Co-crystal Ligand Research Articles

Inhibitor design against JNK1 through e-pharmacophore modeling docking and molecular dynamics simulations

c-Jun-NH2 terminal kinases (JNKs) come under a class of serine/threonine protein kinases and are encoded by three genes, namely JNK1, JNK2 and JNK3. Human JNK1 is a cytosolic kinase belonging to mitogen-activated protein kinase (MAPK) family, which plays a major role in intracrinal signal transduction cascade mechanism. Overexpressed human JNK1, a key kinase interacts with other kinases involved in the etiology of many cancers, such as skin cancer, liver cancer, breast cancer, brain tumors, leukemia, multiple myeloma and lymphoma. Thus, to unveil a novel human JNK1 antagonist, receptor-based pharmacophore modeling was performed with the available eighteen cocrystal structures of JNK1 in the protein data bank. Eighteen e-pharmacophores were generated from the 18 cocrystal structures. Four common e-pharmacophores were developed from the 18 e-pharmacophores, which were used as three-dimensional (3D) query for shape-based similarity screening against more than one million small molecules to generate a JNK1 ligand library. Rigid receptor docking (RRD) performed using GLIDE v6.3 for the 1683 compounds from in-house library and 18 cocrystal ligands with human JNK1 from lower stringency to higher stringency revealed 17 leads. Further to derive the best leads, dock complexes obtained from RRD were studied further with quantum-polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area (MM-GBSA). Four leads have showed lesser binding free energy and better binding affinity towards JNK1 compared to 18 cocrystal ligands. Additionally, JNK1–lead1 complex interaction stability was reasserted using 50 ns MD simulations run and also compared with the best resolute cocrystal structure using Desmond v3.8. Thus, the results obtained from RRD, QPLD, IFD and MD simulations indicated that lead1 might be used as a potent antagonist toward human JNK1 in cancer therapeutics.

An integrated molecular modeling approach for in silico design of new tetracyclic derivatives as ALK inhibitors

Anaplastic lymphoma kinase (ALK), a promising therapeutic target for treatment of human cancers, is a receptor tyrosine kinase that instigates the activation of several signal transduction pathways. In the present study, in silico methods have been employed in order to explore the structural features and functionalities of a series of tetracyclic derivatives displaying potent inhibitory activity toward ALK. Initially docking was performed using GLIDE 5.6 to probe the bioactive conformation of all the compounds and to understand the binding modes of inhibitors. The docking results revealed that ligand interaction with Met 1199 plays a crucial role in binding of inhibitors to ALK. Further to establish a robust 3D-QSAR model using CoMFA and CoMSIA methods, the whole dataset was divided into three splits. Model obtained from Split 3 showed high accuracy ( of 0.700 and 0.682, of 0.971 and 0.974, of 0.673 and 0.811, respectively for CoMFA and CoMSIA). The key structural requirements for enhancing the inhibitory activity were derived from CoMFA and CoMSIA contours in combination with site map analysis. Substituting small electronegative groups at Position 8 by replacing either morpholine or piperidine rings and maintaining hydrophobic character at Position 9 in tetracyclic derivatives can enhance the inhibitory potential. Finally, we performed molecular dynamics simulations in order to investigate the stability of protein ligand interactions and MM/GBSA calculations to compare binding free energies of co-crystal ligand and newly designed molecule N1. Based on the coherence of outcome of various molecular modeling studies, a set of 11 new molecules having potential predicted inhibitory activity were designed.

An expeditious one-pot microwave facilitated versus conventional syntheses: in vivo biological screening and molecular docking studies of some 3,5-disubstituted-4,5-dihydro-(1H)-pyrazole derivatives

