Drug discovery: a historical perspective

Biochemical and toxicity evaluation of Retama sphaerocarpa extracts and in-silico investigation of phenolic compounds as potential inhibitors against HPV16 E6 oncoprotein

Cervical cancer is a type of cancer which affects the cervix cells. The conventional treatments for cervical cancer including surgery, chemotherapy, and radiotherapy are only effective in premature stages and less effective in late stages of this tumor. Therefore, the therapeutic strategies based on biologically active substances from plants are needed to develop for the treatment of cervical cancer. The aim of the present study was to assess in vivo toxicity, hematological and biochemical blood parameters in Wistar rats fed Retama sphaerocarpa aqueous leaf extract (RS-AE), as well as to perform in silico molecular docking studies and dynamic simulation of phenolic compounds against HPV16 oncoprotein E6 in order to identify potential inhibitors. RS-AE was found not to induce acute or sub-acute oral toxicity or significant alterations in hematological and biochemical blood parameters in Wistar rats. A total of 11 phenolic compounds were identified in RS-AE, including dihydrodaidzein glucuronide, chrysoperiol pentoside, genistin and vitexin, which turned out to have the highest binding affinity to HPV16 oncoprotein E6. Based on these results, these RS-AE phenolic compounds could be used as natural drugs against the HPV16 E6 oncoprotein.

Stereoselective Synthesis of β, γ-Fused Bicyclic γ-Ureasultams via an Intramolecular Mannich and aza-Michael Addition Cascade

A novel approach has been developed for the synthesis of bicyclic β, γ-fused bicyclic γ-ureasultams containing two consecutive chiral centers through an intramolecular Mannich and aza-Michael addition cascade of alkenyl sulfamides. The straightforward practical procedure and readily available starting materials enable the synthesis of variously substituted ureasultams. In addition, bicyclic γ-ureasultams is a class of potential biotin analogues.

Metal-Free Photoredox Catalyzed Sulfonylation of Phenylhydrazines with Thiols

The sulfonylation method stands out as a simple and efficient approach for synthesizing sulfonamides. Despite the advancements in constructing the sulfonamide framework, the potential use of phenyl hydrazine as an amination source remains unexplored. Herein, we report a metal-free, environment-friendly photoredox-catalyzed sulfonylation of phenylhydrazines using thiols, employing MeCN:H2O as a green solvent and eosin Y as a photoredox catalyst. This strategy exhibits a broad substrate scope and good functional group compatibility, including hetero(aryl) as well as aliphatic phenylhydrazines. Finally, this protocol also demonstrated good application for the synthesis of pharmaceutical analogues.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Poly-pharmacology of existing drugs: How to crack the code?

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.

Oxidant-Assisted Sulfonylation/Cyclization Cascade Synthesis of Alkylsulfonylated Oxindoles via the Insertion of SO2

An oxidant-assisted tandem sulfonylation/cyclization of electron-deficient alkenes with 4-alkyl-substituted Hantzsch esters and Na2S2O5 for the preparation of 3-alkylsulfonylated oxindoles under mild conditions in the absence of a photocatalyst and transition metal catalyst is established. The mechanism studies show that the alkyl radicals, which come from the cleavage of the C-C bond in 4-substituted Hantzsch esters under oxidant conditions, subsequently undergo the in situ insertion of sulfur dioxide to generate the crucial alkylsulfonyl radical intermediates. This three-component reaction provides an efficient and facile route for the construction of alkylsulfonylated oxindoles and avoids the use of highly toxic alkylsulfonyl chlorides or alkylsulfonyl hydrazines as alkylsulfonyl sources.

Unlocking the mechanism of action: a cost-effective flow cytometry approach for accelerating antimicrobial drug development

Antimicrobial resistance is one of the greatest challenges to global health. While the development of new antimicrobials can combat resistance, low profitability reduces the number of new compounds brought to market. Elucidating the mechanism of action is crucial for developing new antimicrobials. This can become expensive as there are no universally applicable pipelines. Phenotypic heterogeneity of microbial populations resulting from antimicrobial treatment can be captured through flow cytometric fingerprinting. Since antimicrobials are classified into limited groups, the mechanism of action of known compounds can be used for predictive modeling. We demonstrate a cost-effective flow cytometry approach for determining the mechanism of action of new compounds. Cultures of Actinomyces viscosus and Fusobacterium nucleatum were treated with different antimicrobials and measured by flow cytometry. A Gaussian mixture mask was applied over the data to construct phenotypic fingerprints. Fingerprints were used to assess statistical differences between mechanism of action groups and to train random forest classifiers. Classifiers were then used to predict the mechanism of action of cephalothin. Statistical differences were found among the different mechanisms of action groups. Pairwise comparison showed statistical differences for 35 out of 45 pairs for A. viscosus and for 32 out of 45 pairs for F. nucleatum after 3.5 h of treatment. The best-performing random forest classifier yielded a Matthews correlation coefficient of 0.92 and the mechanism of action of cephalothin could be successfully predicted. These findings suggest that flow cytometry can be a cheap and fast alternative for determining the mechanism of action of new antimicrobials.IMPORTANCEIn the context of the emerging threat of antimicrobial resistance, the development of novel antimicrobials is a commonly employed strategy to combat resistance. Elucidating the mechanism of action of novel compounds is crucial in this development but can become expensive, as no universally applicable pipelines currently exist. We present a novel flow cytometry-based approach capable of determining the mechanism of action swiftly and cost-effectively. The workflow aims to accelerate drug discovery and could help facilitate a more targeted approach for antimicrobial treatment of patients.

