverse compounds. This study reports a novel strategy called chemical genomics-facilitated chemical proteomics (CGCP) for multiplexing the target identification of bioactive small molecules. CGCP correlates compounds' perturbation of global transcription, or chemical genomic profiles, with their reactivity toward target proteins, enabling simultaneous identification of targets. We demonstrated the utility of CGCP by studying the targets of celastrol (Cel) and four other electrophilic compounds with varying levels of similarity to Cel based on their chemical genomic profiles. We identified multiple novel targets and binding sites shared by the compounds in a single experiment. CGCP enabled multiplexity and improved the efficiency of target identification for structurally distinct compounds, indicating its potential to accelerate drug discovery.

Integrated Thermal Proteome Profiling and Affinity Ultrafiltration Mass Spectrometry (iTPAUMS): A Novel Paradigm for Elucidating the Mechanism of Action of Natural Products

Natural products (NPs) are foundational to drug discovery, offering a rich repertoire of molecular diversity with multifaceted modes of action against a broad array of targets. Despite their potential, deconvoluting the intricate mechanism of action (MoA) of NPs, characterized by their multicomponent, multitarget, and multilevel interactions, remains a formidable challenge. Here, we introduce an innovative pipeline called integrated thermal proteome profiling and affinity ultrafiltration mass spectrometry (iTPAUMS). This approach combines the high-throughput capacity of thermal proteome profiling (TPP) with the specificity of affinity ultrafiltration mass spectrometry (AUMS), creating a powerful toolkit for elucidating complex MoAs of NPs. Significantly, our investigation represents a pioneering application of TPP to delineate the target group of NPs mixtures and overcome the long-standing obstacle of mapping specific component-target interactions through AUMS. Our findings demonstrate the utility of iTPAUMS in constructing a comprehensive component-target atlas, providing a robust analytical foundation for unraveling the intricate pharmacological landscapes of NPs and advancing drug discovery.

Genome-wide pan-GPCR cell libraries accelerate drug discovery

G protein-coupled receptors (GPCRs) are pivotal in mediating diverse physiological and pathological processes, rendering them promising targets for drug discovery. GPCRs account for about 40% of FDA-approved drugs, representing the most successful drug targets. However, only approximately 15% of the 800 human GPCRs are targeted by market drugs, leaving numerous opportunities for drug discovery among the remaining receptors. Cell expression systems play crucial roles in the GPCR drug discovery field, including novel target identification, structural and functional characterization, potential ligand screening, signal pathway elucidation, and drug safety evaluation. Here, we discuss the principles, applications, and limitations of widely used cell expression systems in GPCR-targeted drug discovery, GPCR function investigation, signal pathway characterization, and pharmacological property studies. We also propose three strategies for constructing genome-wide pan-GPCR cell libraries, which will provide a powerful platform for GPCR ligand screening, and facilitate the study of GPCR mechanisms and drug safety evaluation, ultimately accelerating the process of GPCR-targeted drug discovery.

Low-cost and sustainable smartphone-based tissue-on-chip device for bioluminescence biosensing

Several organ-on-chip and cell-on-chip devices have been reported, however, their main drawback is that they are not interoperable (i.e., they have been fabricated with customized equipment, thus cannot be applied in other facilities, unless having the same setup), and require cell-culture facilities and benchtop instrumentation. As a consequence, results obtained with such devices do not generally comply with the principles of findability, accessibility, interoperability, and reusability (FAIR). To overcome such limitation, leveraging cost-effective 3D printing we developed a bioluminescent tissue on-a-chip device that can be easily implemented in any laboratory. The device enables continuous monitoring of cell co-cultures expressing different bioluminescent reporter proteins and, thanks to the implementation of new highly bioluminescent luciferases having high pH and thermal stability, can be monitored via smartphone camera. Another relevant feature is the possibility to insert the chip into a commercial 24-well plate for use with standard benchtop instrumentation. The suitability of this device for 3D cell-based biosensing for monitoring activation of target molecular pathways, i.e., the inflammatory pathway via nuclear factor kappa-B (NF-κB) activation, and general cytotoxicity is here reported showing similar analytical performance when compared to conventional 3D cell-based assays performed in 24-well plates.

