A novel antiplasmodial compound: integration of in silico and in vitro assays

Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules

The dual inhibition of human acetylcholinesterase (hAChE) and butyrylcholinesterase (hBuChE) plays an important role in Alzheimer’s disease treatment. Thus, this study aims identify promising dual inhibitors against hAChE and hBuChE by in silico approaches (pharmacophore-based virtual screening and molecular docking). Ten 3 D pharmacophore models for dual inhibitors using default genetic parameters were built by GALAHAD™ available on SYBYL-X 2.0. Validation steps were carried out according to Energy (<100.0 kcal/mol), Pareto = 0, Area under the ROC Curve (>0.70), Boltzmann-Enhanced Discrimination of ROC curve (BEDROC >0.50) and structure–activity relationship (SAR) for known inhibitors. The best dual pharmacophore model based on internal/external statistical parameters and SAR data (one hydrogen bond acceptor, two hydrogen bond donors and four hydrophobic centers) was employed in virtual screening at Sigma-Aldrich® subset (n = 214,446) of ZINC database by UNITY module of SYBYL-X 2.0. According to superposition values (QFIT), the best ranked compounds were prioritized for molecular docking and partition coefficient analysis (clog p < 5.0). 37 top-ranked compounds (QFIT > 64.22) from pharmacophore model showed affinity in hAChE (-10.2 < Affinity energy < −6.3 kcal/mol) and hBuChE (-10.9 < Affinity energy < −2.3 kcal/mol) binding sites. Next, liposolubity prediction and commercially available showed that ZINC43198636, ZINC43198637 and ZINC00390718 can be potential dual inhibitors against hAChE and hBuChE. Communicated by Ramaswamy H. Sarma

Drug resistance from affordable drugs has increased the number of deaths from malaria globally. This problem has raised the requirement to design new drugs against multidrug-resistant Plasmodium falciparum parasite. In the current project, we have focused on four important proteins of Plasmodium falciparum and used them as receptors against a dataset of four anti-malarial drugs. In silico analysis of these receptors and ligand dataset was carried out using Autodock 4.2. A pharmacophore model was also established using Ligandscout. Analysis of docking experiments showed that all ligands bind efficiently to four proteins of Plasmodium falciparum. We have used ligand-based pharmacophore modeling and developed a pharmacophore model that has three hydrophobic regions, two aromatic rings, one hydrogen acceptor and one hydrogen donor. Using this pharmacophore model, we have screened a library of 50,000 compounds. The compounds that shared features of our pharmacophore model and exhibited interactions with the four proteins of our receptors dataset are short-listed. As there is a need of more anti-malarial drugs, therefore, this research will be helpful in identifying novel anti-malarial drugs that exhibited bindings with four important proteins of Plasmodium falciparum. The hits obtained in this study can be considered as useful leads in anti-malarial drug discovery.

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC50 values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere–Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a QFIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.

c-KIT is a component of the platelet-derived growth factor receptor family, classified as type-III receptor tyrosine kinase. c-KIT has been reported to be involved in, small cell lung cancer, other malignant human cancers, and inflammatory and autoimmune diseases associated with mast cells. Available c-KIT inhibitors suffer from tribulations of growing resistance or cardiac toxicity. A combined in silico pharmacophore and structure-based virtual screening was performed to identify novel potential c-KIT inhibitors. In the present study, five molecules from the ZINC database were retrieved as new potential c-KIT inhibitors, using Schrödinger's Maestro 9.0 molecular modeling suite. An atom-featured 3D QSAR model was built using previously reported c-KIT inhibitors containing the indolin-2-one scaffold. The developed 3D QSAR model ADHRR.24 was found to be significant (R2 = 0.9378, Q2 = 0.7832) and instituted to be sufficiently robust with good predictive accuracy, as confirmed through external validation approaches, Y-randomization and GH approach [GH score 0.84 and Enrichment factor (E) 4.964]. The present QSAR model was further validated for the OECD principle 3, in that the applicability domain was calculated using a "standardization approach." Molecular docking of the QSAR dataset molecules and final ZINC hits were performed on the c-KIT receptor (PDB ID: 3G0E). Docking interactions were in agreement with the developed 3D QSAR model. Model ADHRR.24 was explored for ligand-based virtual screening followed by in silico ADME prediction studies. Five molecules from the ZINC database were obtained as potential c-KIT inhibitors with high in -silico predicted activity and strong key binding interactions with the c-KIT receptor.

