Epigallocatechin gallate and curcumin inhibit Bcl-2: a pharmacophore and docking based approach against cancer.

The protein Bcl-2, well-known for its anti-apoptotic properties, has been implicated in cancer pathogenesis. Identifying the primary gene responsible for promoting improved cell survival and development has provided compelling evidence for preventing cellular death in the progression of malignancies. Numerous research studies have provided evidence that the abundance of Bcl-2 is higher in malignant cells, suggesting that suppressing Bcl-2 expression could be a viable therapeutic approach for cancer treatment. In this study, we acquired a compound collection using a database that includes constituents from Traditional Chinese Medicine (TCM). Initially, we established a pharmacophore model and utilized it to search the TCM database for potential compounds. Compounds with a fitness score exceeding 0.75 were selected for further analysis. The Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis identified six compounds with favorable therapeutic characteristics. The compounds that successfully passed the initial screening process based on the pharmacodynamic model were subjected to further evaluation. Extra-precision (XP) docking was employed to identify the compounds with the most favorable XP docking scores. Further analysis using the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) method to calculate the overall free binding energy. The binding energy between the prospective ligand molecule and the target protein Bcl-2 was assessed by a 100 ns molecular dynamics simulation for curcumin and Epigallocatechin gallate (EGCG). The findings of this investigation demonstrate the identification of a molecular structure that effectively inhibits the functionality of the Bcl-2 when bound to the ligand EGCG. Consequently, this finding presents a novel avenue for the development of pharmaceuticals capable of effectively addressing both inflammatory and tumorous conditions.

A Computational Approach for the Discovery of Novel DNA Methyltransferase Inhibitors.

Nowadays, the explosion of knowledge in the field of epigenetics has revealed new pathways toward the treatment of multifactorial diseases, rendering the key players of the epigenetic machinery the focus of today's pharmaceutical landscape. Among epigenetic enzymes, DNA methyltransferases (DNMTs) are first studied as inhibition targets for cancer treatment. The increasing clinical interest in DNMTs has led to advanced experimental and computational strategies in the search for novel DNMT inhibitors. Considering the importance of epigenetic targets as a novel and promising pharmaceutical trend, the present study attempted to discover novel inhibitors of natural origin against DNMTs using a combination of structure and ligand-based computational approaches. Particularly, a pharmacophore-based virtual screening was performed, followed by molecular docking and molecular dynamics simulations in order to establish an accurate and robust selection methodology. Our screening protocol prioritized five natural-derived compounds, derivatives of coumarins, flavones, chalcones, benzoic acids, and phenazine, bearing completely diverse chemical scaffolds from FDA-approved "Epi-drugs". Their total DNMT inhibitory activity was evaluated, revealing promising results for the derived hits with an inhibitory activity ranging within 30-45% at 100 µM of the tested compounds.

Identification of novel RANKL inhibitors through in silico analysis.

Receptor activator of nuclear factor-κB ligand (RANKL) is considered the principal regulator of osteoclast differentiation. Therefore, strategies interfering with the RANKL-RANK signaling pathway may effectively inhibit osteoclast differentiation and mitigate bone resorption. Consequently, RANKL has become a promising target for new drug design strategies. Despite extensive research on specific drugs and antibodies, only a few have shown efficacy in treating osteoporosis. To address this challenge, we aimed to explore new approaches for designing drugs for osteoporosis. In this study, a 3D quantitative structure-activity relationship (QSAR) pharmacophore model was built for RANKL with reference to known inhibitor IC50 values. The optimal pharmacophore model was then employed as a 3D query to screen databases for novel lead compounds. The obtained compounds were subjected to ADMET and TOPKAT analyses to predict drug pharmacokinetics and toxicity. Molecular docking and de novo evolution approaches were applied to verify the docking binding affinities of the compounds. Five candidate compounds were subjected to further in vitro analyses to assess their anti-osteoporotic effects, among which compound 4 demonstrated significant inhibitory activity, achieving an inhibitory rate of 92.6 % on osteoclastogenesis at a concentration of 10 µM. Subsequent molecular dynamics (MD) simulations to assess the stability and behavior of compound 4 and its evolved variant, ZINC00059014397_Evo, within the RANKL binding site revealed that the variant is a potential therapeutic agent for targeting osteoclasts. This study offers valuable insights for developing next generation RANKL inhibitors for osteoporosis treatments.

Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches.

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

In-silico identification and exploration of small molecule coumarin-1,2,3-triazole hybrids as potential EGFR inhibitors for targeting lung cancer.

Globally, lung cancer is a significant public health concern due to its role as the leading cause of cancer-related mortalities. The promising target of EGFR for lung cancer treatment has been identified, providing a potential avenue for more effective therapies. The purpose of the study was to design a library of 1843 coumarin-1,2,3-triazole hybrids and screen them based on a designed pharmacophore to identify potential inhibitors targeting EGFR in lung cancer with minimum or no side effects. Pharmacophore-based screening was carried out and 60 hits were obtained. To gain a better understanding of the binding interactions between the compounds and the targeted receptor, molecular docking was conducted on the 60 screened compounds. In-silico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results indicated that coumarin-1,2,3-triazole hybrids COUM-0849, COUM-0935, COUM-0414, COUM-1335, COUM-0276, and COUM-0484 exhibit dock score of - 10.2, - 10.2, - 10.1, - 10.1, - 10, - 10 while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, we performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of coumarin-1,2,3-triazole hybrids as promising EGFR inhibitors for the management of lung cancer.

Biological and Computational Studies of Hydrazone based Transition Metal(II) Complexes.

In the chronicles of human history, infectious diseases played a pivotal role, influencing societies, steering advancements in medicine, and significantly impacting the well-being of people worldwide. Consequently, in the pursuit of identifying effective combating agents for infectious ailments, the Co(II), Ni(II), Cu(II), Zn(II) complexes of N'-(4-nitrobenzylidene)benzohydrazide were synthesized in the current investigation. Numerous spectral and physical analysis were conducted to characterize the compounds which revealed octahedral stereochemistry of complexes. The anti-tuberculosis, anti-inflammatory, antibacterial and antifungal investigations demonstrated that the compounds (1-5) have significant efficacy for these infectious ailments. The [Zn(L)2(H2O)2] complex (5) has comparable TB inhibition potency to streptomycin as shown by MIC value of 0.0196 ± 0.0003 µmol/mL. Additionally, the anti-inflammatory, antibacterial and antifungal studies also revealed the comparable inhibiting property of (5) to standard drugs with significant IC50 (07.49 ± 0.08 µM) and MIC (0.0098 µmol/mL) values. Furthermore, pharmacophore modeling with addition of molecular docking, DFT, MESP, ADMET were employed against (1-5) to give a new insight in biological evaluations. The pharmacophore modeling suggested that (5) has a distinctive pharmacophoric features including cationic sites, hydrogen-bond donors and acceptors which provide valuable insights into drug design for pharmacological applications. Moreover, another in silico investigations authenticate the bioactivity of (5).

Three-Dimensional Quantitative Structure-Activity Relationship Study of Transient Receptor Potential Vanilloid 1 Channel Antagonists Reveals Potential for Drug Design Purposes.