Structural analysis and conformational dynamics of a holo-adhesion GPCR reveal interplay between extracellular and transmembrane domains

Adhesion G Protein-Coupled Receptors (aGPCRs) are key cell-adhesion molecules involved in numerous physiological functions. aGPCRs have large multi-domain extracellular regions (ECR) containing a conserved GAIN domain that precedes their seven-pass transmembrane domain (7TM). Ligand binding and mechanical force applied on the ECR regulate receptor function. However, how the ECR communicates with the 7TM remains elusive, because the relative orientation and dynamics of the ECR and 7TM within a holoreceptor is unclear. Here, we describe the cryo-EM reconstruction of an aGPCR, Latrophilin3/ADGRL3, and reveal that the GAIN domain adopts a parallel orientation to the membrane and has constrained movement. Single-molecule FRET experiments unveil three slow-exchanging FRET states of the ECR relative to the 7TM within the holoreceptor. GAIN-targeted antibodies, and cancer-associated mutations at the GAIN-7TM interface, alter FRET states, cryo-EM conformations, and receptor signaling. Altogether, this data demonstrates conformational and functional coupling between the ECR and 7TM, suggesting an ECR-mediated mechanism of aGPCR activation.

Toward Unified AI Drug Discovery with Multimodal Knowledge

Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.

A synthetic approach towards drug modification: 2-hydroxy-1-naphthaldehyde based imine-zwitterion preparation, single-crystal study, Hirshfeld surface analysis, and computational investigation

The current work is about the modification of primary amine functionalized drugs, pyrimethamine and 4-amino-N-(2,3-dihydrothiazol-2-yl)benzenesulfonamide, via condensation reaction with 2-hydroxy-1-naphthaldehyde to produce new organic zwitterionic compounds (E)-1-(((4-(N-(2,3-dihydrothiazol-2-yl)sulfamoyl)phenyl)iminio)methyl)naphthalen-2-olate (DSPIN) and (E)-1-(((4-amino-5-(4-chlorophenyl)-6-ethylpyrimidin-2-yl)iminio)methyl)naphthalen-2-olate (ACPIN) in methanol as a solvent. The crystal structures of both compounds were confirmed to be imine-based zwitterionic products via single-crystal X-ray diffraction (SC-XRD) analysis which indicated that the stabilization of both crystalline compounds is achieved via various noncovalent interactions. The supramolecular assembly in terms of noncovalent interactions was explored by the Hirshfeld surface analysis. Void analysis was carried out to predict the crystal mechanical response. Compound geometries calculated in the DFT (Density Functional Theory) study showed reasonably good agreement with the experimentally determined structural parameters. Frontier molecular orbital (FMO) analysis showed that the DSPIN HOMO/LUMO gap is by 0.15 eV smaller than the ACPIN HOMO/LUMO gap due to some destabilization of the DSPIN HOMO and some stabilization of its LUMO. The results of the charge analysis implied formation of intramolecular hydrogen bonds and suggested formation of intermolecular hydrogen bonding and dipole-dipole and dispersion interactions.

Enzyme Catalysis Causes Fluid Flow, Motility, and Directional Transport on Supported Lipid Bilayers

The dynamic interplay between the composition of lipid membranes and the behavior of membrane-bound enzymes is critical to the understanding of cellular function and viability, and the design of membrane-based biosensing platforms. While there is a significant body of knowledge about how lipid composition and dynamics affect membrane-bound enzymes, little is known about how enzyme catalysis influences the motility and lateral transport on lipid membranes. Using enzyme-attached lipids in supported bilayers (SLBs), we provide direct evidence of catalysis-induced fluid flow that underlies the observed motility on SLBs. Additionally, by using active enzyme patches, we demonstrate the directional transport of tracer particles on SLBs. As expected, enhancing the membrane viscosity by incorporating cholesterol into the bilayer suppresses the overall movement. These are the first steps in understanding diffusion and transport on lipid membranes due to active, out-of-equilibrium processes that are the hallmark of living systems. In general, our study demonstrates how active enzymes can be used to control diffusion and transport in confined 2-D environments.

Proteomics and Its Current Application in Biomedical Area: Concise Review

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Application scenario-oriented molecule generation platform developed for drug discovery

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.

Progress interrogating TRPMPZQ as the target of praziquantel

The drug praziquantel (PZQ) has served as the long-standing drug therapy for treatment of infections caused by parasitic flatworms. These encompass diseases caused by parasitic blood, lung, and liver flukes, as well as various tapeworm infections. Despite a history of clinical usage spanning over 4 decades, the parasite target of PZQ has long resisted identification. However, a flatworm transient receptor potential ion channel from the melastatin subfamily (TRPMPZQ) was recently identified as a target for PZQ action. Here, recent experimental progress interrogating TRPMPZQ is evaluated, encompassing biochemical, pharmacological, genetic, and comparative phylogenetic data that highlight the properties of this ion channel. Various lines of evidence that support TRPMPZQ being the therapeutic target of PZQ are presented, together with additional priorities for further research into the mechanism of action of this important clinical drug.

Targeting papain-like protease by natural products as novel therapeutic potential SARS-CoV-2

The highly infectious respiratory illness 'COVID-19' was caused by SARS-CoV-2 and is responsible for millions of deaths. SARS-single-stranded viral RNA genome encodes several structural and nonstructural proteins, including papain-like protease (PLpro), which is essential for viral replication and immune evasion and serve as a potential therapeutic target. Multiple computational techniques were used to search the natural compounds that may block the protease and deubiquitinase activities of PLpro. Five compounds showed strong interactions and binding energy (ranges between -8.18 to -8.69 Kcal/mol) in our in-silico studies. Interestingly, those molecules strongly bind in the PLpro active site and form a stable complex, as shown by microscale molecular dynamic simulations (MD). The dynamic movements indicate that PLpro acquires closed conformation by the attachment of these molecules, thereby changing its normal function. In the in-vitro evaluation, compound COMP4 showed the most potent inhibitory potential for PLpro (protease activity: 2.24 ± 0.17 μM and deubiquitinase activity: 1.43 ± 0.14 μM), followed by COMP1, 2, 3, and 5. Furthermore, the cytotoxic effect of COMP1-COMP5 on a human BJ cell line revealed that these compounds demonstrate negligible cytotoxicity at a dosage of 30 μM. The results suggest that these entities bear therapeutic efficacy for SARS-CoV-2 PLpro.

