Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Akt2 inhibitors

An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs

Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1-D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex.

High-throughput virtual screening of phenylpyrimidine derivatives as selective JAK3 antagonists using computational methods

In this study, we used phenylpyrimidine derivatives with known biological activity against JAK3, a critical tyrosine kinase enzyme involved in signaling pathways, to find similar compounds as potential treatments for rheumatoid arthritis. These inhibitors inhibited JAK3 activity by forming a covalent bond with the Cys909 residue, which resulted in a strong inhibitory effect. Phenylpyrimidine is considered a promising therapeutic target. For pharmacophore modeling, 39 phenylpyrimidine derivatives with high pIC50 (Exp) values were chosen. The best pharmacophore model produced 28 molecules, and the five-point common pharmacophore hypothesis from P HASE (DHRRR_1) revealed the requirement for a hydrogen bond donor feature, a hydrophobic group feature, and three aromatic ring features for further design. The validation of the pharmacophore model phase was performed through 3D-QSAR using partial least squares (P LS). The 3D-QSAR study produced two successful models, an atom-based model (R2 = 0.95; Q2 = 0.67) and a field-based model (R2 = 0.93; Q2 = 0.76), which were used to predict the biological activity of new compounds. The pharmacophore model successfully distinguished between active and inactive medications, discovered potential JAK3 inhibitors, and demonstrated validity with a ROC of 0. 77. ADME-Tox was used to eliminate compounds that might have adverse effects. The best pharmacokinetics and affinity derivatives were selected for covalent docking. A molecular dynamics simulation of the selected molecules and the protein complex was performed to confirm the stability of the interaction with JAK3, whereas MM/GBSA simulations further confirmed their binding affinity. By using the principle of retrosynthesis, we were able to map out a pathway for synthesizing these potential drug candidates. This study has the potential to offer valuable and practical insights for optimizing novel derivatives of phenylpyrimidine.Communicated by Ramaswamy H. Sarma.

Discovery of novel and highly potential inhibitors of glycogen synthase kinase 3-beta (GSK-3β) through structure-based pharmacophore modeling, virtual computational screening, docking and in silico ADMET analysis

The protein Glycogen Synthase Kinase 3-Beta (GSK-3β), is a promising therapeutic target for treating various diseases such as neurodegenerative disorders, diabetes, inflammation and cancer. This study aims to investigate the potential of compounds targeting inflammation or carbohydrate metabolism to selectively inhibit GSK3β by binding to its ATP site. To achieve this goal, we filtered a database of 49367 molecules involved in carbohydrate metabolism or targeting inflammation using various computational analyses, including pharmacophore modeling, molecular docking, dynamic simulation, prime MM-GBSA calculation, and in silico ADME studies. We generated a pharmacophore model (hypo S: AADDHRR) using two different crystallographic complexes of GSK3β and evaluated the model's performance in identifying hits using various parameters, including EF, GH, ROC, AUC and BEDROC. Subsequently, we performed various dockings (HTVS, SP, XP and IFD) for the retrieved hits and found that, 5 out of the top 10 ranked compounds had the scaffold of pyrazolidine 3,5-dione, which has never been reported to inhibit kinases. We also conducted ADMET studies to and concluded that compound N6 exhibited the best pharmacokinetic profile passing the blood-brain barrier, possessing high lipophilicity and a high coefficient of skin permeability in the intestines, along with good bioavailability and low toxicity risk assessment. Dynamic simulation were also performed indicating that compounds N6 derived from pyrazolidine 3,5-dione demonstrated better binding potential for GSK3β during the simulation period. Therefore, we propose that compounds derived from pyrazolidine-3,5-dione, which modulate the activity of lysosomal alpha-glucosidase could serve as a novel scaffold for the selective inhibition of GSK-3β.Communicated by Ramaswamy H. Sarma.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

A combination of structure-based virtual screening and experimental strategies to identify the potency of caffeic acid ester derivatives as SARS-CoV-2 3CLpro inhibitor from an in-house database

Drug development requires significant time and resources, and computer-aided drug discovery techniques that integrate chemical and biological spaces offer valuable tools for the process. This study focused on the field of COVID-19 therapeutics and aimed to identify new active non-covalent inhibitors for 3CLpro, a key protein target. By combining in silico and in vitro approaches, an in-house database was utilized to identify potential inhibitors. The drug-likeness criteria were considered to pre-filter 553 compounds from 12 groups of natural products. Using structure-based virtual screening, 296 compounds were identified that matched the chemical features of SARS-CoV-2 3CLpro peptidomimetic inhibitor pharmacophore models. Subsequent molecular docking resulted in 43 hits with high binding affinities. Among the hits, caffeic acid analogs showed significant interactions with the 3CLpro active site, indicating their potential as promising candidates. To further evaluate their efficacy, enzyme-based assays were conducted, revealing that two ester derivatives of caffeic acid (4k and 4l) exhibited more than a 30% reduction in 3CLpro activity. Overall, these findings suggest that the screening approach employed in this study holds promise for the discovery of novel anti-SARS-CoV-2 therapeutics. Furthermore, the methodology could be extended for optimization or retrospective evaluation to enhance molecular targeting and antiviral efficacy of potential drug candidates.

