Two- and three-dimensional quantitative structure–activity relationships for a series of purine nucleoside phosphorylase inhibitors

A Comparative Study of 1D Descriptors Supported CoMFA and CoMSIA QSAR Models to Gain Novel Insights into 1,2,4-Triazoles Acting As Antitubercular Agents

BACKGROUND

Tuberculosis is one of the leading causes of deaths due to infectious disease worldwide. There is an urgent need for developing new drugs due to the rising incidents of drug resistance. Previously, triazole molecules showing antitubercular activity, were reported. Various computational tools pave the way for a rational approach to understanding the structural importance of these compounds in inhibiting the growth of Mycobacterium Tuberculosis.

OBJECTIVE

The aim of this study is to develop and compare two different QSAR models based on a set of previously reported triazole molecules and use the best one for gaining structural insights into those molecules.

METHODS

In this current study, two separate models were made with CoMFA and CoMSIA descriptors based on a dataset of triazole molecules showing antitubercular activity. Several one dimensional (1D) descriptors were added to each of the models and the validation results and contour data generated from them were compared. The best model was analysed to give a detailed understanding of the triazole molecules and their role in the antitubercular activity.

RESULTS

The r², q², predicted r² and SEP (Standard error of prediction) for the CoMFA model were 0.866, 0.573, 0.119 and 0.736 respectively and for the CoMSIA model, the r², q², predicted r² and SEP were calculated to be 0.998, 0.634, 0.013 and 0.869 respectively. Although both the QSAR models produced acceptable internal and external validation scores, but the CoMSIA results were significantly better. The CoMSIA contours also provided a better match than CoMFA with most of the features of the active compound 30b. Hence, the CoMSIA model was chosen and its contours were explored for gaining structural insights into the triazole molecules.

CONCLUSION

The CoMSIA contours helped us understand the role of several atoms and groups of the triazole molecules in their biological activity. The possibilities for substitution in the triazole compounds that would enhance the activity were also analyzed. Thus, this study paves the way for designing new antitubercular drugs in future.

Hydroxyl-related differences for three dietary flavonoids as inhibitors of human purine nucleoside phosphorylase

In this work, the hydroxyl-related differences of binding properties and inhibitory activities of dietary flavonoids, namely chrysin, baicalein and apigenin against purine nucleoside phosphorylase (PNP) were investigated. It was found that the hydroxylation on position C4' of chrysin (→apigenin) mildly decreased the binding affinities for PNP, whereas on the position C6 of chrysin (→baicalein) significantly increased binding affinities. Comparatively, the hydroxylation on position C4' and C6 greatly improved their PNP inhibitory effects. The IC₅₀ values of apigenin and baicalein were 6.09 × 10^-5 M and 8.94 × 10^-5 M, respectively, which is significantly lower than that of chrysin (2.13 × 10^-4 M). Results from molecular modeling revealed that there are two binding sites, i.e. active site (major) and tryptophan site (minor) on PNP, and the binding of these flavonoids might induce a serious conformational destabilization of PNP as a result of altering the micro-environment and morphology by flavonoids.

3D-QSAR analysis of a series of S-DABO derivatives as anti-HIV agents by CoMFA and CoMSIA

In this study, we retrieved a series of 59 dihydroalkylthio-benzyloxopyrimidine (S-DABO) derivatives, which is a class of highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) reported from published articles, and analysed them with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Statistically significant three-dimensional quantitative structure-activity relationship (3D-QSAR) models by CoMFA and CoMSIA were derived from a training set of 46 compounds on the basis of the rigid body alignment. Further, the predictive ability of the QSAR models was validated by a test set of 13 compounds. Based on the information derived from CoMFA and CoMSIA contour maps, we have identified some steric and electrostatic features for improving the activities of these inhibitors, and we validated the 3D-QSAR results by a molecular docking method. On the basis of the obtained results, we designed a new series of S-DABO derivatives with high activities. Therefore, this study could be utilized to design more potent S-DABO analogues as anti-HIV agents.

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x)

Retrieving descriptor information (x information) from a value of an objective variable (y) is a fundamental problem in inverse quantitative structure-property relationship (inverse-QSPR) analysis but challenging because of the complexity of the preimage function. Herewith, we propose using a cluster-wise multiple linear regression (cMLR) model as a QSPR model for inverse-QSPR analysis. x information is acquired as a probability density function by combining cMLR and the prior distribution modeled with a mixture of Gaussians (GMMs). Three case studies were conducted to demonstrate various aspects of the potential of cMLR. It was found that the predictive power of cMLR was superior to that of MLR, especially for data with nonlinearity. Moreover, it turned out that the applicability domain could be considered since the posterior distribution inherits the prior distribution's feature (i.e., training data feature) and represents the possibility of having the desired property. Finally, a series of inverse analyses with the GMMs/cMLR was demonstrated with the aim to generate de novo structures having specific aqueous solubility.

