Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) - a promising target for breast cancer

Combined 4D-QSAR and target-based approaches for the determination of bioactive Isatin derivatives

The hybrid method of the Electron-Conformational Genetic Algorithm (EC-GA) was used to determine the pharmacophore groups and to estimate anticancer activity in isatin derivatives using a robust 4D-QSAR software (EMRE). To build the model, each compound is represented by a set of conformers rather than a single conformation. The Electron Conformational Matrix of Congruity (ECMC) is composed via EMRE software. Electron Conformational Submatrix of Activity (ECSA) was calculated by the comparison of these matrices. Genetic algorithm was used to select important variables to predict theoretical activity. The model with the best seven parameters produced satisfactory results. The E statistics technique was applied to the generated EC-GA model to evaluate the individual contribution of each of the descriptors on biological activity. The r² and q² values of the training set compounds were found to be 0.95 and 0.93, respectively. Because no previous 4D-QSAR studies on isatin derivatives have been conducted, this study is important in the development of new isatin derivatives. In this study, 27 isatin derivatives whose activities were estimated using the hybrid EC-GA method were also investigated through molecular docking and molecular dynamics simulations for their BCL-2 inhibitory activity.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

In silico identification of angiotensin-1 converting enzyme inhibitors using text mining and virtual screening

Cardiovascular diseases are the world's leading cause of death. Hypertension is an important risk factor for cardiovascular and renal diseases. Angiotensin-converting enzyme (ACE) can be a possible therapeutic target for managing angiotensin I conversion to angiotensin II and ultimately controlling hypertension. Indole is an significant fragment used in many medicines approved by FDA. For this reason, the molecules in their fragments containing" indol" keywords were taken from the Specs-SC (small compound) database. The predicted therapeutc activity values (TAV) of these compounds against hypertension were evaluated using binary models of QSAR by MetaCore/MetaDrug. For the 26 separate QSAR models of toxicity, molecules with measured TAV greater than 0.5 were used. 3792 non-toxic compounds were investigated by molecular docking study and molecular dynamics simulations for their ACE inhibitory activity. Glide standard precision (SP) of Maestro Molecular Modeling pocket was used to perform molecular docking. Short molecular dynamics (MD) simulations (5-ns) were carried out by initiating the top docking poses of selected 40 molecules. To quantitatively evaluate the predicted binding affinity of a screened compound, average MM/GBSA scores of screened ligands were calculated and based on their binding free energy values, hit compounds were identified for the long (100-ns) MD simulations. Root mean square deviation and root mean square fluctuations were also calculated to assess the structural characteristics and observe fluctuations of the 100-ns time scale. Thus, with the application of text mining and integrated molecular modeling we reported novel indole-based hit inhibitors for ACE-1.Communicated by Ramaswamy H. Sarma.

Investigation of novel indole-based HIV-1 protease inhibitors using virtual screening and text mining

Human immunodeficiency virus type 1 protease (HIV-1 PR) inhibitors have been used as possible therapeutic agents for HIV-1 infection in clinical study. Most of the HIV therapy-related problems usually stem from long-term opioid usage. The rapid development of drug-resistant variants limits the long-term effectiveness of current inhibitors as therapeutic agents. In addition, different side effects were reported. Further drug development is required to design new compounds which have similar efficacy as the drugs currently used in HIV infection but without having undesirable side effects. Indole derivatives were considered as one of the effective HIV inhibitors. Indole is an important fragment used in many FDAapproved medicines and used in various diseases. For this purpose, in this study the molecules containing" indole" keywords in their fragments are taken from the Specs-SC database which includes 212520 small molecules. 5194 molecules that include indole keywords are selected. These selected molecules are then screened against HIV-1 PR target protein using molecular docking simulations. Then the molecules are ranked according to the their docking scores. Top docking poses of ten ligands and FDA approved drug Amprenavir are subjected to 100 ns Molecular Dynamics (MD) simulations. Thus, by using combination of text mining and integrated molecular modeling approaches, we identified novel indole-based hits against HIV-1 PR.Communicated by Ramaswamy H. Sarma.

Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors

The excessive activity of acetylcholinesterase enzyme (AChE) causes different neuronal problems, especially dementia and neuronal cell deaths. Food and Drug Administration (FDA) approved drugs donepezil, rivastigmine, tacrine and galantamine are AChE inhibitors and in the treatment of Alzheimer’s disease (AD) these drugs are currently prescribed. However, these inhibitors have various adverse side effects. Therefore, there is a great need for the novel selective AChE inhibitors with fewer adverse side effects for the effective treatment. In this study, combined ligand-based and structure-based virtual screening approaches were used to identify new hit compounds from small molecules library of National Cancer Institute (NCI) containing approximately 265,000 small molecules. In the present study, we developed a computational pipeline method to predict the binding affinities of the studied compounds at the specific target sites. For this purpose, a text mining study was carried out initially and compounds containing the keyword “indol” were considered. The therapeutic activity values against AD were screened using the binary quantitative structure activity relationship (QSAR) models. We then performed docking, molecular dynamics (MD) simulations and free energy analysis to clarify the interactions between selected ligands and enzyme. Thus, in this study we identified new promising hit compounds from a large database that may be used to inhibit the enzyme activity of AChE.

Binding Interactions of Ergotamine and Dihydroergotamine to 5-Hydroxytryptamine Receptor 1B (5-HT1b) Using Molecular Dynamics Simulations and Dynamic Network Analysis

Ergotamine (ERG) and dihydroergotamine (DHE), common migraine drugs, have small structural differences but lead to clinically important distinctions in their pharmacological profiles. For example, DHE is less potent than ERG by about 10-fold at the 5-hydroxytrptamine receptor 1B (5-HT_1B). Although the high-resolution crystal structures of the 5-HT_1B receptor with both ligands have been solved, the high similarity between these two complex structures does not sufficiently explain their activity differences and the activation mechanism of the receptor. Hence, an examination of the dynamic motion of both drugs with the receptor is required. In this study, we ran a total of 6.0 μs molecular dynamics simulations on each system. Our simulation data show the subtle variations between the two systems in terms of the ligand-receptor interactions and receptor secondary structures. More importantly, the ligand and protein root-mean-square fluctuations (RMSFs) for the two systems were distinct, with ERG having a trend of lower RMSF values, indicating it to be bound tighter to 5-HT_1B with less fluctuations. The molecular mechanism-general born surface area (MM-GBSA) binding energies illustrate this further, proving ERG has an overall stronger MM-GBSA binding energy. Analysis of several different microswitches has shown that the 5-HT_1B-ERG complex is in a more active conformation state than 5-HT_1B-DHE, which is further supported by the dynamic network model, with reference to mutagenesis data with the critical nodes and the first three low-energy modes from the normal mode analysis. We also identify Trp327^6.48 and Phe331^6.52 as key residues involved in the active state 5-HT_1B for both ligands. Using the detailed dynamic information from our analysis, we made predictions for possible modifications to DHE and ERG that yielded five derivatives that might have more favorable binding energies and reduced structural fluctuations.

Characterizing the interaction modes of PAR4 receptor with agonist and antagonist by molecular simulation approach

Protease-activated receptor 4 (PAR4) is a promising target for antiplatelet therapy. In this study, homology modeling and molecular docking methods were used to investigate the binding modes of PAR4 agonists and antagonists. The outcomes show that agonists have good docking scores, and they also form more hydrogen bonds with PAR4 than antagonists. To reveal the different conformational changes caused by agonist and antagonist, molecular dynamic simulations were carried out on three selected PAR4 systems. Simulation results show that PAR4 activation involves breaking interactions of 3–7 lock switch (Try157 and Tyr322) and ionic lock switch (Arg188 and Asp173), and formation of transmission switch among Tyr161, Asn300 and Phe296. In addition, principal component analysis (PCA) indicates that the major change for agonist bound system takes place in the intracellular region while that for antagonist bound system is in the extracellular region. The binding free energy of BMS-986120 is much lower than AYPGKF, suggesting high affinity of antagonist. Moreover, the electronegative aspartic residues Asp230 and Asp235 at ECL2 are important for PAR4 binding to agonist. Clarifying the PAR4 structural characteristics may be helpful to understand the activation mechanism, giving insights into the molecular design and discovery of novel potential PAR4 antagonists in the future.

Drug repositioning as an effective therapy for protease-activated receptor 2 inhibition

Proteinase-activated receptor 2 (PAR-2) is a G protein-coupled receptor activated by both trypsin and a specific agonist peptide, SLIGKV-NH2. It has been linked to various pathologies, including pain and inflammation. Several peptide and peptidomimetic agonizts for PAR-2 have been developed exhibiting high potency and efficacy. However, the number of PAR-2 antagonists is smaller. We screened the Food and Drug Administration library of approved compounds to retrieve novel antagonists for repositioning in the PAR-2 structure. The most efficacious compound bicalutamide bound to the PAR-2 binding groove near the extracellular domain as observed in the in silico studies. Further, it showed reduced Ca²⁺ release in trypsin activated cells in a dose-dependent manner. Hence, bicalutamide is a novel and potent PAR-2 antagonist which could be therapeutically useful in blocking multiple pathways diverging from PAR-2 signaling. Further, the novel scaffold of bicalutamide represents a new molecular structure for PAR-2 antagonism and can serve as a basis for further drug development.

