Inhibitor design against JNK1 through e-pharmacophore modeling docking and molecular dynamics simulations

Identification of the active constituents and molecular mechanism of Eulophia nuda extract in the treatment of osteoarthritis by network pharmacology, molecular modelling and experimental assays

Osteoarthritis is a degenerative joint disease that worsens over time, often resulting in chronic pain. Eulophia nuda (Orchidaceae), a medicinal herb widely used by folklore and indigenous healers for treating arthritis but the active ingredients and the molecular mechanisms of action are yet to be explored. The present study systematically investigates the underlying anti-osteoarthritic mechanism of ENE through network pharmacology, molecular dynamics simulation and experimental assays. A comprehensive search on IMPPAT, KNApSAcK and Pubchem databases resulted 26 active compounds from E. nuda, of which 23 passed the drug-likeness criteria. A total of 2344 compound targets, 1370 osteoarthritis targets and 81 overlapping compound-disease targets were identified. The compound-disease target network resulted in five active constituents with degree > 23. Topological analysis of the protein-protein interaction network revealed six hub target genes. KEGG analysis revealed IL-17, TNF and AGE-RAGE signalling pathways as the enriched pathways involved in osteoarthritis. Molecular docking showed eulophiol had the good binding energy (>8.0 kcal/mol) with MMP9, JNK1, p38 and NF-kβ. The molecular dynamics simulations and the MMPBSA analysis indicate high stability and greater binding energy of eulophiol with the target proteins. ENE did not show cytotoxicity on SW982 cells up to a concentration of 100 μg/ml. ENE exhibited considerable anti-inflammatory effect by reducing PGE2, IL-6 and IL8 levels as well as reducing the mRNA expression of matrix metalloproteinases (MMP2 and MMP9). Furthermore, ENE effectively inhibited the NF-kβ nuclear translocation and phosphorylation of ERK2, p38 and JNK in SW982 cells. The current study showed that ENE may act as a potential drug candidate for treating osteoarthritis.

Identification and Evaluation of Natural Compounds as Potential Inhibitors of NS2B-NS3 Zika Virus Protease: A Computational Approach

The Zika virus (ZIKV), an arbovirus within the Flavivirus genus, is associated with severe neurological complications, including Guillain-Barré syndrome in affected individuals and microcephaly in infants born to infected mothers. With no approved vaccines or antiviral treatments available, there is an urgent need for effective therapeutic options. This study aimed to identify new natural compounds with inhibitory potential against the NS2B-NS3 protease (PDB ID: 5LC0), an essential enzyme in viral replication. An e-pharmacophore model was generated using a five-point (ADDRR) feature approach in the PHASE module of Schrodinger and used for the virtual screening of 26,689 natural compounds from the PubChem database. The screening yielded 14,277 prioritized compounds based on fitness scores, further refined through extra precision (XP) docking in GLIDE, resulting in 24 compounds. Eight top hits were selected following ADME analysis with SwissADME, and toxicity screening with ProTox-II identified four non-toxic lead candidates. Molecular dynamic simulations confirmed the stability of the three most promising leads, CID 44418637, CID 163078083, and CID 68734190, with binding affinities of - 7.721, - 8.226, and - 8.307 kcal/mol, respectively. MM/GBSA analysis revealed that Compounds 68734190 (- 50.192 kcal/mol) and 163078083 (- 49.947 kcal/mol) possess superior binding affinities to the ZIKV NS2B-NS3 protease compared to the reference compound (- 38.347 kcal/mol). Given their natural origin, these compounds may offer safer options to mitigate severe ZIKV-related symptoms while providing a favourable safety and pharmacokinetic profile. This study lays the groundwork for developing targeted ZIKV therapies, potentially addressing a significant unmet need in public health by reducing the incidence of ZIKV-related complications. Further experimental validation is required to confirm efficacy and address potential development challenges.

Pharmacophore modeling, 3D-QSAR, and MD simulation-based overture for the discovery of new potential HDAC1 inhibitors

Histone deacetylases (HDACs) are important epigenetic regulators that modulate the activity of histone and non-histone proteins leading to various cancers. Histone deacetylase 1 (HDAC1) is a member of class 1 HDAC family related to different cancers. However, the nonselective profile of existing HDAC1 inhibitors restricted their clinical utility. Therefore, the identification of new HDAC1 selective inhibitors may be fruitful against cancer therapy. In this present work, a pharmacophore model was built using 60 benzamide-based known HDAC1 selective inhibitors and it was used further to filter the large epigenetic molecular database of small molecules. Further, the 3D-QSAR model was built using the best common pharmacophore hypothesis consisting of higher PLS statistics of R2 of 0.89, Q2 of 0.83, variance ratio (F) of 65.7 and Pearson-r value of 0.94 revealing the model reliability and its high predictive power. The screened hits of the pharmacophore model were then subjected to molecular docking against HDAC1 to identify high-affinity lead molecules. The top 10 hits were ranked from the docking studies using docking scores for lead optimization. The potential hit molecules M1 and M2 identified from the study showed promising interaction during HDAC1 docking and MD simulation studies with acceptable ADME properties. Also, the newly designed lead compounds M11 and M12 may be considered highly potential inhibitors against HDAC1. The 3D-QSAR analysis, conformational requirements, and observations noticed in the MD simulations study will enable the optimization of lead molecules and to design of novel effective, and selective HDAC1 inhibitors in the future.