Identification of Promising Sulfonamide Chalcones as Inhibitors of SARS-CoV-2 3CLpro through Structure-Based Virtual Screening and Experimental Approaches

3CLpro is a viable target for developing antiviral therapies against the coronavirus. With the urgent need to find new possible inhibitors, a structure-based virtual screening approach was developed. This study recognized 75 pharmacologically bioactive compounds from our in-house library of 1052 natural product-based compounds that satisfied drug-likeness criteria and exhibited good bioavailability and membrane permeability. Among these compounds, three promising sulfonamide chalcones were identified by combined theoretical and experimental approaches, with SWC423 being the most suitable representative compound due to its competitive inhibition and low cytotoxicity in Vero E6 cells (EC50 = 0.89 ± 0.32 μM; CC50 = 25.54 ± 1.38 μM; SI = 28.70). The binding and stability of SWC423 in the 3CLpro active site were investigated through all-atom molecular dynamics simulation and fragment molecular orbital calculation, indicating its potential as a 3CLpro inhibitor for further SARS-CoV-2 therapeutic research. These findings suggested that inhibiting 3CLpro with a sulfonamide chalcone such as SWC423 may pave the effective way for developing COVID-19 treatments.

Search for potentially biased epidermal growth factor receptor (EGFR) inhibitors through pharmacophore modelling, molecular docking, and molecular dynamics (MD) simulation approaches

Epidermal growth factor receptor (EGFR), being one of the most crucial receptor in cancer therapy, has been selected as a potential target for the present study. Ligand-based pharmacophore model (n = 30, R²=0.93 with root mean square deviation = 1.14, ΔCost = 144.27 and configuration cost = 21) was developed and validated with Fischer's randomisation (at 95% confidence), test set (n = 225, R² pred = 0.81), external data set (n = 13, R² pred = 0.95) and decoy set (n = 70), further the model has been used to search for novel EGFR inhibitors. The validated model was used for virtual screening of zinc database. A pool of 115,948 candidate molecules was screened through the model. Subsequently, molecules having predicted IC₅₀<0.2 µM were selected for screening through drug-like properties filter. Based on pharmacokinetic profile (ADMET study), Lipinski's rule of five and Veber's rule, 62 molecules were shortlisted for molecular docking. Using consensus docking, five hit molecules were selected, which were further considered for molecular dynamics simulation. Additionally MM-GBSA analysis was carried which showed that affinity of hits towards the receptor of three compound mainly ZINC305, ZINC131796 and ZINC131785 were similar to the standard vanedtinib. The simulation, performed for 100 ns, revealed that two hit molecules, namely ZINC305 and ZINC131785, showing potential interactions at the ligand-binding domain of EGFR protein with good ligand-protein stability. Communicated by Ramaswamy H. Sarma.

Identification of Activated Cdc42-Associated Kinase Inhibitors as Potential Anticancer Agents Using Pharmacoinformatic Approaches

BACKGROUND

Activated Cdc42-associated kinase (ACK1) is essential for numerous cellular functions, such as growth, proliferation, and migration. ACK1 signaling occurs through multiple receptor tyrosine kinases; therefore, its inhibition can provide effective antiproliferative effects against multiple human cancers. A number of ACK1-specific inhibitors were designed and discovered in the previous decade, but none have reached the clinic. Potent and selective ACK1 inhibitors are urgently needed.

METHODS

In the present investigation, the pharmacophore model (PM) was rationally built utilizing two distinct inhibitors coupled with ACK1 crystal structures. The generated PM was utilized to screen the drug-like database generated from the four chemical databases. The binding mode of pharmacophore-mapped compounds was predicted using a molecular docking (MD) study. The selected hit-protein complexes from MD were studied under all-atom molecular dynamics simulations (MDS) for 500 ns. The obtained trajectories were ranked using binding free energy calculations (ΔG kJ/mol) and Gibb's free energy landscape.

RESULTS

Our results indicate that the three hit compounds displayed higher binding affinity toward ACK1 when compared with the known multi-kinase inhibitor dasatinib. The inter-molecular interactions of Hit1 and Hit3 reveal that compounds form desirable hydrogen bond interactions with gatekeeper T205, hinge region A208, and DFG motif D270. As a result, we anticipate that the proposed scaffolds might help in the design of promising selective ACK1 inhibitors.

