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Structure of the interaction site of the GABA-AT complexed with 3k; a, b orientation of phenyl ring to lipophilic pocket, c orientation of NHNH 2 moiety to mainly polar region of enzyme
Source publication
β-Phenylethylidenehydrazine (PEH) derivatives have been recognized as Gamma-aminobutyric acid aminotransferase (GABA-AT) inhibitors.
In this research a group of newly synthesized of PEH analogs, possessing a variety of substituents (Me, OMe, Cl, and CF3) at the 2-, 3-, and 4-position of the phenyl ring, were subjected to docking study and quantitat...
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Citations
... The results from the established QSAR models help researchers to find a quantitative relationship between structures and biological activities that leads to the design of new compounds with remarkable biological activities with no need for any experimental studies (Muhammad et al. 2018;Ojha Lokendra et al. 2013). In recent years, the three-dimensional (3D) structures for a wide variety of receptors have become available and also the computational methods have greatly improved; thus, the use of descriptors containing information about the interactions of ligands with the active site of receptors has been highly suggested in the QSAR studies (Amini et al. 2016;Chakraborty et al. 2014;Chen and Chen 2012;Coi and Bianucci 2013;Davood and Iman 2011;Ebrahimi and Khayamian 2014;Ebrahimi et al. 2012;Garg et al. 2010;Gharaghani et al. 2013;Rasouli and Davood 2018;Safarizadeh and Garkani-Nejad 2019;Sheikhpour et al. 2017;Singla et al. 2011;Zheng et al. 2014). Among the computational chemistry methods, molecular docking is a powerful tool that provides the LR interaction information (Gharaghani et al. 2013) through the different computational software such as Dock and AutoDock (Kramer et al. 1999;Morris et al. 1998). ...
... The descriptors computed in this way are in fact the same as the structural descriptors; however, because they are calculated from the modified structure of the docked ligand, they have to some extent the interaction information but do not fully reflect the LR interactions. The other way is the extraction of MDDs as the interactions information from the best conformation of LR complex after successful docking of each ligand in the active site of the receptor (Amini et al. 2016;Chakraborty et al. 2014;Chen and Chen 2012;Coi and Bianucci 2013;Davood and Iman 2011;Ebrahimi and Khayamian 2014;Ebrahimi et al. 2012;Garg et al. 2010;Gharaghani et al. 2013;Rasouli and Davood 2018;Safarizadeh and Garkani-Nejad 2019;Sheikhpour et al. 2017;Singla et al. 2011;Zheng et al. 2014). The MDDs computed in this way are of the LR binding energy and enter the LR interaction information into the QSAR models, successfully. ...
... Therefore, MDDs are not a complete representation of the studied ligands and adding some structural descriptors to the MDDs has been suggested to improve the predictability of the QSAR models (Chakraborty et al. 2014;Coi and Bianucci 2013;Ebrahimi and Khayamian 2014;Ebrahimi et al. 2012;Gharaghani et al. 2013;Safarizadeh and Garkani-Nejad 2019;Singla et al. 2011). It is expected that the use of a mixture of MDDs and structural descriptors improves the performance of the presented QSAR models, but when MDDs are combined with a large number of structural descriptors, MDDs are not appeared in the final QSAR model (Amini et al. 2016;Davood and Iman 2011;Sheikhpour et al. 2017). This is probably due to the decrease of the relative importance of MDDs in the data set with too many variables afteradding a large number of structural descriptors. ...
A combination of ligand–receptor interactions and drug-like indexes have been used to develop a quantitative structure–activity relationship model to predict anti-HIV activity (pEC50) of 73 azine derivatives as non-nucleoside reverse transcriptase inhibitors. Ligand–receptor interactions were derived from the best position (best pose) of studied compounds, as ligands, in the active site of receptors using Autodock 4.2 software and named as molecular docking descriptors. The drug-like indexes were calculated using DRAGON 5.5 software. Two groups of descriptors were mixed, and the stepwise regression method was used for the selection of the most relevant descriptors. Four selected descriptors were subsequently used to construct the quantitative structure–activity relationship model using the Levenberg–Marquardt artificial neural network method. Dataset was randomly divided into the train (53 compounds), validation (10 compounds) and test set (10 compounds). The best model was selected according to the lowest mean square error value of the validation set. The accuracy and predictability of the model were evaluated using test and validation sets and the leave-one-out technique. According to the predicted results, the coefficient of determination of the test set (R2 = 0.86) and all data (\({Q}_{LOO}^{2}\)= 0.73) were acceptable. The mean square error value for the test set was equal to 0.11. The obtained results emphasized the good prediction ability and generalizability of the developed model in the prediction of pEC50 values for new compounds.