Novel 3,6-Disubstituted Pyridazine Derivatives Targeting JNK1 Pathway: Scaffold Hopping and Hybridization-Based Design, Synthesis, Molecular Modeling, and In Vitro and In Vivo Anticancer Evaluation

A series of novel 3,6-disubstituted pyridazine derivatives were designed, synthesized, and biologically evaluated as preclinical anticancer candidates. Compound 9e exhibited the highest growth inhibition against most of the NCI-60 cancer cell lines. The in vivo anticancer activity of 9e was subsequently investigated at two dose levels using the Ehrlich ascites carcinoma solid tumor animal model, where a reduction in the mean tumor volume allied with necrosis induction was reported without any signs of toxicity in the treated groups. Interestingly, compound 9e was capable of downregulating c-jun N-terminal kinase-1 (JNK1) gene expression and curbing the protein levels of its phosphorylated form, in parallel with a reduction in its downstream targets, namely, c-Jun and c-Fos in tumors, along with restoring p53 activity. Furthermore, molecular docking and dynamics simulations were carried out to predict the binding mode of 9e and prove its stability in the JNK1 binding pocket.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Virtual Screening, Molecular Docking, and Dynamic Simulations Revealed TGF-β1 Potential Inhibitors to Curtail Cervical Cancer Progression

Cervical cancer is one of the main causes of cancer death in women globally, and its epidemiology is similar to that of a low-infectious venereal illness. Many sexual partners and early age at first intercourse have been demonstrated to have a significant influence on risk. TGF-β1 is a multifunctional cytokine that is required for cervical carcinoma metastasis, tumor development, progression, and invasion. The TGF-β1 signaling system plays a paradoxical function in cancer formation, suppressing early-stage tumor growth while increasing tumor progression and metastasis. Importantly, TGF-β1 and TGF-β receptor 1 (TGF-βR1), two components of the TGF-β signaling system, are substantially expressed in a range of cancers, including breast cancer, colon cancer, gastric cancer, and hepatocellular carcinoma. The current study aims to investigate possible inhibitors targeting TGF-β1 using molecular docking and dynamic simulations. To target TGF-β1, we used anti-cancer drugs and small molecules. MVD was utilized for virtual screening, and the highest scoring compound was then subjected to MD simulations using Schrodinger software package v2017-1 (Maestro v11.1) to identify the most favorable lead interactions against TGF-β1. The Nilotinib compound has shown the least XP Gscore of -2.581 kcal/mol, 30ns MD simulations revealing that the Nilotinib- TGF-β1 complex possesses the lowest energy of -77784.917 kcal/mol. Multiple parameters, including Root Mean Square Deviation, Root Mean Square Fluctuation, and Intermolecular Interactions, were used to analyze the simulation trajectory. Based on the results; we conclude that the ligand nilotinib appears to be a promising prospective TGF-β1inhibitor for reducing TGF-β1 expression ad halting cervical cancer progression.

TRAIL predisposes non-small cell lung cancer to ferroptosis by regulating ASK-1/JNK1 pathway

OBJECTIVE

Our current study aimed to assess the relationship between TNF-related apoptosis-inducing ligand (TRAIL) and ferroptosis in non-small cell lung cancer (NSCLC) development.

METHODS

The expression of TRAIL was detected by western blot, RT-qRCR and immunohistochemistry. The viability of NSCLC cells was analyzed by CCK-8 kit. The migration and invasion of NSCLC cells were detected by wound healing assay and transwell assay, respectively. Labile iron pool (LIP) was detected based on the calcein-acetoxymethyl ester method. Ferrous iron (Fe2+) and iron levels were assessed by detection kits. The levels of superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA) were measured using corresponding detection kits. Mice tumor xenograft models were used for the in vivo research.

RESULTS

The expression of TRAIL was reduced in H1299, NCL-H1395, and A549 cells compared with BEAS-2B cells. The up-regulation of TRAIL expression significantly reduced cell viability, invasion, and migration of H1299 and A549 cells. TRAIL reduced the expression of ferroptosis-related genes (FTH1, GPX4, and SLC7A11), increased the levels of LIP, iron, and Fe2+, and promoted lipid peroxidation, thereby predisposing NSCLC cells to ferroptosis. TRAIL up-regulated the expression of phosphate modification of ASK-1 and JNK. ASKI-1 inhibitor GS-4977 attenuated the effects of TRAIL on the viability, migration, invasion, and ferroptosis of H1299 cells. Furthermore, TRAIL further suppressed tumor growth and ferroptosis in mice tumor xenograft models.

CONCLUSION

We indicated that overexpression of TRAIL induced ferroptosis in NSCLC cells and exerted anti-tumor effects. Mechanistically, TRAIL promoted ferroptosis by the activation of the ASK-1/JNK1 pathway. Our results may provide new therapeutic strategies for NSCLC.

In Silico Insights into the Arsenic Binding Mechanism Deploying Application of Computational Biology-Based Toolsets

An assortment of environmental matrices includes arsenic (As) in its different oxidation states, which is often linked to concerns that pose a threat to public health worldwide. The current difficulty lies in addressing toxicological concerns and achieving sustained detoxification of As. Multiple conventional degradation methods are accessible; however, they are indeed labor-intensive, expensive, and reliant on prolonged laboratory evaluations. Molecular interaction and atomic level degradation mechanisms for enzyme-As exploration are, however, underexplored in those approaches. A feasible approach in this case for tackling this accompanying concern of As might be to cope with undertaking multivalent computational methodologies and tools. This work aimed to provide molecular-level insight into the enzyme-aided As degradation mechanism. AutoDock Vina, CABS-flex 2.0, and Desmond high-performance molecular dynamics simulation (MDS) were utilized in the current investigation to simulate multivalent molecular processes on two protein sets: arsenate reductase (ArsC) and laccase (LAC) corresponding arsenate (ART) and arsenite (AST), which served as model ligands to comprehend binding, conformational, and energy attributes. The structural configurations of both proteins exhibited variability in flexibility and structure framework within the range of 3.5-4.5 Å. The LAC-ART complex exhibited the lowest calculated binding affinity, measuring -5.82 ± 0.01 kcal/mol. Meanwhile, active site residues ILE-200 and HIS-206 were demonstrated to engage in H-bonding with the ART ligand. In contrast to ArsC, the ligand binding affinity of this bound complex was considerably greater. Additional validation of docked complexes was carried out by deploying Desmond MDS of 100 ns to capture protein and ligand conformation behavior. The system achieved stability during the 100 ns simulation run, as confirmed by the average P-L RMSD, which was ∼1 Å. As a preliminary test of the enzyme's ability to catalyze As species, corresponding computational insights might be advantageous for bridging gaps and regulatory consideration.