Discovery of potential inhibitors for stat3: ligand based 3D pharmacophore, virtual screening, molecular docking, dynamic studies and in vitro evaluation

A large analysis of the signal transducer and activator of transcription (STAT3) in cancer is currently being carried out. It regulates gene expression, which is required for normal cellular functions such as differentiation, cell growth, proliferation, survival, maturation, and immunity. A ligand-based pharmacophore model was created using 3 D QSAR pharmacophore generation methodology in Discovery studio 4.1 clients to imagine structurally diverse novel chemical entities as STAT3 inhibitors with improved efficacy. Chemical properties of 48 different derivatives were included in the training package. Hypo1 was chosen as the query model for screening 1,45,000 drug-like molecules from the SPECS database, with these molecules subjected to the Lipinski rule of 5, Verber's rule, and SMART filtration. After filtration, the molecule was examined further using molecular docking analysis on the active site of STAT3. The binding interaction(s) and pharmacophore mapping were used to select the 19 possible inhibitory molecules. These 19 hits were then tested for toxicity using the TOPKAT software. In MD simulations and MM-PBSA calculations, the tested compound specs 28 provided the best results, suggesting that this ligand has the ability to inhibit more effectively. Based in-silico finding 19 compounds are subjected to in vitro anticancer activity against MDA-MB-231 and MCF-7 cell lines. Based on results compounds specs 11 and specs 13 shows significant activity compared to other compounds and these compounds were subjected to apoptosis assay. The tested compounds induced morphologic changes were dose and time dependent by which all the tested compound exhibits stronger anti-tumor effects.Communicated by Ramaswamy H. Sarma.

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

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

New N-Alkylated Heterocyclic Compounds as Prospective NDM1 Inhibitors: Investigation of In Vitro and In Silico Properties

A new family of pyrazole-based compounds (1–15) was synthesized and characterized using different physicochemical analyses, such as FTIR, UV-Visible, ¹H, ¹³C NMR, and ESI/LC-MS. The compounds were evaluated for their in vitro antifungal and antibacterial activities against several fungal and bacterial strains. The results indicate that some compounds showed excellent antibacterial activity against E. coli, S. aureus, C. freundii, and L. monocytogenes strains. In contrast, none of the compounds had antifungal activity. Molecular electrostatic potential (MEP) map analyses and inductive and mesomeric effect studies were performed to study the relationship between the chemical structure of our compounds and the biological activity. In addition, molecular docking and virtual screening studies were carried out to rationalize the antibacterial findings to characterize the modes of binding of the most active compounds to the active pockets of NDM1 proteins.

Discovery of novel Glutaminase allosteric inhibitors through drug repurposing and comparative MMGB/PBSA and molecular dynamics simulation

GLS1 enzymes (Glutaminase C (GAC) and kidney-type Glutaminase (KGA)) are gaining prominence as a target for tumor treatment including lung, breast, kidney, prostate, and colorectal. To date, several medicinal chemistry studies are being conducted to develop new and effective inhibitors against GLS1 enzymes. Telaglenastat, a drug that targets the allosteric site of GLS1, has undergone clinical trials for the first time for the therapy of solid tumors and hematological malignancies. A comprehensive computational investigation is performed to get insights into the inhibition mechanism of the Telaglenastat. Some novel inhibitors are also proposed against GLS1 enzymes using the drug repurposing approach using 2D-fingerprinting virtual screening method against 2.4 million compounds, application of pharmacokinetics, Molecular Docking, and Molecular Dynamic (MD) Simulations. A TIP3P water box of 10 Å was defined to solvate both enzymes to improve MD simulation reliability. The dynamics results were validated further by the MMGB/PBSA binding free energy method, RDF, and AFD analysis. Results of these computational analysis revealed a stable binding affinity of Telaglenastat, as well as an FDA approved drug Astemizole (IC₅₀ ∼ 0.9 nM) and a novel para position oriented methoxy group containing Chembridge compound (Chem-64284604) that provides an effective inhibitory action against GAC and KGA.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Screening of natural compounds from Cyperus rotundus Linn against SARS-CoV-2 main protease (Mpro): An integrated computational approach

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that has been spreading across the globe. The World Health Organization (WHO) declared it as a public health emergency. The treatment of COVID-19 has been hampered due to the lack of effective therapeutic efforts. Main Protease (M^pro) is a key enzyme in the viral replication cycle and its non-specificity to human protease makes it a potential drug target. Cyperus rotundus Linn, which belongs to the Cyperaceae family, is a traditional herbal medicine that has been widely studied for its antiviral properties. In this study, a computational approach was used to screen natural compounds from C. rotundus Linn using BIOVIA Discovery Suite and novel potential molecules against M^pro of SARS-CoV-2 were predicted. Molecular docking was performed using LibDock protocol and selected ligands were further subjected to docking analysis by CDOCKER. The docking scores of the selected ligands were compared with standard antiretroviral drugs such as lopinavir and ritonavir to assess their binding potentials. Interaction pharmacophore analysis was then performed for the compounds exhibiting good binding scores to evaluate their protein–ligand interactions. The selected protein–ligand complexes were subjected to molecular dynamics simulation for 50 ns. Results of binding free energy analysis revealed that two compounds—β-amyrin and stigmasta-5,22-dien-3-ol—exhibited the best binding interactions and stability. Finally, absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were performed to understand the pharmacokinetic properties and safety profile of the compounds. The overall results indicate that the phytochemicals from Cyperus rotundus Linn, namely β-amyrin and stigmasta-5,22-dien-3-ol, can be screened as potential inhibitors of SARS-CoV-2 M^pro.