Current status and trends in small nucleic acid drug development: Leading the future

Small nucleic acid drugs, composed of nucleotides, represent a novel class of pharmaceuticals that differ significantly from conventional small molecule and antibody-based therapeutics. These agents function by selectively targeting specific genes or their corresponding messenger RNAs (mRNAs), further modulating gene expression and regulating translation-related processes. Prominent examples within this category include antisense oligonucleotides (ASO), small interfering RNAs (siRNAs), microRNAs (miRNAs), and aptamers. The emergence of small nucleic acid drugs as a focal point in contemporary biopharmaceutical research is attributed to their remarkable specificity, facile design, abbreviated development cycles, expansive target spectrum, and prolonged activity. Overcoming challenges such as poor stability, immunogenicity, and permeability issues have been addressed through the integration of chemical modifications and the development of drug delivery systems. This review provides an overview of the current status and prospective trends in small nucleic acid drug development. Commencing with a historical context, we introduce the primary classifications and mechanisms of small nucleic acid drugs. Subsequently, we delve into the advantages of the U.S. Food and Drug Administration (FDA) approved drugs and mainly discuss the challenges encountered during their development. Apart from researching chemical modification and delivery system that efficiently deliver and enrich small nucleic acid drugs to target tissues, promoting endosomal escape is a critical scientific question and important research direction in siRNA drug development. Future directions in this field will prioritize addressing these challenges to facilitate the clinical transformation of small nucleic acid drugs.

Pseudo-Natural Products: Expanding chemical and biological space by surpassing natural constraints

This review explores the recent advancements in the design and synthesis of pseudo-natural products (pseudo-NPs) by employing innovative principles and strategies, heralding a transformative era in chemistry and biology. Pseudo-NPs, produced through in silico fragmentation and the de novo recombination of natural product fragments, reveal compounds endowed with distinct biological activities. Their advantage lies in transcending natural product structures, fostering diverse possibilities. Research in this area over the past decade has yielded unconventional combinations of natural product fragments, leading to the identification of novel compounds possessing unique scaffolds and biological significance, thereby contributing to the discovery of new therapeutics. The pseudo-NPs exert potent biological effects through various signaling pathways. In chemical biology and medicinal chemistry, designing pseudo-NPs is an important strategy, harnessing molecular hybridization and bioinspired synthesis to generate diverse compounds with remarkable biological activities, underscoring their immense potential in drug discovery and development.

Towards cost-effective drug discovery: Reusable immobilized enzymes for neurological disease research

Enzyme handling and utilization bears many challenges such as their limited stability, intolerance of organic solvents, high cost, or inability to reuse. Most of these limitations can be overcome by enzyme immobilization on the surface of solid support. In this work, the recombinant form of human cholinesterases and monoamine oxidases as important drug targets for neurological diseases were immobilized on the surface of magnetic non-porous microparticles by a non-covalent bond utilizing the interaction between a His-tag terminus on the recombinant enzymes and cobalt (Co2+) ions immobilized on the magnetic microparticles. This type of binding led to targeted enzyme orientation, which completely preserved the catalytic activity and allowed high reproducibility of immobilization. In comparison with free enzymes, the immobilized enzymes showed exceptional stability in time and the possibility of repeated use. Relevant Km, Vmax, and IC50 values using known inhibitors were obtained using particular immobilized enzymes. Such immobilized enzymes on magnetic particles could serve as an excellent tool for a sustainable approach in the early stage of drug discovery.

Prediction of drug-target binding affinity based on multi-scale feature fusion

Accurate prediction of drug-target binding affinity (DTA) plays a pivotal role in drug discovery and repositioning. Although deep learning methods are widely used in DTA prediction, two significant challenges persist: (i) how to effectively represent the complex structural information of proteins and drugs; (ii) how to precisely model the mutual interactions between protein binding sites and key drug substructures. To address these challenges, we propose a MSFFDTA (Multi-scale feature fusion for predicting drug target affinity) model, in which multi-scale encoders effectively capture multi-level structural information of drugs and proteins are designed. And then a Selective Cross Attention (SCA) mechanism is developed to filter out the trivial interactions between drug-protein substructure pairs and retain the important ones, which will make the proposed model better focusing on these key interactions and offering insights into their underlying mechanism. Experimental results on two benchmark datasets demonstrate that MSFFDTA is superior to several state-of-the-art methods across almost all comparison metrics. Finally, we provide the ablation and case studies with visualizations to verify the effectiveness and the interpretability of MSFFDTA. The source code is freely available at https://github.com/whitehat32/MSFF-DTA/.