Mechanisms and implications of bacterial-fungal competition for soil resources

Elucidating complex interactions between bacteria and fungi that determine microbial community structure, composition, and functions in soil, as well as regulate carbon (C) and nutrient fluxes, is crucial to understand biogeochemical cycles. Among the various interactions, competition for resources is the main factor determining the adaptation and niche differentiation between these two big microbial groups in soil. This is because C and energy limitations for microbial growth are a rule rather than an exception. Here, we review the C and energy demands of bacteria and fungi-the two major kingdoms in soil-the mechanisms of their competition for these and other resources, leading to niche differentiation, and the global change impacts on this competition. The normalized microbial utilization preference showed that bacteria are 1.4-5 times more efficient in the uptake of simple organic compounds as substrates, whereas fungi are 1.1-4.1 times more effective in utilizing complex compounds. Accordingly, bacteria strongly outcompete fungi for simple substrates, while fungi take advantage of complex compounds. Bacteria also compete with fungi for the products released during the degradation of complex substrates. Based on these specifics, we differentiated spatial, temporal, and chemical niches for these two groups in soil. The competition will increase under the main five global changes including elevated CO2, N deposition, soil acidification, global warming, and drought. Elevated CO2, N deposition, and warming increase bacterial dominance, whereas soil acidification and drought increase fungal competitiveness.

The anticancer/cytotoxic effect of a novel gallic acid derivative in non-small cell lung carcinoma A549 cells and peripheral blood mononuclear cells from healthy individuals and lung cancer patients

Gallic acid (GA) is a naturally occurring polyphenol with a strong antioxidant capacity. GA stimulates the apoptosis of cancer cells, thereby suppressing cancer cell invasion. However, the low oral permeability of GA limits its therapeutic use. In order to enhance the antioxidant capacity and oral permeability of GA, a series of compounds analogous to GA were synthesized: 4-methoxybenzenesulfonamide (MBS), 3,4-dimethoxybenzenesulfonamide (DMBS) and 3,4,5-trimethoxybenzenesulfonamide (TMBS). In the new compounds, hydroxyl groups were replaced with various numbers of methoxy groups (stronger electron-donating groups), to increase hydrophobicity and oral permeability compared to GA. In addition, the carboxylic group was replaced with a sulfonyl group (a stronger electron-withdrawing group), to increase the molecular polarity and antioxidative activities of the compounds. The cell counting kit-8 (CCK-8) assay was used to detect the effect of GA, MBS, DMBS, and TMBS on cell proliferation and apoptosis in peripheral blood mononuclear cells (PBMCs) from healthy individuals and non-small cell lung carcinoma A549 cells. Additionally, the comet assay was used to assess the genotoxicity of these compounds in PBMCs from healthy individuals, lung cancer patients, and A549 cells. Compared to untreated cells, TMBS reduced DNA damage more effectively than GA in PBMCs from lung cancer patients and healthy donors. Furthermore, in comparison to GA, TMBS was more cytotoxic in A549 cells. Moreover, TMBS was not cytotoxic in healthy PBMCs, suggesting that TMBS demonstrates therapeutic potential in cancer.

Defining the Cell Surface Cysteinome Using Two-Step Enrichment Proteomics

The plasma membrane proteome is a rich resource of functionally important and therapeutically relevant protein targets. Distinguished by high hydrophobicity, heavy glycosylation, disulfide-rich sequences, and low overall abundance, the cell surface proteome remains undersampled in established proteomic pipelines, including our own cysteine chemoproteomics platforms. Here, we paired cell surface glycoprotein capture with cysteine chemoproteomics to establish a two-stage enrichment method that enables chemoproteomic profiling of cell Surface Cysteinome. Our "Cys-Surf" platform captures >2,800 total membrane protein cysteines in 1,046 proteins, including 1,907 residues not previously captured by bulk proteomic analysis. By pairing Cys-Surf with an isotopic chemoproteomic readout, we uncovered 821 total ligandable cysteines, including known and novel sites. Cys-Surf also robustly delineates redox-sensitive cysteines, including cysteines prone to activation-dependent changes to cysteine oxidation state and residues sensitive to addition of exogenous reductants. Exemplifying the capacity of Cys-Surf to delineate functionally important cysteines, we identified a redox sensitive cysteine in the low-density lipoprotein receptor (LDLR) that impacts both the protein localization and uptake of low-density lipoprotein (LDL) particles. Taken together, the Cys-Surf platform, distinguished by its two-stage enrichment paradigm, represents a tailored approach to delineate the functional and therapeutic potential of the plasma membrane cysteinome.

Artificial intelligence: Machine learning approach for screening large database and drug discovery

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

Melamine-cored glucosides for membrane protein solubilization and stabilization: importance of water-mediated intermolecular hydrogen bonding in detergent performance

Membrane proteins play essential roles in a number of biological processes, and their structures are important in elucidating such processes at the molecular level and also for rational drug design and development. Membrane protein structure determination is notoriously challenging compared to that of soluble proteins, due largely to the inherent instability of their structures in non-lipid environments. Micelles formed by conventional detergents have been widely used for membrane protein manipulation, but they are suboptimal for long-term stability of membrane proteins, making downstream characterization difficult. Hence, there is an unmet need for the development of new amphipathic agents with enhanced efficacy for membrane protein stabilization. In this study, we designed and synthesized a set of glucoside amphiphiles with a melamine core, denoted melamine-cored glucosides (MGs). When evaluated with four membrane proteins (two transporters and two G protein-coupled receptors), MG-C11 conferred notably enhanced stability compared to the commonly used detergents, DDM and LMNG. These promising findings are mainly attributed to a unique feature of the MGs, i.e., the ability to form dynamic water-mediated hydrogen-bond networks between detergent molecules, as supported by molecular dynamics simulations. Thus, MG-C11 is the first example of a non-peptide amphiphile capable of forming intermolecular hydrogen bonds within a protein-detergent complex environment. Detergent micelles formed via a hydrogen-bond network could represent the next generation of highly effective membrane-mimetic systems useful for membrane protein structural studies.