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

AKT, is a serine/threonine protein kinase comprising three isoforms-namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT' inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective

The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.

Ligand based 3D-QSAR model, pharmacophore, molecular docking and ADME to identify potential fibroblast growth factor receptor 1 inhibitors

The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby significantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. In this study, a combination of structure-based drug carriers and molecular docking-based virtual screening was used to screen new potential FGFR1 inhibitors. Forty eight known inhibitors were collected to establish 3 D-QSAR models and pharmacophore models, investigate the relationship between the activity and conformation of compounds, and verify the efficiency of pharmacophore. In Accelrys Discovery Studio 2016, the ZINC database was filtered by Lipinski's Rule of Five and SMART's filtration. Then, Hypo01 was used for virtual screening of ZINC database. Compounds with predicted activity values less than 1 μM were molecularly docked with FGFR1 protein crystals, the docking results were observed, and the interaction between compounds and targets was studied. The absorption, distribution, metabolism and excretion (ADME) and toxicity of potential inhibitors were studied, and a compound with new structural scaffolds were obtained. It could be further studied to explore their better therapeutic effects.Communicated by Ramaswamy H. Sarma.

Virtual Screening of Natural Compounds as Potential PI3K-AKT1 Signaling Pathway Inhibitors and Experimental Validation

A computational screening for natural compounds suitable to bind the AKT protein has been performed after the generation of a pharmacophore model based on the experimental structure of AKT1 complexed with IQO, a well-known inhibitor. The compounds resulted as being most suitable from the screening have been further investigated by molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis and toxicity profiles. Two compounds selected at the end of the computational analysis, i.e., ZINC2429155 (also named STL1) and ZINC1447881 (also named AC1), have been tested in an experimental assay, together with IQO as a positive control and quercetin as a negative control. Only STL1 clearly inhibited AKT activation negatively modulating the PI₃K/AKT pathway.

Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches

Acetylcholinesterase (AChE) and beta-secretase (BACE-1) are the two crucial enzymes involved in the pathology of Alzheimer's disease. The former is responsible for many defects in cholinergic signaling pathway and the latter is the primary enzyme in the biosynthesis of beta-amyloid as the main component of the amyloid plaques. These both abnormalities are found in the brains of Alzheimer's patients. In this study, in silico models were developed, including 3D-pharmacophore, 2D-QSAR (two-dimensional quantitative structure-activity relationship), and molecular docking, to screen virtually a database of compounds for AChE and BACE-1 inhibitory activities. A combinatorial library containing more than 3 million structures of curcumin and flavonoid derivatives was generated and screened for drug-likeness and enzymatic inhibitory bioactivities against AChE and BACE-1 through the validated in silico models. A total of 47 substances (two curcumins and 45 flavonoids), with remarkable predicted pIC₅₀ values against AChE and BACE-1 ranging from 4.24-5.11 (AChE) and 4.52-10.27 (BACE-1), were designed. The in vitro assays on AChE and BACE-1 were performed and confirmed the in silico results. The study indicated that, by using in silico methods, a series of curcumin and flavonoid structures were generated with promising predicted bioactivities. This would be a helpful foundation for the experimental investigations in the future. Designed compounds which were the most feasible for chemical synthesis could be potential candidates for further research and lead optimization.

Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation, protein-ligand interaction fingerprint, 3D pharmacophore, docking and 3D QSAR

HCV NS5B polymerase has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting Hepatitis C Virus genotype 1 (HCV GT1). Hepatitis C virus genotype 4a (HCV GT4a) dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B polymerase of GT4a using homology modeling, protein-ligand interaction fingerprint (PLIF), docking, pharmacophore, and 3D CoMFA quantitative structure activity relationship (QSAR). Firstly, a high-quality 3D model of HCV NS5B polymerase of GT4a was constructed using crystal structure of HCV NS5B polymerase of GT1 (PDB ID: 3hkw) as a template. Then, both the model and the template were simulated to compare conformational stability. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb), which revealed the most important residues and their interactions with the co-crystalized ligands. After that, a 3D pharmacophore model was developed from the generated PLIF data and then used as a screening filter for 17000328 drug-like zinc database compounds. 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. Finally, a 3D CoMFA QSAR was developed using 42 compounds as a training and 19 compounds as a test set. The 3D CoMFA QSAR was used to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1-p4) and compound q1) were selected for further analysis.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics Studies on COX-2 Protein-tyrosine Analogue Complex and Ligand-based Computational Analysis of Halo-substituted Tyrosine Analogues

Background:

The quest for new drug entities and novel structural fragments with applications in therapeutic areas is always at the core of medicinal chemistry.