... In QSAR studies, the biological activity of a compound is predicted using some physicochemical and structural properties called descriptors (Muhammad et al. 2018). Recently, the use of the molecular docking descriptors (MDDs) has proposed in the SBDD method because of the availability of the 3D structure of the protein as the receptor (Amini et al. 2016;Chakraborty et al. 2014;Chen and Chen 2012;Coi and Bianucci 2013;Davood and Iman 2011;Ebrahimi and Khayamian 2014;Ebrahimi et al. 2012;Garg et al. 2010;Gharaghani et al, 2013;Rasouli and Davood 2018;Safarizadeh and Garkani-Nejad 2019;Sheikhpour et al. 2017;Singla et al. 2011;Zheng et al. 2014). There are several ways to extract MDDs (Amini et al. 2016;Coi and Bianucci 2013;Rasouli and Davood 2018). ...
... In the most common method, the interactions of a ligand with the active site of a suitable receptor are first simulated using molecular docking. Then, MDDs are extracted from the ligand-receptor (LR) interaction information derived from the most stable structure of LR complex and used in the QSAR studies (Amini et al. 2016;Chakraborty et al. 2014;Chen and Chen 2012;Coi and Bianucci 2013;Davood and Iman 2011;Garg et al. 2010;Gharaghani et al. 2013;Safarizadeh and Garkani-Nejad 2019;Singla et al. 2011;Zheng et al. 2014). Since MDDs provide valuable information about the interaction of ligands with the target protein, the development of predictive QSAR models is expected using MDDs. ...
... The probable reason for this drawback is that MDDs have essential information about the interactions; nevertheless, they do not take into account the structural features of ligands in QSAR models. Therefore, researchers have suggested the use of a combination of MDDs and structural descriptors to address this limitation (Chakraborty et al. 2014;Safarizadeh and Garkani-Nejad 2019;Singla et al. 2011;Davood and Iman 2011;Sheikhpour et al. 2017). In this approach, MDDs are usually mixed with almost all available structural descriptors (all descriptors calculated with Dragon software), and the mixture is used to build QSAR models. ...
An accurate QSAR model was developed for the prediction of the anti-HIV activities of a set of DAPY-like derivatives as new non-nucleoside reverse transcriptase inhibitors (NNRTIs). The ligand–receptor (LR) interactions for all compounds were studied by the docking of compounds in the active site of appropriate receptors. The binding information of LR complexes at the best pose was called the molecular docking descriptors (MDDs). The mixture of 10 MDDs with about 154 simple, functional group counts was used as a new group of descriptors in the QSAR study of DAPY-like compounds. Among the 164 mixed descriptors, seven descriptors were selected as the most effective descriptors using the linear stepwise regression (SR) method and used as inputs of the artificial neural network (ANN) model. Levenberg–Marquardt (LM) method was used for the training of ANN through the backpropagation of errors algorithm. The Levenberg–Marquardt artificial neural network (LM-ANN) model with the architecture of 6-4-1 was selected as the optimal model. The predictability of the LM-ANN model was estimated by applying the external test set and the leave-one-out (LOO) method. The mean square errors (MSEs) and coefficient of determination (R²) values for the test set were 0.16 and 0.89, respectively. The Q² value for the LOO method was calculated as 0.72. The results obtained demonstrated that a mixture of MDDs and functional group counts provided the required information for the construction of a QSAR model with acceptable prediction ability.
Graphic abstract
... In the usual approach, the ligand structure is docked into the active site of a suitable protein (receptor) and the most stable structure of LR complex (the best pose of the ligand in protein active site) is obtained. After that, an LR structure with minimum energy is used for the generation of MDDs [16][17][18][19][20][21][22][23][24]. In recent years, several researchers have been tried to use MDDs in the development of predictive QSAR models [16][17][18][19][20][21][22][23][24]. ...