Anti-Helicobacter pylori Activity of Six Major Compounds Isolated from Rumex acetosa

Gastric problems are often caused by the well-known Helicobacter pylori (H. pylori) bacterium. One of the biggest obstacles to the treatment of H. pylori infections is increasing the antibiotic resistance. During our search for naturally derived anti-H. pylori compounds, six major compounds were isolated from the methylene chloride (CH2Cl2) and ethyl acetate (EtOAc) fractions of Rumex acetosa that showed anti-H. pylori activity. Three anthraquinones and three anthraquinone glucosides were identified as the major chemical constituents of the CH2Cl2 and EtOAc fractions, respectively. The chemical structures were identified to be emodin (1), chrysophanol (2), physcion (3), emodin-8-O-β-d-glucoside (4), chrysophanol-8-O-β-d-glucoside (5), and physcion-8-O-β-d-glucoside (6) by UV, 1H NMR, 13C NMR, and mass spectrometry. Anti-H. pylori activity, including the minimum inhibitory concentration (MIC) value of each compound, was evaluated against two H. pylori strains. All isolates exhibited anti-H. pylori activity with different potencies, with an MIC value ranging between 3.13 and 25 μM. However, some variations were found between the two strains. While compound 5 displayed the most potent antibacterial activity with an MIC50 value of 8.60 μM and an MIC90 value of 15.7 μM against H. pylori strain 51, compound 1 exhibited the most potent inhibitory activity against H. pylori strain 43504. The two compounds also showed moderate urease inhibitory activity, with compound 1 demonstrating activity higher than that of compound 5. Furthermore, a molecular docking study revealed the high binding ability of compounds 1 and 5 to the active site of H. pylori urease. The present study suggests that the six anthraquinones isolated from R. acetosa with the whole parts of this plant may be natural candidates for the treatment of H. pylori infection. Further studies are required to determine the exact mechanism of action and to evaluate safety issues in the human body.

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

Defining the Cell Surface Cysteinome using Two-step Enrichment Proteomics

The plasma membrane proteome is a rich resource of functional and therapeutically relevant protein targets. Distinguished by high hydrophobicity, heavy glycosylation, disulfide-rich sequences, and low overall abundance, the cell surface proteome remains undersampled in established proteomic pipelines, including our own cysteine chemoproteomics platforms. Here we paired cell surface glycoprotein capture with cysteine chemoproteomics to establish a two-stage enrichment method that enables chemoproteomic profiling of cell Surface Cysteinome. Our "Cys-Surf" platform captures >2,800 total membrane protein cysteines in 1,046 proteins, including 1,907 residues not previously captured by bulk proteomic analysis. By pairing Cys-Surf with an isotopic chemoproteomic readout, we uncovered 821 total ligandable cysteines, including known and novel sites. Cys-Surf also robustly delineates redox-sensitive cysteines, including cysteines prone to activation-dependent changes to cysteine oxidation state and residues sensitive to addition of exogenous reductants. Exemplifying the capacity of Cys-Surf to delineate functionally important cysteines, we identified a redox sensitive cysteine in the low-density lipoprotein receptor (LDLR) that impacts both the protein localization and uptake of LDL particles. Taken together, the Cys-Surf platform, distinguished by its two-stage enrichment paradigm, represents a tailored approach to delineate the functional and therapeutic potential of the plasma membrane cysteinome.

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting

The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can develop new therapies for a range of diseases, leading to better patient outcomes. Identification of DPs by machine learning strategies is more efficient and cost-effective than conventional methods. In this study, a computational predictor, namely Drug-LXGB, is introduced to enhance the identification of DPs. Features are discovered by composition, transition, and distribution (CTD), composition of K-spaced amino acid pair (CKSAAP), pseudo-position-specific scoring matrix (PsePSSM), and a novel descriptor, called multi-block pseudo amino acid composition (MB-PseAAC). The dimensions of CTD, CKSAAP, PsePSSM, and MB-PseAAC are integrated and utilized the sequential forward selection as feature selection algorithm. The best characteristics are provided by random forest, extreme gradient boosting, and light eXtreme gradient boosting (LXGB). The predictive analysis of these learning methods is measured via 10-fold cross-validation. The LXGB-based model secures the highest results than other existing predictors. Our novel protocol will perform an active role in designing novel drugs and would be fruitful to explore the potential target. This study will help better to capture a more universal view of a potential target.Communicated by Ramaswamy H. Sarma.

Kinetic and thermodynamic allostery in the Ras protein family

Allostery, the transfer of information between distant parts of a macromolecule, is a fundamental feature of protein function and regulation. However, allosteric mechanisms are usually not explained by protein structure, requiring information on correlated fluctuations uniquely accessible to molecular simulation. Existing work to extract allosteric pathways from molecular dynamics simulations has focused on thermodynamic correlations. Here, we show how kinetic correlations encode complementary information essential to explain observed variations in allosteric regulation. We applied kinetic and thermodynamic correlation analysis on atomistic simulations of H, K, and NRas isoforms in the apo, GTP, and GDP-bound states of Ras protein, with and without complexing to its downstream effector, Raf. We show that switch I and switch II are the primary components of thermodynamic and kinetic allosteric networks, consistent with the key roles of these two motifs. These networks connect the switches to an allosteric loop recently discovered from a crystal structure of HRas. This allosteric loop is inactive in KRas, but is coupled to the hydrolysis arm switch II in NRas and HRas. We find that the mechanism in the latter two isoforms are thermodynamic and kinetic, respectively. Binding of Raf-RBD further activates thermodynamic allostery in HRas and KRas but has limited effect on NRas. These results indicate that kinetic and thermodynamic correlations are both needed to explain protein function and allostery. These two distinct channels of allosteric regulation, and their combinatorial variability, may explain how subtle mutational differences can lead to diverse regulatory profiles among enzymatic proteins.

AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion

Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.

Visible light-driven photocatalysts, quantum chemical calculations, ADMET-SAR parameters, and DNA binding studies of nickel complex of sulfadiazine

A 3D-supramolecular nickel integrated Ni-SDZ complex was synthesized using sodium salt of sulfadiazine as the ligand and nickel(II) acetate as the metal salt using a condensation process and slow evaporation approach to growing the single crystal. The metal complex was characterized for its composition, functional groups, surface morphology as well as complex 3D structure, by resorting to various analytical techniques. The interacting surface and stability as well as reactivity of the complex were carried out using the DFT platform. From ADMET parameters, human Intestinal Absorbance data revealed that the compound has the potential to be well absorbed, and also Ni-SDZ complex cannot cross the blood-brain barrier (BBB). Additionally, the complex's DNA binding affinity and in-vivo and in-vitro cytotoxic studies were explored utilizing UV-Vis absorbance titration, viscosity measurements, and S. pombe cells and brine shrimp lethality tests. In visible light radiation, the Ni-SDZ complex displayed exceptional photo-degradation characteristics of approximately 70.19% within 70 min against methylene blue (MB).

Determination of Ligand-Binding Affinity (Kd) Using Transverse Relaxation Rate (R2) in the Ligand-Observed 1H NMR Experiment and Applications to Fragment-Based Drug Discovery

High hit rates from initial ligand-observed NMR screening can make it challenging to prioritize which hits to follow up, especially in cases where there are no available crystal structures of these hits bound to the target proteins or other strategies to provide affinity ranking. Here, we report a reproducible, accurate, and versatile quantitative ligand-observed NMR assay, which can determine Kd values of fragments in the affinity range of low μM to low mM using transverse relaxation rate R2 as the observable parameter. In this study, we examined the theory and proposed a mathematical formulation to obtain Kd values using non-linear regression analysis. We designed an assay format with automated sample preparation and simplified data analysis. Using tool compounds, we explored the assay reproducibility, accuracy, and detection limits. Finally, we used this assay to triage fragment hits, yielded from fragment screening against the CRBN/DDB1 complex.

Photoredox-Catalyzed Synthesis of 3-Sulfonylated Pyrrolin-2-ones via a Regioselective Tandem Sulfonylation Cyclization of 1,5-Dienes

A mild, visible-light-induced, regioselective cascade sulfonylation-cyclization of 1,5-dienes with sulfonyl chlorides through the intermolecular radical addition/cyclization of alkenes C(sp²)-H was developed. This procedure proceeds well and affords a mild and efficient route to a range of monosulfonylated pyrrolin-2-ones at room temperatures.

Recent Advances on Cell Culture Platforms for In Vitro Drug Screening and Cell Therapies: From Conventional to Microfluidic Strategies

The clinical translations of drugs and nanomedicines depend on coherent pharmaceutical research based on biologically accurate screening approaches. Since establishing the 2D in vitro cell culture method, the scientific community has improved cell-based drug screening assays and models. Those advances result in more informative biochemical assays and the development of 3D multicellular models to describe the biological complexity better and enhance the simulation of the in vivo microenvironment. Despite the overall dominance of conventional 2D and 3D cell macroscopic culture methods, they present physicochemical and operational challenges that impair the scale-up of drug screening by not allowing a high parallelization, multidrug combination, and high-throughput screening. Their combination and complementarity with microfluidic platforms enable the development of microfluidics-based cell culture platforms with unequivocal advantages in drug screening and cell therapies. Thus, this review presents an updated and consolidated view of cell culture miniaturization's physical, chemical, and operational considerations in the pharmaceutical research scenario. It clarifies advances in the field using gradient-based microfluidics, droplet-based microfluidics, printed-based microfluidics, digital-based microfluidics, SlipChip, and paper-based microfluidics. Finally, it presents a comparative analysis of the performance of cell-based methods in life research and development to achieve increased precision in the drug screening process.

Bacterial synthetic biology: tools for novel drug discovery

INTRODUCTION

Bacterial synthetic biology has provided powerful tools to revolutionize the drug discovery process. These tools can be harnessed to generate bacterial novel pharmaceutical compounds with enhanced bioactivity and selectivity or to create genetically modified microorganisms as living drugs.

AREAS COVERED

This review provides a current overview of the state-of-the-art in bacterial synthetic biology tools for novel drug discovery. The authors discuss the application of these tools including bioinformatic tools, CRISPR tools, engineered bacterial transcriptional regulators, and synthetic biosensors for novel drug discovery. Additionally, the authors present the recent progress on reprogramming bacteriophages as living drugs to fight against antibiotic-resistant pathogens.

EXPERT OPINION

The field of using bacterial synthetic biology tools for drug discovery is rapidly advancing. However, challenges remain in developing reliable and robust methods to engineer bacteria. Further advancements in synthetic biology hold promise to speed up drug discovery, facilitating the development of novel therapeutics against various diseases.

In vitro PCR verification that lysozyme inhibits nucleic acid replication and transcription

Lysozyme can kill bacteria by its enzymatic activity or through a mechanism involving its cationic nature, which can facilitate electrostatic interactions with the viral capsid, the negatively charged parts of nucleic acids, and polymerase, so binding to nucleic acids may be another biological function of lysozyme. Here, PCR was used as a research tool to detect the effects of lysozyme on the replication and transcription of nucleic acids after treatment in different ways. We found that lysozyme and its hydrolysate can enter cells and inhibit PCR to varying degrees in vitro, and degraded lysozyme inhibited nucleic acid replication more effectively than intact lysozyme. The inhibition of lysozyme may be related to polymerase binding, and the sensitivity of different polymerases to lysozyme is inconsistent. Our findings provide a theoretical basis for further explaining the pharmacological effects of lysozyme, such as antibacterial, antiviral, anticancer, and immune regulatory activities, and directions for the development of new pharmacological effects of lysozyme and its metabolites.