Coding Therapeutic Nucleic Acids from Recombinant Proteins to Next-Generation Vaccines: Current Uses, Limitations, and Future Horizons

This study aims to highlight the potential use of cTNAs in therapeutic applications. The COVID-19 pandemic has led to significant use of coding therapeutic nucleic acids (cTNAs) in terms of DNA and mRNA in the development of vaccines. The use of cTNAs resulted in a paradigm shift in the therapeutic field. However, the injection of DNA or mRNA into the human body transforms cells into biological factories to produce the necessary proteins. Despite the success of cTNAs in the production of corona vaccines, they have several limitations such as instability, inability to cross biomembranes, immunogenicity, and the possibility of integration into the human genome. The chemical modification and utilization of smart drug delivery cargoes resolve cTNAs therapeutic problems. The success of cTNAs in corona vaccine production provides perspective for the eradication of influenza viruses, Zika virus, HIV, respiratory syncytial virus, Ebola virus, malaria, and future pandemics by quick vaccine design. Moreover, the progress cTNAs technology is promising for the development of therapy for genetic disease, cancer therapy, and currently incurable diseases.

Antileishmanial natural products as potential inhibitors of the Leishmania pteridine reductase: insights from molecular docking and molecular dynamics simulations

Although many natural product-derived compounds possess anti-leishmanial activities in vitro and in vivo, their molecular targets in the Leishmania parasite remain elusive. This is a major challenge in optimizing these compounds into leads. The Leishmania pteridine reductase (PTR1) is peculiar for folate and pterin metabolism and has been validated as a drug target. In this study, 17 compounds with anti-leishmanial activities were screened against Leishmania major PTR1 (LmPTR1) using molecular docking and molecular dynamics (MD) simulations. All ligands were bound in the active site pocket of LmPTR1 with binding affinities ranging from -11.2 to -5.2 kcal/mol. Agnuside, betulin, betulinic acid, gerberinol, ismailin, oleanolic acid, pristimerin, and ursolic acid demonstrated binding affinities similar to a known inhibitor, methyl 1-(4-{[2,4-diaminopteridin-6-yl) methyl] amino} benzoyl) piperidine-4-carboxylate (DVP). MD simulations revealed that betulin, betulinic acid, ismailin, oleanolic acid, pristimerin, and ursolic acid formed stable complexes with LmPTR1. The binding free energies of the complexes were very good (-87 to -148 kJ/mol), and much higher than the complex of the standard DVP inhibitor and LmPTR1 (-27 kJ/mol). Betulin, betulinic acid, ismailin, oleanolic acid, pristimerin, and ursolic acid likely exert their antileishmanial action by inhibiting PTR1 and could thus be used as a basis for the development of potential antileishmanial chemotherapeutic agents.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00247-8.

CACTI: an in silico chemical analysis tool through the integration of chemogenomic data and clustering analysis

It is well-accepted that knowledge of a small molecule's target can accelerate optimization. Although chemogenomic databases are helpful resources for predicting or finding compound interaction partners, they tend to be limited and poorly annotated. Furthermore, unlike genes, compound identifiers are often not standardized, and many synonyms may exist, especially in the biological literature, making batch analysis of compounds difficult. Here, we constructed an open-source annotation and target hypothesis prediction tool that explores some of the largest chemical and biological databases, mining these for both common name, synonyms, and structurally similar molecules. We used this Chemical Analysis and Clustering for Target Identification (CACTI) tool to analyze the Pathogen Box collection, an open-source set of 400 drug-like compounds active against a variety of microbial pathogens. Our analysis resulted in 4,315 new synonyms, 35,963 pieces of new information and target prediction hints for 58 members.Scientific contributionsWith the employment of this tool, a comprehensive report with known evidence, close analogs and drug-target prediction can be obtained for large-scale chemical libraries that will facilitate their evaluation and future target validation and optimization efforts.

Machine learning in preclinical drug discovery

Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.

Antitumoral activity of different Amaryllidaceae alkaloids: In vitro and in silico assays

ETHNOPHARMACOLOGY RELEVANCE

The plants of Amaryllidaceae family, such as Amaryllis belladonna L., have been used as herbal remedies for thousands of years to address various disorders, including diseases that might today be identified as cancer.

AIM OF THE STUDY

The objective of this work was to evaluate the potential of three Amaryllidaceae alkaloids against four cancer cell lines.

