Chemoenzymatic synthesis and high-throughput screening of an aminoglycoside-polyamine library: identification of high-affinity displacers and DNA-binding ligands

Chemoenzymatic parallel synthesis and high-throughput screening were employed to develop a multivalent aminoglycoside-polyamine library for use as high-affinity cation-exchange displacers and DNA-binding ligands. Regioselective lipase-catalyzed acylation, followed by chemical aminolysis, was used to generate vinyl carbonate and vinyl carbamate linkers, respectively, of the aminoglycosidic cores. These were further derivatized with polyamines, leading to library generation. A parallel batch-displacement assay was employed to identify the efficacy of the library candidates as potential displacers for protein purification. Using this approach, low-molecular-mass displacers with affinities higher than those previously observed have been identified. The aminoglycoside-polyamine library was also screened for DNA binding efficacy using an ethidium bromide displacement assay. These highly cationic molecules exhibited strong DNA-binding properties and may have potential for enhanced gene delivery.     

CoMFA, CoMSIA, and molecular hologram QSAR studies of novel neuronal nAChRs ligands-open ring analogues of 3-pyridyl ether

3-Pyridyl ethers are excellent nAChRs ligands, which show high subtype selectivity and binding affinity to alpha4beta2 nAChR. Although the quantitative structure-activity relationship (QSAR) of nAChRs ligands has been widely investigated using various classes of compounds, the open ring analogues of 3-pyridyl ethers have been less involved in these studies due to the greater flexibility of this kind of molecule. In this study, two three-dimensional QSAR techniques and one two-dimensional QSAR technique were used to correlate the molecular structure with the biological activity of 64 analogues of 3-pyridyl ethers. Three different QSAR models were established. Their performances in the QSAR studies of open ring analogues of 3-pyridyl ethers were evaluated by the statistical values in the corresponding models. All models exhibited satisfactory predictive power. Of these models, the HQSAR behaved optimally in terms of the statistical values with q2=0.845, r2=0.897. Finally, graphic interpretation of three different models provided coincident information about the interaction of the ligand-receptor complex and supplied useful guidelines for the synthesis of novel, potent ligands.     

Influencing receptor-ligand binding mechanisms with multivalent ligand architecture

Multivalent ligands can function as inhibitors or effectors of biological processes. Potent inhibitory activity can arise from the high functional affinities of multivalent ligand-receptor interactions. Effector functions, however, are influenced not only by apparent affinities but also by alternate factors, including the ability of a ligand to cluster receptors. Little is known about the molecular features of a multivalent ligand that determine whether it will function as an inhibitor or effector. We envisioned that, by altering multivalent ligand architecture, ligands with preferences for different binding mechanisms would be generated. To this end, a series of 28 ligands possessing structural diversity was synthesized. This series provides the means to explore the effects of ligand architecture on the inhibition and clustering of a model protein, the lectin concanavalin A (Con A). The structural parameters that were varied include scaffold shape, size, valency, and density of binding elements. We found that ligands with certain architectures are effective inhibitors, but others mediate receptor clustering. Specifically, high molecular weight, polydisperse polyvalent ligands are effective inhibitors of Con A binding, whereas linear oligomeric ligands generated by the ring-opening metathesis polymerization have structural properties that favor clustering. The shape of a multivalent ligand also influences specific aspects of receptor clustering. These include the rate at which the receptor is clustered, the number of receptors in the clusters, and the average interreceptor distance. Our results indicate that the architecture of a multivalent ligand is a key parameter in determining its activity as an inhibitor or effector. Diversity-oriented syntheses of multivalent ligands coupled with effective assays that can be used to compare the contributions of different binding parameters may afford ligands that function by specific mechanisms.     

Hidden active information in a random compound library: extraction using a pseudo-structure-activity relationship model

We propose a hypothesis that "a model of active compound can be provided by integrating information of compounds high-ranked by docking simulation of a random compound library". In our hypothesis, the inclusion of true active compounds in the high-ranked compound is not necessary. We regard the high-ranked compounds as being pseudo-active compounds. As a method to embody our hypothesis, we introduce a pseudo-structure-activity relationship (PSAR) model. Although the PSAR model is the same as a quantitative structure activity relationship (QSAR) model, in terms of statistical methodology, the implications of the training data are different. Known active compounds (ligands) are used as training data in the QSAR model, whereas the pseudo-active compounds are used in the PSAR model. In this study, Random Forest was used as a machine-learning algorithm. From tests for four functionally different targets, estrogen receptor antagonist (ER), thymidine kinase (TK), thrombin, and acetylcholine esterase (AChE), using five scoring functions, we obtained three conclusions: (1) the PSAR models significantly gave higher percentages of known ligands found than random sampling, and these results are sufficient to support our hypothesis; (2) the PSAR models gave higher percentages of known ligands found than normal scoring by scoring function, and these results demonstrate the practical usefulness of the PSAR model; and (3) the PSAR model can assess compounds failed in the docking simulation. Note that PSAR and QSAR models are used in different situations; the advantage of the PSAR model emerges when no ligand is available as training data or when one wants to find novel types of ligands, whereas the QSAR model is effective for finding compounds similar to known ligands when the ligands are already known.     