... After that, an LR structure with minimum energy is used for the generation of MDDs [16][17][18][19][20][21][22][23][24]. In recent years, several researchers have been tried to use MDDs in the development of predictive QSAR models [16][17][18][19][20][21][22][23][24]. Nevertheless, in cases, there is no acceptable relationship between MDDs and biological activity [18,21,24] and/or such descriptors have not appeared in the final QSAR model [17]. ...
... In recent years, several researchers have been tried to use MDDs in the development of predictive QSAR models [16][17][18][19][20][21][22][23][24]. Nevertheless, in cases, there is no acceptable relationship between MDDs and biological activity [18,21,24] and/or such descriptors have not appeared in the final QSAR model [17]. One of the reasons for the inadequacy of the reported QSAR models is probably that the MDDs have insufficient information about the chemical structures of compounds. ...
In this paper, we report the use of a mixture of radial distribution functions (RDFs) and molecular docking descriptors (MDDs), as a new group of descriptors, to construct a predictive quantitative structure-activity relationship (QSAR) model. The performance of the proposed mixed descriptors as the independent variables was checked with QSAR modeling of the anti-cancer activities of a series of 4-anilinoquinazoline analogs as the potent epidermal growth factor receptor (EGFR) inhibitors. The RDF descriptors were calculated using the available software. The docking descriptors were extracted by docking the understudied compounds into the active site of the protein with the PDB Code of 1M17 using molecular docking software. The stepwise linear regression was used to select the most important descriptors. The selected relevant descriptors were used as the inputs in the Bayesian regularization-artificial neural network (BR-ANN) as the QSAR model. The data set was randomly divided into training (35 compounds) and external test (8 compounds) sets. The mean square error (MSE) of the training set was applied for the selection of the optimal BR-ANN model. The validation of the proposed BR-ANN model was accomplished by the prediction of pIC50 of compounds in the external test set and all molecules through the leave-one-out (LOO) technique. The results obtained confirmed the acceptable accuracy of the model (\( {R}_{\mathrm{test}}^2=0.90 \) and\( \kern0.50em {R}_{\mathrm{LOO}}^2=0.79 \)).
... Recently, researchers have made efforts to use ligand-receptor interaction information as descriptors in the QSAR studies [16]. However, in many cases, such descriptors are not included in the final QSAR model as vital descriptors [17][18][19] and/or the constructed models do not have desirable prediction ability. This can be attributed to the fact that the information obtained from the interaction of the receptor-ligand alone cannot be descriptive enough [20]. ...
... In the design of other type of GABA-AT inhibitors via computational methods, Bansal has designed novel GABA-AT inhibitors based on a molecular field analysis kNN-MFA 3DQSAR model for phenyl-substituted β-phenylethylidene hydrazine analogues [38,39]. Davood, identified β-phenylethylidene hydrazide GABA analogues with a variety of substituents at the phenyl ring by QSAR techniques [40]. On the other hand, Abdulfatai has reported QSAR models for the prediction of biological activities of quinoxaline and thiadiazoles derivatives as GABA-AT inhibitors [41,42] and Jain studied the effect of halogen substitution on the docking and 3DQSAR properties of a series of aryl-substituted thiosemicarbazones [43]. ...
We have previously reported the synthesis, in vitro and in silico activities of new GABA analogues as inhibitors of the GABA-AT enzyme from Pseudomonas fluorescens, where the nitrogen atom at the γ-position is embedded in heterocyclic scaffolds. With the goal of finding more potent inhibitors, we now report the synthesis of a new set of GABA analogues with a broader variation of heterocyclic scaffolds at the γ-position such as thiazolidines, methyl-substituted piperidines, morpholine and thiomorpholine and determined their inhibitory potential over the GABA-AT enzyme from Pseudomonas fluorescens. These structural modifications led to compound 9b which showed a 73% inhibition against this enzyme. In vivo studies with PTZ-induced seizures on male CD1 mice show that compound 9b has a neuroprotective effect at a 0.50 mmole/kg dose. A QSAR study was carried out to find the molecular descriptors associated with the structural changes in the GABA scaffold to explain their inhibitory activity against GABA-AT. Employing 3D molecular descriptors allowed us to propose the GABA analogues enantiomeric active form. To evaluate the interaction with Pseudomonas fluorescens and human GABA-AT by molecular docking, the constructions of homology models was carried out. From these calculations, 9b showed a strong interaction with both GABA-AT enzymes in agreement with experimental results and the QSAR model, which indicates that bulky ligands tend to be the better inhibitors especially those with a sulfur atom on their structure.
