Common Feature Pharmacophore Research Articles

Integration of Consensus‐Based Pharmacophore Mapping and Molecular Docking in Sequential Virtual Screening: Toward the Discovery of Novel PI3Kγ Inhibitors

AbstractNumerous research studies have demonstrated the significant correlation of phosphatidylinositol‐3 kinase gamma (PI3Kγ) with the onset and progression of various human diseases, highlighting PI3Kγ as a promising therapeutic target. However, PI3Kγ demonstrates considerable similarity with other isoforms in the PI3K family, presenting significant challenges in the creation of PI3Kγ inhibitors. This study presents an ensemble‐based virtual screening approach to discover novel inhibitors targeting PI3Kγ. Eganelisib (IPI‐549) is the sole selective PI3Kγ inhibitor that has progressed to clinical trials, making it a significant model for the advancement of novel PI3Kγ inhibitors. Initially, common feature pharmacophore and receptor–ligand pharmacophore models were independently developed using IPI‐549 and its potent derivatives, in conjunction with the crystal complex of PI3Kγ/IPI‐549. Both qualitative pharmacophore models proved highly effective at distinguishing between active and inactive compounds. Then, four widely utilized docking programs were chosen for assessment, where the Glide_SP mode demonstrated superior predictive accuracy in sampling ligand conformations during binding, effectively distinguishing between PI3Kγ inhibitors and noninhibitors. Finally, a virtual screening protocol was conducted to screen the ChEMBL database, utilizing similarity search, consensus‐based pharmacophore mapping, and sequential molecular docking. This process resulted in the identification of multiple molecules exhibiting notable promise as potent PI3Kγ inhibitors.

In vitro and In silico Antibacterial Evaluation of N-Methyl-2-phenylmaleimides

Background: Novel antibiotics are needed to stem the rise of antimicrobial resistance. N-Methyl-2-phenylmaleimide (NMP) compounds previously synthesised by our research group are structural analogues of 2,3,5-substituted perhydropyrrolo[3,4-d]isoxazole-4,6-diones found by others to have antibacterial activity. Objectives: This study aims to explain the significance of NMPs and their antibacterial activity. The antibacterial activity of the NMPs was determined against Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. The partition coefficient of the NMPs and a pharmacophore model were used to explain their antibacterial activity. Methods: The Kirby Bauer Disc diffusion method was used to screen the NMPs for activity, while the broth microdilution method was used to determine the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the active NMPs. Using the in vitro antibacterial activity of 2,3,5-substituted perhydropyrrolo[3,4-d]isoxazole-4,6-diones, a common feature pharmacophore model was constructed and validated. The rank score, fit value, enrichment factor (EF20%), and receiver operating characteristic area under the curve (ROC-AUC) were used as validation metrics. Results: The NMPs were only active against S. aureus, with compound 3 (4 µg/ml) being the most active. The majority of NMPs were bacteriostatic. A common feature pharmacophore model was validated (rank score: 120.5; fit value: 4; EF20%: 4.3; ROC-AUC: 0.9 ± 0.03) and showed that three hydrogen bond acceptors and a ring aromatic region are important for activity. Comparing the partition coefficient of the NMPs to their MIC a statistically significant correlation was found. Conclusion: NMPs can be used as lead compounds in future studies. The validated pharmacophore model and partition coefficient can be used to develop more active compounds.

Flavopiridol inhibits adipogenesis and improves metabolic homeostasis by ameliorating adipose tissue inflammation in a diet-induced obesity model

