Development Of Pharmacophore Model Research Articles

Watermelon: setup and validation of an in silico fragment-based approach

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target’s binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

Receptor-based pharmacophore modeling, molecular docking, synthesis and biological evaluation of novel VEGFR-2, FGFR-1, and BRAF multi-kinase inhibitors

A receptor-based pharmacophore model describing the binding features required for the multi-kinase inhibition of the target kinases (VEGFR-2, FGFR-1, and BRAF) were constructed and validated. It showed a good overall quality in discriminating between the active and the inactive in a compiled test set compounds with F1 score of 0.502 and Mathew’s correlation coefficient of 0.513. It described the ligand binding to the hinge region Cys or Ala, the glutamate residue of the Glu-Lys αC helix conserved pair, the DFG motif Asp at the activation loop, and the allosteric back pocket next to the ATP binding site. Moreover, excluded volumes were used to define the steric extent of the binding sites. The application of the developed pharmacophore model in virtual screening of an in-house scaffold dataset resulted in the identification of a benzimidazole-based scaffold as a promising hit within the dataset. Compounds 8a-u were designed through structural optimization of the hit benzimidazole-based scaffold through (un)substituted aryl substitution on 2 and 5 positions of the benzimidazole ring. Molecular docking simulations and ADME properties predictions confirmed the promising characteristics of the designed compounds in terms of binding affinity and pharmacokinetic properties, respectively. The designed compounds 8a-u were synthesized, and they demonstrated moderate to potent VEGFR-2 inhibitory activity at 10 µM. Compound 8u exhibited a potent inhibitory activity against the target kinases (VEGFR-2, FGFR-1, and BRAF) with IC50 values of 0.93, 3.74, 0.25 µM, respectively. The benzimidazole derivatives 8a-u were all selected by the NCI (USA) to conduct their anti-proliferation screening. Compounds 8a and 8d resulted in a potent mean growth inhibition % (GI%) of 97.73% and 92.51%, respectively. Whereas compounds 8h, 8j, 8k, 8o, 8q, 8r, and 8u showed a mean GI% > 100% (lethal effect). The most potent compounds on the NCI panel of 60 different cancer cell lines were progressed further to NCI five-dose testing. The benzimidazole derivatives 8a, 8d, 8h, 8j, 8k, 8o, 8q, 8r and 8u exhibited potent anticancer activity on the tested cell lines reaching sub-micromolar range. Moreover, 8u was found to induce cell cycle arrest of MCF-7 cell line at the G2/M phase and accumulating cells at the sub-G1 phase as a result of cell apoptosis.

Structure-based drug design and molecular dynamics studies of an allosteric modulator targeting the protein-protein interaction site of PDK1.

Protein-protein interaction interfaces play a major role in cell signaling pathways. There is always agreat interest in developing protein-protein interaction (PPI) inhibitors of kinases, as they are challenging due to their hydrophobicity, flat surface, specificity, potency, etc. 3 Phosphoinositide-dependent kinase-1 (PDK1), which is involved in the PI3K/PDK1/AKT pathway, is a cancer target that has gained insight for the past two decades. PDK1 possesses a protein interaction fragment (PIF) pocket, which is a well-known PPI that targets allosteric modulators. This work focusses on energy-based pharmacophore model development which on virtual screening could yield novel scaffolds towards the drug designing objective for the kind of PDK1 modulators. A novel pyrazolo pyridine molecule was identified as an allosteric modulator that binds to the PPI site. The metadynamics simulations with free energy profiles further revealed the conformational allosteric changes stimulated on the protein structure upon ligand binding. The cytotoxic activity (IC50-20 μM) of the identified compound against five different cancer cell lines and cell cycle analysis supported the anticancer activity of the identified compound. All the computational works were carried out by the most commonly used Schrodinger Suite software. The pharmacophore was validated by Receiver Operation Characteristics (ROC) and screening against allosteric Enamine database library. The Optimized Potential Liquid Simulations (OPLS-2005) was used to minimize the structures for molecular docking calculations, and inbuilt scoring method of ranking the compounds based on docking score and Glide energy was used. Molecular dynamics simulations were conducted by Desmond implemented in Maestro. The hit compound was purchased from Enamine and tested against different cancer cell lines by MTT assay, apoptosis by western blotting technique, and by flow cytometry analysis.