Transient receptor potential vanilloid 1 (TRPV1) was reported to be a putative target for recovery from chronic pain, producing analgesic effects after its inhibition. A series of drug candidates were previously developed, without the ability to ameliorate the therapeutic outcome. Starting from previously designed compounds, derived from the hybridization of antagonist SB-705498 and partial agonist MDR-652, we performed a virtual screening on a pharmacophore model built by exploiting the Cryo-EM 3D structure of a nanomolar antagonist in complex with the human TRPV1 channel. The pharmacophore model was described by three pharmacophoric features, taking advantage of both the bioactive pose of the antagonist and the receptor exclusion spheres. The results of the screening were implemented inside a 3D-QSAR model, correlating with the negative decadic logarithm of the inhibition rate of the ligands. After the validation of the obtained 3D-QSAR model, we designed a new series of compounds by introducing key modifications on the original scaffold. Again, we determined the compounds' binding poses after alignment to the pharmacophoric model, and we predicted their inhibition rates with the validated 3D-QSAR model. The obtained values resulted in being even more promising than parent compounds, demonstrating that ongoing research still leaves much room for improvement.

A Systematic Hierarchical Virtual Screening Model for RhlR Inhibitors Based on PCA, Pharmacophore, Docking, and Molecular Dynamics.

RhlR plays a key role in the quorum sensing of Pseudomonas aeruginosa. The current structure-activity relationship (SAR) studies of RhlR inhibitors mainly focus on elucidating the functional groups. Based on a systematic review of previous research on RhlR inhibitors, this study aims to establish a systematic, hierarchical screening model for RhlR inhibitors. We initially established a database and utilized principal component analysis (PCA) to categorize the inhibitors into two classes. Based on the training set, pharmacophore models were established to elucidate the structural characteristics of ligands. Subsequently, molecular docking, molecular dynamics simulations, and the calculation of binding free energy and strain energy were performed to validate the crucial interactions between ligands and receptors. Then, the screening criteria for RhlR inhibitors were established hierarchically based on ligand structure characteristics, ligand-receptor interaction, and receptor affinity. Test sets were finally employed to validate the hierarchical virtual screening model by comparing it with the current SAR studies of RhlR inhibitors. The hierarchical screening model was confirmed to possess higher accuracy and a true positive rate, which holds promise for subsequent screening and the discovery of active RhlR inhibitors.

Multidimensional Criteria for Virtual Screening of PqsR Inhibitors Based on Pharmacophore, Docking, and Molecular Dynamics.

Pseudomonas aeruginosa is a clinically challenging pathogen due to its high resistance to antibiotics. Quorum sensing inhibitors (QSIs) have been proposed as a promising strategy to overcome this resistance by interfering with the bacterial communication system. Among the potential targets of QSIs, PqsR is a key regulator of quorum sensing in Pseudomonas aeruginosa. However, the current research on PqsR inhibitors is limited by the lack of diversity in the chemical structures and the screening methods. Therefore, this study aims to develop a multidimensional screening model for PqsR inhibitors based on both ligand- and receptor-based approaches. First, a pharmacophore model was constructed from a training set of PqsR inhibitors to identify the essential features and spatial arrangement for the activity. Then, molecular docking and dynamics simulations were performed to explore the core interactions between PqsR inhibitors and their receptor. The results indicate that an effective PqsR inhibitor should possess two aromatic rings, one hydrogen bond acceptor, and two hydrophobic groups and should form strong interactions with the following four amino acid residues: TYR_258, ILE_236, LEU_208, and GLN_194. Moreover, the docking score and the binding free energy should be lower than -8 kcal/mol and -40 kcal/mol, respectively. Finally, the validity of the multidimensional screening model was confirmed by a test set of PqsR inhibitors, which showed a higher accuracy than the existing screening methods based on single characteristics. This multidimensional screening model would be a useful tool for the discovery and optimization of PqsR inhibitors in the future.

Structural Models for a Series of Allosteric Inhibitors of IGF1R Kinase.