MATERIAL AND METHODS

The alkaloids lycorine, 1-O-acetylcaranine, and montanine were evaluated in vitro against colon adenocarcinoma cell line (HCT-116) and breast carcinoma cell lines (MCF-7, MDAMB231, and Hs578T). Computational experiments (target prediction and molecular docking) were conducted to gain a deeper comprehension of possible interactions between these alkaloids and potential targets associated with these tumor cells.

RESULTS

Montanine presented the best results against HCT-116, MDAMB231, and Hs578T cell lines, while lycorine was the most active against MCF-7. In alignment with the target prediction outcomes and existing literature, four potential targets were chosen for the molecular docking analysis: CDK8, EGFR, ER-alpha, and dCK. The docking scores revealed two potential targets for the alkaloids with scores similar to co-crystallized inhibitors and substrates: CDK8 and dCK. A visual analysis of the optimal docked configurations indicates that the alkaloids may interact with some key residues in contrast to the other docked compounds. This observation implies their potential to bind effectively to both targets.

CONCLUSIONS

In vitro and in silico results corroborate with data literature suggesting the Amaryllidaceae alkaloids as interesting molecules with antitumoral properties, especially montanine, which showed the best in vitro results against colorectal and breast carcinoma. More studies are necessary to confirm the targets and pharmaceutical potential of montanine against these cancer cell lines.

Thermostability-assisted limited proteolysis-coupled mass spectrometry for capturing drug target proteins and sites

BACKGROUND

Identifying drug-binding targets and their corresponding sites is crucial for drug discovery and mechanism studies. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a sophisticated method used for the detection of compound and protein interactions. However, in some cases, LiP-MS cannot identify the target proteins due to the small structure changes or the lack of enrichment of low-abundant protein. To overcome this drawback, we developed a thermostability-assisted limited proteolysis-coupled mass spectrometry (TALiP-MS) approach for efficient drug target discovery.

RESULTS

We proved that the novel strategy, TALiP-MS, could efficiently identify target proteins of various ligands, including cyclosporin A (a calcineurin inhibitor), geldanamycin (an HSP90 inhibitor), and staurosporine (a kinase inhibitor), with accurately recognizing drug-binding domains. The TALiP protocol increased the number of target peptides detected in LiP-MS experiments by 2- to 8-fold. Meanwhile, the TALiP-MS approach can not only identify both ligand-binding stability and destabilization proteins but also shows high complementarity with the thermal proteome profiling (TPP) and machine learning-based limited proteolysis (LiP-Quant) methods. The developed TALiP-MS approach was applied to identify the target proteins of celastrol (CEL), a natural product known for its strong antioxidant and anti-cancer angiogenesis effect. Among them, four proteins, MTHFD1, UBA1, ACLY, and SND1 were further validated for their strong affinity to CEL by using cellular thermal shift assay. Additionally, the destabilized proteins induced by CEL such as TAGLN2 and CFL1 were also validated.

SIGNIFICANCE

Collectively, these findings underscore the efficacy of the TALiP-MS method for identifying drug targets, elucidating binding sites, and even detecting drug-induced conformational changes in target proteins in complex proteomes.

Leveraging binding pose metadynamics to optimise target fishing predictions for three diverse ligands and their true targets

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.

Unmasking the Antifungal Activity of Anacardium occidentale Leaf Extract against Candida albicans

Invasive fungal disease causes high morbidity and mortality among immunocompromised patients. Resistance to conventional antifungal drugs and the toxicity associated with high doses highlight the need for effective antifungal therapies. In this study, the antifungal potential of the ethanolic extract of Anacardium occidentale (Cashew Leaf) leaves were evaluated against Candida albicans and C. auris. The antifungal activity was tested by the broth microdilution method and growth kinetic test. To further explore its antifungal action mode, spectrofluorophotometry, confocal microscopy and scanning and transmission electron microscopy were performed. Additionally, heterozygous knockout strains associated with resistance to oxidative stress were included in the study. We found that A. occidentale could inhibit the proliferation and growth of C. albicans at concentrations of 62.5 and 125 μg/mL. The doubling time was also drastically affected, going from 2.8 h to 22.5 h, which was also observed in C. auris. The extract induced the accumulation of intracellular reactive oxygen species (ROS), resulting in endoplasmic reticulum stress and mitochondrial dysfunction, while it did not show cytotoxicity or hemolytic activity at the concentrations evaluated. Our work preliminarily elucidated the potential mechanisms of A. occidentale against C. albicans on a cellular level, and might provide a promising option for the design of a new treatment for invasive candidiasis.

