Intervals and the deduction of drug binding site models

In the search for new drugs, it often occurs that the binding affinities of several compounds to a common receptor macromolecule are known experimentally, but the structure of the receptor is not known. This article describes an extraordinarily objective computer algorithm for deducing the important geometric and energetic features of the common binding site, starting only from the chemical structures of the ligands and their observed binding. The user does not have to propose a pharmacophore, guess the bioactive conformations of the ligands, or suggest ways to superimpose the active compounds. The method takes into account conformational flexibility of the ligands, stereospecific binding, diverse or unrelated chemical structures, inaccurate or qualitative binding data, and the possibility that chemically similar ligands may or may not bind to the receptor in similar orientations. The resulting model can be viewed graphically and interpreted in terms of one or more binding regions of the receptor, each preferring to be occupied by various sorts of chemical groups. The model always fits the given data completely and can predict the binding of any other ligand, regardless of chemical structure. The method is an outgrowth of distance geometry and Voronoi polyhedra site modeling but incorporates several novel features. The geometry of the ligand molecules and the site is described in terms of intervals of internal distances. Determining the site model consists of reducing the uncertainty in the interregion distance intervals, and this uncertainty is described as intervals of intervals. Similarly, the given binding affinities and their experimental uncertainties are treated as intervals in the affinity scale. The final site model specifies an entire region of interaction energy parameters that satisfy the training set rather than a single set of parameters. Predicted binding for test compounds results in an interval which, when compared to the experimental interval, may be correct, incorrect, or vague. There is a pervasive ternary logic involved in the assessment of predictions, in the search for a satisfactory model, and in judging whether a given molecule may bind in a particular orientation: true, false, or maybe. The approach is illustrated on an extremely simple artificial example and on a real data set of cocaine analogues binding to a nerve membrane receptor in vitro. © 1995 by John Wiley & Sons, Inc.     

Identification of Novel β3-Adrenoceptor Agonists Using Energetic Analysis, Structure Based Pharmacophores and Virtual Screening

β3 Adrenergic receptor (β3-AR), is a potential therapeutic target for the treatment of type II diabetes and obesity. We report the identification of novel compounds as β3-AR agonists by integrating different approaches of energetic analysis, structure based pharmacophore designing and virtual screening. In a step wise filtering protocol, structure based virtual screening of 2,33,450 compounds was done. These molecules were docked into the active site of the receptor utilizing three levels of accuracy; ligands passing the HTVS (high throughput virtual screening) step were subsequently analyzed in Glide SP (Standard Precision) and finally in Glide XP (Extra Precision) to estimate the receptor ligand binding affinities. In the second step a total of 300 pharmacophore hypotheses were generated from a set of known and diverse β3-AR agonists. The best hypothesis showed six features: three hydrogen bond acceptors, one positively charged group, and two aromatic rings. To cross validate, pharmacophore filtering was done on the set of shortlisted compounds from structure based VS (virtual screening). The different screening techniques employed were validated using enrichment factor calculations. The energetic based Pharmacophore performed fairly well at distinguishing active from the inactive compounds and yielded a greater diversity of active molecules whereas the number of actives retrieved in the case of structure based screening was the highest.     

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.     

Antibacterial,DFT and molecular docking studies of Rh(III) complexes of Coumarinyl‐Thiosemicarbazone nuclei based ligands

Coumarinyl thiosemicarbazone derivatives (1E)‐1‐(1‐(2‐oxo‐2H‐chromen‐3‐yl)ethylidene)thiosemicarbazide (OCET), (1E)‐1‐(1‐(6‐bromo‐2‐ oxo‐2H‐chromen‐3‐yl)ethylidene)thiosemicarbazide (BOCET) and 1‐(1‐(3‐oxo‐3H‐benzo[f]chromen‐2‐yl)ethylidene)thiosemicarbazide (NOCET) and their Rh(III) complexes were synthesized, the characterization was carried out by elemental analysis, IR, UV–Visible, mass, magnetic measurement and molar conductance techniques. Data interpretation of the Rh(III) complexes indicates that the ligands of coumarinyl thiosemicarbazone derivatives were formed in stoichiometric ratios as 1:2 (metal: ligand). The studied ligands act as a bidentate ligand by using both azomethine nitrogen and thiol sulphur as monoanion center of donation. The theoretical conformational structure analyses were performed using density functional theory for ligands and complexes at B3LYP functional with 6‐31G(++)d,p basis set for ligands and LANL2DZ basis set for complexes. The charge distribution within the ligands and its Rh(III) complexes was calculated using Mulliken population analysis of (MPA) and natural population analysis (NPA). The antibacterial activity of the prepared compounds was tested against some types of Gram positive and negative bacteria. Molecular docking investigation proved that, the ligands and complexes had interesting interactions with active site amino acids of ribosyltransferase (code: 3GEY).     