Chemical library design, QSAR modeling and molecular dynamics simulations of naturally occurring coumarins as dual inhibitors of MAO-B and AChE

Coumarins are a highly privileged scaffold in medicinal chemistry. It is present in many natural products and is reported to display various pharmacological properties. A large plethora of compounds based on the coumarin ring system have been synthesized and were found to possess biological activities such as anticonvulsant, antiviral, anti-inflammatory, antibacterial, antioxidant as well as neuroprotective properties. Despite the wide activity spectrum of coumarins, its naturally occurring derivatives are yet to be investigated in detail. In the current study, a chemical library was created to assemble all chemical information related to naturally occurring coumarins from the literature. Additionally, a multi-stage virtual screening combining QSAR modeling, molecular docking, and ADMET prediction was conducted against monoamine oxidase B and acetylcholinesterase, two relevant targets known for their neuroprotective properties and 'disease-modifying' potential in Parkinson's and Alzheimer's disease. Our findings revealed ten coumarin derivatives that may act as dual-target drugs against MAO-B and AChE. Two coumarin candidates were selected from the molecular docking study: CDB0738 and CDB0046 displayed favorable interactions for both proteins as well as suitable ADMET profiles. The stability of the selected coumarins was assessed through 100 ns molecular dynamics simulations which revealed promising stability through key molecular interactions for CDB0738 to act as dual inhibitor of MAO-B and AChE. However, experimental studies are necessary to evaluate the bioactivity of the proposed candidate. The current results may generate an increasing interest in bioprospecting naturally occurring coumarins as potential candidates against relevant macromolecular targets by encouraging virtual screening studies against our chemical library.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors

Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features. The dataset inhibitors of best hypotheses used to build the 3D QSAR model and the generated 3D QSAR model resulted in good PLS statistics with a regression coefficient (R2) of 0.89, predictive coefficient (Q2) of 0.88, and Pearson-R factor of 0.94 indicating its excellent predictive ability. The hits retrieved from pharmacophore-based virtual screening were subjected to docking against HDAC3 for the identification of potential inhibitors. A total of 10 hitsM1 to M10 were ranked using their scoring functions and further subject to lead optimization. The Prime MM/GBSA, AutoDock binding free energies, and ADMET studies were implemented for the selection of lead candidates. The four ligand molecules M1, M2, M3, and M4 were identified as potential leads against HDAC3 after lead optimization. The top two leads M1 and M2 were subjected to MD simulations for their stability evaluation with HDAC3. The newly designed leads M11 and M12 were identified as HDAC3 potential inhibitors from MD simulations studies. Therefore, the outcomes of the present study could provide insights into the discovery of new potential HDAC3 inhibitors with improved selectivity and activity against a variety of cancers and neurodegenerative diseases.

Exploring bixin from Bixa orellana L. seeds: quantification and in silico insights into its anti-cancer potential

Bixin, the key pigment of Bixa orellana L., is an apo-carotenoid found in the seed arils. The present study aimed to quantitatively determine the bixin content of seeds and explore its anti-cancer activity through in silico studies. The bixin content from the seeds of the local genotype, TNMTP8, quantified by RP-HPLC was 4.58 mg per gram. The prediction of pharmacological activity suggested that bixin may serve as a BRAF, MMP9, TNF expression inhibitors, and TP53 expression enhancer. According to molecular docking analysis, bixin interacted with eight different skin cancer targets and had the lowest binding energy compared to the standard drug, 5-fluorouracil. The binding score between bixin and the targets ranged from -4.7 to -8.7 kcal/mol. The targets BRAF and SIRT3 interacted well with bixin, with binding energies as low as -8.3 and -8.7 kcal/mol, respectively. Hence, the dynamic behavior of these two docked complexes throughout a 500 ns trajectory run was investigated further. The Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) values, and total contacts as a function of time recorded during scrutiny suggest that both complexes were stable. This was validated by post-molecular dynamics analysis using Molecular Mechanics Generalized Born Surface Area (MM-GBSA). Principal component analysis (PCA) was used to analyze the significant differences in motion exhibited by BRAF-Bixin and SIRT3-Bixin. The results showed that bixin is a promising source for potential treatment interventions in skin cancer therapies.Communicated by Ramaswamy H. Sarma.

Identification of BACE-1 Inhibitors against Alzheimer’s Disease through E-Pharmacophore-Based Virtual Screening and Molecular Dynamics Simulation Studies: An Insilco Approach

Alzheimer is a severe memory and cognitive impairment neurodegenerative disease that is the most common cause of dementia worldwide and characterized by the pathological accumulation of tau protein and amyloid-beta peptides. In this study, we have developed E-pharmacophore modeling to screen the eMolecules database with the help of a reported co-crystal structure bound with Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE-1). Flumemetamol, florbetaben, and florbetapir are currently approved drugs for use in the clinical diagnosis of Alzheimer’s disease. Despite the benefits of commercially approved drugs, there is still a need for novel diagnostic agents with enhanced physicochemical and pharmacokinetic properties compared to those currently used in clinical practice and research. In the E-pharmacophore modeling results, it is revealed that two aromatic rings (R19, R20), one donor (D12), and one acceptor (A8) are obtained, and also that similar pharmacophoric features of compounds are identified from pharmacophore-based virtual screening. The identified screened hits were filtered for further analyses using structure-based virtual screening and MM/GBSA. From the analyses, top hits such as ZINC39592220 and en1003sfl.46293 are selected based on their top docking scores (−8.182 and −7.184 Kcal/mol, respectively) and binding free energy (−58.803 and −56.951 Kcal/mol, respectively). Furthermore, a molecular dynamics simulation and MMPBSA study were performed, which revealed admirable stability and good binding free energy throughout the simulation period. Moreover, Qikprop results revealed that the selected, screened hits have good drug-likeness and pharmacokinetic properties. The screened hits ZINC39592220 and en1003sfl.46293 could be used to develop drug molecules against Alzheimer’s disease.