Predicting protein functions using positive-unlabeled ranking with ontology-based priors

Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive samples are trained as negatives. We introduce a novel approach named PU-GO, wherein we address function prediction as a positive-unlabeled ranking problem. We apply empirical risk minimization, i.e. we minimize the classification risk of a classifier where class priors are obtained from the Gene Ontology hierarchical structure. We show that our approach is more robust than other state-of-the-art methods on similarity-based and time-based benchmark datasets.

AVAILABILITY AND IMPLEMENTATION

Data and code are available at https://github.com/bio-ontology-research-group/PU-GO.

An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model

Identification of interactions between chemical compounds and proteins is crucial for various applications, including drug discovery, target identification, network pharmacology, and elucidation of protein functions. Deep neural network-based approaches are becoming increasingly popular in efficiently identifying compound-protein interactions with high-throughput capabilities, narrowing down the scope of candidates for traditional labor-intensive, time-consuming and expensive experimental techniques. In this study, we proposed an end-to-end approach termed SPVec-SGCN-CPI, which utilized simplified graph convolutional network (SGCN) model with low-dimensional and continuous features generated from our previously developed model SPVec and graph topology information to predict compound-protein interactions. The SGCN technique, dividing the local neighborhood aggregation and nonlinearity layer-wise propagation steps, effectively aggregates K-order neighbor information while avoiding neighbor explosion and expediting training. The performance of the SPVec-SGCN-CPI method was assessed across three datasets and compared against four machine learning- and deep learning-based methods, as well as six state-of-the-art methods. Experimental results revealed that SPVec-SGCN-CPI outperformed all these competing methods, particularly excelling in unbalanced data scenarios. By propagating node features and topological information to the feature space, SPVec-SGCN-CPI effectively incorporates interactions between compounds and proteins, enabling the fusion of heterogeneity. Furthermore, our method scored all unlabeled data in ChEMBL, confirming the top five ranked compound-protein interactions through molecular docking and existing evidence. These findings suggest that our model can reliably uncover compound-protein interactions within unlabeled compound-protein pairs, carrying substantial implications for drug re-profiling and discovery. In summary, SPVec-SGCN demonstrates its efficacy in accurately predicting compound-protein interactions, showcasing potential to enhance target identification and streamline drug discovery processes.Scientific contributionsThe methodology presented in this work not only enables the comparatively accurate prediction of compound-protein interactions but also, for the first time, take sample imbalance which is very common in real world and computation efficiency into consideration simultaneously, accelerating the target identification and drug discovery process.

Roadmap for Clinical Translation of Mobile Microrobotics

Medical microrobotics is an emerging field to revolutionize clinical applications in diagnostics and therapeutics of various diseases. On the other hand, the mobile microrobotics field has important obstacles to pass before clinical translation. This article focuses on these challenges and provides a roadmap of medical microrobots to enable their clinical use. From the concept of a "magic bullet" to the physicochemical interactions of microrobots in complex biological environments in medical applications, there are several translational steps to consider. Clinical translation of mobile microrobots is only possible with a close collaboration between clinical experts and microrobotics researchers to address the technical challenges in microfabrication, safety, and imaging. The clinical application potential can be materialized by designing microrobots that can solve the current main challenges, such as actuation limitations, material stability, and imaging constraints. The strengths and weaknesses of the current progress in the microrobotics field are discussed and a roadmap for their clinical applications in the near future is outlined.

Premix technologies for drug delivery: manufacturing, applications, and opportunities in regulatory filing

Active pharmaceutical ingredients (APIs) and excipients can be carefully combined in premix-based materials before being added to dosage forms, providing a flexible platform for the improvement of drug bioavailability, stability, and patient compliance. This is a promising and transformative approach in novel and generic product development, offering both the potential to overcome challenges in the delivery of complex APIs and viable solutions for bypassing patent hurdles in generic product filing. We discuss the different types of premixes; manufacturing technologies such as spray drying, hot melt extrusion, wet granulation, co-crystal, co-milling, co-precipitation; regulatory filing opportunities; and major bottlenecks in the use of premix materials in different aspects of pharmaceutical product development.

