Molecular Modeling and 3D-QSAR Studies in 2-Aziridinyl-and 2,3-Bis(Aziridinyl)-1,4-Naphthoquinonyl Sulfonate and Acylate Derivatives as Potential Antimalarial Agents

Abstract

Malaria is still continuing to be one of the most dreadful diseases of the tropical countries particularly due to the development of resistance to the existing antimalarials. From observed, antimalarial activity of 2-aziridinyl- and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives acting through redox cycling mechanism, molecular modeling and three dimensional-quantitative structure activity relationship (3D-QSAR) studies have been carried out on a set of 63 compounds to identify important pharmacophors. Among several 3D-QSAR models generated, three models with correlation coefficient r > 0.82, match > 0.60 and chance = 0.00 have shown two common biophoric sites: one being the oxygen atom at position 1 of the naphthoquinone ring in terms of π-population, charge and electron donating ability while the second being the center of the phenyl ring in terms of its 6π-electrons. In addition to these sites, the models also share two common secondary sites: one positively contributing H-acceptor site while the second site contributing negatively in terms of steric refractivity. All these models showed good agreement between the experimental, calculated and predicted antimalarial activities.     

4D-QSAR analysis of a series of antifungal p450 inhibitors and 3D-pharmacophore comparisons as a function of alignment

A training set of 55 antifungal p450 analogue inhibitors was used to construct receptor-independent four-dimensional quantitative structure-activity relationship (RI 4D-QSAR) models. Ten different alignments were used to build the models, and one alignment yields a significantly better model than the other alignments. Two different methodologies were used to measure the similarity of the best 4D-QSAR models of each alignment. One method compares the residual of fit between pairs of models using the cross-correlation coefficient of their residuals of fit as a similarity measure. The other method compares the spatial distributions of the IPE types (3D-pharmacophores) of pairs of 4D-QSAR models from different alignments. Optimum models from several different alignments have nearly the same correlation coefficients, r(2), and cross-validation correlation coefficients, xv-r(2), yet the 3D-pharmacophores of these models are very different from one another. The highest 3D-pharmacophore similarity correlation coefficient between any pair of 4D-QSAR models from the 10 alignments considered is only 0.216. However, the best 4D-QSAR models of each alignment do contain some proximate common pharmacorphore sites. A test set of 10 compounds was used to validate the predictivity of the best 4D-QSAR models of each alignment. The "best" model from the 10 alignments has the highest predictivity. The inferred active sites mapped out by the 4D-QSAR models suggest that hydrogen bond interactions are not prevalent when this class of P450 analogue inhibitors binds to the receptor active site. This feature of the 4D-QSAR models is in agreement with the crystal structure results that indicate no ligand-receptor hydrogen bonds are formed.     

4D-QSAR analysis of a set of ecdysteroids and a comparison to CoMFA modeling.

The ecdysteroid-responsive Drosophila melanogaster B(II) cell line is a prototypical homologous inducible gene expression system. A training set of 71 ecdysteroids, for which the -log(EC(50)) potencies in the ecdysteroid-responsive B(II) cell line were measured, was used to construct 4D-QSAR models. Four nearly equivalent optimum 4D-QSAR models, for two modestly different alignments, were identified (Q(2) = 0.76-0.80). These four models, together with two CoMFA models, were used in consensus modeling to arrive at a three-dimensional pharmacophore. The C-2 and C-22 hydroxyls are identified as hydrogen-bond acceptor sites which enhance activity. A hydrophobic site near C-12 is consistent with increasing activity. The side-chain substituents at C-17 are predicted to adopt semiextended "active" conformations which could fit into a cylinder-shaped binding pocket lined largely with nonpolar residues for enhanced activity. A test set of 20 ecdysteroids was used to evaluate the QSAR models. Two 4D-QSAR models for one alignment were identified to be superior to the others based on having the smallest average residuals of prediction for the prediction set (0.69 and 1.13 -log[EC(50)] units). The correlation coefficients of the optimum 4D-QSAR models (R(2) = 0.87 and 0.88) are nearly the same as those of the best CoMFA model (R(2) = 0.92) determined for the same training set. However, the cross-validation correlation coefficient of the CoMFA model is less significant (Q(2) = 0.59) than those of the 4D-QSAR models (Q(2) = 0.80 and 0.80).     