In silico trials to design potent inhibitors against matrilysin (MMP-7)

The study deals with structure-based rational drug design against the chief zinc-rely endopeptidase called matrilysin (MMP-7) that is involved in inflammatory and metastasis process of several carcinomas. Hyperactivated matrilysin of human was targeted, because of its hydrolytic actions on extracellular matrix (ECM) protein components constitutes fibrillar collagens, gelatins, fibronectins and it also activates zymogen forms of vital matrix metalloproteinases (gelatinase A-MMP-2 and B-MMP-9) responsible for ECM destruction in many cancers. In the present work, e-pharmacophores were generated for the respective five co-crystal structures of human matrilysin by mapping ligand's pharmacophoric features. During the lead-optimization campaign, the five e-pharmacophores-based shape screening against an in-house library of >21 million compounds created a dataset of 5000 structural analogs. The subsequent three different docking strategies, including rigid-receptor docking, quantum-polarized-ligand docking, induced-fit docking and free energy binding calculations resulted four leads as novel and potent MMP-7 binders. These four leads were observed with good pharmacological features and good receiver operating characteristics curve metrics (ROC: 0.93) in post-docking evaluations against five existing co-crystal inhibitors and 1000 decoy molecules with MMP-7. Moreover, stability and dynamics behavior of matrilysin-lead1 complex and matrilysin-cocrystal ligand (TQJ) complex were analyzed in natural physiological milieu of 1000 ns or 1 µs molecular dynamics simulations. Lead1-MMP-7 complex was found with an average Cα root-mean-square deviation (RMSD) of 2.35 Å, average ligand root-mean-square fluctuations (RMSF) of 0.66 Å and the strong metallic interactions with E220, a key residue for proteolytic action thereby hinders ECM proteolysis that in turn can halt metastatic cancerous condition.Communicated by Ramaswamy H. Sarma.

Design and evaluation of pyrimidine derivatives as potent inhibitors of ABCG2, a breast cancer resistance protein

The protein ATP-binding cassette subfamily G member 2 (ABCG2) is one of the major factors behind multidrug resistance (MDR) in breast cancer. We performed three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling, docking, and molecular dynamics (MD) simulation to design pyrimidine-based ABCG2 antagonists. The developed QSAR model (r 2 = 0.92, q 2 = 0.82, and good cross-validated r 2 = 0.73) dictate requirement of electrostatic, and hydrophobic fields for modulating bioactivity. Based on this rationale, we designed and screened 1010 new compounds, among them 2 (ND-510 and ND-500) exhibit excellent drug-like features. Comparative molecular docking, MM/GBSA and ADMET profiles were determined to understand the interactive poses, affinity, and drug-likeness of the designed compounds. Furthermore, MD simulations were performed with the ABCG2 receptor, and the results were compared with the two earlier synthesized active compounds. The outcomes of the study will help researchers to develop new antagonists for treatment of MDR breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03231-1.

Identification of NEK7 inhibitors: structure based virtual screening, molecular docking, density functional theory calculations and molecular dynamics simulations

NEK7 is a NIMA related-protein kinase that plays a crucial role in spindle assembly and cell division. Dysregulation of NEK7 protein leads to development and progression of different types of malignancies including colon and breast cancers. Therefore, NEK7 could be considered as an attractive target for anti-cancer drug discovery. However, few efforts have been made for the development of selective inhibitors of NIMA-related kinase but still no FDA approved drug is known to selectively inhibit the NEK7 protein. Dacomitinib and Neratinib are two Enamide derivatives that were approved for treatment against non-small cell lung cancer and breast cancer respectively. Drug repurposing is a time and cost-efficient method for re-evaluating the activities of previously authorized medications. Thus, the present research involves the repurposing of two FDA-approved medications via comprehensive in silico approach including Density functional theory (DFTs) studies which were conducted to determine the electronic properties of the Dacomitinib and Neratinib. Afterward, binding orientation of selected drugs inside NEK7 activation loop was evaluated through molecular docking approach. Selected drugs exhibited potential molecular interactions engaging important amino acid residues of active site. The docking score of Dacomitinib and Neratinib was -30.77 and -26.78 kJ/mol, respectively. The top ranked pose obtained from molecular docking was subjected to Molecular Dynamics (MD) Simulations for investigating the stability of protein-ligand complex. The RMSD pattern revealed the stability of protein-ligand complex throughout simulated trajectory. In conclusion, both drugs displayed inhibitory efficacy against NEK7 protein and provide a prospective therapy option for malignant malignancies linked with NEK7. Communicated by Ramaswamy H. Sarma.

Network analysis and ligand-based pharmacophore modeling for discovery of small molecule against glioblastoma multiforme

Aim: This study uses network pharmacology to design a c-Src inhibitor followed by pharmacophore modeling to combat glioblastoma multiforme. These in silico approaches are suitable for designing and developing new molecules of interest. Materials & methods: The authors performed virtual screening, pharmacophore analysis and validation of results using various in silico tools and reliable data from different types of literature and databases. Results: The in silico pipeline the authors followed produced reliable chemical information to combat glioblastoma. The authors identified a chemical template against the c-Src protein, which was validated statistically and computationally. Conclusion: The authors have successfully identified a chemical template against c-Src, which will be developed into a promising inhibitor in future studies.

Wani T, Batiha GES, Siddique F, Alqarni M, Akintola AA. Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of -42.67 kJ/mol, better than Dabrafenib (-33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein-ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7-Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.

