Molecular mechanics study of transannular amine–ketone (N→C(DOUBLE BOND)O) interaction in medium-sized heterocycles

Medium-sized nitrogen-containing heterocycles have considerable potential as structurally novel templates for new medicinal agents. In order to evaluate this potential and to investigate their binding to various target receptors, satisfactory modeling of the properties of such compounds with force-field based computational methods is required, especially the conformations accessible to the molecules at and around their global minimum conformation. This is currently only possible with selected force fields for compounds that show a special intramolecular interaction such as the transannular interaction between a basic nitrogen atom and a carbonyl carbon atom. This article substantiates this claim and discusses two approaches to modify the commercially available CFF91 force field. The different approaches are discussed and assessed by their performance in reproducing the conformation in the crystal for a series of known model compounds. In summary, very good agreement with the experimental structure is achieved. The modified force fields are then used to investigate a potentially bioactive lead compound. The lead compound is predicted to be able to mimic the shape of a fused-ring compound with biological activity. © 1997 John Wiley & Sons, Inc. J Comput Chem 18: 1211–1221     

(13)C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroids binding the aromatase enzyme

Five quantitative spectroscopic data-activity relationships (QSDAR) models for 50 steroidal inhibitors binding to aromatase enzyme have been developed based on simulated (13)C nuclear magnetic resonance (NMR) data. Three of the models were based on comparative spectral analysis (CoSA), and the two other models were based on comparative structurally assigned spectral analysis (CoSASA). A CoSA QSDAR model based on five principal components had an explained variance (r(2)) of 0.78 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.71. A CoSASA model that used the assigned (13)C NMR chemical shifts from a steroidal backbone at five selected positions gave an r(2) of 0.75 and a q(2) of 0.66. The (13)C NMR chemical shifts from atoms in the steroid template position 9, 6, 3, and 7 each had correlations greater than 0.6 with the relative binding activity to the aromatase enzyme. All five QSDAR models had explained and cross-validated variances that were better than the explained and cross-validated variances from a five structural parameter quantitative structure-activity relationship (QSAR) model of the same compounds. QSAR modeling suffers from errors introduced by the assumptions and approximations used in partial charges, dielectric constants, and the molecular alignment process of one structural conformation. One postulated reason that the variances of QSDAR models are better than the QSAR models is that (13)C NMR spectral data, based on quantum mechanical principles, are more reflective of binding than the QSAR model's calculated electrostatic potentials and molecular alignment process. The QSDAR models provide a rapid, simple way to model the steroid inhibitor activity in relation to the aromatase enzyme.     

Affinity screening by packed capillary high-performance liquid chromatography using molecular imprinted sorbents. I. Demonstration of feasibility

Molecular imprint polymers (MIPs) are synthetic polymers capable of selectively binding a template molecule. In this work, the potential utility of MIP-based chromatographic sorbents for affinity screening of structurally similar compounds was investigated as alternatives to in vitro bioassays and biological targets bound to chromatographic supports. A group of structurally similar tricyclic antidepressant drugs and related compounds were used to simulate a combinatorial library. One of the antidepressants, nortriptyline (NOR), was selected as the template species. Using capillary HPLC columns packed with NOR-imprinted MIP particles, the simulated library was screened and the degree of selective interaction of each compound was determined. This correlated with each compound's affinity for the NOR binding site in the polymer. The results of the study revealed that library species which possess the major structural features of the template, specifically the ring structure and pendant secondary amine, were best "recognized" by the MIP, while the most structurally dissimilar compounds exhibited the least selective interaction. An investigation of the retention mechanism on these MIPs provided evidence that hydrogen bonding between the pendant amine group on the antidepressants and a methacrylic acid moiety on the polymer surface was critical in the molecular recognition process.     