Methods:

As part of our efforts to develop novel selective cyclooxygenase-2 (COX-2) inhibitors containing tyrosine scaffold. The objective of this study was to identify potent COX-2 inhibitors by dynamic simulation, pharmacophore and 3D-QSAR methodologies. Dynamics simulation was performed for COX-2/tyrosine derivatives complex to characterise structure validation and binding stability. Certainly, Arg120 and Tyr355 residue of COX-2 protein formed a constant interaction with tyrosine inhibitor throughout the dynamic simulation phase. A four-point pharmacophore with one hydrogen bond acceptor, two hydrophobic and one aromatic ring was developed using the HypoGen algorithm. The generated, statistically significant pharmacophore model, Hypo 1 with a correlation coefficient of r2, 0.941, root mean square deviation, 1.15 and total cost value of 96.85.

Results:

The QSAR results exhibited good internal (r2, 0.992) and external predictions (r2pred, 0.814). The results of this study concluded the COX-2 docked complex was stable and interactive like experimental protein structure. Also, it offered vital chemical features with geometric constraints responsible for the inhibition of the selective COX-2 enzyme by tyrosine derivatives.

Conclusion:

In principle, this work offers significant structural understandings to design and develop novel COX-2 inhibitors.

Identification of Novel Scaffolds with Dual Role as Antiepileptic and Anti-Breast Cancer

Aromatase inhibitors with an $\mathrm{IC}_{50}$ IC 50 value ranging from 1.4 to 49.7 µM are known to act as antiepileptic drugs besides being potential breast cancer inhibitors. The aim of the present study is to identify novel antiepileptic aromatase inhibitors with higher activity exploiting the ligand-based pharmacophore approach utilizing the experimentally known inhibitors. The resultant Hypo1 consists of four features and was further validated by using three different strategies. Hypo1 was allowed to screen different databases to identify lead molecules and were further subjected to Lipinski's Rule of Five and ADMET to establish their drug-like properties. Consequently, the obtained 68-screened molecules were subjected to molecular docking by GOLD v5.2.2. Furthermore, the compounds with the highest dock scores were assessed for molecular interactions. Later, the MD simulation was applied to evaluate the protein backbone stabilities and binding energies adapting GROMACS v5.0.6 and MM/PBSA which was followed by the density functional theory (DFT), to analyze their orbital energies, and further the energy gap between them. Eventually, the number of Hit molecules was culled to three projecting Hit1, Hit2, and Hit3 as the potential lead compounds based on their highest dock scores, hydrogen bond interaction, lowest energy gap, and the least binding energies and stable MD results.

Understanding the riboflavin biosynthesis pathway for the development of antimicrobial agents

Life on earth depends on the biosynthesis of riboflavin, which plays a vital role in biological electron transport processes. Higher mammals obtain riboflavin from dietary sources; however, various microorganisms, including Gram-negative pathogenic bacteria and yeast, lack an efficient riboflavin-uptake system and are dependent on endogenous riboflavin biosynthesis. Consequently, the inhibition of enzymes in the riboflavin biosynthesis pathway would allow selective toxicity to a pathogen and not the host. Thus, the riboflavin biosynthesis pathway is an attractive target for designing novel antimicrobial drugs, which are urgently needed to address the issue of multidrug resistance seen in various pathogens. The enzymes involved in riboflavin biosynthesis are lumazine synthase (LS) and riboflavin synthase (RS). Understanding the details of the mechanisms of the enzyme-catalyzed reactions and the structural changes that occur in the enzyme active sites during catalysis can facilitate the design and synthesis of suitable analogs that can specifically inhibit the relevant enzymes and stop the generation of riboflavin in pathogenic bacteria. The present review is the first compilation of the work that has been carried out over the last 25 years focusing on the design of inhibitors of the biosynthesis of riboflavin based on an understanding of the mechanisms of LS and RS. This review aimed to address the fundamental advances in our understanding of riboflavin biosynthesis as applied to the rational design of a novel class of inhibitors. These advances have been aided by X-ray structures of ligand-enzyme complexes, rotational-echo, double-resonance nuclear magnetic resonance spectroscopy, high-throughput screening, virtual screenings, and various mechanistic probes.

Pharmacophore-based drug design for the identification of novel butyrylcholinesterase inhibitors against Alzheimer's disease

BACKGROUND

Alzheimer's disease is a severe neurodegenerative disease of the central nervous system in the elderly.

HYPOTHESIS/PURPOSE

In our study, we aimed to find the best potential small molecule for AD treatment.