In silico analytical toolset for predictive degradation and toxicity of hazardous pollutants in water sources

Different phenolic compounds, including multimeric lignin derivatives in the β-O-4 form, are among the most prevalent compounds in wastewater, often generated from paper industries. Relatively small concentrations of lignin are hazardous to aquatic organisms and can trigger severe environmental hazards. Herein, we present a predictive toolset to insight the induced toxic hazards prediction, and their Lignin peroxidase (LiP)-assisted degradation mechanism of selected multimeric lignin model compounds. T.E.ST and Toxtree toolset were deployed for toxic hazards estimation in different endpoints. To minimize the concerning hazards, we screened multimeric compounds for binding affinity with LiP. The binding affinity was found to be significantly lower than the reference compound. An Extra precision (XP) Glide score of -6.796 kcal/mol was found for dimer (guaiacyl 4-O-5 guaiacyl) complex as lowest compared to reference compound (-4.007 kcal/mol). The active site residues ASP-153, HIP-226, VAL-227, ARG-244, GLU-215, 239, PHE-261 were identified as site-specific key binding AA residues actively involved with corresponding ligands, forming Hydrophobic, H-Bond, π-Stacking, π-π type interactions. The DESMOND-assisted molecular dynamics simulation's (MDS) trajectories of protein-ligand revealed the considerable binding behavior and attained stability and system equilibrium state. Such theoretical and predictive conclusions indicted the feasibility of LiP assisted sustainable mitigation of lignin-based compounds, and such could be used to protect the environment from the potential hazards posed by recognized similar pollutants.

Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein–ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.

A network pharmacology strategy to investigate the anti-osteoarthritis mechanism of main lignans components of Schisandrae Fructus

Osteoarthritis (OA) is a chronic age-related progressive joint disorder. Degradation of the cartilage extracellular matrix (ECM) is considered a hallmark of OA and may be a target for new therapeutic methods. Schisandrae Fructus (SF) has been shown to be effective in treating OA. The major active components of SF are lignans. However, the targets of SF and the pharmacological mechanisms underlying the effects of SF lignans in the treatment of OA have not been elucidated. Therefore, based on network pharmacology, this research predicted the treatment targets of six lignans in SF, constructed a protein-protein interaction network and identified 15 hub genes in the OA-target protein-protein interaction network. Through Gene Ontology function and pathway analyses, the gene functions of lignans in the treatment of OA were determined. Finally, the anti-OA effects of lignans and underlying mechanisms identified in the network pharmacology analysis were verified by molecular docking, real-time PCR and western blotting in vitro. The biological processes of the genes and proteins targeted by lignans in the treatment of OA included the immune response, inflammatory response, cell signal transduction and phospholipid metabolism. Moreover, 20 metabolic pathways were enriched. Network pharmacology, molecular docking and in vitro and in vivo experimental results revealed that SF, schisanhenol and gamma-schisandrin inhibited EGFR and MAPK14 gene expression by inhibiting SRC gene expression and activity and then decreased MMP 13 and collagen II protein and gene expression. This research provides a basis for further study of the anti-OA effects and mechanisms of SF, schisanhenol and gamma-schisandrin.

In silico exploration of lignin peroxidase for unraveling the degradation mechanism employing lignin model compounds

Lignin peroxidase is a heme-containing biocatalyst, well-known for its diverse applications in the fields from environmental chemistry to biotechnology. LiP-mediated oxidative catalysis is H₂O₂-dependent, and can oxidize phenolic, and non-phenolic substrates by oxidative cleavage of the C-C and C-O bonds of lignin. In contrast to fungi-derived LiP, the binding affinity of bacterial-derived LiP to lignin at the molecular level is poorly known to date. Tremendous wet-lab studies have been unveiled that provide degradation and biotransformation information on kraft lignin, whilst studies on the completely transformed compounds and the degradation of each transformed compounds simultaneously during degradation are scarce. To gain an understanding of the degradation process using docking, and MDS based studies, we assessed the binding affinity of selected lignin model compounds with bacterial origin LiP and validated such docked complexes exploiting 30 ns molecular dynamics simulations. We selected and picked a total of 12 lignin model compounds for molecular modeling analysis, namely two chlorinated lignin model compounds (monomer) (2-chlorosyringaldehyde and 5-chlorovanillin), eight standard lignin model compounds (veratryl alcohol, syringyl alcohol, sinapyl alcohol, methyl hydroquinone, guaiacol, coniferyl alcohol, catechol, and 4-methoxy phenol), while, two 4-O-5, and β-O-4 linkage-based multimeric model compounds (dimer: 2-methoxy-6-(2-methoxy-4-methylphenoxy)-4-methylphenol; trimer: syringyl β-O-4 syringyl β-O-4 sinapyl alcohol). Far more specific binding residues were observed from XP-Glide docking, as TYR, HIP (protonated histidine), PHE, VAL, ASP, THR, LYS and GLN. The binding affinity was confirmed by the Gibbs free energy or binding energy (ΔG) score; furthermore, it is found that the maximum binding energy seems to be observed for 4-methoxyphenol with a Glide score of -3.438 with Pi-Pi stacking and H-bond type bonding interactions, whilst the lowest XP Gscore as -8.136 with Pi-Pi stacking and H-bond (side chain) type bonding interactions were found for the trimer model compound. The docked complexes were further evaluated for deep rigorous structural and functional fluctuation analyses through high-performance molecular dynamics simulations-DESMOND, after a post simulation run of 30 ns. The RMSD trajectory analyses of the protein-ligands were found to be in the equilibrium state at the end of simulation run for multimeric lignin model compounds. In addition, ionic ligand-protein interaction occurs among chlorinated compounds, while hydrophobic and H-bond contacts have frequently been observed in all lignin-model compounds. The findings herein demonstrate that bacterial LiP can effectively catalyze multiple lignin model compounds, and it might further be used as an effective tool for sustainable mitigation of diverse environmental contaminants.