Repositioning of FDA approved/clinical phase drugs has recently opened a new opportunity for rapid approval of drugs, as it shortens the overall process of drug discovery and development. In previous studies, we predicted the possibility of better activity profiles of flavopiridol, the FDA approved orphan drug with better fit value 2.79 using a common feature pharmacophore model for anti-adipogenic compounds (CFMPA). The present study aimed to investigate the effect of flavopiridol on adipocyte differentiation and to determine the underlying mechanism. Flavopiridol inhibited adipocyte differentiation in different cell models like 3T3-L1, C3H10T1/2, and hMSCs at 150 nM. Flavopiridol was around 135 times more potent than its parent molecule rohitukine. The effect was mediated through down-regulation of key transcription factors of adipogenesis i.e. Peroxisome proliferator-activated receptor gamma (PPARγ), CCAAT/enhancer-binding protein alpha (C/EBPα), and their downstream targets, including adipocyte protein −2 (aP2) and fatty acid synthase (FAS). Further, results revealed that flavopiridol arrested the cell cycle in G1/S phase during mitotic clonal expansion by suppressing cell cycle regulatory proteins i.e. Cyclins and CDKs. Flavopiridol inhibited insulin-stimulated signalling in the early phase of adipocyte differentiation by downregulation of AKT/mTOR pathway. In addition, flavopiridol improved mitochondrial function in terms of increased oxygen consumption rate (OCR) in mature adipocytes. In the mouse model of diet-induced obesity, flavopiridol attenuated obesity-associated adipose tissue inflammation and improved serum lipid profile, glucose tolerance as well as insulin sensitivity. In conclusion, the FDA approved drug flavopiridol could be placed as a potential drug candidate for the treatment of cancer and obesity comorbid patients.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking.

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

In vitro and in silico antibacterial evaluation of nitrocatechol chalcone and pyrazoline derivatives

The aim of this study was to determine the in vitro antibacterial activity of nitrocatechol chalcone and pyrazoline derivatives previously synthesised by our research group against Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii and Klebsiella aerogenes, and to create and validate a pharmacophore model using this data. The enrichment factor (EF10%) and the area under the receiver operating characteristic (ROC-AUC) curve were used to validate the pharmacophore model. Using the validated pharmacophore model novel derivatives were designed and synthesised, whereafter the in vitro antibacterial activity of these derivatives was also determined against the susceptible bacteria. After the initial screening, the derivatives only had activity against S.aureus, with compound 2a, 2b and 1b (1–2 µg/ml) having comparable activity to tetracycline (2 µg/ml). A common feature pharmacophore model (max. fit: 4, rank score: 84.02) was able to accurately identify active nitrocatechol chalcones and pyrazoline derivatives within a decoy test set. The best performing pharmacophore model, i.e., hypothesis 9 (EF10%: 6.7, ROC-AUC: 0.85 ± 0.00) indicated that four hydrogen bond acceptors are important for antibacterial activity. This model was used to guide the design and synthesis of novel nitrocatechol chalcone and pyrazoline derivatives of which the in vitro antibacterial activity against both the susceptible and resistant S. aureus strains were determined. The most active compounds were 3i (0.5 µg/ml) and 3c (0.5 µg/ml) against the susceptible and resistant strain respectively, which were more active than tetracycline.

Molecular modeling strategy for detailing the primary mechanism of action of copanlisib to PI3K: combined ligand-based and target-based approach

Since dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3K has emerged as an attractive target for drug development. Although copanlisib is the first pan-PI3K inhibitor to be approved for clinical use, the precise mechanism by which it acts on PI3K has not been fully elucidated. To reveal the binding mechanisms and structure-activity relationship between PI3K and copanlisib, a comprehensive modeling approach that combines 3D-quantitative structure-activity relationship (3D-QSAR), pharmacophore model, and molecular dynamics (MD) simulation was utilized. Initially, the structure-activity relationship of copanlisib and its derivatives were explored by constructing a 3D-QSAR. Then, the key chemical characteristics were identified by building common feature pharmacophore models. Finally, MD simulations were performed to elucidate the important interactions between copanlisib and different PI3K subtypes, and highlight the key residues for tight-binding inhibitors. The present study uncovered the principal mechanism of copanlisib’s action on PI3K at the theoretical level, and these findings might provide guidance for the rational design of pan-PI3K inhibitors. Communicated by Ramaswamy H. Sarma

Developing new PI3Kγ inhibitors by combining pharmacophore modeling, molecular dynamic simulation, molecular docking, fragment-based drug design, and virtual screening

Since dysregulation of the phosphatidylinositol 3-kinase gamma (PI3Kγ) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3Kγ has emerged as an attractive target for drug development. IPI-549 is the only selective PI3Kγ inhibitor that has advanced to clinical trials, thus, IPI-549 could serve as a promising template for designing novel PI3Kγ inhibitors. In this present study, a modeling strategy consisting of common feature pharmacophore modeling, receptor-ligand pharmacophore modeling, and molecular dynamics simulation was utilized to identify the key pharmacodynamic characteristic elements of the target compound and the key residue information of the PI3Kγ interaction with the inhibitors. Then, 10 molecules were designed based on the structure-activity relationships, and some of them exhibited satisfactory predicted binding affinities to PI3Kγ. Finally, a hierarchical multistage virtual screening method, involving the developed common feature and receptor-ligand pharmacophore model and molecular docking, was constructed for screening the potential PI3Kγ inhibitors. Overall, we hope these findings would provide some guidance for the development of novel PI3Kγ inhibitors.

Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation.

Ribosomal protein S6 kinase beta-1 (S6K1) is considered a potential target for the treatment of various diseases, such as obesity, type II diabetes, and cancer. Development of novel S6K1 inhibitors is an urgent and important task for the medicinal chemists. In this research, an effective ensemble-based virtual screening method, including common feature pharmacophore model, 3D-QSAR pharmacophore model, naïve Bayes classifier model, and molecular docking, was applied to discover potential S6K1 inhibitors from BioDiversity database with 29,158 compounds. Finally, 7 hits displayed considerable properties and considered as potential inhibitors against S6K1. Further, carefully analyzing the interactions between these 7 hits and key residues in the S6K1 active site, and comparing them with the reference compound PF-4708671, it was found that 2 hits exhibited better binding patterns. In order to further investigate the mechanism of the interactions between 2 hits and S6K1 at simulated physiological conditions, the molecular dynamics simulation was performed. The ΔGbind energies for S6K1-Hit1 and S6K1-Hit2 were - 111.47 ± 1.29 and - 54.29 ± 1.19kJmol-1, respectively. Furthermore, deep analysis of these results revealed that Hit1 was the most stable complex, which can stably bind to S6K1 active site, interact with all of the key residues, and induce H1, H2, and M-loop regions changes. Therefore, the identified Hit1 may be a promising lead compound for developing new S6K1 inhibitor for various metabolic diseases treatment.

Pharmacophore-Oriented Identification of Potential Leads as CCR5 Inhibitors to Block HIV Cellular Entry.

Cysteine-cysteine chemokine receptor 5 (CCR5) has been discovered as a co-receptor for cellular entry of human immunodeficiency virus (HIV). Moreover, the role of CCR5 in a variety of cancers and various inflammatory responses was also discovered. Despite the fact that several CCR5 antagonists have been investigated in clinical trials, only Maraviroc has been licensed for use in the treatment of HIV patients. This indicates that there is a need for novel CCR5 antagonists. Keeping this in mind, the present study was designed. The active CCR5 inhibitors with known IC50 value were selected from the literature and utilized to develop a ligand-based common feature pharmacophore model. The validated pharmacophore model was further used for virtual screening of drug-like databases obtained from the Asinex, Specs, InterBioScreen, and Eximed chemical libraries. Utilizing computational methods such as molecular docking studies, molecular dynamics simulations, and binding free energy calculation, the binding mechanism of selected inhibitors was established. The identified Hits not only showed better binding energy when compared to Maraviroc, but also formed stable interactions with the key residues and showed stable behavior throughout the 100 ns MD simulation. Our findings suggest that Hit1 and Hit2 may be potential candidates for CCR5 inhibition, and, therefore, can be considered for further CCR5 inhibition programs.

A strategy for screening novel umami dipeptides based on common feature pharmacophore and molecular docking

To shorten the complex and tedious process of traditional umami peptide identification, a novel model based on common feature pharmacophore (HipHop, a ligand molecule-based screening method) and molecular docking (a receptor protein-based screening method) was established for umami peptide screening. In this study, HipHop was used to perform a preliminary screening of peptides. Dipeptides with potential umami activity were docked into the umami taste receptor T1R1/T1R3 for a second screening. Twenty previously unreported umami dipeptides identified through virtual screening were validated using sensory evaluation and electronic tongue analysis. All 20 dipeptides (HE, HD, KE, EH, ET, EQ, DH, DR, DQ, DN, DY, DM, DI, DV, QE, QD, NE, ND, CE, and SE) had umami taste with umami threshold values ranging from 0.094 to 1.517 mmol/L. Therefore, when we increased the screening criteria for docking energy to −60 kcal/mol, the virtual screening results had 100% accuracy. The T1R1-peptide complexes of the four dipeptides with the lowest umami threshold values were subjected to molecular dynamics (MD) simulations for 100 ns, and the results showed that the four umami dipeptides remained in the starting active cavity. Overall, this screening strategy could be applied to the rapid screening of umami peptides in food products.