The allosteric inhibition of insulin-like growth factor receptor 1 kinase (IGF1RK) is a potential strategy to overcome selectivity barriers for targeting receptor tyrosine kinases. We constructed structural models of a series of 12 indole-butyl-amine derivatives that have been reported as allosteric inhibitors of IGF1RK. We further studied the dynamics and interactions of each inhibitor in the allosteric pocket via all-atom explicit-solvent molecular dynamics (MD) simulations. We discovered that a bulky carbonyl substitution at the R1 indole ring is structurally unfavorable for inhibitor binding in the IGF1RK allosteric pocket. Moreover, we found that the most potent derivative (termed C11) acquires a distinct conformation: forming an allosteric pocket channel with better shape complementarity and interactions with the receptor. In addition to a hydrogen-bonding interaction with V1063, the cyano derivative C11 forms a stable hydrogen bond with M1156, which is responsible for its unique binding conformation in the allosteric pocket. Our findings show that the positioning of chemical substituents with different pharmacophore features at the R1 indole ring influences molecular interactions and binding conformations of indole-butyl-amine derivatives and, hence, dramatically affects their potencies. Our results provide a structural framework for the design of allosteric inhibitors with improved affinities and specificities against IGF1RK.

Antifungal Potential of Secondary Metabolites Derived from Arcangelisia flava (L.) Merr.: An Analysis of In Silico Enzymatic Inhibition and In Vitro Efficacy against Candida Species.

Considering the escalating resistance to conventional antifungal medications, it is critical to identify novel compounds that can efficiently counteract this challenge. The purpose of this research was to elucidate the fungicidal properties of secondary metabolites derived from Arcangelisia flava, with a specific focus on their efficacy against Candida species. This study utilized a combination approach comprising laboratory simulations and experiments to discern and evaluate the biologically active constituents present in the dichloromethane extract of A. flava. The in vitro experiments demonstrated that compounds 1 (palmatine) and 2 (fibraurin) exhibited antifungal properties. The compounds exhibited minimum inhibitory concentrations (MICs) ranging from 15.62 to 62.5 µg/mL against Candida sp. Moreover, compound 1 demonstrated a minimum fungicidal concentration (MFC) of 62.5 µg/mL against Candida glabrata and C. krusei. In contrast, compound 2 exhibited an MFC of 125 µg/mL against both Candida species. Based on a molecular docking study, it was shown that compounds 1 and 2 have a binding free energy of -6.6377 and -6.7075 kcal/mol, respectively, which indicates a strong affinity and specificity for fungal enzymatic targets. This study utilized pharmacophore modeling and Density Functional Theory (DFT) simulations to better understand the interaction dynamics and structural properties crucial for antifungal activity. The findings underscore the potential of secondary metabolites derived from A. flava to act as a foundation for creating novel and highly efficient antifungal treatments, specifically targeting fungal diseases resistant to existing treatment methods. Thus, the results regarding these compounds can provide references for the next stage in antifungal drug design. Further investigation is necessary to thoroughly evaluate these natural substances' clinical feasibility and safety characteristics, which show great potential as antifungal agents.

Virtual screening and biological evaluation to identify pharmaceuticals potentially causing hypertension and hypokalemia by inhibiting steroid 11ß-hydroxylase.

Several drugs were found after their market approval to unexpectedly inhibit adrenal 11ß-hydroxylase (CYP11B1)-dependent cortisol synthesis. Known side-effects of CYP11B1 inhibition include hypertension and hypokalemia, due to a feedback activation of adrenal steroidogenesis, leading to supraphysiological concentrations of 11-deoxycortisol and 11-deoxycorticosterone that can activate the mineralocorticoid receptor. This results in potassium excretion and sodium and water retention, ultimately causing hypertension. With the risk known but usually not addressed in preclinical evaluation, this study aimed to identify drugs and drug candidates inhibiting CYP11B1. Two conceptually different virtual screening methods were combined, a pharmacophore based and an induced fit docking approach. Cell-free and cell-based CYP11B1 activity measurements revealed several inhibitors with IC50 values in the nanomolar range. Inhibitors include retinoic acid metabolism blocking agents (RAMBAs), azole antifungals, α2-adrenoceptor ligands, and a farnesyltransferase inhibitor. The active compounds share a nitrogen atom embedded in an aromatic ring system. Structure activity analysis identified the free electron pair of the nitrogen atom as a prerequisite for the drug-enzyme interaction, with its pKa value as an indicator of inhibitory potency. Another important parameter is drug lipophilicity, exemplified by etomidate. Changing its ethyl ester moiety to a more hydrophilic carboxylic acid group dramatically decreased the inhibitory potential, most likely due to less efficient cellular uptake. The presented work successfully combined different in silico and in vitro methods to identify several previously unknown CYP11B1 inhibitors. This workflow facilitates the identification of compounds that inhibit CYP11B1 and therefore pose a risk for inducing hypertension and hypokalemia.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches.

