H-bond Acceptor Fields Research Articles

Discovery of anti-colon cancer agents targeting wild-type and mutant p53 using computer-aided drug design

Mutations in the p53 gene are common and occur in over 50% of all cancers, as it is involved in DNA damage repair, cell cycle regulation and apoptosis. Moreover, the p53 gene is mutated in 70% of colon cancers. Therefore, the development of drugs to combat this mutation requires urgent attention. With this in mind, in silico drug design approaches were applied on quinoline derivatives with anticancer activity. In 3D-QSAR study, steric, electrostatic, hydrophobic and H-bond acceptor fields (SEHA) play an important role in prediction and design of new colon cancer compounds. Indeed, the two best CoMSIA/SEHA models with (Q 2 = 0.737, R 2 = 0.914, R pred 2 = 0.720) and (Q 2 = 0.738, R 2 = 0.919, R pred 2 = 0.739) show good prediction of human colon carcinoma HCT 116 (p53+/+) and (p53−/−) activities, respectively. Furthermore, the predictive ability and robustness of these models were tested by several validation methods. Molecular docking analyses reveal crucial interactions with the active sites of the p53 protein in both wild type and mutant. Based on these theoretical studies, we designed 10 new compounds with good anticancer activity potential, which were evaluated using ADMET properties. Molecular dynamics simulations were performed to confirm the detailed binding mode of the docking results. Finally, the MM-GBSA based on molecular dynamics simulation confirmed that the designed compounds were able to form stable hydrogen bonding interactions with the crucial residues, which are essential to overcome the p53 mutation in colon cancer. Communicated by Ramaswamy H. Sarma

Development of novel monoamine oxidase B (MAO-B) inhibitors by combined application of docking-based alignment, 3D-QSAR, ADMET prediction, molecular dynamics simulation, and MM_GBSA binding free energy

Unsaturated ketone derivatives are known as inhibitors of monoamine oxidase B (MAO-B), a potential drug target of Parkinson’s disease. Here, docking-based alignment, 3 D-QSAR (three-dimensional quantitative structure-activity relationship) studies, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, molecular dynamics (MD) simulation, and MM_GBSA binding free energy were performed on a novel series of MAO-B inhibitors. The objective is to predict new MAO-B inhibitors with high potency activity. The 3 D-QSAR models were created using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Molecular docking findings indicated that compounds with strong inhibitory efficacy also had a high binding affinity. 3 D-QSAR studies showed the importance of steric, electrostatic, and H-bond acceptor fields on the inhibitory activity of MAO-B. Based on the appropriate 3 D-QSAR model, a new series of MAO-B inhibitors were predicted and their pharmacokinetic characteristics were evaluated using in silico ADMET prediction. All screened compounds show good oral bioavailability without any side effects. Moreover, the dynamic behavior and stability of the most active compounds were evaluated using MD simulations. The results showed that unsaturated ketone derivatives are stable and compact during the 100 ns of MD simulation. Finally, the binding free energy of complexes was determined using the MM_GBSA method; the findings indicated that the T1 compound is more stable (ΔGbinding = −409.506 KJ/mol) than the data set's highest active compound (ΔGbinding = −31.883 KJ/mol). Communicated by Ramaswamy H. Sarma

3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors

Resistance to folate antagonists is caused by mutations in the dihydrofolate reductase (DHFR) genes. These mutations affect the amino acids at positions 51, 59, 108 and 164 of DHFR, which appear to play a major role in malaria treatment failure. Therefore, the design of new drugs able to overcome the problem of antifolate drug resistance should receive urgent attention. In this study, a three-dimensional quantitative structure-activity relationship (3 D-QSAR) and molecular docking studies have been performed on antimalarial quinazoline derivatives. The CoMFA (Q2 = 0.63, R2 = 0.83 and = 0.70) and the CoMSIA (Q2 = 0.584, R2 = 0.816, and = 0.73) models show a good prediction of antimalarial activity. The reliability and robustness of the proposed models have been tested using several validation methods, which showed that the steric, electrostatic, hydrophobic and H-bond acceptor fields of the CoMSIA model play a key role in the prediction of antimalarial activity. Molecular docking studies reveal important interactions between two isomeric compounds (meta and para) and the DHFR receptor in its wild and mutant forms. The obtained outcomes of molecular docking studies have been validated using a new method based on visual inspection. The DFT study of the two isomeric compounds confirms clearly the trends of 3 D-QSAR and molecular docking for the design of new compounds. Moreover, the consistency between theoretical, 3 D-QSAR and molecular docking analysis provides guidance for the design of new drug candidates, which have been tested using ADMET properties and drug likeness analysis. Communicated by Ramaswamy H. Sarma