Cyano Enone-Bearing Triterpenoid Soloxolone Methyl Inhibits Epithelial-Mesenchymal Transition of Human Lung Adenocarcinoma Cells In Vitro and Metastasis of Murine Melanoma In Vivo

Introduction of α-cyano α,β-unsaturated carbonyl moiety into natural cyclic compounds markedly improves their bioactivities, including inhibitory potential against tumor growth and metastasis. Previously, we showed that cyano enone-bearing derivatives of 18βH-glycyrrhetinic (GA) and deoxycholic acids displayed marked cytotoxicity in different tumor cell lines. Moreover, GA derivative soloxolone methyl (SM) was found to induce ER stress and apoptosis in tumor cells in vitro and inhibit growth of carcinoma Krebs-2 in vivo. In this work, we studied the effects of these compounds used in non-toxic dosage on the processes associated with metastatic potential of tumor cells. Performed screening revealed SM as a hit compound, which inhibits motility of murine melanoma B16 and human lung adenocarcinoma A549 cells and significantly suppresses colony formation of A549 cells. Further study showed that SM effectively blocked transforming growth factor β (TGF-β)-induced epithelial-mesenchymal transition (EMT) of A549 cells: namely, inhibited TGF-β-stimulated motility and invasion of tumor cells as well as loss of their epithelial characteristics, such as, an acquisition of spindle-like phenotype, up- and down-regulation of mesenchymal (vimentin, fibronectin) and epithelial (E-cadherin, zona occludens-1 (ZO-1)) markers, respectively. Network pharmacology analysis with subsequent verification by molecular modeling revealed that matrix metalloproteinases MMP-2/-9 and c-Jun N-terminal protein kinase 1 (JNK1) can be considered as hypothetical primary targets of SM, mediating its marked anti-EMT activity. The inhibitory effect of SM on EMT revealed in vitro was further confirmed in a metastatic model of murine B16 melanoma: SM was found to effectively block metastatic dissemination of melanoma B16 cells in vivo, increase expression of E-cadherin and suppress expression of MMP-9 in lung metastatic foci. Altogether, our data provided valuable information for a better understanding of the antitumor activity of cyano enone-bearing semisynthetic compounds and revealed SM as a promising anti-metastatic drug candidate.

Identification of Phosphoinositide-3 Kinases Delta and Gamma Dual Inhibitors Based on the p110δ/γ Crystal Structure

Background:

Phosphoinositide-3 kinases (PI3Ks) are key signaling molecules that affect a diverse array of biological processes in cells, including proliferation, differentiation, survival, and metabolism. The abnormal activity of PI3K signals is closely related to the occurrence of many diseases, which has become a very promising drug target, especially for the treatment of cancer. PI3Kδ/γ inhibitors can reduce toxicity concerns for chronic indications such as asthma and rheumatoid arthritis compared with pan PI3Ks inhibitors.

Methods:

With the aim of finding more effective PI3Kδ/γ dual inhibitors, virtual screening, ADMET prediction Molecular Dynamics (MD) simulations and MM-GBSA were executed based on the known p110δ/γ crystal structure. Compound ZINC28564067 with high docking score and low toxicity was obtained.

Results:

By MD simulations and MM-GBSA, we could observe that ZINC28564067 had more favorable conformation binding to the PI3Kδ/γ than the original ligands.

Conclusion:

The results provided a rapid approach for the discovery of novel PI3Kδ/γ dual inhibitors which might be a potential anti-tumor lead compound.

Novel and potent inhibitors for dihydropteroate synthase of Helicobacter pylori

An endless drug-resistant strains of Helicobacter pylori and multitudinous drug reactions are obstacles in the treatment of H. pylori infections, thereby ambitious novel proof-of-concept for inhibitor design was practiced in advancement of medication. Dihydropteroate synthase (DHPS) is an alluring target that plays a great role in folate synthesis pathway essential for amino acids biosynthesis was selected for designing novel drugs to prevent infections caused by pathogenic H. pylori. In the present study, a reliable tertiary structure of DHPS in complex with inhibitor 6MB was constructed by Modeler 9v19. DrugBank compounds of DHPS, published inhibitors, and co-crystal ligand (6MB) were docked against DHPS. The best docked compounds were screened against 28.5 million compounds resulted 1186 structural analogs. Virtual screening workflow and quantum polarized ligand dockings of these compounds against DHPS resulted three leads that showed better XP Gscores, ADME properties, and binding-free energies compared to 6MB, DrugBank compounds, and published inhibitors. The proposed leads were also validated by receiver operative characteristic (ROC) curve metrics in the presence of thousand decoys and the best docked existing compounds against DHPS. Long-range molecular dynamics (MD) simulations for 100 ns were executed after post-docking evaluations. Trajectory analysis showed the lead-DHPS docking complex's inter-molecular interactions were stable throughout the entire runtime of MD simulations than 6MB-DHPS complex and Eliglustat-DHPS complex. The study outcomes showed good competitive binding propensity and active-tunneling of leads over the existing inhibitors, thereby these leads could be ideal inhibitors against DHPS to target H. pylori.

Inhibition of Oncogenic Kinases: An In Vitro Validated Computational Approach Identified Potential Multi-Target Anticancer Compounds

Tumorigenesis in humans is a multistep progression that imitates genetic changes leading to cell transformation and malignancy. Oncogenic kinases play a central role in cancer progression, rendering them putative targets for the design of anti-cancer drugs. The presented work aims to identify the potential multi-target inhibitors of oncogenic receptor tyrosine kinases (RTKs) and serine/threonine kinases (STKs). For this, chemoinformatics and structure-based virtual screening approaches were combined with an in vitro validation of lead hits on both cancerous and non-cancerous cell lines. A total of 16 different kinase structures were screened against ~739,000 prefiltered compounds using diversity selection, after which the top hits were filtered for promising pharmacokinetic properties. This led to the identification of 12 and 9 compounds against RTKs and STKs, respectively. Molecular dynamics (MD) simulations were carried out to better comprehend the stability of the predicted hit kinase-compound complexes. Two top-ranked compounds against each kinase class were tested in vitro for cytotoxicity, with compound F34 showing the most promising inhibitory activity in HeLa, HepG2, and Vero cell lines with IC50 values of 145.46 μM, 175.48 μM, and 130.52 μM, respectively. Additional docking of F34 against various RTKs was carried out to support potential multi-target inhibition. Together with reliable MD simulations, these results suggest the promising potential of identified multi-target STK and RTK scaffolds for further kinase-specific anti-cancer drug development toward combinatorial therapies.