STUDY DESIGN

We used many models in Discovery Studio 2016 to find new potential inhibitors of butyrylcholinesterase (BChE), including pharmacophore model, virtual screening model, molecular docking model, de novo evolution model.

METHODS

Ligand-based pharmacophore models were used to identify the critical chemical features of BChE inhibitors using the module of 3D QSAR Pharmacophore Generation in Discovery Studio 2016. The best pharmacophore model was then validated by cost analysis, Fischer's randomization method, 3D-QSAR Method of the training set and test set. The compounds that match the best pharmacophore model with the predicted activity <1 μM filtered by Lipinski's rule of five were subjected to molecular docking.

RESULT

After virtual screening, 35 compounds filtered by Lipinski's rule of five and ADMET analysis were subjected to molecular docking and then the number were narrowed down on 10 compounds based on -CDOCKER_ENERGY. Finally, we obtained and modified the best potential candidate ENA739155.

CONCLUSION

Ultimately, ENA739155_Evo with -CDOCKER_ENERGY of 47.12, estimate activity of 0.012, fit value of 10.02 could be further subjected to drug development and forwarded as better alternatives to the current batch of medicines used for the treatment of AD.

Ligand-based Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors

Camptothecin (CPT), a natural product and its synthetic derivatives exert potent anticancer activity by selectively targeting DNA Topoisomerase I (Top1) enzyme. CPT and its clinically approved derivatives are used as Top1 poisons for cancer therapy suffer from many limitations related to stability and toxicity. In order to envisage structurally diverse novel chemical entity as Top1 poison with better efficacy, Ligand-based-pharmacophore model was developed using 3D QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery studio 4.1 clients. The chemical features of 29 CPT derivatives were taken as the training set. The selected pharmacophore model Hypo1 was further validated by 33 test set molecules and used as a query model for further screening of 1,087,724 drug-like molecules from ZINC databases. These molecules were subjected to several assessments such as Lipinski rule of 5, SMART filtration and activity filtration. The molecule obtained after filtration was further scrutinized by molecular docking analysis on the active site of Top1 crystal structure (PDB ID: 1T8I). Six potential inhibitory molecules have been selected by analyzing the binding interaction and Ligand-Pharmacophore mapping with the validated pharmacophore model. Toxicity assessment TOPKAT program provided three potential inhibitory ‘hit molecules’ ZINC68997780, ZINC15018994 and ZINC38550809. MD simulation of these three molecules proved that the ligand binding into the protein-DNA cleavage complex is stable and the protein-ligands conformation remains unchanged. These three hit molecules can be utilized for designing future class of potential topoisomerase I inhibitor.

Reverse pharmacophore mapping and molecular docking studies for discovery of GTPase HRas as promising drug target for bis-pyrimidine derivatives

BACKGROUND

Pyrimidine is an important pharmacophore in the field of medicinal chemistry and exhibit a broad spectrum of biological potentials. A study was carried out to identify the target protein of potent bis-pyrimidine derivatives using reverse docking program. PharmMapper, a robust online tool was used for identifying the target proteins based on reverse pharmacophore mapping. The murine macrophage (RAW 264.7) and human embryonic kidney (HEK-293) cancer cell line used for selectivity and safety study.

METHODS

An open web server PharmMapper was used to identify the possible target of the developed compounds through reverse pharmacophore mapping. The results were analyzed and validated through docking with Schrodinger v9.6 using 10 protein GTPase HRas selected as possible target. The docking studies with Schrödinger validated the binding behavior of bis-pyrimidine compounds within GTP binding pocket. MTT and sulforhodamine assay were used as antiproliferative activity.

RESULTS AND DISCUSSION

The protein was found one of the top scored targets of the compound 18, hence, the GTPase HRas protein was found crucial to be targeted for competing cancer. Toxicity study demonstrated the significant selectivity of most active compounds, 12, 16 and 18 showed negligible cell toxicity at their IC₅₀ concentration.

CONCLUSION

From the results, we may conclude that GTPase HRas as a possible target of studied bis-pyrimidine derivatives where the retrieved information may be quite useful for rational drug designing.

Targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based molecular modelling approaches

Human epidermal growth factor receptors are implicated in several types of cancers characterized by aberrant signal transduction. This family comprises of EGFR (ErbB1), HER2 (ErbB2, HER2/neu), HER3 (ErbB3), and HER4 (ErbB4). Amongst them, HER2 is associated with breast cancer and is one of the most valuable targets in addressing the breast cancer incidences. For the current investigation, we have performed 3D-QSAR based pharmacophore search for the identification of potential inhibitors against the kinase domain of HER2 protein. Correspondingly, a pharmacophore model, Hypo1, with four features was generated and was validated employing Fischer's randomization, test set method and the decoy test method. The validated pharmacophore was allowed to screen the colossal natural compounds database (UNPD). Subsequently, the identified 33 compounds were docked into the proteins active site along with the reference after subjecting them to ADMET and Lipinski's Rule of Five (RoF) employing the CDOCKER implemented on the Discovery Studio. The compounds that have displayed higher dock scores than the reference compound were scrutinized for interactions with the key residues and were escalated to MD simulations. Additionally, molecular dynamics simulations performed by GROMACS have rendered stable root mean square deviation values, radius of gyration and potential energy values. Eventually, based upon the molecular dock score, interactions between the ligands and the active site residues and the stable MD results, the number of Hits was culled to two identifying Hit1 and Hit2 has potential leads against HER2 breast cancers.