Development of pharmacophore models for AcrB protein and the identification of potential adjuvant candidates for overcoming efflux-mediated colistin resistance.

Growing multi-drug resistance (MDR) among ESKAPE pathogens is a huge challenge. Increased resistance to last-resort antibiotics, like colistin, has further aggravated this. Efflux is identified as a major route of colistin resistance. So, finding an FDA-approved efflux inhibitor for potential application as an adjuvant to colistin was the primary objective of this study. E. coli-AcrB pump inhibitors and substrates were used to develop and validate the pharmacophoric model. Drugs confirming this pharmacophore were subjected to molecular docking to identify hits for the AcrB binding pocket. The efflux inhibition potential of the top hit was validated through the in vitro evaluation of the minimum inhibitory concentration (MIC) in combination with colistin. The checkerboard assay was done to demonstrate synergism, which was further corroborated by the Time-kill assay. Ten common pharmacophore hypotheses were successfully generated using substrate/inhibitors. Following enrichment analysis, AHHNR.100 was identified as the top-ranked hypothesis, and 207 unique compounds were found to conform to this hypothesis. The multi-step docking of these compounds against the AcrB protein revealed argatroban as the top non-antibiotic hit. This significantly inhibited the efflux activity of colistin-resistant clinical isolates K. pneumoniae (n = 1) and M. morganii (n = 2). Further, their combination with colistin enhanced the susceptibility of these isolates, and the effect was found to be synergistic. Accordingly, the time-kill assay of this combination showed 8-log and 2-log reductions against K. pneumoniae and M. morganii, respectively. In conclusion, this study found argatroban as a bacterial efflux inhibitor that can be potentially used to overcome efflux-mediated resistance.

Design, synthesis, biological evaluation and molecular dynamics of some novel 3-phenylpyrazolo[1,5-a]pyrimidine-2,7(1H,4H)-dione based compounds as anti-tubercular agents

Decaprenylphosphoryl-β-d-ribose-2′-epimerase (DprE1) is a druggable target which is being exploited for the development of new anti-TB agents. In the present work, we report developing a pharmacophore model and performing virtual screening of Asinex database using the developed pharmacophore model to get eight hits as potential DprE1 inhibitors. The hits were used as leads to design new 3-phenylpyrazolo[1,5-a]pyrimidine-2,7(1H,4H)-dione based potential anti-TB agents. On the basis of the identified lead molecules, a total of 18 compounds were synthesized and evaluated for their anti-TB activity by using MABA. ADMET predictions for all the compounds revealed that these compounds have drug-like and lead-like properties. One of the final compounds was found to exhibit potent anti-TB activity against Mycobacterium bovis. Communicated by Ramaswamy H. Sarma

Identification of a PD1/PD-L1 inhibitor by Structure-Based Pharmacophore Modelling, Virtual Screening, Molecular docking and Biological Evaluation.

PD-1/PD-L1 is a critical druggable target for immunotherapy against sepsis. Chemoinformatics techniques involved the structure-based 3D pharmacophore model development followed by virtual screening of small molecule databases to identify the small molecules against PD-L1 pathway inhibition. Raltitrexed and Safinamide act as potent repurposed drugs, and three other Specs database compounds using in silico methods. These compounds were screened based on the pharmacophore fit score and binding affinity towards the active site of the PD-L1 protein. In silico pharmacokinetic profiling of these screened compounds was done to test their biological activity. Next, experimental validation of the best four virtually screened hits was done in vitro for its hemocompatibility and cytotoxicity. Among these, Raltitrexed, Safinamide and Specs compound (AK-968/40642641) effectively increased the proliferation of immune cells and IFN-γ production. These compounds can act as potent PDL-1 inhibitors for adjuvant therapy against sepsis.