In Silico Design of Novel Tetra-Substituted Pyridinylimidazoles Derivatives as c-Jun N-Terminal Kinase-3 Inhibitors, Using 2D/3D-QSAR Studies, Molecular Docking and ADMET Prediction

The c-Jun N-terminal Kinase 3 (JNK3) is a family of protein kinases that plays an important role in the neurodegenerative diseases such as Alzheimer’s disease. Here, we have performed 2D and 3D quantitative structure–activity relationship (2D/3D-QSAR), molecular docking, absorption, distribution, metabolism, excretion and toxicity (ADMET) approaches on a series of tetra-substituted pyrdinylimidazoles derivatives as JNK3 inhibitors. The aim was to design the new potent JNK3 inhibitors with high inhibitory activity. The significant 2D-QSAR models showed a good correlation between observed activity and predicted ones (R2PLS = 0.87 and R2ANN = 0.88) using partial least squares (PLS) and artificial neural network (ANN) methods. The three descriptors vsurf_G, lip-don and SMR_VSA1 that are used in the construction of 2D-QSAR models play a significant role in JNK3 inhibition. Comparative molecular similarity indices analysis (CoMSIA) was used to construct the best 3D-QSAR model using PLS method, showing good correlative and predictive capabilities (R2 = 0.96, Q2 = 0.67 and SEE = 0.14). The steric and H-bond acceptor fields play an important role in the variation of biological activity with four principal components. Molecular docking of selective inhibitors confirms the results obtained by 2D/3D-QSAR and indicate that Met 146 and Met 149 are the main residues that used to stabilize the ligand in the active site of JNK3 protein. Based on those satisfactory results, three new compounds were designed and analyzed by in silico ADMET method.

3D-QSAR modeling of maximum steady-state fluxes of some substituted benzenes and quinolone derivatives through polydimethylsiloxane membrane

The maximum steady-state flux of 79 compounds (substituted benzenes, and quinolones and their derivatives) with a wide range of polarity through a PDMS membrane was predicted using a comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods. Moreover, the contribution of partial atomic charge to mass transport phenomena was further verified by the correlation of atomic charge to apparent permeability through polydimethylsiloxane (PDMS) membranes. The obtained results indicated superiority of CoMSIA model over CoMFA model. The best CoMSIA model is developed based on the combination of electrostatic and hydrophobic and H-bond acceptor fields (CoMSIA-EHA). The contour maps of electrostatic and hydrophobic and H-bond acceptor fields of CoMSIA model provide an interpretable and logical relationship between chemicals structure and their fluxes, which give useful insights for designing new compounds with higher penetration through the membranes.

Comparative molecular similarity indices analysis of some 1-substituted imidazole analogs as Candida albicans P450-demethylase inhibitors

A three-dimensional quantitative structure–activity relationship of 66 structurally and functionally diverse series of 1-substituted imidazoles with antifungal activity was studied using the CoMSIA method. The compounds were divided into a training set of 56 molecules and a test set of 10 molecules. The optimum CoMSIA model obtained for the training set were all statistically significant with cross-validated coefficients (q 2) of 0.725 and conventional coefficients (r ncv 2 ) of 0.998. The predictive ability of CoMSIA was determined using a test set of ten imidazole derivatives. CoMSIA model (Model 1) obtained from steric, electrostatic, and H-bond acceptor fields were found to have best predictivity with a predictive correlation coefficient (r pred 2 ) of 0.60. Based upon the information derived from CoMSIA, it is evident that steric, electrostatic, and hydrogen bond acceptor groups may be important for the design of more potent imidazole analogs as potent Candida P450DM inhibitors.