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure–activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure–activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log?P data from a widely used steroid benchmark data set.     

Subtype selectivity of dopamine receptor ligands: insights from structure and ligand-based methods

Subtype selective dopamine receptor ligands have long been sought after as therapeutic and/or imaging agents for the treatment and monitoring of neurologic disorders. We report herein on a combined structure- and ligand-based approach to explore the molecular mechanism of the subtype selectivity for a large class of D?-like dopamine receptor ligands (163 ligands in total). Homology models were built for both human D(?L) and D? receptors in complex with haloperidol. Other ligands, which included multiple examples of substituted phenylpiperazines, were aligned against the binding conformations of haloperidol, and three-dimensional quantitative structure activity relationship (3D-QSAR) analyses were carried out. The receptor models show that although D? and D? share highly similar folds and 3D conformations, the slight sequence differences at their extracellular loop regions result in the binding cavity in D? being comparably shallower than in D?, which may explain why some larger ligands bind with greater affinity at D? compared to D? receptors. The QSAR models show excellent correlation and high predictive power even when evaluated by the most stringent criteria. They confirm that the origins of subtype selectivity for the ligands arise primarily due to differences in the contours of the two binding sites. The predictive models suggest that while both steric and electrostatic interactions contribute to the compounds' binding affinity, the major contribution arises from hydrophobic interactions, with hydrogen bonding conferring binding specificity. The current work provides clues for the development of more subtype selective dopamine receptor ligands. Furthermore, it demonstrates the possibility of being able to apply similar modeling methods to other subtypes or classes of receptors to study GPCR receptor-ligand interactions at a molecular level.     

Combined QSAR-based virtual screening and fluorescence binding assay to identify natural product mediators of Interferon Regulatory Factor 7 (IRF-7) in pulmonary infection

Abstract

Interferon regulatory factor-7 (IRF-7) is involved in pulmonary infection and pneumonia. Here, a synthetic strategy that combined quantitative structure–activity relationship (QSAR)-based virtual screening and in vitro binding assay was described to identify new and potent mediator ligands of IRF-7 from natural products. In the procedure, a QSAR scoring function was developed and validated using Gaussian process (GP) regression and a structure-based set of protein–ligand affinity data. By integrating hotspot pocket prediction, pharmacokinetics profile analysis and molecular docking calculations, the scoring function was successfully applied to virtual screening against a large library of structurally diverse, drug-like natural products. With the method we were able to identify a number of potential hits, from which several compounds were found to have moderate or high affinity to IRF-7 using fluorescence binding assays, with dissociation constants K_d at micromolar level. We have also examined the structural basis and noncovalent interactions of computationally modelled IRF-7 complex with its potent ligands. It is revealed that hydrophobic forces and van der Waals contacts play a central role in stabilization of the complex architecture, while few hydrogen bonds confer additional specificity for the protein–ligand recognition.     

Promotion of sugar-lectin recognition through the multiple sugar presentation offered by regioselectively addressable functionalized templates (RAFT): a QCM-D and SPR study

The investigation of recognition events between carbohydrates and proteins, especially the understanding of how spatial factors and binding avidity are correlated, remains a great interest for glycobiology. In this context we have investigated by nanogravimetry (QCM-D) and surface plasmon resonance (SPR), the kinetics and thermodynamics of the interaction between concanavalin A (Con A) and various neoglycopeptide ligands of low molecular weight. Regioselectively addressable functionalized templates (RAFT) have been used as scaffolds for the design of multivalent neoglycopeptides bearing thiol or biotin functions for their anchoring on transducer surfaces. Although these multivalent neoglycopeptide ligands cannot span multiple binding sites within the same Con A protein, they have increased activities relative to their monovalent counterpart. Our results emphasize that the multivalent RAFT ligands function by clustering several lectins, which leads to enhanced affinities.     