Development of Pharmacophore Models for the Important Off-Target 5-HT2B Receptor.

Toxicity is a major cause of attrition in the development of pharmaceuticals, and the off-target effects are a frequent contributor. The 5-HT2B receptor agonism is known to be responsible for a variety of safety concerns including valvular heart disease which was the cause for the withdrawal of several compounds from the market. An early detection of potential binding to this receptor is thus desirable. Herein, we present the identification of key amino acid residues in the active site of 5-HT2B by molecular dynamics simulations, the development of pharmacophore models and their performance on in-house data, and a structurally highly diverse subset of Enamine REAL labeled for 5-HT2B activity by a machine learning model. These models may be used as filters employed on screening compound sets for the early filtration of compounds with potential 5-HT2B off-target liabilities.

A Way for Finding Ligands for New Binding Sites

Analysis of protein structures shows that most of them have potential binding sites that may be considered as applicable for new ligand design. The lack of known ligands interacting with such binding sites seriously complicated potential ligands selection. We have developed an approach that can increase the effectiveness of virtual screening for such ligands. It integrates methods of de novo ligand design, pharmacophore modeling, molecular docking, molecular dynamics, calculation of binding energies by MM- GBSA. This approach starts by the de novo design of virtual library of potential compounds followed by selection of favourable substructures and their correct positioning in a new ligand binding site. This generated library has been used for a development of pharmacophore models that have been used for a virtual screening of molecular databases. The selected compounds were docked to the putative binding site to check their ability to accommodate into it and their ability to locate the identified favorable fragments in the same region of the binding site as de novo generated molecules. The further evaluation of the selected ligands can be carried out by standard CADD methods.

The Development of Pharmacophore Models for the Search of New Natural Inhibitors of SARS-CoV-2 Spike RBD-ACE2 Binding Interface.

To date, some succeeding variants of SARS-CoV-2 have become more contagious. This virus is known to enter human cells by binding the receptor-binding domain (RBD) of spike protein with the angiotensin-converting enzyme 2 (ACE2), the latter being a membrane protein that regulates the renin-angiotensin system. Since the host cell receptor plays a critical role in viral entry, inhibition of the RBD-ACE2 complex is a promising strategy for preventing COVID-19 infection. In the present communication, we propose and utilize an approach based on the generation of a complex of pharmacophore models and subsequent Induced Fit Docking (IFD) to identify potential inhibitors of the main binding sites of the Omicron SARS-CoV-2 RBD(S1)-ACE2 complex (PDB ID: 7T9L) among a number of natural products of various types and origins. Several natural compounds have been found to provide a high affinity for the receptor of interest. It is expected that the present results will stimulate further research aimed at the development of specialized drugs against this virus.

Development of pharmacophore model to identify potential DNA gyrase inhibitors

There is great concern in the medical community due to rapid increase in antibiotic resistance, causing 700,000 deaths annually worldwide. Therefore, there is paramount need to develop novel and innovative antibacterial agents active against resistant bacterial strains. DNA gyrase is a crucial enzyme in bacterial replication that is absent in eukaryotes, making it effective curative target for antibacterials. To identify potential DNA gyrase inhibitors by virtual screening of NCI database using a 3-step approach. A total of 271 compounds with known IC50 values against Escherichia coli DNA GyrA were selected to develop a pharmacophore model for dual screening approach to identify new potential hits from the NCI database. In the second step, the NCI database was also screened using in-house built NN-QSAR model. Molecular docking of common 5298 compounds screened from both methods were performed against E. coli DNA GyrA (PDB id- 6RKU), and 3004 compounds are reported to exhibit lower binding energies than ciprofloxacin (−6.77 Kcal/mol). The top three compounds (NCI371878, NCI371876 and NCI142159) reported with binding energy of −13.5, −13.19 and −13.03 Kcal/mol were further subjected to MD simulation studies for 100 ns supporting the stability of the docked complexes.