Developing 13C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroid binding to the corticosteroid binding globulin

We have developed four quantitative spectrometric data-activity relationship (QSDAR) models for 30 steroids binding to corticosteroid binding globulin, based on comparative spectral analysis (CoSA) of simulated ¹³C nuclear magnetic resonance (NMR) data. A QSDAR model based on 3 spectral bins had an explained variance (r ²) of 0.80 and a cross-validated variance (q ²) of 0.78. Another QSDAR model using the 3 atoms from the comparative structurally assigned spectral analysis (CoSASA) of simulated ¹³C NMR on a steroid backbone template gave an explained variance (r ²) of 0.80 and a cross-validated variance (q ²) of 0.73. Positions 3 and 14 from the steroid backbone template have correlations with the relative binding activity to corticosteroid binding globulin that are greater than 0.52. The explained correlation and cross-validated correlation of these QSDAR models are as good as previously published quantitative structure-activity relationship (QSAR), self-organizing map (SOM) and electrotopological state (E-state) models. One reason that the cross-validated variance of QSDAR models were as good as the other models is that simulated ¹³C NMR spectral data are more accurate than the errors introduced by the assumptions and approximations used in calculated electrostatic potentials, E-states, HE-states, and the molecular alignment process of QSAR modeling. The QSDAR models developed provide a rapid, simple way to predict the binding activity of a steroid to corticosteroid binding globulin.     

Biological mechanism profiling using an annotated compound library

We present a method for testing many biological mechanisms in cellular assays using an annotated library of 2036 small organic molecules. This annotated compound library represents a large-scale collection of compounds with diverse, experimentally confirmed biological mechanisms and effects. We found that this chemical library is (1) more structurally diverse than conventional, commercially available libraries, (2) enriched in active compounds in a tumor cell viability assay, and (3) capable of generating hypotheses regarding biological mechanisms underlying cellular processes. We elucidated biological mechanisms relevant to the antiproliferative activity of 85 compounds from this library that were selected using a high-throughput cell viability screen. We developed a novel automated scoring system for identifying statistically enriched mechanisms among such a subset of compounds. This scoring system can identify both previously known and potentially novel antiproliferative mechanisms.     

Pharmacophore Mode lingand Virtual Screening to Design the Potential Influenza Virus Endonuclease Inhibitors

Influenza virus endonuclease is an attractive target for antiviral therapy in the treatment of influenza infection. The purpos e of this study is to design a novel antiviral agent with improved biological activities against the influenza virus endonuclease. In this study, chemical feature‐based 3D pharmacophore models were developed from 41 known influenza virus endonuclease inhibitors. The best quantitative pharmacohore model (Hypo 1), which consists of two hydrogen‐bond acceptors and two hydrophobic features, yields the highest correlation coefficient (R = 0.886). Hypo 1 was further validated by the cross validation method and the test set compounds. The application of this model for predicting the activities of 11 known influenza virus endonuclease inhibitors in the test set shows great success. The correlation coefficient of 0.942 and a cross validation of 95;% confidence level prove that this model is reliable in identifying structurally diverse compounds for influenza virus endonuclease inhibition. The most active compound (compound 1) from the training set was docked into the active site of the influenza virus endonuclease as an additional verification that the pharmacophore model is accurate. The docked conformation showed important hydrogen bond interactions between the compound and two amino acids, Lys 134 and Lys 137. After validation, this model was used to screen the NCI chemical database to identify new influenza virus endonuclease inhibitors. Our study shows that the to pranking compound out of the 10 newly identified compounds using fit value ranking has an estimated activity of 0.049 μM. These newly identified lead compounds can be further experimentally validated using in vitro techniques.     

Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.     

A new class of HIV-1 inhibitors and the target identification via proteomic profiling

Anti-HIV screening with the MT-4/MTT assay on a focused library of structurally diverse natural products has led to the discovery of a group of steroids with potent activities, which include four new ergostane-type steroids, named amotsterols A-D (1-4), together with two known analogs. Among them, the most potent amotsterol D (4) exhibited anti-HIV activity against wildtype and some clinically relevant multidrug resistant HIV-1 strains. Subsequent studies on its target identification through a proteomic approach found that compound 4 might target PKM2, a rate limiting enzyme of glycolysis, in host cells to restrict HIV replication. The docking model of compound 4 to PKM2 showed that the two hydroxyl groups of 4 form hydrogen bonds with the two parallel Y390 in each subunit of PKM2 separately, and the ring C of 4 is sandwiched between the two parallel aromatic rings of F26. The identified hit compound may have the potential to be further developed as a novel anti-HIV agent. These results demonstrated that an integrated approach, which combines new chemical structures and phenotypic screening with a proteomic approach, could not only identify novel HIV-1 inhibitors, but also elucidate the unknown targets of compound interactions in antiviral drug discovery.     