Computationally identified novel diphenyl- and phenylpyridine androgen receptor antagonist structures

We have identified and profiled a set of androgen receptor (AR) binding compounds representing two nonsteroidal scaffolds from a public chemical database supplied by Asinex with virtual screening procedure incorporating our recently published 3D QSAR model of AR ligands. The diphenyl- and phenylpyridine-based compounds act as antagonists in wild-type AR in CV1 cells and also retain this antagonistic character in CV1 cells expressing T877A mutant receptor. This mutation is frequently associated with prostate cancer. Two of the compounds repress the androgen-dependent cell growth of LNCaP prostate cancer cells expressing the T877A AR mutant. Molecular modeling of the observed in vitro antagonism with induced fit docking suggests that W741 and M895 could be mechanistically involved in the initiation of the antagonism. The results indicate finding of nonsteroidal AR antagonist compounds from a public chemical database with computational methods. Compounds could serve as a novel platform to develop more potent AR antagonists with inhibitory activity in both wild-type and T877A mutant AR.     

Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands

The use of high throughput screening (HTS) to identify lead compounds has greatly challenged conventional quantitative structure-activity relationship (QSAR) techniques that typically correlate structural variations in similar compounds with continuous changes in biological activity. A new QSAR-like methodology that can correlate less quantitative assay data (i.e., "active" versus "inactive"), as initially generated by HTS, has been introduced. In the present study, we have, for the first time, applied this approach to a drug discovery problem; that is, the study of the estrogen receptor ligands. The binding affinities of 463 estrogen analogues were transformed into a binary data format, and a predictive binary QSAR model was derived using 410 estrogen analogues as a training set. The model was applied to predict the activity of 53 estrogen analogues not included in the training set. An overall accuracy of 94% was obtained.     

An Approach for Visualization of the Active Site of Enzymes with Unknown Three-Dimensional Structures

Abstract

A new approach for virtual characterization of the active site structure of enzymes with unknown three-dimensional (3D) structure has been proposed. It includes analysis of data on enzyme interaction with reversible competitive inhibitors, their 3D structures and moulding of the substrate-binding region. The superposition of ligands in biologically active conformations allows to determine the shape and dimension of the active site cavity accommodating these compounds. Monoamine oxidase A (MAO-A), a “typical” enzyme with unknown spatial organisation, was used to test this method. The correctness of such approach was validated by the analysis of HIV protease interaction with its inhibitors using 3D structures of their complexes. Mould of the substrate/inhibitor binding site can be used for the visualization of this binding site and for searching new ligands in molecular databases.     

PharmID: pharmacophore identification using Gibbs sampling

The binding of a small molecule to a protein is inherently a 3D matching problem. As crystal structures are not available for most drug targets, there is a need to be able to infer from bioassay data the key binding features of small molecules and their disposition in space, the pharmacophore. Fingerprints of 3D features and a modification of Gibbs sampling to align a set of known flexible ligands, where all compounds are active, are used to discern possible pharmacophores. A clique detection method is used to map the features back onto the binding conformations. The complete algorithm is described in detail, and it is shown that the method can find common superimposition for several test data sets. The method reproduces answers very close to the crystal structure and literature pharmacophores in the examples presented. The basic algorithm is relatively fast and can easily deal with up to 100 compounds and tens of thousands of conformations. The algorithm is also able to handle multiple binding mode problems, which means it can superimpose molecules within the same data set according to two different sets of binding features. We demonstrate the successful use of this algorithm for multiple binding modes for a set of D2 and D4 ligands.     

Molecular Structure and Affinity to 5-HT1A Receptor of Chlorophenyl(Piperazinylalkyl)Phthalimides

Abstract

X-ray crystallography, quantum-chemical calculations and conformational analysis have been employed to study chlorophenyl(piperazinylalkyl)phthalimides, potential ligands of 5-HT_1A receptor. The molecular recognition of investigated compounds by 5-HT_1Aserotonin receptor has been estimated according to their ability to inhibit the [³H8]-DPAT binding. The model for 5-HT_1A pharmacophore has been proposed based on crystal structures of N-(aryl)piperazinyl — alkylphthalimides.     

Different binding modes of structurally diverse ligands for human D3DAR

Five different dopamine D3 receptors (D3DARs) models were created considering some suggested binding modes for D3DAR antagonists reported in earlier computational studies. Different hypotheses are justified because of the lack of experimental information about the putative site of interaction and are also due to the variability in scaffolds and size of D3DAR ligands. In this study 114 potent and selective D3DAR antagonists or partial agonists are used as key experimental information to discriminate the most reliable receptor model and to build a docking based 3D quantitative structure-activity relationship model able to indicate the ligand properties and the residues important for activity. The ability of this D3DAR model to discriminate the binding mode of different classes of ligands, showing a good quantitative correlation with their activity, encourages us to use it for screening novel lead compounds.     

Interaction between vitamin D receptor and vitamin D ligands: two-dimensional alanine scanning mutational analysis

We present a new method to investigate the details of interaction between vitamin D nuclear receptor (VDR) and various ligands, namely a two-dimensional alanine scanning mutational analysis. In this method, the transactivation of various ligands is studied in conjunction with a series of alanine scanning mutations of the residues lining the ligand binding pocket (LBP) of VDR, and the complete set of results is profiled in a patch table. We investigated examples from four structurally diverse groups of known VDR ligands: the native vitamin D hormone and two compounds with the same side chain configuration; four 20-epi compounds; three 19-nor compounds; and two nonsecosteroids. The patch table of the results indicates characteristics of each group in terms of its interaction with 18 LBP residues. We demonstrate the validity of this approach by application to docking studies of the two nonsecosteroids.