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

Structure-based drug design of potential inhibitors of FBXW8, the substrate recognition component of Cullin-RING ligase 7.

FBXW8 plays an irreplaceable role in the substrate recognition of ubiquitin-dependent proteolysis, which further regulates cell cycle progression and signal transduction. However, the abnormal expression of FBXW8 triggers malignancy, inflammation, and autophagy irregulation. FBXW8 is considered as an effective therapeutic target for Cullin-RING ligase 7 (CRL7)-related cancers. Still, the lack of selective inhibitors hinders further therapeutic development and limits the exploration of its biological mechanism. This study constructed an integrated protocol that combines pharmacophore modeling, structure-based virtual screening, and Molecular Dynamic Simulation. It was then used as a screening query to identify hit compounds targeted at the substrate recognition site of FBXW8 from a large-scale compound database including 120 million compounds. Then, ten lead compounds were retrieved by using molecular docking analysis and ADMET prediction. Finally, MD simulations were performed to validate the binding stability of selected drug candidates. The result indicated that three newly obtained compounds, namely ZINC96179876, ZINC72174069, and ZINC97730272, might be potent FBXW8 inhibitors against CRL7-related cancers such as endometrial cancer.

Integrated use of ligand and structure-based virtual screening, molecular dynamics, free energy calculation and ADME prediction for the identification of potential PTP1B inhibitors.

Protein tyrosine phosphatases (PTPs) are the group of enzymes that control both cellular activity and the dephosphorylation of tyrosine (Tyr)-phosphorylated proteins. Dysregulation of PTP1B has contributed to numerous diseases including Diabetes Mellitus, Alzheimer's disease, and obesity rendering PTP1B as a legitimate target for therapeutic applications. It is highly challenging to target this enzyme because of its highly conserved and positively charged active-site pocket motivating researchers to find novel lead compounds against it. The present work makes use of an integrated approach combining ligand-based and structure-based virtual screening to find hit compounds targeting PTP1B. Initially, pharmacophore modeling was performed to find common features like two hydrogen bond acceptors, an aromatic ring and one hydrogen bond donor from the potent PTP1B inhibitors. The dataset of compounds matching with the common pharmacophoric features was filtered to remove Pan-Assay Interference substructure and to match the Lipinski criteria. Then, compounds were further prioritized using molecular docking and top fifty compounds with good binding affinity were selected for absorption, distribution, metabolism, and excretion (ADME) predictions. The top five compounds with high solubility, absorption and permeability holding score of - 10 to - 9.3 kcal/mol along with Ertiprotafib were submitted to all-atom molecular dynamic (MD) studies. The MD studies and binding free energy calculations showed that compound M4, M5 and M8 were having better binding affinity for PTP1B enzyme with ∆Gtotal score of - 24.25, - 31.47 and - 33.81 kcal/mol respectively than other compounds indicating that compound M8 could be a suitable lead compound as PTP1B inhibitor.

Identification of potential Akt activators: a ligand and structure-based computational approach.