Interface inhibitory action on Interleukin-1β using selected anti-inflammatory compounds to mitigate the depression: A computational investigation

Interleukin-1β (IL1β) is a keynote mediator of inflammation with diverse physiological functions, playing a fundamental role in memory and mood regulation. The pleiotropic effects of IL-1β have been proposed to be implicated in the pathogenesis and etiology of depression. Thus, targeting IL-1β offers an inimitable opportunity to develop new strategies for an alternative therapy to treat depression. The focus of this study is to find out the potential inhibitors against IL-1β. Since, there is no oral specific drug reported yet thus, demanding an urgent need to develop new immunomodulatory drugs to combat chronic diseases. In this study, ligand-based pharmacophore modeling integrated with virtual screening and molecular docking strategy was designed to identify novel compounds capable of inhibiting the interactions towards cognitive receptor IL-1RI. In this connection, a set of 30,000 compounds were screened by a developed pharmacophore model that led to the retrieval of 2043 molecules from the in-house library and ZINC Database. Primarily, specific binding regions for IL-1β inhibitors have been explored by blind docking studies. After the selection of the binding site, the hits identified as actives based on the 3D-pharmacophore model were assessed by molecular docking studies. In a stepwise screening, six potential virtual hits were shortlisted for molecular dynamic simulation to acquire insights into their dynamic behavior. The obtained results highlighted that these compounds are stabilized in the targeted pocket of IL-1β and possibly block the formation of an active heterocomplex, subsequently locking the associated signaling cascade. Further in vitro experiments confirmed the inhibitory potential of Compound-157 and compound-283 with the IC50 of 1.6 ± 0.1 and 9.1 ± 1.7 µg/mL respectively.

Common Structural Features of Hydrophobic α-Helical Hot Spots: Insights for the Design of Novel α-Helix Mimetics.

The binding conformations of α-helical hydrophobic hot spots are convergent into two spatial areas in protein-protein complex structures. The physical basis for convergence was disclosed, which allows the development of pharmacophore models for i/i + 4/i + 7 or i/i + 3/i + 4 α-helical hot spots. Further investigation revealed that this convergence of binding conformations is common among all hydrophobic hot spots regardless of their α-helical positions. This observation led to a streamlined generation of pharmacophore models for hydrophobic hot spots at any positions along the α-helix. These successfully evaluated pharmacophore models may be useful for designing novel α-helical hot spot mimetics.

Discovery of Novel Antimalarial Drugs Based on Thiosemicarbazone Derivatives: An In Silico Approach

Background: Thiosemicarbazones belong to the group of semicarbazides, which contains a sulfur atom instead of an oxygen atom. Several studies have shown that they are effective against extracellular protozoans like Trichomonas vaginalis, Plasmodium falciparum, Trypanosoma cruzi, and other parasites. Objective: The current research involves pharmacophore model design, 3-D-QSAR, virtual screening, and docking studies, all of which are evaluated using various parameters. Methods: The present study was performed by Schrodinger software. A total of 40 ligands were selected for the development of 3D QSAR models. To predict the pIC50 values in 3D-QSAR analysis, the entire dataset was divided into two sets, training, and test sets, in a 7:3 ratio. The selected pharmacophore hypothesis has been used for the virtual screening study. Results: DHHRR_1 emerged as the best pharmacophore model with a survival score of 5.80. The 3D QSAR study showed a significant model with R2 = 0.91 and Q2 = 0.73. The series top-scoring compound 7e had a docking score of -10.44 and showed interactions with the amino acids, ARG-265, PHE-227, and LEU-531, required for activity. The developed pharmacophore model had been used for the screening of ZINC compounds, where ZINC26244107, ZINC13469100, ZINC01290725, and ZINC01350173 showed the best XP docking scores (-11.60, -11.27, -11.35, -10.52, respectively) while binding important amino acids ARG265, HIE185 and LEU 531 against plasmodium falciparum, PDB ID:5TBO. The docking study was further evaluated by taking standard drug chloroquine, showing similar binding interactions as shown by other compounds. The ADME studies showed the drug-likeness properties of the compounds. Conclusion: The results of the present study may be helpful for the future development of antimalarial compounds against Plasmodium falciparum.