Three-dimensional quantitative structure-activity relationship (QSAR) and receptor mapping of cytochrome P-450(14 alpha DM) inhibiting azole antifungal agents

Molecular modeling was performed by a combined use of conformational analysis and 3D-QSAR methods to distinguish structural attributes common to a series of azole antifungal agents. Apex-3D program was used to recognize the common biophoric structural patterns of 13 diverse sets of azole antifungal compounds demonstrating different magnitudes of biological activity. Apex-3D identified three common biophoric features significant for activity: N1 atom of azole ring, the aromatic ring centroid 1, and aromatic ring centroid 2. A common biophore model proposed from the Apex-3D analysis can be useful for the design of novel cytochrome P-450(14 alpha DM) inhibiting antifungal agents.     

Characterization of β<Subscript>3</Subscript>-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β<Subscript>3</Subscript> adrenergic receptor agonism

Summary The β₃-adrenoreceptor (β₃-AR) has been shown to mediate various pharmacological and physiological effects such as lipolysis, thermogenesis, and intestinal smooth muscle relaxation. It also plays an important role in glucose homeostasis and energy balance. Molecular modeling studies were undertaken to develop predictive pharmacophoric hypothesis and 3D-QSAR model, which may explain variations in β₃-AR agonistic activity in terms of chemical features and physicochemical properties. The two softwares, CATALYST for pharmacophoric alignment and APEX-3D for 3D-QSAR modeling were used to establish the structure activity relationships for β₃-AR agonistic activity. Among the several statistically significant models, the selection of the best pharmacophore and 3D-QSAR model was based on its ability to estimate the activity of external test sets of similar and different structural types along with the reasonable consistency of the model with the limited information of the active site of β₃-AR. The final 3D-QSAR model was derived using the pharmacophoric alignments from the hypothesis which consisted of four chemical features: basic or positive ionizable feature on the nitrogen of the aryloxypropylamino group, two ring aromatic features corresponding to the phenyl ring of the phenoxide and the benzenesulphonamido groups and a hydrogen-bond donor (HBD) in the vicinity of the nitrogen atom of the benzenesulphonamido group with the most active molecule mapping in an energetically favorable extended conformation. This hypothesis was in agreement with the site directed mutagenesis studies on human β₃-AR and correlated well the observed and estimated activity both in, training and both the external test sets. It also mapped reasonably well to six β₃-AR agonists of different structural classes under clinical development and thus this hypothesis may have a universal applicability in providing a powerful template for virtual screening and also for designing new chemical entities (NCEs) as β₃-AR agonists.^★CDRI communication number 6202. *To whom correspondence should be addressed. Fax: +91-0522-223405; E-mail: anilsak@hotmail.com     

A proposed common spatial pharmacophore and the corresponding active conformations of some peptide leukotriene receptor antagonists

Summary Molecular modeling studies were carried out by a combined use of conformational analysis and 3D-QSAR methods to identify molecular features common to a series of hydroxyacetophenone (HAP) and non-hydroxyacetophenone (non-HAP) peptide leukotriene (pLT) receptor antagonists. In attempts to develop a ligand-binding model for the pLT receptor, the Apex-3D program was used to identify biophoric structural patterns that are common to 13 diverse sets of compounds showing different levels of biological activity. A systematic conformational analysis was carried out to obtain sterically accessible conformations for these flexible compounds. Apex-3D was then utilized to propose common biophoric regions based on the selection of one of several conformations (MOPAC-minimized AM1) from each compound's data set that best fits the biophoric pattern and the resulting superimposition with all the other data-set compounds. Apex-3D identified three common biophoric features important for activity: one as the hydroxyl, acetyl, carbonyl and carboxyl groups, which mimic the acid-binding region of an agonist, the other as the hydrogen-bond donating site, and the third part is represented by a plane in which lipophilic aromatic groups align. The structure-activity relationships were then assessed by using the 3D-QSAR model. A common biophore model is proposed from the Apex-3D analysis which may be useful in designing new pLT antagonists. Molecular volumes and electrostatic potential similarities were also calculated in order to obtain the important structural requirements for the activity.     

Improving the quality of 3D-QSAR by using flexible-ligand receptor models

To address the problems associated with molecular conformations and alignments in the 3D-QSAR studies, we have developed the Flexible Ligand - Atomic Receptor Model (FLARM) 2.0 method. The FLARM 2.0 method has three unique features as compared to other pseudoreceptor model methods: (1) the training ligands are flexibly optimized inside the receptors to achieve minimal docking energies; (2) the receptor atoms are spatially moveable in the process of genetic evolving in order to avoid improper initial receptor shapes; and (3) void receptor sites are specially favored in order to obtain open receptor models that allow large gaps. Advantages of an open model include less noise information, a smaller risk of overfitting, and ease of locating the key interaction sites. The latter two features, inherited from the previous FLARM 1.0 method, can improve the predictive ability of the 3D-QSAR models, while the first feature is newly implemented to relieve the uncertainty caused by improper conformation and alignment. Three FLARM 2.0 case studies were performed, and the results show that FLARM 2.0 models are highly predictive and robust. FLARM 2.0 pseudoreceptor models can correspond well with the pharmacophore models and/or the binding sites of the real protein receptors.     