Targeting norovirus infection-multivalent entry inhibitor design based on NMR experiments

Noroviruses attach to their host cells through histo blood group antigens (HBGAs), and compounds that interfere with this interaction are likely to be of therapeutic or diagnostic interest. It is shown that NMR binding studies can simultaneously identify and differentiate the site for binding HBGA ligands and complementary ligands from a large compound library, thereby facilitating the design of potent heterobifunctional ligands. Saturation transfer difference (STD) NMR experiments, spin-lock filtered NMR experiments, and interligand NOE (ILOE) experiments in the presence of virus-like particles (VLPs), identified compounds that bind to the HBGA binding site of human norovirus. Based on these data two multivalent prototype entry-inhibitors against norovirus infection were synthesized. A surface plasmon resonance based inhibition assay showed avidity gains of 1000 and one million fold over a millimolar univalent ligand. This suggests that further rational design of multivalent inhibitors based on our strategy will identify potent entry-inhibitors against norovirus infections.     

Possible allosteric interactions of monoindazole-substituted P2 cyclic urea analogues with wild-type and mutant HIV-1 protease

Our ongoing efforts to understand the difference in the binding pattern of HIV-1 protease inhibitor (HIVPI) with the wild-type and mutant HIV-1 protease (HIVPR) and to provide mechanistic insight are continued further. We report here the results of a recent quantitative structure-activity relationship (QSAR) study on monoindazole-substituted P2 analogues of cyclic urea HIVPIs. The QSAR models revealed an inverted parabolic relationship between biological activity and calculated molar refractivity (CMR). That is, biological activity first decreases with increase in CMR and at a certain minimum point (inversion point) it suddenly changes and increases with further increase in CMR. CMR is a measure of volume-dependent-polarizability and is an indication of the polar interactions between ligand and receptor. The results seem to be best rationalized by larger molecules inducing a change in a receptor unit that allows for a new mode of interaction. Similar QSAR models were also observed for the biological activity of these molecules tested against a panel of mutant viruses including mutant strains with single amino acid substitution (I84V), double amino acid substitutions (I84V/V82F), and multiple amino acid changes corresponding to mutations observed in clinical isolates of patients treated with Ritonavir((R)). Interestingly the inversion points for these mutant strains were found larger than for wild-type. The subtle but significant difference in the inversion point indicates change in the shape and size of the binding pocket. Earlier QSAR studies have shown that the correlation of biological activity with an inverted parabola is an indicative of the 'allosteric interaction' of the ligands with the receptor. This report presents a detail analysis of these observations.     

Control of multivalent interactions by binding epitope density

Receptor clustering by multivalent ligands can activate signaling pathways. In principle, multivalent ligand features can control clustering and the downstream signals that result, but the influence of ligand structure on these processes is incompletely understood. Using a series of synthetic polymers that vary systematically, we studied the influence of multivalent ligand binding epitope density on the clustering of a model receptor, concanavalin A (Con A). We analyze three aspects of receptor clustering: the stoichiometry of the complex, rate of cluster formation, and receptor proximity. Our experiments reveal that the density of binding sites on a multivalent ligand strongly influences each of these parameters. In general, high binding epitope density results in greater numbers of receptors bound per polymer, faster rates of clustering, and reduced inter-receptor distances. Ligands with low binding epitope density, however, are the most efficient on a binding epitope basis. Our results provide insight into the design of ligands for controlling receptor-receptor interactions and can be used to illuminate mechanisms by which natural multivalent displays function.     

Insight into crucial inhibitor–enzyme interaction of arylamides as novel direct inhibitors of the enoyl ACP reductase (InhA) from Mycobacterium tuberculosis: computer-aided molecular design

Abstract  

The enoyl ACP reductase enzyme (InhA) involved in the type II fatty acid biosynthesis pathway of Mycobacterium tuberculosis is an attractive target enzyme for antitubercular drug development. Arylamide derivatives are a novel class of InhA inhibitors used to overcome the drug-resistance problem of isoniazid, the frontline drug for tuberculosis treatment. Their remarkable property of inhibiting the InhA enzyme directly without requiring any coenzyme, makes them especially appropriate for the design of new antibacterials. In order to find a sound binding conformation for the different arylamide analogs, molecular docking experiments were performed with subsequent QSAR investigations. The X-ray conformation of one arylamide within its cocrystallized complex with InhA was used as a starting conformation for the docking experiments. The results thus obtained are perfectly consistent (rmsd = 0.73 ?) with the results from X-ray analysis. A thorough investigation of the arylamide binding modes with InhA provided ample information about structural requirements for appropriate inhibitor–enzyme interactions. Three different QSAR models were established using two three-dimensional (CoMFA and CoMSIA) and one two-dimensional (HQSAR) techniques. With statistically ensured models, the QSAR results obtained had high correlation coefficients between molecular structure properties of 28 arylamide derivatives and their biological activity. Molecular fragment contributions to the biological activity of arylamides could be obtained from the HQSAR model. Finally, a graphic interpretation designed in different contour maps provided coincident information about the ligand–receptor interaction thus offering guidelines for syntheses of novel analogs with enhanced biological activity.