... Docking was carried out using AutoDock 4.2 software [34][35][36][37][38][39][40]. Some compounds (such as compound 14 in Fig. 1) in the dataset and the predicted structure from the 3D-QSAR study, and the verapamil molecule as reference inhibitor for this protein, were docked to the three-dimensional structure of p-gp (pdb code: 3g60). ...
Background:
The multidrug resistance (MDR) of cancer cells has become a great barrier to the success of chemotherapy.
Objective:
In this study quantitative structure activity relationship (QSAR) modeling was applied to 461,4-dihydropyridine structures (DHPs),and some selected compounds were docked.
Methods:
QSAR was used to generate models and predict the MDR inhibitory activity for a series of 1,4-dihydropyridines (DHP). The DHPs were built and optimized using the Sybyl program (x1.2 version). Descriptor generation was done using the DRAGON package. Docking was carried out using Auto Dock 4.2 software. Multiple linear regression, and partial least square were used as QSAR model-generation methods. External validation, cross-validation (leave one out) and y-randomization were used as validation methods.
Results:
The constructed model using stepwise-MLR and GA-PLS revealed good statistical parameters. In the final step all compounds were divided into two parts: symmetric (PLS) and asymmetric (MLR) 1,4-dihydropyridines and two other models were built. The square correlation coefficient (R^2) and root mean square error (RMSE) for train set for GA-PLS was (R^2=0.734, RMSE train=0.26).
Conclusion:
The predictive ability of the models was found to be satisfactory and could be used for designing new 1,4-dihydropyridines as potent MDR inhibitors in cancer treatment. 1,4- dihydropyridine ring containing protonable nitrogen as scaffold could be proposed. Sulfur, ester, amide, acyle, ether … fragments are connected to a 1,4-dihydropyridine ring. Phenyl groups (with an electronegative substituent) as a lipophilic part are essential for the inhibitory effect.
... The docking and molecular modeling procedures are based on our previous articles [29][30][31][32][33]. The chemical structure of compound 5c was built using HyperChem software. ...
Curcumin is a polyphenolic natural compound with multiple targets that used for the prophylaxis and treatment of some type of cancers like cervical and pancreatic cancers. Objective: In this study, ten new curcumin derivatives were designed and synthesized and their cytostatic activity evaluated against the Hela and Panc cell lines that some of them showed more activity than curcumin. Method: In the present study, a series of mono-carbonyl derivatives of curcumin were designed and prepared. The details of the synthesis and chemical characterization of the synthesized compounds are described. The cytostatic activities of the designed compounds are assessed in two different tumor cell lines using MTT test. Results: In vitro screening for human cervix carcinoma cell lines (Hela) and pancreatic cell lines (Panc-1) at 24 and 48 hour showed that all the analogs possessed good activity against these tumor cell lines and compounds 5a, 5c and 6 with high potency can be used as a new lead compounds for the designing and finding new and potent cytostatic agents. Docking studies indicated that compound 5c readily binds the active site of human glyoxalase I protein via two strong hydrogen bonds engaging residues of Glu-99 and Lys-156. Conclusion: Our results are useful in guiding a design of optimized ligands with improved pharmacokinetic properties and increased of anti-cancer activity vs. the prototype curcumin compound.
... The inhibition-constant (Ki) is calculated in AutoDock4.2 as Ki = exp ((∆G ×1000)/(R cal × TK)) where ∆G is the docking energy, R cal is 1.98719, and TK is 298.15 [25]. The low inhibition constant values indicated the efficiency of the compounds to inhibit the enzyme and prove its greater affinity towards the catalytic site of the enzyme. ...
Background:
Degradation of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) is mainly catalysed by GABA aminotransferase (GABA-AT), excessive activity of which leads to convulsions. Inhibition of GABA-AT increases the concentration of GABA and can terminate the convulsions. Several studies have revealed that GABA analogues could be the outstanding scaffolds for the design of potent inhibitors of GABA-AT. The poor ability of GABA analogues to cross the blood-brain barrier (BBB), always produces low therapeutic index. However, Vigabatrin, a mechanism-based inhibitor of GABA-AT, is currently approved treatment of epilepsy, but it has harmful side effects, leaving a need for improved GABA-AT inactivators.