Design,synthesis and biological evaluation of antimalarial activity of new derivatives of 2,4,6-<Emphasis Type="Italic">s</Emphasis>-triazine

Dihydrofolate reductase (DHFR) is an important enzyme for de novo synthesis of nucleotides in Plasmodium falciparum and it is essential for cell proliferation. DHFR is a well known antimalarial target for drugs like cycloguanil and pyrimethamine which target its inhibition for their pharmacological actions. However, the clinical efficacies of these antimalarial drugs have been compromising due to multiple mutations occurring in DHFR that lead to drug resistance. In this background, we have designed 22 s -triazine compounds using the best five parameters based 3D-QSAR model built by using genetic function approximation. In-silico designed compounds were further filtered to 6 compounds based upon their ADME properties, docking studies and predicted minimum inhibitory concentrations (MIC). Out of 6 compounds, 3 compounds were synthesized in good yield over 95% and characterized using IR, ¹HNMR, ¹³CNMR and mass spectroscopic techniques. Parasitemia inhibition assay was used to evaluate the antimalarial activity of s -triazine compounds against 3D7 strain of P. falciparum. All the three compounds (7, 13 and 18) showed 30 times higher potency than cycloguanil (standard drug). It was observed that compound 18 was the most active while the compound 13 was the least active. On the closer inspection of physicochemical properties and SAR, it was observed that the presence of electron donating groups, number of hydrogen bond formation, lipophilicity of ligands and coulson charge of nitrogen atom present in the triazine ring enhances the DHFR inhibition significantly. This study will contribute to further endeavours of more potent DHFR inhibitors.     

Receptor-independent 4D-QSAR analysis of a set of norstatine derived inhibitors of HIV-1 protease

A training set of 27 norstatine derived inhibitors of HIV-1 protease, based on the 3(S)-amino-2(S)-hydroxyl-4-phenylbutanoic acid core (AHPBA), for which the -log IC(50) values were measured, was used to construct 4D-QSAR models. Five unique RI-4D-QSAR models, from two different alignments, were identified (q(2) = 0.86-0.95). These five models were used to map the atom type morphology of the lining of the inhibitor binding site at the HIV protease receptor site as well as predict the inhibition potencies of seven test set compounds for model validation. The five models, overall, predict the -log IC(50) activity values for the test set compounds in a manner consistent with their q(2) values. The models also correctly identify the hydrophobic nature of the HIV protease receptor site, and inferences are made as to further structural modifications to improve the potency of the AHPBA inhibitors of HIV protease. The finding of five unique, and nearly statistically equivalent, RI-4D-QSAR models for the training set demonstrates that there can be more than one way to fit structure-activity data even within a given QSAR methodology. This set of unique, equally good individual models is referred to as the manifold model.     

Assessing how well a modeling protocol captures a structure-activity landscape

We introduce the notion of structure-activity landscape index (SALI) curves as a way to assess a model and a modeling protocol, applied to structure-activity relationships. We start from our earlier work [ J. Chem. Inf. Model., 2008, 48, 646-658], where we show how to study a structure-activity relationship pairwise, based on the notion of "activity cliffs"-pairs of molecules that are structurally similar but have large differences in activity. There, we also introduced the SALI parameter, which allows one to identify cliffs easily, and which allows one to represent a structure-activity relationship as a graph. This graph orders every pair of molecules by their activity. Here, we introduce the new idea of a SALI curve, which tallies how many of these orderings a model is able to predict. Empirically, testing these SALI curves against a variety of models, ranging over two-dimensional quantitative structure-activity relationship (2D-QSAR), three-dimensional quantitative structure-activity relationship (3D-QSAR), and structure-based design models, the utility of a model seems to correspond to characteristics of these curves. In particular, the integral of these curves, denoted as SCI and being a number ranging from -1.0 to 1.0, approaches a value of 1.0 for two literature models, which are both known to be prospectively useful.     

QSAR modeling and chemical space analysis of antimalarial compounds

Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.     

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theoretical predictions was observed. Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the molecular discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.     