A series of 3,5-disubstituted-2-pyrazoline derivatives (2a–2t) were synthesized by reacting different aromatic/heteroaromatic aldehydes and ketones, in a two-step reaction through Claisen Schmidt condensation, followed by cyclization of the resulted chalcones with hydrazine hydrate in the presence of a base using conventional and microwave approaches. The synthesized derivatives were characterized by various physicochemical methods, and their chemical structures were established by IR, Mass, 1H-NMR, 13C-NMR spectroscopic data and elemental analysis. The antidepressant with tail suspension test and forced swim test and anti-anxiety with Elevated Plus Maze Test activities were evaluated using suitable animal models. Compounds 2i, and 2j showed noticeable antidepressant activity, by reducing the duration of immobility in both the tests, while compounds 2a and 2b were found to possess good anxiolytic activity, by increasing the number of arm entries and open arm exploratory time at the tested doses (50 and 100 mg/kg b.w.), when compared to the standard drugs imipramine and diazepam, respectively. In order to ascertain the binding interactions of the synthesized derivatives to the MAO-A target protein, molecular docking was employed which demonstrated the key interactions with the amino acid residues Asn181, Phe208, Tyr69, Tyr197, Tyr444 and Met445 at the binding site. In addition, the most active derivatives 2i and 2b showed some imperative conserved interactions of the PDB co-crystal ligand 2Z5X with the amino acid residues at the binding site of MAO-A protein. The results of the study also demonstrated that the Glide gscores of the synthesized derivatives were in close correlation with the in vivo biological activity data, in particular with the forced swim test of the antidepressant activity with a very good correlation coefficient of 0.754103. Furthermore, the ADME properties of the synthesized derivatives were predicted and found to be within the affirmed limits.

Exploring the physicochemical profile and the binding patterns of selected novel anticancer Himalayan plant derived active compounds with macromolecular targets

Abstract Plants are vital source of compounds offering plethora of therapeutic effects against various ailments without much side effects. Due to wide spread prevalence and drug resistance in cancer; there is an urgent need for discovery of new anti-cancer drugs. In the present study, selected novel anti-cancer plants derived compounds (cmpd1 to cmpd15) from Himalayan region were docked with defined molecular targets that regulate cell proliferation and apoptosis. The binding energies of best docked compounds ranged between −8.0 kcal/mol and −11.71 kcal/mol. Further analysis revealed critical hydrogen bonds and hydrophobic interactions between compounds and targets. The best docked compounds viz., cmpd15 against cyclin-dependent kinase-2 (CDK-2), cmpd8 against CDK-6 and cmpd9 against Topoisomerase I and II showed higher binding affinities than the native co-crystal ligands. The root mean square deviation (RMSD) and potential energy plot clearly indicates the stability of the complexes during 20 ns molecular dynamics (MD) simulation. The Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) binding energy analysis revealed Van der Waals energy component which is the principal stabilizing energy for their interactions except CDK-2/cmpd15 complex. The polar solvation energy did not have favorable contribution to their stabilization. The binding energy decomposition analysis revealed per residue contribution for each docked complexes. Physicochemical profile studies showed that majority of the compounds conform to Lipinski's rule of five (ROF) having low to high blood brain barrier (BBB) penetration, human intestinal absorption, plasma binding protein inhibition and P glycoprotein inhibition.

E-pharmacophore-based virtual screening to identify GSK-3β inhibitors

Glycogen synthase kinase-3β (GSK-3β) is a serine/threonine kinase which has attracted significant attention during recent years in drug design studies. The deregulation of GSK-3β increased the loss of hippocampal neurons by triggering apoptosis-mediating production of neurofibrillary tangles and alleviates memory deficits in Alzheimer’s disease (AD). Given its role in the formation of neurofibrillary tangles leading to AD, it has been a major therapeutic target for intervention in AD, hence was targeted in the present study. Twenty crystal structures were refined to generate pharmacophore models based on energy involvement in binding co-crystal ligands. Four common e-pharmacophore models were optimized from the 20 pharmacophore models. Shape-based screening of four e-pharmacophore models against nine established small molecule databases using Phase v3.9 had resulted in 1800 compounds having similar pharmacophore features. Rigid receptor docking (RRD) was performed for 1800 compounds and 20 co-crystal ligands with GSK-3β to generate dock complexes. Interactions of the best scoring lead obtained through RRD were further studied with quantum polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area. Comparing the obtained leads to 20 co-crystal ligands resulted in 18 leads among them, lead1 had the lowest docking score, lower binding free energy and better binding orientation toward GSK-3β. The 50 ns MD simulations run confirmed the stable nature of GSK-3β-lead1 docking complex. The results from RRD, QPLD, IFD and MD simulations confirmed that lead1 might be used as a potent antagonist for GSK-3β.