Theoretical studies on the interaction of partial agonists with the 5-HT2A receptor

A series of 51 5-HT(2A) partial agonistic arylethylamines (primary or benzylamines) from different structural classes (indoles, methoxybenzenes, quinazolinediones) was investigated by fragment regression analysis (FRA), docking and 3D-QSAR approaches. The data, pEC(50) values and intrinsic activities (E(max)) on rat arteries, show high variability of pEC(50) from 4 to 10 and of E(max) from 15 to 70%. FRA indicates which substructures affect potency or intrinsic activity. The high contribution of halogens in para position of phenethylamines to pEC(50) points to a specific hydrophobic pocket. Other results suggest the significance of hydrogen bonds of the aryl moiety for activation and the contrary effect of benzyl groups on affinity (increasing) and intrinsic activity (decreasing). Results from fragment regression and data on all available mutants were considered to derive a common binding site at the rat 5-HT(2A) receptor. After generation and MD simulations of a receptor model based on the β(2)-adrenoceptor structure, typical derivatives were docked, leading to the suggestion of common interactions, e.g., with serines in TM3 and TM5 and with a cluster of aromatic amino acids in TM5 and TM6. The whole series was aligned by docking and minimization of the complexes. The pEC(50) values correlate well with Sybyl docking energies and hydrophobicity of the aryl moieties. With this alignment, CoMFA and CoMSIA approaches based on a training set of 36 and a test set of 15 compounds were performed. The correlation of pEC(50) with steric, electrostatic, hydrophobic and H-bond acceptor fields resulted in sufficient fit (q (2): 0.75-0.8, r (2): 0.92-0.95) and predictive power (r (pred) (2) : 0.85-0.88). The important interaction regions largely reflect the patterns provided by the putative binding site. In particular, the fit of the aryl moieties and benzyl substituents to two hydrophobic pockets is evident.

Identification of novel 5-hydroxy-1H-indole-3-carboxylates with anti-HBV activities based on 3D QSAR studies

Infection with hepatitis B virus (HBV) is a major cause of liver diseases such as cirrhosis and hepatocellular carcinoma. In our previous studies, we identified indole derivatives that have anti-HBV activities. In this study, we optimize a series of 5-hydroxy-1H-indole-3-carboxylates, which exhibited potent anti-HBV activities, using three-dimensional quantitative structure-activity relationship (3D QSAR) studies with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The lowest energy conformation of compound 3, which exhibited the most potent anti-HBV activity, obtained from systematic search was used as the template for alignment. The best predictions were obtained with the CoMFA standard model (q (2) = 0.689, r (2) = 0.965, SEE = 0.082, F = 148.751) and with CoMSIA combined steric, electrostatic, hydrophobic and H-bond acceptor fields (q (2) = 0.578, r (2) = 0.973, SEE = 0.078, F = 100.342). Both models were validated by an external test set of six compounds giving satisfactory prediction. Based on the clues derived from CoMFA and CoMSIA models and their contour maps, another three compounds were designed and synthesized. Pharmacological assay demonstrated that the newly synthesized compounds possessed more potent anti-HBV activities than before (IC(50): compound 35a is 3.1 μmol/l, compound 3 is 4.1 μmol/l). Combining the clues derived from the 3D QSAR studies and from further validation of the 3D QSAR models, the activities of the newly synthesized indole derivatives were well accounted for. Furthermore, this showed that the CoMFA and CoMSIA models proved to have good predictive ability.

3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds

Inhibitory activities of flavonoid derivatives against aldose reductase (AR) enzyme were modelled by using CoMFA, CoMSIA and GALAHAD methods. CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 55 compounds, after which they were evaluated for predictive ability with additional 14 compounds. The best CoMFA model included both steric and electrostatic fields, meanwhile, the best CoMSIA model included steric, hydrophobic and H-bond acceptor fields. These models had a good predictive quality according to both internal and external validation criteria. On the other hand, GALAHAD was used for deriving a 3D pharmacophore model. Twelve active compounds were used for deriving this model. The obtained model included hydrophobe, hydrogen bond acceptor and hydrogen bond donor features; it was able to identify the active AR inhibitors from the remaining compounds. These in silico tools might be useful in the rational design of new AR inhibitors.