Combined pharmacophore, virtual screening and molecular dynamics studies to identify Bruton's tyrosine kinase inhibitors

Bruton's tyrosine Kinase (BTK) is a cytoplasmic, non-receptor tyrosine kinase expressed in hematopoietic cells. BTK plays a critical role in many cellular signalling pathways making it a potential target to treat autoimmune diseases and cancer. BTK signalling is important for the production of arthritis-associated antibodies, and inhibiting BTK will help the system to block the production of disease-associated antibodies. In this study, we have implemented ligand-based pharmacophore modelling and virtual screening against natural compounds followed by molecular docking, density functional theory and molecular dynamics studies for 50 ns. Four compounds with high affinity towards BTK were identified, and it could be used as a potent lead molecule for designing BTK inhibitor.

Identification of New Potential APE1 Inhibitors by Pharmacophore Modeling and Molecular Docking

Apurinic/apyrimidinic endonuclease 1 (APE1) is an enzyme responsible for the initial step in the base excision repair pathway and is known to be a potential drug target for treating cancers, because its expression is associated with resistance to DNA-damaging anticancer agents. Although several inhibitors already have been identified, the identification of novel kinds of potential inhibitors of APE1 could provide a seed for the development of improved anticancer drugs. For this purpose, we first classified known inhibitors of APE1. According to the classification, we constructed two distinct pharmacophore models. We screened more than 3 million lead-like compounds using the pharmacophores. Hits that fulfilled the features of the pharmacophore models were identified. In addition to the pharmacophore screen, we carried out molecular docking to prioritize hits. Based on these processes, we ultimately identified 1,338 potential inhibitors of APE1 with predicted binding affinities to the enzyme.

Computational Exploration for Lead Compounds That Can Reverse the Nuclear Morphology in Progeria

Progeria is a rare genetic disorder characterized by premature aging that eventually leads to death and is noticed globally. Despite alarming conditions, this disease lacks effective medications; however, the farnesyltransferase inhibitors (FTIs) are a hope in the dark. Therefore, the objective of the present article is to identify new compounds from the databases employing pharmacophore based virtual screening. Utilizing nine training set compounds along with lonafarnib, a common feature pharmacophore was constructed consisting of four features. The validated Hypo1 was subsequently allowed to screen Maybridge, Chembridge, and Asinex databases to retrieve the novel lead candidates, which were then subjected to Lipinski's rule of 5 and ADMET for drug-like assessment. The obtained 3,372 compounds were forwarded to docking simulations and were manually examined for the key interactions with the crucial residues. Two compounds that have demonstrated a higher dock score than the reference compounds and showed interactions with the crucial residues were subjected to MD simulations and binding free energy calculations to assess the stability of docked conformation and to investigate the binding interactions in detail. Furthermore, this study suggests that the Hits may be more effective against progeria and further the DFT studies were executed to understand their orbital energies.

Sulfonanilide Derivatives in Identifying Novel Aromatase Inhibitors by Applying Docking, Virtual Screening, and MD Simulations Studies

Breast cancer is one of the leading causes of death noticed in women across the world. Of late the most successful treatments rendered are the use of aromatase inhibitors (AIs). In the current study, a two-way approach for the identification of novel leads has been adapted. 81 chemical compounds were assessed to understand their potentiality against aromatase along with the four known drugs. Docking was performed employing the CDOCKER protocol available on the Discovery Studio (DS v4.5). Exemestane has displayed a higher dock score among the known drug candidates and is labeled as reference. Out of 81 ligands 14 have exhibited higher dock scores than the reference. In the second approach, these 14 compounds were utilized for the generation of the pharmacophore. The validated four-featured pharmacophore was then allowed to screen Chembridge database and the potential Hits were obtained after subjecting them to Lipinski's rule of five and the ADMET properties. Subsequently, the acquired 3,050 Hits were escalated to molecular docking utilizing GOLD v5.0. Finally, the obtained Hits were consequently represented to be ideal lead candidates that were escalated to the MD simulations and binding free energy calculations. Additionally, the gene-disease association was performed to delineate the associated disease caused by CYP19A1.