SARS-CoV-2 has posed serious threat to the health and has inflicted huge costs in the world. Discovering potent compounds is a critical step to inhibit coronavirus. 3CLpro and RdRp are the most conserved targets associated with COVID-19. In this study, three-dimensional pharmacophore modeling, scaffold hopping, molecular docking, structure-based virtual screening, QSAR-based ADMET predictions and molecular dynamics analysis were used to identify inhibitors for these targets. Binding free energies estimated by molecular docking for each ligand in different binding sites of RdRp were used to predict the active site. Previously reported active 3CLpro and RdRp inhibitors were used to build a pharmacophore model to develop different scaffolds. Structure-based simulations and pharmacophore modeling based on Hip Hop algorithm converged in a state that suggest hydrogen bond acceptor and donor features have a critical role in the two binding sites. Further validations indicated that the best pharmacophore model has fairly good correlation values compared with approved inhibitors. Structure-based simulation results approved that GLu166 and Gln189 in 3CLpro and Lys551 and Glu811 in RdRp, are critical residues for dual activities. Ten compounds were extracted from pharmacophore-based virtual screening in six databases. The results, gained by repurposing approach, suggest the effectiveness of these ten compounds with different scaffolds as possible inhibitors of the two targets. Some quinoline-based hybrid derivatives also were designed. QSAR descriptors plot predicted that the scaffolds have had accepted pharmacokinetic profiles. Multiple molecular dynamics simulations in 100 ns and MM/PBSA studies of some reference inhibitors and the novel compounds in complex with both targets demonstrated stable complexes and confirmed the interaction modes. Based on different computational methods, COVID-19 multi-target inhibitors are proposed. Communicated by Ramaswamy H. Sarma

The highly polymorphic human cytochrome P450 2D6 (CYP2D6) metabolizes about 25% of currently used drugs. In this study, we have explored the interaction of a large number of substrates (n = 120) with wild-type and mutated CYP2D6 by molecular docking using the CDOCKER module. Before we conducted the molecular docking and virtual mutations, the pharmacophore and QSAR models of CYP2D6 substrates were developed and validated. Finally, we explored the interaction of a traditional Chinese herbal formula, Fangjifuling decoction, with CYP2D6 by virtual screening. The optimized pharmacophore model derived from 20 substrates of CYP2D6 contained two hydrophobic features and one hydrogen bond acceptor feature, giving a relevance ratio of 76% when a validation set of substrates were tested. However, our QSAR models gave poor prediction of the binding affinity of substrates. Our docking study demonstrated that 117 out of 120 substrates could be docked into the active site of CYP2D6. Forty one out of 117 substrates (35.04%) formed hydrogen bonds with various active site residues of CYP2D6 and 53 (45.30%) substrates formed a strong π-π interaction with Phe120 (53/54), with only carvedilol showing π-π interaction with Phe483. The active site residues involving hydrogen bond formation with substrates included Leu213, Lys214, Glu216, Ser217, Gln244, Asp301, Ser304, Ala305, Phe483, and Phe484. Furthermore, the CDOCKER algorithm was further applied to study the impact of mutations of 28 active site residues (mostly non-conserved) of CYP2D6 on substrate binding modes using five probe substrates including bufuralol, debrisoquine, dextromethorphan, sparteine, and tramadol. All mutations of the residues examined altered the hydrogen bond formation and/or aromatic interactions, depending on the probe used in molecular docking. Apparent changes of the binding modes have been observed with the Glu216Asp and Asp301Glu mutants. Overall, 60 compounds out of 130 from Fangjifuling decoction matched our pharmacophore model for CYP2D6 substrates. Fifty four out of these 60 compounds could be docked into the active site of CYP2D6 and 24 of 54 compounds formed hydrogen bonds with Glu216, Asp301, Ser304, and Ala305 in CYP2D6. These results have provided further insights into the factors that determining the binding modes of substrates to CYP2D6. Screening of high-affinity ligands for CYP2D6 from herbal formula using computational models is a useful approach to identify potential herb-drug interactions.