Developing an antituberculosis compounds database and data mining in the search of a motif responsible for the activity of a diverse class of antituberculosis agents

A novel data mining procedure to look for new antitubercular agents and targets as well as to find a minimum common bioactive substructure (MCBS), has been reported here. The methodology extracts MCBS, both across the diverse chemical classes and within the particular chemical class, known to be present in the various marketed drugs alongside antimycobacterial compounds with known MICs. For this purpose a small in-house database of compounds has been created, for which MICs against Mycobacterium are known. The compounds have been collected from literature available on the synthetic compounds, having known MICs against Mycobacterium tuberculosis. An elaborate HQSAR (Hologram QSAR) study has been attempted to extract active fragment from a diverse class of compounds, in combination with the clustering technique to select a homogeneous group of compounds having good a profile toward the activity. The 2D pharmacophore (the 2D fragments extracted from HQSAR) has been validated searching the database. It has been found further that this validated 2D pharmacophore could be used for searching the orphan target in Mycobacterium effectively.     

Comparative structural connectivity spectra analysis (CoSCoSA) models of steroid binding to the corticosteroid binding globulin

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) model was developed that is built by combining NMR spectral information with structural information in a 3D-connectivity matrix. The 3D-connectivity matrix is built by displaying all possible carbon-to-carbon connections with their assigned carbon NMR chemical shifts and distances between the carbons. Selected 2D (13)C-(13)C COrrelation SpectroscopY (COSY) (through-bond nearest neighbors) and selected theoretical 2D (13)C-(13)C distance connectivity spectral slices from the 3D-connectivity matrix to produce a relationship among the spectral patterns for 30 steroids binding to corticosteroid binding globulin. We call this technique a comparative structural connectivity spectra analysis (CoSCoSA) modeling. A CoSCoSA principal component linear regression model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra principal components (PCs) had an r(2) of 0.96 and a leave-one-out (LOO) cross-validation q(2) of 0.92. A CoSCoSA parallel distributed artificial neural network (PD-ANN) model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra had an r(2) of 0.96, a leave-three-out q(3)(2) of 0.78, and a leave-ten-out q(10)(2) of 0.73. CoSCoSA modeling attempts to uniquely combine the quantum mechanics information from the NMR chemical shifts with internal molecular atom-to-atom distances into an accurate modeling technique. The CoSCoSA modeling technique has the flexibility and accuracy to outperform the cross-validated variance q(2) of previously published quantitative structure-activity relationship (QSAR), quantitative spectral data-activity relationship (QSDAR), self-organizing map (SOM), and electrotopological state (E-state) models.     

Affinity screening by packed capillary high performance liquid chromatography using molecular imprinted sorbents. II. Covalent imprinted polymers

This study concentrates on the production of covalent molecular imprint polymers (MIPs) as highly selective sorbents for nortriptyline (NOR), a representative tricyclic antidepressant (TCA). The functionalized template contains a polymerizable 4-vinylphenyl carbamate moiety used to bind the template molecule to the polymer matrix. Polymerization with a cross-linker followed by hydrolytic cleavage of the labile carbamate functionality leaves an MIP with selective binding sites capable of binding template through hydrogen bonding interactions. Demonstrated chromatographically through a "selection index", these MIPs showed high selectivity for the template molecule (NOR) among a library of structurally similar compounds. The recognition was found to correlate with structural similarity to the template compound. A direct comparison between covalent and non-covalent molecular imprinting strategies reveals a great deal of improvement in the peak shape of the retained compound resulting from covalent imprinting (evidenced by peak asymmetry factors A.).     

Assignments of the chemical shifts of carbon-13 nuclear magnetic resonance spectra of some 2-methyl-3-(3,4-dimethoxy/dihydroxyphenylethyl)-4-quinazolones

The ¹³C nmr chemical shifts of a series of 2-methyl-3-(3,4-dimethoxy/dihydroxyphenylethyl)-4-quinazolones are reported. The carbon resonances have been assigned on the basis of chemical shift theory, intensity of the signals, multiplicities generated in SFORD spectra and the comparison with the structurally related compounds.     