A promising in silico protocol to develop novel PPARγ antagonists as potential anticancer agents: Design, synthesis and experimental validation via PPARγ protein activity and competitive binding assay

Peroxisome proliferator-activated receptor gamma (PPARγ), a member of the nuclear receptor superfamily is an excellent example of targets that orchestrates cancer, inflammation, lipid and glucose metabolism. We report a protocol for the development of novel PPARγ antagonists by employing 3D QSAR based virtual screening for the identification of ligands with anticancer properties. The models are generated based on a large and diverse set of PPARγ antagonist ligands by the HYPOGEN algorithm using Discovery Studio 2019 drug design software. Among the 10 hypotheses generated, Hypotheses 2 showed the highest correlation coefficient values of 0.95 with less RMS deviation of 1.193. Validation of the developed pharmacophore model was performed by Fischer’s randomization and screening against test and decoy set. The GH score or goodness score was found to be 0.81 indicating moderate to a good model. The selected pharmacophore model Hypo 2 was used as a query model for further screening of 11,145 compounds from the PubChem, sc-PDB structure database, and designed novel ligands. Based on fit values and ADMET filter, the final 10 compounds with the predicated activity of ≤ 3 nM were subjected for docking analysis. Docking analysis revealed the unique binding mode with hydrophobic amino acid that can cause destabilization of the H12 which is an important molecular mechanism to prove its antagonist action. Based on high CDocker scores, Cpd31 was synthesized, purified, analyzed and screened for PPARγ competitive binding by TR-FRET assay. The biochemical protein binding results matched the predicted results. Further, Cpd31 was screened against cancer cells and validated the results.

Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease.

Recent fragment‐based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS‐CoV‐2 Mpro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to Mpro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo‐ or small molecule fragment‐bound structures of Mpro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS‐CoV‐2 Mpro. It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC‐AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.

Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors.

Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic, and steric features that characterize small-molecule inhibitors binding stably to 3CLpro and by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics (MD) simulation, machine learning, and in vitro experimental validation analyses, which have led to the identification of small-molecule inhibitors of 3CLpro with micromolar activity and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with the available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM) and synthetic compounds previously not characterized (e.g., compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, MD simulations, and machine learning can facilitate 3CLpro-targeted small-molecule screening investigations. Different receptor-, ligand-, and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of the identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small-molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.

A Computational approach to discover potential quinazoline derivatives against CDK4/6 kinase

CDK is a protein (serine-threonine) kinase family that plays an important role in the cell cycle and oncogenic transformation processes. This controls the activity of an enzyme involved in cell proliferation in response to endogenous and exogenous signals. Quinazoline derivatives are essential components in the inhibition or destruction of cancer cell processes such as breast cancer. The current research entails the development of pharmacophore models, 3-D-QSAR, virtual screening, and docking studies, with evaluation by different parameters. A total of 46 ligands were chosen for the analysis, yielding ADRRR_1 as the best pharmacophore model with a survival score of 5.48. The 3D QSAR study showed significant model with R2 =0.98 and Q2 = 0.55. The series top-scoring compound was 4a with a docking score of -13.62 and showed interactions with the amino acids LYS-43, GLU-61, and VAL-101. The developed pharmacophore model was used to screen ZINC compounds, that were further screened by using a virtual screening study. The compounds ZINC75260693, ZINC68584027, ZINC20411715, ZINC08928348, and ZINC11964703 showing the highest XP docking scores (-10.71, -10.60, -10.51, -9.86, -9.83, consecutively). The compound with the highest score, ZINC75260693, showed binding interactions with VAL-101, PHE-98, LYS-43, GLU-61, and ASP-163 (PDB ID: 2EUF). The compounds' drug-like properties were revealed through ADME study. The findings of this study may aid in the production of quinazoline derivatives that target CDK4/6 kinase in the future.