Immobilized purine nucleoside phosphorylase from Aeromonas hydrophila as an on-line enzyme reactor for biocatalytic applications

We described the development of a biochromatographic system which uses a purine nucleoside phosphorylase from Aeromonas hydrophila (AhPNP) for the evaluation of the substrate specificity on nucleoside libraries. AhPNP has been covalently immobilized on a fused silica Open Tubular Capillary (OTC) via Schiff base chemistry. The resulting bioreactor has been characterized by the determination of kinetic constants (Km and Vmax) for a natural substrate (inosine) and then assayed versus all natural purine (deoxy)ribonucleosides and a small library of 6-substituted purine ribosides. Characterization of the bioreactor has been carried out through a bidimensional chromatographic system with the sample on-line transfer from the bioreactor to the analytical column for the separation and quantification of substrate and product. Comparison with the soluble enzyme showed that the AhPNP-based bioreactor is reliable as the same ranking order, with respect to the standard activity assay, was obtained. The stability of the IMER was also assessed and the system was found to be stable up to 60 reactions.

Computational studies of TGF-βRI (ALK-5) inhibitors: analysis of the binding interactions between ligand-receptor using 2D and 3D techniques

ALK-5 (Activin-Like Kinase 5) is a biological receptor involved in a variety of pathological processes such as cancer and fibrosis. ALK-5 receptor propagates an intracellular signaling that forms a protein complex capable of reaching the nucleus and modulating the gene transcription. In the present study, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent ALK-5 inhibitors. Significant correlation coefficients (CoMFA, r(2)=0.99 and q(2)=0.85; HQSAR, r(2)=0.92 and q(2)=0.72) were obtained, indicating the predictive potential of the 2D and 3D models for untested compounds. The models were then used to predict the potency of a test set, and the predicted values from the HQSAR and CoMFA models were in good agreement with the experimental results. The final QSAR models, along with the information obtained from 3D (steric and electrostatic) contour maps and 2D contribution maps, can be useful for the design of novel bioactive ligands.

Docking and CoMSIA studies on steroids and non-steroidal chemicals as androgen receptor ligands

While some synthetic chemicals have been demonstrated to disrupt normal endocrine function by binding to the androgen receptor (AR), the mechanism by which ligands bind to the ligand binding domain (LBD) remained unclear. In this study, docking and comparative molecular similarity index analysis (CoMSIA) were performed to study the AR ligand binding mechanism of steroids and non-steroidal chemicals. The obtained docking conformations and predictive CoMSIA models (r(pred)(2)values as 0.842 and 0.554) indicated the primary interaction site and key residues in the binding process. The major factors influence the binding affinity of steroids and non-steroidal chemicals were electrostatic and hydrophobic interactions, respectively. The results indicated that besides amino-acid residues Gln711, Arg752 and Thr877 which have previously been reported to be important in binding ligands, Leu701 and Leu704 are also important. Residues Val746, Met749 and Phe764 are crucial only for steroids, while Met742 and Met787 are important only for non-steroidal chemicals. This knowledge of key interactions and important amino-acid residues governing ligands to the AR allow better prediction of potency of AR agonists so that their potential to disrupt AR-mediated pathways and to design less potent alternatives.

Hologram QSAR models of 4-[(diethylamino)methyl]-phenol inhibitors of acetyl/butyrylcholinesterase enzymes as potential anti-Alzheimer agents

Hologram QSAR models were developed for a series of 36 inhibitors (29 training set and seven test set compounds) of acetyl/butyrylcholinesterase (AChE/BChE) enzymes, an attractive molecular target for Alzheimer’s disease (AD) treatment. The HQSAR models (N = 29) exhibited significant cross-validated (AChE, q² = 0.787; BChE, q² = 0. 904) and non-cross-validated (AChE, r² = 0.965; BChE, r² = 0.952) correlation coefficients. The models were used to predict the inhibitory potencies of the test set compounds, and agreement between the experimental and predicted values was verified, exhibiting a powerful predictive capability. Contribution maps show that structural fragments containing aromatic moieties and long side chains increase potency. Both the HQSAR models and the contribution maps should be useful for the further design of novel, structurally related cholinesterase inhibitors.

Discovery of new inhibitors of Schistosoma mansoni PNP by pharmacophore-based virtual screening

Schistosomiasis is considered the second most important tropical parasitic disease, with severe socioeconomic consequences for millions of people worldwide. Schistosoma mansoni , one of the causative agents of human schistosomiasis, is unable to synthesize purine nucleotides de novo, which makes the enzymes of the purine salvage pathway important targets for antischistosomal drug development. In the present work, we describe the development of a pharmacophore model for ligands of S. mansoni purine nucleoside phosphorylase (SmPNP) as well as a pharmacophore-based virtual screening approach, which resulted in the identification of three thioxothiazolidinones (1-3) with substantial in vitro inhibitory activity against SmPNP. Synthesis, biochemical evaluation, and structure-activity relationship investigations led to the successful development of a small set of thioxothiazolidinone derivatives harboring a novel chemical scaffold as new competitive inhibitors of SmPNP at the low-micromolar range. Seven compounds were identified with IC(50) values below 100 μM. The most potent inhibitors 7, 10, and 17 with IC(50) of 2, 18, and 38 μM, respectively, could represent new potential lead compounds for further development of the therapy of schistosomiasis.