Discovery and evaluation of novel Mycobacterium tuberculosis ketol-acid reductoisomerase inhibitors as therapeutic drug leads

Tuberculosis (TB) remains a major threat to human health. This due to the fact that current drug treatments are less than optimal and the increasing occurrence of multi drug-resistant strains of etiological agent, Mycobacterium tuberculosis (Mt). Given the wide-spread significance of this disease, we have undertaken a design and evaluation program to discover new anti-TB drug leads. Here, we focused on ketol-acid reductoisomerase (KARI), the second enzyme in the branched-chain amino acid biosynthesis pathway. Importantly, this enzyme is present in bacteria but not in humans, making it an attractive proposition for drug discovery. In the present work, we used molecular docking to identify seventeen potential inhibitors of KARI using an in-house database. Compounds were selected based on docking scores, which were assigned as the result of favourable interactions between the compound and the active site of KARI. The inhibitory constant values for two leads, compounds 14 and 16 are 3.71 and 3.06 µM respectively. To assess the mode of binding, 100 ns molecular dynamics simulations for these two compounds in association with Mt KARI were performed and showed that the complex was stable with an average root mean square deviation of less than 3.5 Å for all atoms. Furthermore, compound 16 showed a minimum inhibitory concentration of 2.06 ± 0.91 µM and a 1.9 fold logarithmic reduction in the growth of Mt in an infected macrophage model. The two compounds exhibited low toxicity against RAW 264.7 cell lines. Thus, both compounds are promising candidates for development as an anti-TB drug leads.

Identification of isobavachalcone as a potential drug for rice blast disease caused by the fungus Magnaporthe grisea

The rice blast disease caused by the fungus Magnaporthe grisea is one of the most devastating rice diseases, but there is no effective fungicide toward chitinase which is a key enzyme of M. grisea. In this study, we observed that distortion and cell-wall damage of M. grisea hyphae were significantly under the scanning electron micrograph after a 24-h treatment with 10 mg/L isobavachalcone (IBC) extracted from Psoralea corylifolia L. To further explore the effect of IBC on the cell wall of M. grisea, we examined changes in enzymes associated with cell wall degradation by enzyme activity experiments, treated liquid culture mycelia with 10 mg/L IBC for 1 h. Results displayed that chitinase was obviously more active than control group. To illustrate the interactions between IBC and chitinase, the studies of homology modeling and molecular docking were carried out successively. The results revealed that IBC had hydrogen bonds with residues ASP267 and ARG276 of chitinase. Besides, these nonpolar residues TYR270, PRO271, VAL272, LEU310, PRO311, TYR316, and LEU317 were able to form strong hydrophobic interactions. Binding energies of the chitinase-IBC complexes were calculated by MM-GBSA showed that the ΔG_bind score of molecular dynamics had lower binding energy and more stable than docking complexes. All above, IBC owns significant agonistic activity in chitinase and would be a potent fungicide to inhibit the growth of M. grisea. We hope the above information provides an important insight for understanding the interactions between IBC and chitinase, which may be useful in the discovery of a novel potent agonist. Communicated by Ramaswamy H. Sarma.

In silico Prediction, Characterization, Molecular Docking, and Dynamic Studies on Fungal SDRs as Novel Targets for Searching Potential Fungicides Against Fusarium Wilt in Tomato

Vascular wilt of tomato caused by Fusarium oxysporum f.sp. lycopersici (FOL) is one of the most devastating diseases, that delimits the tomato production worldwide. Fungal short-chain dehydrogenases/reductases (SDRs) are NADP(H) dependent oxidoreductases, having shared motifs and common functional mechanism, have been demonstrated as biochemical targets for commercial fungicides. The 1,3,6,8 tetra hydroxynaphthalene reductase (T4HNR) protein, a member of SDRs family, catalyzes the naphthol reduction reaction in fungal melanin biosynthesis. We retrieved an orthologous member of T4HNR, (complexed with NADP(H) and pyroquilon from Magnaporthe grisea) in the FOL (namely; FOXG_04696) based on homology search, percent identity and sequence similarity (93% query cover; 49% identity). The hypothetical protein FOXG_04696 (T4HNR like) had conserved T-G-X-X-X-G-X-G motif (cofactor binding site) at N-terminus, similar to M. grisea (1JA9) and Y-X-X-X-K motif, as a part of the active site, bearing homologies with two fungal keto reductases T4HNR (M. grisea) and 17-β-hydroxysteroid dehydrogenase from Curvularia lunata (teleomorph: Cochliobolus lunatus PDB ID: 3IS3). The catalytic tetrad of T4HNR was replaced with ASN115, SER141, TYR154, and LYS158 in the FOXG_04696. The structural alignment and superposition of FOXG_04696 over the template proteins (3IS3 and 1JA9) revealed minimum RMSD deviations of the C alpha atomic coordinates, and therefore, had structural conservation. The best protein model (FOXG_04696) was docked with 37 fungicides, to evaluate their binding affinities. The Glide XP and YASARA docked complexes showed discrepancies in results, for scoring and ranking the binding affinities of fungicides. The docked complexes were further refined and rescored from their docked poses through 50 ns long MD simulations, and binding free energies (ΔGbind) calculations, using MM/GBSA analysis, revealed Oxathiapiprolin and Famoxadone as better fungicides among the selected one. However, Famoxadone had better interaction of the docked residues, with best protein ligand contacts, minimum RMSD (high accuracy of the docking pose) and RMSF (structural integrity and conformational flexibility of docking) at the specified docking site. The Famoxadone was found to be acceptable based on in silico toxicity and in vitro growth inhibition assessment. We conclude that the FOXG_04696, could be employed as a novel candidate protein, for structure-based design, and screening of target fungicides against the FOL pathogen.