Current Progress in the Science of Novel Adjuvant Nano-Vaccine-Induced Protective Immune Responses

Vaccinations are vital as they protect us from various illness-causing agents. Despite all the advancements in vaccine-related research, developing improved and safer vaccines against devastating infectious diseases including Ebola, tuberculosis and acquired immune deficiency syndrome (AIDS) remains a significant challenge. In addition, some of the current human vaccines can cause adverse reactions in some individuals, which limits their use for massive vaccination program. Therefore, it is necessary to design optimal vaccine candidates that can elicit appropriate immune responses but do not induce side effects. Subunit vaccines are relatively safe for the vaccination of humans, but they are unable to trigger an optimal protective immune response without an adjuvant. Although different types of adjuvants have been used for the formulation of vaccines to fight pathogens that have high antigenic diversity, due to the toxicity and safety issues associated with human-specific adjuvants, there are only a few adjuvants that have been approved for the formulation of human vaccines. Recently, nanoparticles (NPs) have gain specific attention and are commonly used as adjuvants for vaccine development as well as for drug delivery due to their excellent immune modulation properties. This review will focus on the current state of adjuvants in vaccine development, the mechanisms of human-compatible adjuvants and future research directions. We hope this review will provide valuable information to discovery novel adjuvants and drug delivery systems for developing novel vaccines and treatments.

Drug repurposing for neurodegenerative diseases

Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.

Insights into membrane interactions and their therapeutic potential

Recent research into membrane interactions has uncovered a diverse range of therapeutic opportunities through the bioengineering of human and non-human macromolecules. Although the majority of this research is focussed on fundamental developments, emerging studies are showcasing promising new technologies to combat conditions such as cancer, Alzheimer's and inflammatory and immune-based disease, utilising the alteration of bacteriophage, adenovirus, bacterial toxins, type 6 secretion systems, annexins, mitochondrial antiviral signalling proteins and bacterial nano-syringes. To advance the field further, each of these opportunities need to be better understood, and the therapeutic models need to be further optimised. Here, we summarise the knowledge and insights into several membrane interactions and detail their current and potential uses therapeutically.

Tailoring Biomaterials Ameliorate Inflammatory Bone Loss

Inflammatory diseases, such as rheumatoid arthritis, periodontitis, chronic obstructive pulmonary disease, and celiac disease, disrupt the delicate balance between bone resorption and formation, leading to inflammatory bone loss. Conventional approaches to tackle this issue encompass pharmaceutical interventions and surgical procedures. Nevertheless, pharmaceutical interventions exhibit limited efficacy, while surgical treatments impose trauma and significant financial burden upon patients. Biomaterials show outstanding spatiotemporal controllability, possess a remarkable specific surface area, and demonstrate exceptional reactivity. In the present era, the advancement of emerging biomaterials has bestowed upon more efficacious solutions for combatting the detrimental consequences of inflammatory bone loss. In this review, the advances of biomaterials for ameliorating inflammatory bone loss are listed. Additionally, the advantages and disadvantages of various biomaterials-mediated strategies are summarized. Finally, the challenges and perspectives of biomaterials are analyzed. This review aims to provide new possibilities for developing more advanced biomaterials toward inflammatory bone loss.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Anti-Malassezia globosa (MYA-4889, ATCC) activity of Thai propolis from the stingless bee Geniotrigona thoracica

Malassezia globosa, a lipophilic pathogen, is known to be involved in various chronic skin diseases. Unfortunately, the available treatments have unwanted side effects and microbial drug resistance is evolving. As the antimicrobial activity of propolis is outstanding, this study aimed to examine the potential of propolis from the stingless bee Geniotrigona thoracica against the yeast. Anti-M. globosa growth activity was ascertained in agar well diffusion and broth microdilution assays and the inhibitory concentration value at 50 % (IC50) was determined. Since the yeast cannot synthesize its own fatty acids, extracellular lipase is important for its survival. Here, anti-M. globosa extracellular lipase activity was additionally investigated by colorimetric and agar-based methods. Compared to the crude hexane and crude dichloromethane extracts, the crude methanol partitioned extract (CMPE) exhibited the best anti-M. globosa growth activity with an IC50 of 1.22 mg/mL. After CMPE was further enriched by silica gel column chromatography, fraction CMPE1 (IC50 of 0.98 mM or 184.93 μg/mL) presented the highest activity and was later identified as methyl gallate (MG) by nuclear magnetic resonance analysis. Subsequently, MG was successfully synthesized and shown to have a similar activity, and a minimal fungicidal concentration of 43.44 mM or 8.00 mg/mL. However, lipase assay analysis suggested that extracellular lipase might not be the main target mechanism of MG. This is the first report of MG as a new anti-Malassezia compound. It could be a good candidate for further developing alternative therapeutic agents.