Novel Sulfonamide-Triazine Hybrid Derivatives: Docking, Synthesis, and Biological Evaluation as Anticancer Agents

The biological benefits of trisubstituted 1,3,5-triazine derivatives include their ability to reduce inflammation and fight cancer. A unique series of sulfonamide-triazine hybrid molecules were produced chemically by synthesizing triazine derivatives utilizing the usual nucleophilic aromatic substitution of cyanuric chloride via the solvent-free/neat fusion method. Fourier-transform infrared spectroscopy (FTIR), ¹H NMR, and ¹³C NMR spectroscopic analyses were used to identify novel trisubstituted synthetic compounds. The synthesized compounds have a moderate inhibition percentage when tested at 100 μM against the phosphoinositol 3-kinases (PI3Kα) enzyme; compounds 20 and 34 showed 46 and 68% anti-PI3Kα activity, respectively. To comprehend the anticipated interactions, the most successful compounds were subsequently docked into a PI3Kα protein's binding site (PDB code: 6OAC, resolution: 3.15 Å). The final synthetic compounds' anticancer activity was tested on the breast (MCF-7) and lung (A549) cancer cell lines at doses of 100 and 50 μM for additional evaluation of anticancer characteristics. The IC₅₀ values for the sulfaguanidine-triazine derivatives 27, 28, 29, 31, and 35 ranged from 14.8 to 33.2 μM, showing that compounds containing sulfaguanidine and diethylamine in their structures significantly inhibited the activity. Compound 34 could be a promising lead compound for developing new target-selected anticancer compounds with low toxicity and high selectivity.

Structure-based identification of potential substrate antagonists for isethionate sulfite-lyase enzyme of Bilophila Wadsworthia: Towards novel therapeutic intervention to curb gut-associated illness

Bilophila wadsworthia is one of the prominent sources of hydrogen sulfide (H₂S) production in appendices, excessive levels of which can result in a weaker colonic mucus barrier, inflammatory bowel disease, and colorectal cancer. Isethionate sulfite-lyase (IslA) enzyme catalyzes H2S production by cleaving CS bond in isethionate, producing acetaldehyde and sulfite. In this study, we aimed to identify potential substrate antagonists for IsIA using a structure-based drug design. Initially, pharmacophore-based computational screening of the ZINC20 database yielded 66 hits that were subjected to molecular docking targeting the isethionate binding site of IsIA. Based on striking docking scores, nine compounds showed strong interaction with critical IsIA residues (Arg189, Gln193, Glu470, Cys468, and Arg678), drug-like features, appropriate adsorption, metabolism, excretion, and excretion profile with non-toxicity. Molecular dynamics simulations uncovered the significant impact of binding the compounds on protein conformational dynamics. Finally, binding free energies revealed substantial binding affinity (ranging from -35.23 to -53.88 kcal/mol) of compounds (ZINC913876497, ZINC913856647, ZINC914263733, ZINC914137795, ZINC915757996, ZINC914357083, ZINC913934833, ZINC9143362047, and ZINC913854740) for IsIA. The compounds proposed herein through a multi-faceted computational strategy can be experimentally validated as potential substrate antagonists of B. wadsworthia's IsIA for developing new medications to curb gut-associated illness in the future.

Construction of multiple concentration gradients for single-cell level drug screening

Isolation and manipulation of single cells play a crucial role in drug screening. However, previously reported single-cell drug screening lacked multiple-dose concentration gradient studies, which limits their ability to predict drug performance accurately. To solve this problem, we constructed a multiconcentration gradient generator in which a Tai Chi-spiral mixer can accelerate solution mixing in a short time and produce a linear concentration gradient. Later, a gradient generator combined with a single-cell capture array was adopted to investigate the effects of single or combined doses of 5-fluorouracil and cisplatin on human hepatoma cells and human breast carcinoma cells (at the single-cell level). The results showed that both drugs were effective in inhibiting the growth of cancer cells, and the combination was more effective for human hepatoma cells. In addition, the relationship between the biomechanical heterogeneity (e.g., deformability and size) of tumor cells and potential drug resistance at the single-cell level was investigated, indicating that small and/or deformable cells were more resistant than large and/or less deformable cells. The device provides a simple and reliable platform for studying the optimal dosage of different drug candidates at the single-cell level and effectively screening single-agent chemotherapy regimens and combination therapies.

Polymer-based microfluidic device for on-chip counter-diffusive crystallization and in situ X-ray crystallography at room temperature

Proteins are long chains of amino acid residues that perform a myriad of functions in living organisms, including enzymatic reactions, signalling, and maintaining structural integrity. Protein function is determined directly by the protein structure. X-ray crystallography is the primary technique for determining the 3D structure of proteins, and facilitates understanding the effects of protein structure on function. The first step towards structure determination is crystallizing the protein of interest. We have developed a centrifugally-actuated microfluidic device that incorporates the fluid handling and metering necessary for protein crystallization. Liquid handling takes advantage of surface forces to control fluid flow and enable metering, without the need for any fluidic or pump connections. Our approach requires only the simple steps of pipetting the crystallization reagents into the device followed by either spinning or shaking to set up counter-diffusive protein crystallization trials. The use of thin, UV-curable polymers with a high level of X-ray transparency allows for in situ X-ray crystallography, eliminating the manual handling of fragile protein crystals and streamlining the process of protein structure analysis. We demonstrate the utility of our device using hen egg white lysozyme as a model system, followed by the crystallization and in situ, room temperature structural analysis of the hub domain of calcium-calmodulin dependent kinase II (CaMKIIβ).

Non-classical C-saccharide linkage of dehydroalanine: synthesis of C-glycoamino acids and C-glycopeptides

Herein, a non-classical C-saccharide linkage is reported via a C5 radical of pentose or C6 radical of hexose addition to Michael acceptors. C(sp³)-S cleaved glycosyl thianthrenium salts are developed as the glycosyl radical agents. The reaction provides an efficient toolkit to synthesize β-glycosyl substituted unnatural amino acids as well as for the late-stage C-saccharide modification of peptides.