Models of polychlorinated dibenzodioxins, dibenzofurans, and biphenyls binding affinity to the aryl hydrocarbon receptor developed using (13)c NMR data

Quantitative spectroscopic data-activity relationship (QSDAR) models for polychlorinated dibenzofurans (PCDFs), dibenzodioxins (PCDDs), and biphenyls (PCBs) binding to the aryl hydrocarbon receptor (AhR) have been developed based on simulated (13)C nuclear magnetic resonance (NMR) data. All the models were based on multiple linear regression of comparative spectral analysis (CoSA) between compounds. A 1.0 ppm resolution CoSA model for 26 PCDF compounds based on chemical shifts in five bins had an explained variance (r(2)) of 0.93 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.90. A 2.0 ppm resolution CoSA model for 14 PCDD compounds based on chemical shifts in five bins had an r(2) of 0.91 and a q(2) of 0.81. The 1.0 ppm resolution CoSA model for 12 PCB compounds based on chemical shifts in five bins had an r(2) of 0.87 and a q(2) of 0.45. The models with more compounds had a better q(2) because there are more multiple chemical shift populated bins available on which to base the linear regression. A 1.0 ppm resolution CoSA model for all 52 compounds that was based on chemical shifts in 12 bins had an r(2) of 0.85 and q(2) of 0.71. A canonical variance analysis of the 1.0 ppm CoSA model for all 52 compounds when they were separated into 27 strong binding and 25 weak binding compounds was 98% correct. Conventional quantitative structure-activity relationship (QSAR) modeling suffer from errors introduced by the assumptions and approximations involved in calculated electrostatic potentials and the molecular alignment process. QSDAR modeling is not limited by such errors since electrostatic potential calculations and molecular alignment are not done. The QSDAR models provide a rapid, simple and valid way to model the PCDF, PCDD, and PCB binding activity in relation to the aryl hydrocarbon receptor (AhR).     

INFERCNMR: a 13C NMR interpretive library search system

INFERCNMR is an automated (13)C NMR spectrum interpretation aid for use either as a stand-alone program or as a component of a comprehensive, computer-based system for the characterization of chemical structure. The program is an interpretive library search which requires a database of assigned (13)C NMR spectra. An interpretive library search does not require overall structural similarity between an unknown and a library entry in order to retrieve a substructure common to both. Input consists of the chemical shift and one-bond proton-carbon multiplicity of each signal in the spectrum, and the molecular formula of the unknown. Program output is one or more substructures predicted to be present in the unknown, each of which is assigned an estimated prediction accuracy.     

Using high-throughput screening data to discriminate compounds with single-target effects from those with side effects

The most desirable compound leads from high-throughput assays are those with novel biological activities resulting from their action on a single biological target. Valuable resources can be wasted on compound leads with significant 'side effects' on additional biological targets; therefore, technical refinements to identify compounds that primarily have effects resulting from a single target are needed. This study explores the use of multiple assays of a chemical library and a statistic based on entropy to identify lead compound classes that have patterns of assay activity resulting primarily from small molecule action on a single target. This statistic, called the coincidence score, discriminates with 88% accuracy compound classes known to act primarily on a single target from compound classes with significant side effects on nonhomologous targets. Furthermore, a significant number of the compound classes predicted to have primarily single-target effects contain known bioactive compounds. We also show that a compound's known biological target or mechanism of action can often be suggested by its pattern of activities in multiple assays.     

Chemical substructure identification by mass spectral library searching

A library-search procedure that identifies structural features of an unknown compound from its electron-ionization mass spectrum is described. Like other methods, this procedure first retrieves library compounds whose spectra are most similar to the spectrum of an unknown compound. It then deduces structural features of the unknown compound from the chemical structures of the retrievals. Unlike other methods, the significance of each retrieved spectrum is weighted according to its similarity to the spectrum of the unknown compound. Also, a “peaks-in-common” screening step serves to reduce search times and an optimized dot product function provides the match factor. If the molecular weight of the unknown compound is provided, the identification of certain substructures can be improved by including “neutral loss” peaks. Correlations between the presence of a substructure in a test compound and its presence among library retrievals were derived from the results of searching the NIST/EPA/NIH reference library with a 7891 compound test set. These correlations allow the estimation of probabilities of substructure occurrence and absence in an unknown compound from the results of a library search. This method may be viewed as an optimization of the “K-nearest neighbor” method of Isenhour and co-workers, with improvements that arise from spectrum screening, peak scaling, an optimal distance measure, a relative-distance weighting scheme, and a larger reference library.