From Quinoline to Quinazoline-Based S. aureus NorA Efflux Pump Inhibitors by Coupling a Focused Scaffold Hopping Approach and a Pharmacophore Search.

Antibiotic resistance breakers, such as efflux pump inhibitors (EPIs), represent a powerful alternative to the development of new antimicrobials. Recently, by using previously described EPIs, we developed pharmacophore models able to identify inhibitors of NorA, the most studied efflux pump of Staphylococcus aureus. Herein we report the pharmacophore‐based virtual screening of a library of new potential NorA EPIs generated by an in‐silico scaffold hopping approach of the quinoline core. After chemical synthesis and biological evaluation of the best virtual hits, we found the quinazoline core as the best performing scaffold. Accordingly, we designed and synthesized a series of functionalized 2‐arylquinazolines, which were further evaluated as NorA EPIs. Four of them exhibited a strong synergism with ciprofloxacin and a good inhibition of ethidium bromide efflux on resistant S. aureus strains coupled with low cytotoxicity against human cell lines, thus highlighting a promising safety profile.

Homology modeling of actin protein in Leishmania donovani and in silico prediction of active drugs

Objectives: Leishmaniasis is a disease caused by leishmania parasite which is genus of trypanosome protozoa. Leishmania donovani promastigote inhibits biogenesis of phagolysosome due to the accumulation of periphagosomal F-actin. This inhibition of phagosome maturation gives favorable environment for differentiation of promastigote-to-amastigote and causes disease progression. L. donovani actin (LdACT) has been found to have unconventional biochemical behavior due to the different amino acid region in its sequence suggesting that it must have a three-dimensional (3D) structure different from eukaryotic actins making it a more specific for predication of antileishmanial drugs which is main objective of this study. Material and Methods: For carrying out this study, protein sequence was retrieved from the database SWISSPROT, analyzed by BIOEDIT software followed by primary and secondary structure prediction by PROTPARAM and SOPMA. A 3D structure of same was constructed by homology modeling using the yeast actin-human gelsolin segment 1 complex (protein data bank [PDB] ID:1yag) as a template with the help of Swiss model. The final model obtained was further accessed by PROCHECK and VERIFY 3D software which ensured the reliability of the model. This model of actin protein was further used for screening different chemical compounds with high binding affinity by GOLD and DISCOVERY STUDIO. Results: The results give information about the some inhibitors having highest binding affinity to the actin protein. Conclusion: This study will be useful for the development of pharmacophore models for in silico predication of active drugs as a part of antileishmanial drug therapy.

Discovery of potential inhibitors for phosphodiesterase 5A, sodium-potassium pump and beta-adrenergic receptor from Terminalia arjuna: in silico approach

The aim of this work was to perform in silico analysis of selected biomolecules from Terminalia arjuna (T. arjuna) by using virtual screening, molecular docking and pharmacophore modeling. Reported 30 biomolecules of T. arjuna were used as ligands. Grip-based docking was carried out to produce the target-specific complex model using vLife MDS 4.4 software. Docked conformations of the selected T. arjuna biomolecules resulted in eight potential biomolecules namely Casuarinin, Luteolin, Pelargonidin, Arjunin, Castalagin, Punicalagin, Kaempferol and Quercetin with major interactions and exhibited good affinity to the residues of protein targets. Developed pharmacophore models have suggested minimum pharmacophoric features required in the biomolecule so as to show standard like activity. Interestingly, Casuarinin showed multiple inhibitions on phosphodiesterase 5A and sodium-potassium pump whereas Pelargonidin on phosphodiesterase 5A and beta-adrenergic protein targets. Conclusively, this study provides a suitable platform for discovery of novel inhibitors from natural source for heart disorders.