A 2D-QSAR and Grid-Independent Molecular Descriptor (GRIND) Analysis of Quinoline-Type Inhibitors of Akt2: Exploration of the Binding Mode in the Pleckstrin Homology (PH) Domain

Protein kinase B-β (PKBβ/Akt2) is a serine/threonine-specific protein kinase that has emerged as one of the most important regulators of cell growth, differentiation, and division. Upregulation of Akt2 in various human carcinomas, including ovarian, breast, and pancreatic, is a well-known tumorigenesis phenomenon. Early on, the concept of the simultaneous administration of anticancer drugs with inhibitors of Akt2 was advocated to overcome cell proliferation in the chemotherapeutic treatment of cancer. However, clinical studies have not lived up to the high expectations, and several phase II and phase III clinical studies have been terminated prematurely because of severe side effects related to the non-selective isomeric inhibition of Akt2. The notion that the sequence identity of pleckstrin homology (PH) domains within Akt-isoforms is less than 30% might indicate the possibility of the development of selective antagonists against the Akt2 PH domain. Therefore, in this study, various in silico tools were utilized to explore the hypothesis that quinoline-type inhibitors bind in the Akt2 PH domain. A Grid-Independent Molecular Descriptor (GRIND) analysis indicated that two hydrogen bond acceptors, two hydrogen bond donors and one hydrophobic feature at a certain distance from each other were important for the selective inhibition of Akt2. Our docking results delineated the importance of Lys30 as an anchor point for mapping the distances of important amino acid residues in the binding pocket, including Lys14, Glu17, Arg25, Asn53, Asn54 and Arg86. The binding regions identified complement the GRIND-based pharmacophoric features.

DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit

During the past few years, pharmacophore modeling has become one of the key components in computer-aided drug design and in modern drug discovery. DrugOn is a fully interactive pipeline designed to exploit the advantages of modern programming and overcome the command line barrier with two friendly environments for the user (either novice or experienced in the field of Computer Aided Drug Design) to perform pharmacophore modeling through an efficient combination of the PharmACOphore, Gromacs, Ligbuilder and PDB2PQR suites. Our platform features a novel workflow that guides the user through each logical step of the iterative 3D structural optimization setup and drug design process. For the pharmacophore modeling we are focusing on either the characteristics of the receptor or the full molecular system, including a set of selected ligands. DrugOn can be freely downloaded from our dedicated server system at www.bioacademy.gr/bioinformatics/drugon/.

Pharmacophore approaches in protein kinase inhibitors design

Protein kinases constitute a superfamily of therapeutic targets for a number of human and animal diseases that include more than 500 members accordingly to sequencing data of the human genome. The well characterized nature of protein kinases makes them excellent targets for drug development. Pharmacophore approaches have become one of the major tools in the area of drug discovery. Application of pharmacophore modeling approaches allows reducing of expensive overall cost associated with drug development project. Pharmacophore models are important functional groups of atoms in the proper spatial position for interaction with target protein. Various ligand-based and structure-based methods have been developed for pharmacophore model generation. Despite the successes in pharmacophore models generation these approaches have not reached their full capacity in application for drug discovery. In the following review, we summarize the published data on pharmacophore models for inhibitors of tyrosine protein kinases (EGFR, HER2, VEGFR, JAK2, JAK3, Syk, ZAP-70, Tie2) and inhibitors of serine/threonine kinases (Clk, Dyrk, Chk1, IKK2, CDK1, CDK2, PLK, JNK3, GSK3, mTOR, p38 MAPK, PKB). Here, we have described the achievements of pharmacophore modeling for protein kinase inhibitors, which provide key points for further application of generated pharmacophore hypotheses in virtual screening, de novo design and lead optimization.

Ligand-Based Pharmacophore Modeling and Virtual Screening of RAD9 Inhibitors

Human RAD9 is a key cell-cycle checkpoint protein that participates in DNA repair, activation of multiple cell cycle phase checkpoints, and apoptosis. Aberrant RAD9 expression has been linked to breast, lung, thyroid, skin, and prostate tumorigenesis. Overexpression of RAD9 interacts with BCL-2 proteins and blocks the binding sites of BCL-2 family proteins to interact with chemotherapeutic drugs and leads to drug resistance. Focusing on this interaction, the present study was designed to identify the interaction sites of RAD9 to bind BCL-2 protein and also to inhibit RAD9-BCL-2 interactions by designing novel small molecule inhibitors using pharmacophore modeling and to restore BCL-2 for interacting with anticancer drugs. The bioactive molecules of natural origin act as excellent leads for new drug development. Thus, in the present study, we used the compounds of natural origin like camptothecin, ascididemin, and Dolastatin and also compared them with synthetic molecule NSC15520. The results revealed that camptothecin can act as an effective inhibitor among all the ligands taken and can be used as an RAD9 inhibitor. The amino acids ARG45 and ALA134 of RAD9 protein are interacting commonly with the drugs and BCL-2 protein.