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan-Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state-of-the-art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top-ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data.

MAPK8 mediates resistance to temozolomide and apoptosis of glioblastoma cells through MAPK signaling pathway

OBJECTIVE

In this study, we aimed to evaluate the expression and functions of MAPK8 in temozolomide (TMZ) -resistant glioblastoma cells as well as to explore the mechanism of TMZ resistance in glioblastoma cells.

METHODS

Gene Expression Omnibus (GEO) database was used for identifying the differentially expressed genes (DEGs) in TMZ resistant samples. The functional partner genes of TMZ were screened out by Gene-drug interaction network (STITCH) and the glioblastoma-related genes were selected by gene search engine with evidence sentences (Digsee). The interactions among identified DEGs and glioblastoma-related genes were detected by Search Tool for the Retrieval of Interacting Genes (STRING). The dysregulated pathways were identified by Gene set enrichment analysis (GSEA). qRT-PCR was performed to detect the expression level of MAPK8 in glioblastoma cells. Western blot was used to detect the expressions of MAPK8 and MAPK signaling pathway-related proteins. MTT assay was utilized to measure the cell viability of TMZ sensitive and resistant cells. Colony formation assay was performed to detect the clone ability and flow cytometry (FCM) assay was applied to identify the apoptosis rate of TMZ resistant glioblastoma cells.

RESULTS

MAPK8 was one of the DEGs and was up-regulated in TMZ resistant glioblastoma cells. The MAPK signaling pathway was activated in TMZ resistant glioblastoma cells under the condition of over-expression of MAPK8. The inhibition of MAPK8 restrained the colony formation, inducing apoptosis of TMZ resistant glioblastoma cells and suppressed the MAPK signaling pathway.

CONCLUSION

MAPK8 promoted the resistance to TMZ, accelerated cell proliferation and inhibited the apoptosis of glioblastoma cells via activating MAPK signaling pathway.

Computational investigation of TGF-β receptor inhibitors for treatment of idiopathic pulmonary fibrosis: Field-based QSAR model and molecular dynamics simulation

The discovery of drugs relevant to transforming growth factor β (TGF-β) receptor inhibitors have been considered as a considerable challenge during therapy idiopathic pulmonary fibrosis diseases. For the first time, herein we illustrate a field-based quantitative structure-activity relationship (QSAR) model and molecular dynamics (MD) simulations for novel 7-substituted-pyrazolo [4, 3-b] pyridine derivatives with biological activity for the TGF-β receptor, with an attempt of elucidating the 3D structural features that are essential for the activity. Results demonstrate that the field-based model (Q² = 0.548, R²_training = 0.840, R²_test = 0.750) are acceptable with good predictive capabilities. In addition, MD studies were also carried out on the training set with the aim of exploring their binding modes in the active pocket of TGF-β receptor, resulting in some of the crucial structural fragments which are responsible for inhibitory activity. Therefore, we summarized the following features required for TGF-β receptor inhibition: electronegative in region1, bulky groups in region2 and smaller groups in region3. Based on the model and related information, we hope the above information provides an important insight for understanding the interactions of the inhibitors and TGF-β receptor, which may be useful in discovering novel potent inhibitors.

Design, sythesis and evaluation of a series of 3- or 4-alkoxy substituted phenoxy derivatives as PPARs agonists

Peroxisome proliferators-activated receptors (PPARα, γ and δ) are potentially effective targets for Type 2 diabetes mellitus therapy. The severe effects of known glitazones and the successfully approved agents (saroglitazar and lobeglitazone) motivated us to study novelly potent PPARs drugs with improved safety profile. In this work, we received 15 carboxylic acids based on the combination principle to integrate the polar head of bezafibrate with the hydrophobic tail of pioglitazone. Another 12 tetrazoles based on the bioisosterism principle were obtained accordingly. Furthermore, in vitro PPARs transactivation assays on these 3- or 4-alkoxy substituted phenoxy derivatives afforded six compounds. Interactions and binding stability from the docking analysis and 20 ns molecular dynamic simulations confirmed the representative compounds to be suitable and plausible for PPARs pockets. The above-mentioned results demonstrated that the compounds may be used as reference for further optimization for enhanced PPARs activities and wide safety range.

Virtual Screening, pharmacophore development and structure based similarity search to identify inhibitors against IdeR, a transcription factor of Mycobacterium tuberculosis

ideR, an essential gene of Mycobacterium tuberculosis, is an attractive drug target as its conditional knockout displayed attenuated growth phenotype in vitro and in vivo. To the best of our knowledge, no inhibitors of IdeR are identified. We carried out virtual screening of NCI database against the IdeR DNA binding domain followed by inhibition studies using EMSA. Nine compounds exhibited potent inhibition with NSC 281033 (I-20) and NSC 12453 (I-42) exhibiting IC₅₀ values of 2 µg/ml and 1 µg/ml, respectively. We then attempted to optimize the leads firstly by structure based similarity search resulting in a class of inhibitors based on I-42 containing benzene sulfonic acid, 4-hydroxy-3-[(2-hydroxy-1-naphthalenyl) azo] scaffold with 4 molecules exhibiting IC₅₀ ≤ 10 µg/ml. Secondly, optimization included development of energy based pharmacophore and screening of ZINC database followed by docking studies, yielding a molecule with IC₅₀ of 60 µg/ml. More importantly, a five-point pharmacophore model provided insight into the features essential for IdeR inhibition. Five molecules with promising IC₅₀ values also inhibited M. tuberculosis growth in broth culture with MIC₉₀ ranging from 17.5 µg/ml to 100 µg/ml and negligible cytotoxicity in various cell lines. We believe our work opens up avenues for further optimization studies.