The Akt pathway plays a significant role in various diseases like Alzheimer's, Parkinson's, and Diabetes. Akt is the central protein whose phosphorylation controls many downstream pathways. Binding of small molecules to the PH domain of Akt facilitates its phosphorylation in the cytoplasm and upregulates the Akt pathway. In the current study, to identify Akt activators, ligand-based approaches like 2D QSAR, shape, and pharmacophore-based screening were used, followed by structure-based approaches such as docking, MM-GBSA, ADME prediction, and MD simulation. The top twenty-five molecules from the Asinex gold platinum database found to be active in most 2D QSAR models were used for shape and pharmacophore-based screening. Later docking was performed using the PH domain of Akt1 (PDB: 1UNQ), and 197105, 261126, 253878, 256085, and 123435 were selected based on docking score and interaction with key residues, which were druggable and formed a stable protein-ligand complex. MD simulations of 261126 and 123435 showed better stability and interactions with key residues. To further investigate the SAR of 261126 and 123435, derivatives were downloaded from PubChem, and structure-based approaches were employed. MD simulation of derivatives 12289533, 12785801, 83824832, 102479045, and 6972939 was performed, in which 83824832 and 12289533 showed interaction with key residues for a longer duration of time, proving that they may act as Akt activators.

Pathogenomic in silico approach identifies NSP-A and Fe-IIISBP as possible drug targets in Neisseria Meningitidis MC58 and development of pharmacophores as novel therapeutic candidates.

Meningitis creates a life-threatening clinical crisis. Moreover, the administered antibiotics result into multi-drug resistance, thereby necessitating development of alternative therapeutic strategies. This study aimed at identifying novel-drug targets in Neisseria meningitidis and therapeutic molecules which can be exploited for the treatment of meningitis. Novel targets were identified by applying a pathogenomic approach involving protein data-set mining, subtractive channel analysis and subsequent qualitative analysis comprising of in silico pharmacokinetics, molecular docking and pharmacophore generation. Pathogenomic studies revealed Neisserial Surface Protein A (NSP-A) and Iron-III-Substrate Binding Protein (Fe-IIISBP) as potential targets. Two pharmacophore models comprising of 2-(biaryl) carbapenems, efavirenz, praziquantel and pyrimethamine for NSP-A and 2-(biaryl) carbapenems, trimipramine and pyrimethamine for Fe-IIISBP, showed successful docking, followed drug-likeness criteria and generated pharmacophore model with a score of 8.08 and 8.818, respectively, which had further been docked to the target stably. Thus, our study identifies NSP-A and Fe-IIISBP as novel targets in Neisseria meningitidis for which 2-(biaryl) carbapenems, efavirenz, praziquantel, trimipramine and pyrimethamine may be employed for effective treatment of meningitis.

Identification of Anti-Influenza A Compounds Inhibiting the Viral Non-Structural Protein 1 (NS1) Using a Type I Interferon-Driven Screening Strategy.

There is an urgent need to identify efficient antiviral compounds to combat existing and emerging RNA virus infections, particularly those related to seasonal and pandemic influenza outbreaks. While inhibitors of the influenza viral integral membrane proton channel protein (M2), neuraminidase (NA), and cap-dependent endonuclease are available, circulating influenza viruses acquire resistance over time. Thus, the need for the development of additional anti-influenza drugs with novel mechanisms of action exists. In the present study, a cell-based screening assay and a small molecule library were used to screen for activities that antagonized influenza A non-structural protein 1 (NS1), a highly conserved, multifunctional accessory protein that inhibits the type I interferon response against influenza. Two potential anti-influenza agents, compounds 157 and 164, were identified with anti-NS1 activity, resulting in the reduction of A/PR/8/34(H1N1) influenza A virus replication and the restoration of IFN-ß expression in human lung epithelial A549 cells. A 3D pharmacophore modeling study of the active compounds provided a glimpse of the structural motifs that may contribute to anti-influenza virus activity. This screening approach is amenable to a broader analysis of small molecule compounds to inhibit other viral targets.