A pharmacoinformatic approach on Cannabinoid receptor 2 (CB2) and different small molecules: Homology modelling, molecular docking, MD simulations, drug designing and ADME analysis

CB2 receptor belongs to the family of G-protein coupled receptors (GPCRs), which extensively controls a range of pointer transduction. CB2 plays an essential role in the immune system. It also associates in the pathology of different ailment conditions. In this scenario, the synthetic drugs are inducing side effects to the human beings after the drug use. Therefore, this study is seeking novel alternate drug molecules with least side effects than conventional drugs. The alternative drug molecules were chosen from the natural sources. These molecules were selected from cyanobacteria with the help of earlier research findings. The target and ligand molecules were obtained from recognized databases. The bioactive molecules are selected from various cyanobacterial species, which are selected by their biological and pharmacological properties, after, which we incorporated to the crucial findings such as homology modelling, molecular docking, MD simulations along with absorption, distribution, metabolism, and excretion (ADME) analysis. Initially, the homology modelling was performed to frame the target from unknown sequences of CB2, which revealed 44% of similarities and 66% of identities with the A2A receptor. Subsequently, the CB2 protein molecule has docked with already known and prepared bioactive molecules, agonists and antagonist complex. In the present study, the agonists (5) and antagonist (1) were also taken for comparing the results with natural molecules. At the end of the docking analysis, the cyanobacterial molecules and an antagonist TNC-201 are revealed better docking scores with well binding contacts than the agonists. Especially, the usneoidone shows better results than other cyanobacterial molecules, and it is very close docking scores with that of TCN-201. Therefore, the usneoidone has incorporated to MD simulation with Cannabinoid receptors 2 (CB2). In MD simulations, the complex (CB2 and usneoidone) reveals better stability in 30 ns. Based on the computational outcome, we concluded that usneoidone is an effectual and appropriate drug candidate for activating CB2 receptors and it will be serving as a better component for the complications of CB2. Moreover, these computational approaches can be motivated to discover novel drug candidates in the pharmacological and healthcare sectors.

Novel ligand-based docking; molecular dynamic simulations; and absorption, distribution, metabolism, and excretion approach to analyzing potential acetylcholinesterase inhibitors for Alzheimer's disease

Acetylcholinesterase (AChE) plays an important role in Alzheimer's disease (AD). The excessive activity of AChE causes various neuronal problems, particularly dementia and neuronal cell deaths. Generally, anti-AChE drugs induce some serious neuronal side effects in humans. Therefore, this study sought to identify alternative drug molecules from natural products with fewer side effects than those of conventional drugs for treating AD. To achieve this, we developed computational methods for predicting drug and target binding affinities using the Schrodinger suite. The target and ligand molecules were retrieved from established databases. The target enzyme has 539 amino acid residues in its sequence alignment. Ligand molecules of 20 bioactive molecules were obtained from different kinds of plants, after which we performed critical analyses such as molecular docking; molecular dynamic (MD) simulations; and absorption, distribution, metabolism, and excretion (ADME) analysis. In the docking studies, the natural compound rutin showed a superior docking score of −12.335 with a good binding energy value of −73.313 kcal/mol. Based on these findings, rutin and the target complex was used to perform MD simulations to analyze rutin stability at 30 ns. In conclusion, our study demonstrates that rutin is a superior drug candidate for AD. Therefore, we propose that this molecule is worth further investigation using in vitro studies.

Protease activated receptor-2 (PAR2): possible target of phytochemicals

The use of phytochemicals either singly or in combination with other anticancer drugs comes with an advantage of less toxicity and minimal side effects. Signaling pathways play central role in cell cycle, cell growth, metabolism, etc. Thus, the identification of phytochemicals with promising antagonistic effect on the receptor/s playing key role in single transduction may have better therapeutic application. With this background, phytochemicals were screened against protease-activated receptor 2 (PAR2). PAR2 belongs to the superfamily of GPCRs and is an important target for breast cancer. Using in silico methods, this study was able to identify the phytochemicals with promising binding affinity suggesting their therapeutic potential in the treatment of breast cancer. The findings from this study acquires importance as the information on the possible agonists and antagonists of PAR2 is limited due its unique mechanism of activation.