E-Pharmacophore Based Virtual Screening to Identify Agonist for PKA-Cα

Owing to PKA-Cα unique functions in regulating tau splicing alternatively in neurons results in aggregation of tau which contributes to neurofibrillary tangles and taupathies generation a hallmark of Alzheimer’s disease (AD). PKACα also inhibits GSK3β thus it has been a therapeutic target for AD intervention. In this study, e-pharmacophore and multiple docking strategies were followed to propose a novel PKA-Cα agonists. Nine e-pharmacophores were developed from nine co-crystal structures such that all the critical pharmacophoric features involved in their bioactivity of PKA-Cα were effectively mapped. Rigid receptor docking (RRD) was performed with the library of PKA-Cα activators having 3512 shape screened compounds towards PKA-Cα. To derive the best leads, dock complexes were further subjected to QPLD, IFD and MM-GBSA calculations. PKA-Cα-lead1 dock complex was subjected to 50 ns MD simulations run. Comparative analysis between obtained 25 leads and 9 co-crystal ligands revealed three the best leads. Among the three, lead1 has the least docking score with lowest binding free energy with better binding orientation towards PKA-Cα. The constancy of PKA-Cα-lead1 interactions was revealed by 50 ns MD simulations run. Thus ADME predictions and results from RRD, QPLD, IFD and MD simulations affirmed that the proposed three leads might be used as a potent agonists for PKA-Cα.

PyPLIF-ASSISTED REDOCKING INDOMETHACIN-(R)-ALPHA-ETHYL-ETHANOLAMIDE INTO CYCLOOXYGENASE-1

Identification of Protein-Ligand Interaction Fingerprints (PLIF) has been performed as the rescoring strategy to identify the best pose for the docked poses of indomethacin-(R)-α-ethyl-etanolamide (IMM) in the binding site of cyclooxygenase-1 (COX-1) from simulations using PLANTS molecular docking software version 1.2 (PLANTS1.2). Instead of using the scoring functions included in the docking software, the strategy presented in this article used external software called PyPLIF that could identify the interactions of the ligand to the amino acid residues in the binding pocket and presents them as binary bitstrings, which subsequently were compared to the interaction bitstrings of the co-crystal ligand pose. The results show that PyPLIF-assisted redocking strategy could select the correct pose much better compared to the pose selection without rescoring. Out of 1000 iterative attempts, PyPLIF-assisted redocking simulations could identify 971 correct poses (more than 95%), while the redocking simulations without PyPLIF could only identify 500 correct poses (50%).These works have also provided us with the initial step of the construction of a valid Structure-Based Virtual Screening (SBVS) protocol to identify COX-1 inhibitors.

Conformation Guides Molecular Efficacy in Docking Screens of Activated β-2 Adrenergic G Protein Coupled Receptor

A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures, which predicted only inverse-agonists. In addition, two hits recapitulated the signaling profile of the co-crystal ligand with respect to the G protein and arrestin mediated signaling. This functional fidelity has important implications in drug design, as the ability to predict ligands with predefined signaling properties is highly desirable. However, the agonist-bound state provides an uncertain template for modeling the activated conformation of other GPCRs, as a dopamine D2 receptor (DRD2) activated model templated on the activated β2AR structure returned few hits of only marginal potency.

Development of robust QSAR model using rapid overlay of crystal structures (ROCS) based alignment: a test case of Tubulin inhibitors