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods.

Owing to several mutations, the oncogene Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) is activated in the majority of cancers, and targeting it has been pharmacologically challenging. In this study, using an in silico approach comprised of pharmacophore modeling, molecular docking, and molecular dynamics simulations, potential KRAS G12D inhibitors were investigated. A ligand-based common feature pharmacophore model was generated to identify the framework necessary for effective KRAS inhibition. The chemical features in the selected pharmacophore model comprised two hydrogen bond donors, one hydrogen bond acceptor, two aromatic rings and one hydrophobic feature. This model was used for screening in excess of 214,000 compounds from InterBioScreen (IBS) and ZINC databases. Eighteen compounds from the IBS and ten from the ZINC database mapped onto the pharmacophore model and were subjected to molecular docking. Molecular docking results highlighted a higher affinity of four hit compounds towards KRAS G12D in comparison to the reference inhibitor, BI-2852. Sequential molecular dynamics (MD) simulation studies revealed all four hit compounds them possess higher KRAS G12D binding free energy and demonstrate stable polar interaction with key residues. Further, Principal Component Analysis (PCA) analysis of the hit compounds in complex with KRAS G12D also indicated stability. Overall, the research undertaken provides strong support for further in vitro testing of these newly identified KRAS G12D inhibitors, particularly Hit1 and Hit2.

Coelogin ameliorates metabolic dyshomeostasis by regulating adipogenesis and enhancing energy expenditure in adipose tissue

Obesity and associated metabolic disorders are heading up with an alarming rate in developing nations. One of highly sought solution for metabolic disorders is to identify natural molecule with an ability to reduce obesity and increase insulin sensitivity. Coelogin (CLN) is a phenanthrene derivative isolated from the ethanolic extract of Coelogyne cristata. In our constant efforts to identify novel anti-dyslipidemic and anti-adipogenic compounds using CFPMA (common feature pharmacophore model using known anti-adipogenic compounds) model, predicted possible anti-adipogenic activity of CLN. In vitro results showed significant inhibition of adipogenesis in 3T3-L1 and C3H10T1/2 cell by CLN. It arrests the cell cycle in G1 phase of interphase and inhibits mitotic clonal expansion to regulate adipogenesis. CLN elicits insulin sensitizing effect in mature adipocytes. During extracellular flux assessment studies, it increases oxidative respiration and energy expenditure in adipocytes. In vivo, CLN reversed HFD-induced dyslipidemia as well as insulin resistance in C57BL/6 mice. It promoted the expression of genes involved in improved mitochondrial function and fatty acid oxidation in eWAT. CLN restored energy expenditure and increased the capacity of energy utilization in HFD fed mice. Taken together, the study indicated beneficial effects of CLN in combating obesity-associated metabolic complications.

Discovery of novel tubulin inhibitors targeting taxanes site by virtual screening, molecular dynamic simulation, and biological evaluation.

Microtubules play crucial role in process of mitosis and cell proliferation, which have been considered as attractive drug targets for anticancer therapy. The aim of this study was to discover novel and chemically diverse tubulin inhibitors for treatment of cancer. In this investigation, the multilayer virtual screening methods, including common feature pharmacophore model, structure-based pharmacophore model and molecular docking, were developed to screen BioDiversity database with 30,000 compounds. A total of 102 compounds were obtained by the virtual screening, and further filtered by diverse chemical clusters with desired properties and PAINS analysis. Finally, 50 compounds were selected and submitted to the biological evaluation. Among these hits, hits 8 and 30 with novel scaffolds displayed stronger antiproliferative activity on four human tumor cells including Hela, A549, MCF-7, and HepG2. Moreover, the two hits were subsequently submitted to molecular dynamic simulations of 90 ns with the aim of exploring the stability of ligand-protein interactions into the binding pocket, and further probing the mechanism of the interaction between tubulin and hits. The molecular dynamic simulation results revealed there had stronger interactions between tubulin and hits in equilibrium state. Therefore, the hits 8 and 30 have been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.

Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discovery. In this investigation, the multistep virtual screening methods, including machine learning methods and common feature pharmacophore model, were developed and used to identify novel DGAT1 inhibitors from BioDiversity database with 30,000 compounds. 531 compounds were predicted as DGAT1 inhibitors by combination of machine learning methods comprising of SVM, NB and RP models. Then, 12 agents were filtered from 531 compounds by using the common feature pharmacophore model. The 3D chemical structures of the 12 hits coordinated with surface charges and isosurface have been carefully analyzed by the established 3D-QSAR model. Finally, 8 compounds with desired properties were retained from the final hits and have been assigned to another research group to complete the follow-up compound synthesis and biologic evaluation.

Repurposing of Some Natural Product Isolates as SARS-COV-2 Main Protease Inhibitors via In Vitro Cell Free and Cell-Based Antiviral Assessments and Molecular Modeling Approaches.

The emergence of the SARS-CoV-2 pandemic has prompted scientists to search for an efficient antiviral medicine to overcome the rapid spread and the marked increase in the number of patients worldwide. In this regard natural products could be a potential source of substances active against coronavirus infections. A systematic computer-aided virtual screening approach was carried out using commercially available natural products found on the Zinc Database in addition to an in-house compound library to identify potential natural product inhibitors of SARS-CoV-2 main protease (MPRO). The top eighteen hits from the screening were selected for in vitro evaluation on the viral protease (SARS-CoV-2 MPRO). Five compounds (naringenin, 2,3′,4,5′,6-pentahydroxybenzophenone, apigenin-7-O-glucoside, sennoside B, and acetoside) displayed high activity against the viral protein. Acteoside showed similar activity to the positive control GC376. The most potent compounds were tested in vitro on SARS-CoV-2 Egyptian strain where only naringenin showed moderate anti-SARS-CoV-2 activity at non-cytotoxic micromolar concentrations in vitro with a significant selectivity index (CC50/IC50 = 178.748/28.347 = 6.3). Moreover; a common feature pharmacophore model was generated to explain the requirements for enzyme inhibition by this diverse group of active ligands. These results pave a path for future repurposing and development of natural products to aid in the battle against COVID-19.

Ligand-based pharmacophore modeling, virtual screening and biological evaluation to identify novel TGR5 agonists.

Takeda G-protein-coupled receptor 5 (TGR5) is emerging as an important and promising target for the development of anti-diabetic drugs. To understand the structural characteristics of TGR5 agonists, the common feature pharmacophore models were generated and molecular docking was performed. The ligand-based virtual screening combined with pharmacophore mapping and molecular docking was performed to identify novel nonsteroidal TGR5 agonists. Finally, 20 compounds were screened for in vitro TGR5 agonistic activity assay, and results showed most compounds exhibiting TGR5 agonistic activity at 40 μM. Among these compounds, V12 and V14 displayed obvious TGR5 agonist activity, with the EC50 values of 19.5 μM and 7.7 μM, respectively. Compounds V12 and V14 could be considered potential TGR5 agonist candidates and also may be used as initial hits for developing novel TGR5 agonists.

Identification of selective Lyn inhibitors from the chemical databases through integrated molecular modelling approaches

ABSTRACT In the current study, the Asinex and ChEBI databases were virtually screened for the identification of potential Lyn protein inhibitors. Therefore, a multi-steps molecular docking study was carried out using the VSW utility tool embedded in Maestro user interface of the Schrödinger suite. On initial screening, molecules having a higher XP-docking score and binding free energy compared to Staurosporin were considered for further assessment. Based on in silico pharmacokinetic analysis and a common-feature pharmacophore mapping model developed from the Staurosporin, four molecules were proposed as promising Lyn inhibitors. The binding interactions of all proposed Lyn inhibitors revealed strong ligand efficiency in terms of energy score obtained in molecular modelling analyses. Furthermore, the dynamic behaviour of each molecule in association with the Lyn protein-bound state was assessed through an all-atoms molecular dynamics (MD) simulation study. MD simulation analyses were confirmed with notable intermolecular interactions and consistent stability for the Lyn protein-ligand complexes throughout the simulation. High negative binding free energy of identified four compounds calculated through MM-PBSA approach demonstrated a strong binding affinity towards the Lyn protein. Hence, the proposed compounds might be taken forward as potential next-generation Lyn kinase inhibitors for managing numerous Lyn associated diseases or health complications after experimental validation.