Imidazole-containing farnesyltransferase inhibitors: 3D quantitative structure–activity relationships and molecular docking

One of the most promising anticancer and recent antimalarial targets is the heterodimeric zinc-containing protein farnesyltransferase (FT). In this work, we studied a highly diverse series of 192 Abbott-initiated imidazole-containing compounds and their FT inhibitory activities using 3D-QSAR and docking, in order to gain understanding of the interaction of these inhibitors with FT to aid development of a rational strategy for further lead optimization. We report several highly significant and predictive CoMFA and CoMSIA models. The best model, composed of CoMFA steric and electrostatic fields combined with CoMSIA hydrophobic and H-bond acceptor fields, had r (2) = 0.878, q (2) = 0.630, and r (pred) (2) = 0.614. Docking studies on the statistical outliers revealed that some of them had a different binding mode in the FT active site based on steric bulk and available active site space, explaining why the predicted activities differed from the experimental activities.

Insight into the effects of chiral isomers quinidine and quinine on CYP2D6 inhibition

The distinct inhibitory effects against CYP2D6 enzyme of the stereoisomers quinidine and quinine were investigated in this work by employing various methods, including the comparative molecular field analysis (CoMFA), the comparative molecular similarity indices analysis (CoMSIA), the molecular electrostatic potential (MEP) analysis and the docking method. Several 3D-QSAR models with proper reliability were well established, with a CoMFA model with steric and electrostatic fields exhibiting 0.67, 0.99 and 0.88 of q 2, r 2 and r pred 2 , respectively, a CoMSIA model with steric, electrostatic and H-bond acceptor fields displaying 0.72, 0.97 and 0.84 of q 2, r 2 and r pred 2 , respectively. These models and related docking results reveal that quinidine binds to CYP2D6 in an inverse orientation as compared with quinine. Moreover, quinidine blocks the entrance of the active pocket of CYP2D6 more closely than quinine does, which explains well why the inhibitory activity of quinidine is of 2 magnitudes larger than quinine. This investigation provides a better understanding of the stereoisometric effects on the bioactivities of the chiral isomers quinidine and quinine interacting with CYP2D6.

A CoMSIA study on the adenosine kinase inhibition of pyrrolo[2,3- d]pyrimidine nucleoside analogues

The structural requirements of pyrrolo[2,3- d]pyrimidine nucleoside (PPN) analogues as adenosine kinase (AK) inhibitors were in silico studied by using CoMSIA method. All models were trained with 32 compounds, after which they were evaluated for predictive ability with additional 5 compounds. Quantitative information on structure–activity trends is provided for further rational development and direction of selective synthesis. The best CoMSIA model included hydrophobic, H-bond donor and H-bond acceptor fields and had a good predictive quality according to internal validation criteria. In addition, this model predicted adequately the compounds contained in the test set. The analysis of the model gives a comprehensive qualitative and quantitative description of the molecular features at C4 and C5 positions of the pyrrolo[2,3- d]pyrimidine scaffold and C5-position of the β- d-ribofuranose of PPN analogues, relevant for a high AK inhibitory activity.

3D-QSAR studies on malonyl coenzyme A decarboxylase inhibitors

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of Malonyl Co-A decarboxylase (MCD) inhibitors (Cheng et al. J. Med. Chem. 2006, 49, 1517–1525 and Cheng et al. Bioorg. Med. Chem. Lett. 2006, 16, 695–700). These inhibitors have shown protective action on the ischemic heart by inhibiting fatty acid oxidation. The CoMFA model produced statistically significant results, with the cross-validated and conventional correlation coefficients being 0.544 and 0.986, respectively. The best results were obtained by combining steric, electrostatic, hydrophobic, and H-bond acceptor fields in CoMSIA, in which case the respective cross-validated and conventional correlation coefficients were 0.551 and 0.918. The predictive ability of CoMFA and CoMSIA, determined using a test set of 24 compounds, gave predictive correlation coefficients of 0.718 and 0.725, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps may be of utility in the design of more potent MCD inhibitors.