Conformational alignment is the most important step in 3D-QSAR methodology. There are various methods available to create an alignment hypothesis for the compounds being studied in 3D-QSAR. Most famous alignment methods include: alignment on a suitable template conformation, field fit alignment, database alignment, pharmacophore-based alignment, atom fit, and docking based alignment. All of the above-mentioned methods were explored previously to sample the conformational space in 3D-QSAR. Current study is dealing with the situation where most of the alignment methods were failed to produce a statistically robust 3D-QSAR model. Therefore, a relatively new alignment scheme was employed using ROCS (Rapid Overlay of Chemical Structures) method from OpenEyes. In the present study, Combretastatin A-4 based Tubulin inhibitors were used as case study. Co-crystal ligand conformation (i.e., Colchicine) in the Tubulin complex structure (PDB ID: 1SA0) was utilized as a template molecule. To date, very few applications of ROCS-based alignments in 3D-QSAR are reported in literature. Therefore, current study will serve as an addition to the existing status of using ROCS in 3D-QSAR. Genetic algorithm was utilized to build final 3D-QSAR model. Correlation obtained from final model suggesting that robust 3D-QSAR model was constructed.

In silico design of potent agonists for human PPAR γ

AbstractPeroxisome proliferator-activated receptor (PPAR γ) acts as a key regulator on adipocyte differentiation and glucose homeostasis. PPAR γ has been implicated in the pathology of type 2 diabetes. As human PPAR γ activity is considered important in improving insulin sensitivity, in silico screening was carried out to find potent agonists for human PPAR γ protein. The co-crystal structure of PPAR γ, solved through X-Ray diffraction method was retrieved from the protein data bank. Four PPAR γ agonists selected from literature were submitted to subsequent 2D searching protocol using Ligand.Info, which yielded 1699 structural analogs. The PPAR γ co-crystal structure and ligand dataset were preprocessed using protein preparation wizard and LigPrep, respectively. Further, docking was performed by using three phased docking protocol of Maestro v9.2 that implements Glide v5.7. The obtained thirteen leads through docking were compared with the existing inhibitors and seven leads with good binding affinity with PPAR γ were proposed. The binding orientations of the seven leads were coinciding well with the native co-crystal structure of human PPAR γ. Thus, the proposed seven leads can be suggested as potential agonists for improving insulin sensitivity in the treatment of type 2 diabetes mellitus if synthesized and validated in animal models

Identification of small molecule inhibitor of cyclophilin-A using high throughput virtual screening and molecular docking Studies

The inhibitors of cyclophilin A (CyPA) have drawn a great deal of attention due to their promising potential as small-molecule therapeutics for the treatment of cardiovascular diseases. This ultimately prompts to explore structural geometries of these inhibitors to obtain insights on next generation CyPA inhibitors through rational drug designing. Herein, 2D similarity search for the seven CyPA inhibitors was performed using Ligand.Info database. Small subsets of 2800 molecules from one million compounds were predicted to have activity against cardiovascular drug target CyPA. The binding strength of 2800 ligands with CyPA was assessed through molecular docking analysis using Schrodinger software 2011. The CyPA co-crystal structure and ligand dataset were preprocessed using protein preparation wizard and LigPrep, respectively. Lipinski filter and reactive filter evaluations were applied to 10,351 tautomeric states of 2800 ligands to achieve conformations with good pharmacological properties. Further, systematic Glide HTVS, SP and XP docking had resulted 151 ligands with good binding affinities towards CyPA. Ten best ranked compounds having good correlation with seven published CyPA inhibitors were proposed as potential leads. These ten leads had the advantage of good drug like properties compared to experimentally validated inhibitors. Lead1 (SQ29548) showed XP GScore of -12.11 Kcal/mol and two hydrogen bonds with Asp102 of CyPA which is in well agreement with crystallographic data of ‘CyPA - Sanglifehrin A’ complex. SQ29548 is already used as an antagonist for human recombinant thromboxane receptor which supports ten proposed potential inhibitors identified through virtual screening approach as a starting point for new generation drug designing to combat cardiovascular associated diseases.

Structural studies of B-type Aurora kinase inhibitors using computational methods

To characterize the structural features of quinazoline-based Aurora B inhibitors that influence its inhibitor activity. Two geometrical methods, Method 1 and Method 2, were used to develop the 3D-QSAR models. The most active ligand was used as the template for the alignment of all the ligands in Method 1, and a conformer of the cocrystal ligand was used as the template for the alignment of all the ligands in Method 2. The models suggest that highly active ligands can be designed by varying the R1 substituent at position 7 of the quinazoline ring with positively charged, bulky, hydrophobic groups, while bulky and hydrophobic groups around the thiazole ring are desirable for higher activity. This study emphasizes that the bioactive conformer is rather different from the minima. The steric, electrostatic, and hydrophobic field effects contribute to its inhibitory activity.