In silico identification of small molecule modulators for disruption of Hsp90–Cdc37 protein–protein interaction interface for cancer therapeutic application

The protein–protein interactions (PPIs) in the biological systems are important to maintain a number of cellular processes. Several disorders including cancer may be developed due to dysfunction in the assembly of PPI networks. Hence, targeting intracellular PPIs can be considered as a crucial drug target for cancer therapy. Among the enormous and diverse group of cancer-enabling PPIs, the Hsp90–Cdc37 is prominent for cancer therapeutic development. The successful inhibition of Hsp90–Cdc37 PPI interface can be an important therapeutic option for cancer management. In the current study, a set of more than sixty thousand compounds belong to four databases were screened through a multi-steps molecular docking study in Glide against the Hsp90–Cdc37 interaction interface. The Glide-score and Prime-MM-GBSA based binding free energy of DCZ3112, standard Hsp90–Cdc37 inhibitor were found to be −6.96 and −40.46 kcal/mol, respectively. The above two parameters were used as cut-off score to reduce the chemical space from all successfully docked molecules. Furthermore, the in-silico pharmacokinetics parameters, common-feature pharmacophore analyses and the molecular binding interactions were used to wipe out the inactive molecules. Finally, four molecules were found to be important to modulate the Hsp90–Cdc37 interface. The potentiality of the final four molecules was checked through several drug-likeness characteristics. The molecular dynamics (MD) simulation study explained that all four molecules retained inside the interface of Hsp90–Cdc37. The binding free energy of each molecule obtained from the MD simulation trajectory was clearly explained the strong affection towards the Hsp90–Cdc37. Hence, the proposed molecule might be crucial for successful inhibition of the Hsp90–Cdc37 interface. Communicated by Ramaswamy H. Sarma

Methylene blue analogues: In vitro antimicrobial minimum inhibitory concentrations and in silico pharmacophore modelling

It has been shown that methylene blue has antimicrobial properties although few studies have determined its minimum inhibitory concentration (MIC), which is the gold standard used to measure antimicrobial activity. The exact antimicrobial mode of action of methylene blue is still unclear and to our knowledge no pharmacophore model has yet been created to investigate methylene blue's mode of action. The aim of this study was to determine the MIC of methylene blue and a number of its analogous against Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, Klebsiella pneumoniae, Salmonella enterica and Candida albicans, and to use these data to develop and validate a common feature pharmacophore model. Three statistical metrics, i.e. the enrichment factor (EF), hit rate (HR) and the area under the curve of a receiver operator characteristic (ROC-AUC), were used to determine the pharmacophore model's ability to differentiate between active/decoy compounds. The validated pharmacophore model was used to map similar antibacterial compounds in an attempt to elucidate methylene blue's antimicrobial mode of action. Most test compounds only had activity against S. aureus, S. epidermidis and K. pneumoniae. Dimethyl methylene blue, new methylene blue and acriflavine proved to be the most active compounds against S. aureus [1 µg/ml (dimethyl methylene blue); 4 µg/ml (new methylene blue); 8 µg/ml (acriflavine)], S. epidermidis [1 µg/ml (dimethyl methylene blue); 4 µg/ml (new methylene blue); 2 µg/ml (acriflavine)] and K. pneumoniae [8 µg/ml (dimethyl methylene blue); 0.5 µg/ml (new methylene blue); 2 µg/ml (acriflavine)]. A common feature pharmacophore model was created (rank score: 26.664, max. fit value: 4), which was able to accurately identify active methylene blue analogous out of the test set (EF2%: 51, HR2%: 100%, ROC-AUC: 1.00 ± 0.00). Six phenothiazine derivatives with known antibacterial activity had high fit values when mapped with the validated pharmacophore model, i.e. >3.4. Dimethyl methylene blue, new methylene blue and acriflavine had potent antibacterial activity against S. aureus, S. epidermidis and K. pneumoniae. The MIC data will allow other researchers to compare the activity across different laboratories. The common feature pharmacophore model was able to identify methylene blue analogous with known in vitro antibacterial activity out of a database containing active/decoy compounds and highlighted the importance of two hydrophobic features, an aromatic feature and a hydrogen bond acceptor. The validated pharmacophore model also correctly mapped six phenothiazine derivatives with known antibacterial activity suggesting that they may share a common antibacterial mechanism of action.