Virtual Screening Strategy and In Vitro Tests to Identify New Inhibitors of the Immunoproteasome.

Immunoproteasome inhibition is a promising strategy for the treatment of hematological malignancies, autoimmune diseases, and inflammatory diseases. The design of non-covalent inhibitors of the immunoproteasome ß1i/ß5i catalytic subunits could be a novel approach to avoid the drawbacks of the known covalent inhibitors, such as toxicity due to off-target binding. In this work, we report the biological evaluation of thirty-four compounds selected from a commercially available collection. These hit compounds are the outcomes of a virtual screening strategy including a dynamic pharmacophore modeling approach onto the ß1i subunit and a pharmacophore/docking approach onto the ß5i subunit. The computational studies were first followed by in vitro enzymatic assays at 100 µM. Only compounds capable of inhibiting the enzymatic activity by more than 50% were characterized in detail using Tian continuous assays, determining the dissociation constant (Ki) of the non-covalent complex where Ki is also the measure of the binding affinity. Seven out of thirty-four hits showed to inhibit ß1i and/or ß5i subunit. Compound 3 is the most active on the ß1i subunit with Ki = 11.84 ± 1.63 µM, and compound 17 showed Ki = 12.50 ± 0.77 µM on the ß5i subunit. Compound 2 showed inhibitory activity on both subunits (Ki = 12.53 ± 0.18 and Ki = 31.95 ± 0.81 on the ß1i subunit and ß5i subunit, respectively). The induced fit docking analysis revealed interactions with Thr1 and Phe31 of ß1i subunit and that represent new key residues as reported in our previous work. Onto ß5i subunit, it interacts with the key residues Thr1, Thr21, and Tyr169. This last hit compound identified represents an interesting starting point for further optimization of ß1i/ß5i dual inhibitors of the immunoproteasome.

mPGES-1 Inhibitor Discovery Based on Computer-Aided Screening: Pharmacophore Models, Molecular Docking, ADMET, and MD Simulations.

mPGES-1 is an enzyme, which, when activated by inflammatory factors, can cause prostaglandin E synthesis. Traditional non-steroidal anti-inflammatory drugs are capable of inhibiting prostaglandin production, yet they can also cause gastrointestinal reactions and coagulation disorders. mPGES-1, the enzyme at the conclusion of prostaglandin production, does not cause any adverse reactions when inhibited. Numerous studies have demonstrated that mPGES-1 is more abundant in cancerous cells than in healthy cells, indicating that decreasing the expression of mPGES-1 could be a potential therapeutic strategy for cancer. Consequently, the invention of mPGES-1 inhibitors presents a fresh avenue for the treatment of inflammation and cancer. Incorporating a database of TCM compounds, we collected a batch of compounds that had an inhibitory effect on mPGES-1 and possessed IC50 value. Firstly, a pharmacophore model was constructed, and the TCM database was screened, and the compounds with score cut-off values of more than 1 were retained. Then, the compounds retained after being screened via the pharmacodynamic model were screened for docking at the mPGES-1 binding site, followed by high-throughput virtual screening [HTVS] and standard precision [SP] and super-precision [XP] docking, and the compounds in the top 20% of the XP docking score were selected to calculate the total free binding energy of MM-GBSA. The best ten compounds were chosen by comparing their score against the reference ligand 4U9 and the MM-GBSA_dG_Bind score. ADMET analysis resulted in the selection of ten compounds, three of which had desirable medicinal properties. Finally, the binding energy of the target protein mPGES-1 and the candidate ligand compound was analyzed using a 100 ns molecular dynamics simulation of the reference ligand 4U9 and three selected compounds. After a gradual screening study and analysis, we identified a structure that is superior to the reference ligand 4U9 in all aspects, namely compound 15643. Taken together, the results of this study reveal a structure that can be used to inhibit mPGES-1 compound 15643, thereby providing a new option for anti-inflammatory and anti-tumor drugs.