In this study, a virtual screening approach based on pharmacophore and molecular docking was proposed to identify endothelin converting enzyme-1 (ECE-1) (EC 3.4.24.71) inhibitors from Salvia miltiorrhiza. First, the pharmacophore models were generated to recognize the common features of the ECE-1 inhibitors. The models were validated by a test database composed by a set of compounds known as ECE-1 inhibitors and nonactive compounds and proven to be successful in discriminating active and inactive inhibitors. Then, the best pharmacophore model was used to screen the compounds from S. miltiorrhiza. Furthermore, the Surflex-Dock procedure was used for molecular docking. All compounds from S. miltiorrhiza were docked into the active site of the target protein. An empirical scoring function was used to evaluate the affinity of the compounds and the target protein. Comparing the virtual screening results based on pharmacophore and molecular docking, respectively, 11 communal compounds with higher QFIT and docking score were hit, and the activity of some compounds was validated in the literature. The binding modes between these compounds and the ECE-1 binding site were predicted and used to identify the key interactions that contribute to the inhibitory activity of ECE-1 activity. The results show that the two methods have good consistency and can be validated and supplemented with each other.

Background: In the past decade, several antibodies directed against the PD-1/PD-L1 interaction have been approved. However, therapeutic antibodies also exhibit some shortcomings. Using small molecules to regulate the PD-1/PD-L1 pathway may be another way to mobilize the immune system to fight cancer. Method: 52,765 marine natural products were screened against PD-L1(PDBID: 6R3K). To identify natural compounds, a structure-based pharmacophore model was generated, following by virtual screening and molecular docking. Then, the absorption, distribution, metabolism, and excretion (ADME) test was carried out to select the most suitable compounds. Finally, molecular dynamics simulation was also performed to validate the binding property of the top compound. Results: Initially, 12 small marine molecules were screened based on the pharmacophore model. Then, two compounds were selected for further evaluation based on the molecular docking scores. After ADME and toxicity studies, molecule 51320 was selected for further verification. By molecular dynamics analysis, molecule 51320 maintains a stable conformation with the target protein, so it has the chance to become an inhibitor of PD-L1. Conclusions: Through structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET approaches, and molecular dynamics (MD) simulation, the marine natural compound 51320 can be used as a small molecule inhibitor of PD-L1.

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

BackgroundFibroblast growth factor receptor 3 is known as a favorable aim in vast range of cancers, particularly in bladder cancer treatment. Pharmacophore and QSAR modeling approaches are broadly utilized for developing novel compounds for the determination of inhibitory activity versus the biological target. In this study, these methods employed to identify FGFR3 potential inhibitors.MethodsTo find the potential compounds for bladder cancer targeting, ZINC and NCI databases were screened. Pharmacophore and QSAR modeling of FGFR3 inhibitors were utilized for dataset screening. Then, with regard to several factors such as Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties and Lipinski’s Rule of Five, the recognized compounds were filtered. In further step, utilizing the flexible docking technique, the obtained compounds interactions with FGFR3 were analyzed.ResultsThe best five compounds, namely ZINC09045651, ZINC08433190, ZINC00702764, ZINC00710252 and ZINC00668789 were selected for Molecular Dynamics (MD) studies. Off-targeting of screened compounds was also investigated through CDD search and molecular docking. MD outcomes confirmed docking investigations and revealed that five selected compounds could make steady interactions with the FGFR3 and might have effective inhibitory potencies on FGFR3.ConclusionThese compounds can be considered as candidates for bladder cancer therapy with improved therapeutic properties and less adverse effects.