Recent advances in fragment-based QSAR and multi-dimensional QSAR methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Anti-androgen activity of polybrominated diphenyl ethers determined by comparative molecular similarity indices and molecular docking

Some polybrominated diphenyl ethers (PBDEs) may have endocrine-disrupting (ED) potencies. In this study, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) were performed to explore the possible anti-androgenicity of PBDEs. Based on the alignment generated by docking conformations, a highly predictive comparative molecular similarity indices analysis (CoMSIA) model was developed with q(2) value of 0.642 and r(2) value of 0.973. The contributions of the steric, electrostatic, hydrophobic fields to the CoMSIA model are 13.1%, 61.0% and 25.9%, respectively. Br substitutions which are at meta and para positions of PBDEs will be unfavorable for androgen receptor (AR) antagonism and ortho Br substitutions for PBDEs are favorable for anti-androgen activity. Mapping the 3D-QSAR models to the active site of the AR provides new insight into the AR-PBDEs interaction. CoMSIA field contributions showed good consistency with structural features of the AR binding site and can be used to predict anti-androgen activities of other PBDE congeners.

Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpha (hERalpha) and beta (hERbeta). Because the levels and relative proportion of hERalpha and hERbeta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hERalpha and hERbeta. Significant statistical coefficients were obtained (hERalpha, q(2) = 0.76; hERbeta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hERalpha and hERbeta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design of novel hER modulators with improved selectivity.

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

Two- and three-dimensional quantitative structure-activity relationships studies on a series of liver x receptor ligands

Liver X receptor (LXR) is an attractive drug target for the development of novel therapeutic agents for the treatment of dyslipidaemia and cholestasis. In the present work, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent LXR ligands. Significant correlation coefficients (CoMFA, r(2) = 0.98 and q(2) = 0.69; HQSAR, r(2) = 0.99 and q(2) = 0.85) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted values obtained from the 2D and 3D models were in good agreement with the experimental results. The final QSAR models, along with the information obtained from 3D steric and electrostatic contour maps and 2D contribution maps should be useful for the design of novel LXR ligands having improved potency.

Hologram QSAR model for the prediction of human oral bioavailability

A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.

Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators

The estrogen receptor-beta subtype (ERbeta) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERbeta binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q(2) = 0.73 and r(2) = 0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4-7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERbeta modulators having improved affinity.

2D QSAR studies on thyroid hormone receptor ligands

2D QSAR studies were carried out for a series of 55 ligands for the Thyroid receptors, TRalpha and TRbeta. Significant cross-validated correlation coefficients (q(2)=0.781 (TRalpha) and 0.693 (TRbeta)) were obtained. The models' predictive abilities were proved more valuable than the classical 2D-QSAR, and were further investigated by means of an external test set of 13 compounds. The predicted values are in good agreement with experimental values, suggesting that the models could be useful in the design of novel, more potent TR ligands. Contribution map analysis identified a number of positions that are promising for the development of receptor isoform specific ligands.

Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies

The estrogen receptor (ER) is an important drug target for the development of novel therapeutic agents for the treatment of breast cancer. Progress towards the design of more potent and selective ER modulators requires the optimization of multiple ligand-receptor interactions. Comparative molecular field analyses (CoMFA) and hologram quantitative structure-activity relationships (HQSAR) were conducted on a large set of ERalpha modulators. Two training sets containing either 127 or 69 compounds were used to generate QSAR models for in vitro binding affinity and potency, respectively. Significant correlation coefficients (affinity models, CoMFA, r(2)=0.93 and q(2)=0.79; HQSAR, r(2)=0.92 and q(2)=0.71; potency models, CoMFA, r(2)=0.94 and q(2)=0.72; HQSAR, r(2)=0.92 and q(2)=0.74) were obtained, indicating the potential of the models for untested compounds. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. The final QSAR models as well as the information gathered from 3D contour maps should be useful for the design of novel ERalpha modulators having improved affinity and potency.

3D QSAR comparative molecular field analysis on nonsteroidal farnesoid X receptor activators

Three-dimensional quantitative structure-activity relationships (3D QSAR) were performed for a series of farnesoid X receptor activators using comparative molecular field analysis (CoMFA). A training set containing 77 compounds served to establish the models. The best statistical results among all models were obtained with region focusing weighted by a S.D. x coefficient values of 0.8 and a grid spacing of 1.0 (r2=0.963, SEE=0.097; q2=0.742, SEP=0.255). The model was used to predict the potency of 20 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final CoMFA model along with the information obtained from 3D contour maps should be useful for the design of novel FXR ligands having improved potency.