Comparative molecular field analysis of flavonoid inhibitors of the PIM-1 kinase

The PIM-1 protein, the product of the pim-1 oncogene, is a serine/threonine kinase. Dysregulation of the PIM-1 kinase has been implicated in the development of human malignancies including lymphomas, leukemias, and prostate cancer. Comparative molecular field analysis (CoMFA) is a 3-D QSAR technique that has been widely used, with notable success, to correlate biological activity with the steric and electrostatic properties of ligands. We have used a set of 15 flavonoid inhibitors of the PIM-1 kinase, aligned de novo by common substructure, to generate a CoMFA model for the purpose of elucidating the steric and electrostatic properties involved in flavonoid binding to the PIM-1 kinase. Partial least squares correlation between observed and predicted inhibitor potency (expressed as −log IC 50), using a non-cross-validated partial least squares analysis, generated a non-cross-validated q 2 = 0.805 for the training set ( n = 15) of flavonoids. The CoMFA generated steric map indicated that the PIM-1-binding site was sterically hindered, leading to more efficient binding of planar molecules over ( R) or ( S) compounds. The electrostatic map identified that positive charges near the flavonoid atom C8 and negative charges near C4′ increased flavonoid binding. The CoMFA model accurately predicted the potency of a test set of flavonoids ( n = 6), generating a correlation between observed and predicted potency of q 2 = 0.825. CoMFA models generated from additional alignment rules, which were guided by co-crystal structure ligand orientations, did not improve the correlative value of the model. Superimposing the PIM-1 kinase crystal structure onto the CoMFA contours validated the steric and electrostatic maps, elucidating the amino acid residues that potentially contribute to the CoMFA fields. Thus we have generated the first predictive model that may be used for the rational design of small-molecule inhibitors of the PIM-1 kinase.

HierVLS hierarchical docking protocol for virtual ligand screening of large-molecule databases.

To provide practical means for rapidly scanning the extensive experimental combinatorial chemistry libraries now available for high-throughput screening (HTS), it is essential to establish computational virtual ligand screening (VLS) techniques to rapidly identify out of a large library all active compounds against a particular protein target. Toward this goal we developed HierVLS, a fast hierarchical docking approach that starts with a coarse grain conformational search over a large number of configurations filtered with a fast but crude energy function, followed by a succession of finer grain levels, using successively more accurate but more expensive descriptions of the ligand-protein-solvent interactions to filter successively fewer cases. The final step of this procedure optimizes one configuration of the ligand in the protein site using our most accurate energy expression and description of the solvent, which would be impractical for all conformations and sites sampled in the coarse level. HierVLS is based on the HierDock approach, but rather than allowing an hour or more to determine the best binding site and energy for each ligands (as in HierDock), we have adapted our procedure so that it can lead to reliable results while using only 4 min (866 MHz Pentium III processor) per ligand. To validate the accuracy for HierVLS to predict the experimentally observed binding conformation, we considered 37 cocrystal structures comprising 11 target proteins. We find that HierVLS identifies the correct binding mode for all 37 cocrystals. In addition, the calculated binding energies correlate well with available experimental binding constants. To validate how well HierVLS can identify the correct ligand in an extensive library of decoys, we considered a library of over 10 000 molecules. HierVLS identifies 26 out of the 37 cases in the top 2% ranked by binding affinity among the 10 037 molecules. The failures result from either metal-containing sites on the protein or water-mediated ligand-protein interactions, which we anticipate can be solved within the constraints of practical VLS. We then applied HierVLS to screen a 55000-compound virtual library against the target protein-tyrosine phosphatase 1B (ptp1b). The top 250 compounds by binding affinity included all six ptp1b cocrystal ligands added to the library plus three other experimentally confirmed binders. The best (top 1) binder is an experimentally confirmed positive. We conclude that HierVLS is useful for selecting leads for a particular target out of large combinatorial databases.