Critical review on Rumex dentatus L. a strong pharmacophore and the future medicine: Pharmacology, phytochemical analysis and traditional uses

Objectives

Rumex dentatus L. (polygonaceae) is one of the most important species of genus Rumex widely utilized for the treatment of various human diseases. Most parts of the plant species like leaves, shoots and roots are found to be rich in many pharmacologically important bioactive constituents that are useful against many diseases like acariasis, eczema, diarrhea, constipation, diuretic, astringent, refrigerants and various types of skin diseases. The main aim of the presented review is to highlight and document research findings carried out by different research groups on Rumex dentatus, like pharmacological potential, phytochemistry investigations based on presence of phytoconstituents, traditional uses and economic importance till date. The information so collected and documented will become available to researchers, scientists and botanists to explore the medicinal benefits of this prized herb for the assistance of mankind which in turn will open up new opportunities for more organized and collective research efforts towards utilization and scientific validation of its pharmacological potential.

Methods

In-vitro and in-vivo preclinical animal studies have been included in detail. The reports and results have been taken from Scopus, Google Scholar, Web of Science, PubMed and Science Direct, Research gate, Articles & Advice, databases. Plant taxonomy studies were taken and confirmed from the available databases. "The Plant List", and "Mansfeld's Encyclopedia". Additional information on traditional uses, botany were obtained from published books.

Key findings

From results and findings, it has been concluded that Rumex dentatus is a rich source of secondary metabolites such as flavonoids, anthraquinones, phenolics, phytosterols, phytoesteryl esters etc. The bactericidal, anti-inflammatory, antimicrobial, anti-tumor and anti-dermatitis properties of Rumex dentatus have been attributed due to the presence of these phytochemicals. In this review, we present a critical account of its habitat, morphology, phyto-constituent profile, pharmacology and traditional uses, which will provide a source of information to the researchers for further studies.

Conclusions

The disclosed review endorses that Rumex dentatus emerged a unique source of Endocrocin, Emodin, Emodin-glycoside, Chrysophenol-glycoside, Quercetin, Helonioside-A and a number of other important bioactive compounds. These isolated compounds have been found to be active against cancer, inflammation, tumor, dermatitis, acariasis, eczema and various bacterial infections, thus providing new insights for further promising investigations on isolated compounds. In addition, Rumex dentatus was found as an excellent traditional medicine against many cutaneous disorders. Taking into account the astounding pharmacological properties of Rumex dentatus in consideration, the plant species is a library of bio-active compounds with a strong biological profile, therefore needs an attention of botanical community around the globe to improve its growth for medicinal uses and commit to broaden research in this field for its proper utilization and scientific exploitation.

Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.

DFT calculations, molecular docking, in vitro antimicrobial and antidiabetic studies of green synthesized Schiff bases: as Covid-19 inhibitor

In this investigation, we synthesized Schiff bases 2-(2-methoxyphenoxy)-N-(4-methylbenzylidene)ethanamine, N-(4-methoxybenzylidene)-2-(2-methoxyphenoxy)ethanamine and 2-(2-methoxyphenoxy)-N-(4-nitrobenzylidene)ethanamine from 2-(2-methoxyphenoxy)ethanamine and various aromatic aldehydes by the environmentally friendly sonication method. The B3LYP method with a 6-311++G (d, p) basis set was used in the DFT calculation to obtain the optimized structure of the Schiff base MPEA-NIT. The compounds were tested in vitro for inhibition of bacterial growth (disc well method) and inhibition of α-amylase (starch-iodine method). The compounds tested showed inhibitory activities. In addition, they were subjected to PASS analysis, drug likeness, and bioactivity score predictions using online software. To confirm the experimental findings, molecular docking analyses of synthesized compounds on α-amylase (PDB ID: 1SMD), tRNA threonylcarbamoyladenosine (PDB ID: 5MVR), glycosyl transferase (PDB ID: 6D9T), and peptididoglycan D,D-transpeptidase (PDB ID: 6HZQ) were performed. The emergence of a new coronavirus epidemic necessitates the development of antiviral medications (SARS-CoV-2). Docking active site interactions were investigated to predict compounds' activity against COVID-19 by binding with the SARS-CoV-2 (PDB ID: 6Y84).Communicated by Ramaswamy H. Sarma.

Precise assembly of inside-out cell membrane camouflaged nanoparticles via bioorthogonal reactions for improving drug leads capturing

Cell membrane camouflaged nanoparticles have been widely used in the field of drug leads discovery attribute to their unique biointerface targeting function. However, random orientation of cell membrane coating does not guarantee effective and appropriate binding of drugs to specific sites, especially when applied to intracellular regions of transmembrane proteins. Bioorthogonal reactions have been rapidly developed as a specific and reliable method for cell membrane functionalization without disturbing living biosystem. Herein, inside-out cell membrane camouflaged magnetic nanoparticles (IOCMMNPs) were accurately constructed via bioorthogonal reactions to screen small molecule inhibitors targeting intracellular tyrosine kinase domain of vascular endothelial growth factor recptor-2. Azide functionalized cell membrane acted as a platform for specific covalently coupling with alkynyl functionalized magnetic Fe₃O₄ nanoparticles to prepare IOCMMNPs. The inside-out orientation of cell membrane was successfully verified by immunogold staining and sialic acid quantification assay. Ultimately, two compounds, senkyunolide A and ligustilidel, were successfully captured, and their potential antiproliferative activities were further testified by pharmacological experiments. It is anticipated that the proposed inside-out cell membrane coating strategy endows tremendous versatility for engineering cell membrane camouflaged nanoparticles and promotes the development of drug leads discovery platforms.