The estrogen receptor alpha (ERα) plays an important role in breast development and pro-proliferation signal activation in the normal and cancerous breast. The ERα inhibitors were potentially active as cytotoxic agents against breast cancer. This study was conducted in order to find Asymmetrical Hexahydro-2H-Indazole Analogs of Curcumin (AIACs) as hits of ERα inhibitor. A training set of 17 selected ERα inhibitors was used to create 10 pharmacophore models using LigandScout 4.2. The pharmacophore models were validated using 383 active compounds as positive data and 20674 decoys as negative data obtained from DUD.E. Model 2 was found as the best pharmacophore model and consisted of three types of pharmacophore features, viz. one hydrophobic, one hydrogen bond acceptor, and aromatic interactions. Model 2 was utilized for ligand-based virtual screening 186 of AIACs, AMACs, intermediates, and Mannich base derivative compounds. The hits obtained were further screened using molecular docking, analyzed using drug scan, and tested for its synthesis accessibility. Fourteen compounds were fulfilled as hits in pharmacophore modeling, in which 10 hits were selected by molecular docking, but only seven hits met Lipinski’s rule of five and had medium synthesis accessibility. In conclusion, seven compounds were suggested to be potentially active as ERα inhibitors and deserve to be synthesized and further investigated.

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease Mpro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective Mpro inhibitors against SARS-CoV-2. The 33.8 kDa Mpro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (Mpro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with Mpro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

Three neurokinin (NK) antagonist pharmacophore models (Models 1-3) accounting for hydrogen bonding groups in the 'head' and 'tail' of NK receptor ligands have been developed by use of a new procedure for treatment of hydrogen bonds during superimposition. Instead of modelling the hydrogen bond acceptor vector in the strict direction of the lone pair, an angle is allowed between the hydrogen bond acceptor direction and the ideal lone pair direction. This approach adds flexibility to hydrogen bond directions and produces more realistic RMS values. By using this approach, two novel pharmacophore models were derived (Models 2 and 3) and a hydrogen bond acceptor was added to a previously published NK2 pharmacophore model [Poulsen et al., J. Comput.-Aided Mol. Design, 16 (2002) 273] (Model 1). Model 2 as well as Model 3 are described by seven pharmacophore elements: three hydrophobic groups, three hydrogen bond acceptors and a hydrogen bond donor. Model 1 contains the same hydrophobic groups and hydrogen bond donor as Models 2 and 3, but only one hydrogen bond acceptor. The hydrogen bond acceptors and donor are represented as vectors. Two of the hydrophobic groups are always aromatic rings whereas the other hydrophobic group can be either aromatic or aliphatic. In Model 1 the antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. Model 2 has the same two aromatic rings in a parallel displaced conformation whereas Model 3 has the rings in an edge to face conformation. The pharmacophore models were evaluated using both a structure (NK receptor homology models) and a ligand based approach. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 21 non-peptide antagonists from several structurally diverse classes were fitted to the pharmacophore models. More antagonists could be fitted to Model 2 with a low RMS and a low conformational energy penalty than to Models 1 and 3. Pharmacophore Model 2 was also able to explain the NK1, NK2 and NK3 subtype selectivity of the compounds fitted to the model. Three NK 7TM receptor models were constructed, one for each receptor subtype. The location of the antagonist binding site in the three NK receptor models is identical. Compounds fitted to pharmacophore Model 2 could be docked into the NK1, NK2 and NK3 receptor models after adjustment of the conformation of the flexible linker connecting the head and tail. Models I and 3 are not compatible with the receptor models.

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.