两类促肾上腺皮质释放激素(CRH)拮抗剂的分子场分析

采用比较分子相似性指数分析方法(CoMSIA)及比较分子场分析方法(CoMSIA)研究了两组CRH拮抗剂结构与活性的关系。在两种方法中,都考虑了静电场、立体场以及氢键场对构效关系的影响,结果表明采用CoMSIA得到构效关系模型要明显优于采用CoMFA得到的构效关系模型,在CoMSIA计算中,当引入疏水场时,三维构效关系模型能得到明显的改善,通过这个三维构效关系模型,可以较为精确地预测化合物的活性。通过分析分子场等值面图在空间的分布,可以观察到叠合分子周围的立体、静电以及疏水特征对化合物活性的影响。     

基于分子对接的6-萘甲基取代HEPT类HIV-1逆转录酶抑制剂构效关系研究

采用分子对接方法得到了一系列6-萘甲基取代HEPT类逆转录酶抑制剂分子与HIV-1逆转录酶复合物模型,从中抽取出抑制剂分子的活性构象,进一步应用CoMFA和CoMSIA方法建立了具有较好预测能力的3D-QSAR模型,深入探讨了这些化合物的定量构效关系,为进一步的药物设计奠定了良好的基础.另外,以化合物13及其相应的β异构体24为代表,结合量子化学从头算分子轨道理论方法考察了它们的前线轨道,为阐明α和β系列化合物的活性差异提供了理论依据.     

Combining ligand-based and structure-based drug design approaches to study the structure-activity relationships of a β-carboline derivative series

In this study, we investigated the structure-activity relationships of a series of β-carboline alkaloid derivatives using the 2D-QSAR and molecular docking, in order to identify the mode of interaction between β-carboline derivatives and the PLK1 kinase, and determine their key substituents responsible for the cytotoxic activity. The obtained QSAR models using multiple linear regression (MLR) and partial least squares (PLS) methods showed a high correlation between the experimental activity and the predicted one by PLS (R²_PLS?=?0.82, q²?=?0.72) and MLR (R²_MLR?=?0.82, q²?=?0.72). An external dataset was used to test the extrapolation power of the models which resulted in an R²_PLS (EV)?=?0.76; RMSE?=?0.39. The 2D-QSAR analysis reveals that lipophilicity plays an important role in the cytotoxic activity of this group of β-carboline derivatives. Indeed, the molecular docking study into the active site of the polo-like kinase (PLK1) revealed that the most active ligand 57 shows higher binding energy and interacts, especially by H-bonds and hydrophobic interactions, with the active site of the PLK1 kinase. Consequently, the results obtained from the 2D-QSAR and docking studies provided a useful tool to design new and potent β-carboline derivatives as cytotoxic agents.     

Modeling robust QSAR 3: SOM-4D-QSAR with iterative variable elimination IVE-PLS: application to steroid, azo dye, and benzoic acid series

In the current paper we present a receptor-independent 4D-QSAR method based on self-organizing mapping (SOM-4D-QSAR) and in particular focus on its pharmacophore mapping ability. We use a novel stochastic procedure to verify the predictive ability of the method for a large population of 4D-QSAR models generated. This systematic study was conducted on a series of benzoic acids, azo dyes, and steroids that bind aromatase. We show that the 4D-QSAR method coupled with IVE-PLS provides a very stable and predictive modeling technique. The method enables us to identify the molecular motifs contributing the most to the fiber-dye affinity and the aromatase enzyme binding activity of the steroid. However, the method appeared much less effective for the benzoic acid series, in which the efficacy was limited by electronic effects strictly correlated to a single conformer.     

CoMFA 3D-QSAR analysis of HIV-1 RT nonnucleoside inhibitors, TIBO derivatives based on docking conformation and alignment

HIV-1 RT is one of the key enzymes in the duplication of HIV-1. Inhibitors of HIV-1 RT are classified as nonnucleoside RT inhibitors (NNRTIs) and nucleoside analogues. NNRTIs bind in a region not associated with the active site of the enzyme. Within the NNRTI category, there is a set of inhibitors commonly referred to as TIBO inhibitors. Fifty TIBO inhibitors were used in the work to build 3-D QSAR models. The two known crystal structures of complexes are used to investigate and validate the docking protocol. The results show that the docking simulations reproduce the crystal complexes very well with RMSDs of approximately 1 A and approximately 0.6 A for 1REV and 1COU, respectively. The alignment of molecules and "active" conformation selection are the key to a successful 3D-QSAR model by CoMFA. The flexible docking (Autodock3) was used on determination of "active" conformation and molecular alignment, and CoMFA and CoMSIA were used to develop 3D-QSAR models of 50 TIBOs in the work. The 3D-QSAR models demonstrate a good ability to predict the activity of studied compounds (r2 = 0.972, 0.944, q2 = 0.704, 0.776). It is shown that the steric and electrostatic properties predicted by CoMFA contours can be related to the binding structure of the complex. The results demonstrate that the combination of ligand-based and receptor-based modeling is a powerful approach to build 3D-QSAR models.