Synthesis and characterization of supermacroporous cryogel with immobilized p-aminobenzenesulfonamide as affinity ligand for the purification of lactoperoxidase from whey

This study proposed the development of a monolithic supermacroporous affinity column for direct capture of lactoperoxidase, a glycoprotein present in milk, whey, and colostrum, with several applications due to its wide antimicrobial activity. A poly(acrylamide)-based cryogel was produced by radical co-polymerization of monomers in frozen aqueous solution and activated with p-aminobenzenesulfonamide as a ligand for specific interaction with the lactoperoxidase. The axial liquid dispersion coefficients at different liquid flow rates were determined by measuring residence time distributions using the tracer pulse-response method. The axial dispersion coefficient was low and the height equivalent to theoretical plate was not dependent on the flow velocity. The adsorptive capacity of affinity cryogel was studied as a function of flow velocity and the best condition was 0.9 cm/min. The response surface methodology was applied to optimize the capture of the enzyme, as a function of pH and salt concentration. Higher purification factor value was found at a salt concentration of 80 mmol/L and pH of 8.0 (p < 0.05). There was no influence of the variables under study on the yield (p > 0.05). The results indicated that affinity cryogel is a promising chromatography support for the use in high-throughput one-step purification of lactoperoxidase from whey.

Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph

AI-driven approaches are widely used in drug discovery, where candidate molecules are generated and tested on a target protein for binding affinity prediction. However, generating new compounds with desirable molecular properties such as Quantitative Estimate of Drug-likeness (QED) and Dopamine Receptor D2 activity (DRD2) while adhering to distinct chemical laws is challenging. To address these challenges, we proposed a graph-based deep learning framework to generate potential therapeutic drugs targeting the SARS-CoV-2 protein. Our proposed framework consists of two modules: a novel reinforcement learning (RL)-based graph generative module with knowledge graph (KG) and a graph early fusion approach (GEFA) for binding affinity prediction. The first module uses a gated graph neural network (GGNN) model under the RL environment for generating novel molecular compounds with desired properties and a custom-made KG for molecule screening. The second module uses GEFA to predict binding affinity scores between the generated compounds and target proteins. Experiments show how fine-tuning the GGNN model under the RL environment enhances the molecules with desired properties to generate 100 % valid and 100 % unique compounds using different scoring functions. Additionally, KG-based screening reduces the search space of generated candidate molecules by 96.64 % while retaining 95.38 % of promising binding molecules against SARS-CoV-2 protein, i.e., 3C-like protease (3CLpro). We achieved a binding affinity score of 8.185 from the top rank of generated compound. In addition, we compared top-ranked generated compounds to Indinavir on different parameters, including drug-likeness and medicinal chemistry, for qualitative analysis from a drug development perspective.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00409-2.

Direct Reaction of Nitroarenes and Thiols via Photodriven Oxygen Atom Transfer for Access to Sulfonamides

Sulfonamide is a common motif in medicines and agrochemicals. Typically, this class of functional groups is prepared by reacting amines with sulfonyl chlorides that are presynthesized from nitro compounds and thiols, respectively. Here, we report a novel strategy that directly couples nitro compounds and thiols to form sulfonamides atom- and redox-economically. Mechanistic studies suggest our reaction proceeds via direct photoexcitation of nitroarenes that eventually transfers the oxygen atoms from the nitro group to the thiol unit.

In silico and in vitro approaches allow the identification of the Prosystemin molecular network

Tomato Prosystemin (ProSys), the precursor of Systemin, a small peptidic hormone, is produced at very low concentration in unchallenged plants, while its expression greatly increases in response to several different stressors triggering an array of defence responses. The molecular mechanisms that underpin such a wide array of defence barriers are not fully understood and are likely correlated with the intrinsically disordered (ID) structure of the protein. ID proteins interact with different protein partners forming complexes involved in the modulation of different biological mechanisms. Here we describe the ProSys-protein network that shed light on the molecular mechanisms underpinning ProSys associated defence responses. Three different approaches were used. In silico prediction resulted in 98 direct interactors, most clustering in phytohormone biosynthesis, transcription factors and signal transduction gene classes. The network shows the central role of ProSys during defence responses, that reflects its role as central hub. In vitro ProSys interactors, identified by Affinity Purification-Mass Spectrometry (AP-MS), revealed over three hundred protein partners, while Bimolecular Fluorescent Complementation (BiFC) experiments validated in vivo some interactors predicted in silico and in vitro. Our results demonstrate that ProSys interacts with several proteins and reveal new key molecular events in the ProSys-dependent defence response of tomato plant.

Synthesis and antimalarial activity of 7-chloroquinoline-tethered sulfonamides and their [1,2,3]-triazole hybrids

Aim & background: Drugs with multiple bioactive moieties have the advantages of multiple modes of action and fewer chances of drug resistance. In continuation of our previous work of developing hybrid antimalarials, we present herein the synthesis and antimalarial activity of two different series of 7-chloroquinoline-sulfonamide hybrids. Materials & methods: The first series of compounds were synthesized by using p-dodecylbenzenesulfonic acid as a Bronsted acid catalyst in ethanol. The second series' compounds were synthesized by 1,3-dipolar cycloaddition of azides and alkynes under click reaction conditions. Results & conclusion: The majority of these compounds demonstrated noncytotoxicity and significant antimalarial activity against Plasmodium falciparum (3D7) with IC₅₀ values in the range of 1.49-13.49 μM. The most promising hybrids (12d, 13a and 13c) may be good starting points for next-generation antimalarials.

Therapeutic approaches to combat the global antibiotic resistance challenge

Antimicrobial resistance (AMR) has become a major concern for healthcare workers due to the emergence of new variants of resistant markers, especially carbapenemases. Combinational antibiotic therapy is one of the best and easiest approaches to handle the current situation of AMR. Although some antibiotic combinations are already in clinical use, they remain to be studied in detail. This review focuses on therapeutic options for AMR mechanisms of resistance in bacteria that can be overcome by combinational therapy and testing methods for synergy. The integration of diverse approaches may provide information that is imperative in mitigating the threat of AMR.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.