Experimental design:
In our present in silico investigation, AutoDock 4.2,-based on Lamarckian genetic algorithm was employed for virtual screen of a compound library with 35 entries (Schiff's bases of GABA) in search for novel and selective inhibitors of GABA-AT.
Results:
By means of flexible type of molecular docking, we proposed that these designed molecules could successfully bind into the active pocket of GABA-AT with good predicted affinities in comparison to standard vigabatrin. Among the designed analogues, HIG18, HIG28 and HIG30 showed significant binding free energy of -10.25, -9.88 and -9.31 kcal/mol with predicted inhibitory constant values of 0.03, 0.05 and 0.15 µM respectively.
Conclusion:
Using ligand-based drug design, we proposed that electron withdrawing phenyl substituted heterocyclic imines of GABA could be considered as promising structures for synthesis and testing of new GABA-AT inhibitors from this class. We hypothesize that novel GABA analogues with an azomethine linkage incorporated with heterocyclic system can have increased affinity and more lipophilic character that would provide a probability of having less toxic effect in the therapy of convulsions.
... They applied it to QSAR/QSPR studies of rate constants of the O-methylation of 36 phenol derivatives and the activities of 31 antifilarial antimycin compounds. There are also different reports in literature on the QSAR/QSPR studies of different compounds by Iranian chemometricians124125126127128129130131. Fatemi and Gharaghani proposed a novel QSAR model for the prediction of the apoptosis-inducing activity of 4-aryl-4H-chromenes based on support vector machine [132]. ...
Although the first publications in the field of chemometrics by Iranian researchers go back nearly 23 years, Iran's chemometrics has experienced significant growth after the year 2000. More than 1500 papers have been published by Iranian chemometricians between 2005 and 2012 which is about seven times more than those published before 2005. The aim of this review is to present a perspective about Iran's chemometrics developments. The role of enthusiastic students, motivated professors, annual workshops, and international collaborations is discussed in this work. After introducing the first papers in each field of chememotrics, the papers published during 2005–2012 were screened, and some of them were introduced.
... Docking protocols aid in elucidation of the most energetically favorable binding pose of a ligand to its receptor. The objective of our current docking study is to elucidate the mode of interaction of phthalimide pharmacophore derivatives with sodium channel (10). In the present study, we report the molecular modeling and drugreceptor interaction profile of 19 phthalimide derivatives which had been designed and synthesized before. ...
... Among all energy minima conformers, the global minimum of compounds were used in docking calculations and the resulted geometry was transferred into Autodock (version 4.2) program package, which was developed by Arthur J. Olson Chemometrics Group (12). The structure of docked N-phenyl substituent of phthalimide (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) Table 1 (11). The model constructed by homology with potassium channel structures was reasonably successful in accounting for inner pore residue interactions with local anesthetics and anticonvulsant drugs like phenytoin. ...
... Our docking results reveal that based on the predicted binding energy, compounds 4, 8, 15, 6, 7, 13 and 14 with -6.46, -6.30, -6.09, -6.02, -5.90, -5.88 and -5.84 kcal/mol binding energy, respectively, are more potent than phenytoin with -5.83 kcal/mol binding energy, as a reference drug. Based on the binding energy, compounds 1, 2, 3,5,9,10,11,12,16,17,18,19 Our docking studies reveals while phenytoin interacted with the domain IV-S6 of NaV1.2 ( Figure 1), but N-Phenyl Phthalimide derivatives, interacted mainly with the domain II-S6 by making a hydrogen bond and have additional hydrophobic interaction mainly with domains I , II, IV and sometimes with domain III in the channel's inner pore (Figure 2A). This molecular modeling shows that the oxygen in NO2 forms a hydrogen bonding interaction with the OH of TYR87 in compounds 8 and 4 ( Figure 2B) and with LYS7 in compound 14. ...
Objective(s): Recently, phthalimide derivatives were designed based on ameltolide and thalidomide as they possess a similar degree of anticonvulsant potency due to their phenytoin-like profile. The ability of phthalimide pharmacophore to interact with neuronal voltage-dependent sodium channels was studied in the batrachotoxin affinity assay. Therefore, in the present study, a series of 19 compounds of phthalimide pharmacophore possessing a variety of substituents (NO2, NH2, Me, Cl, COOH, MeO) at 2-, 3-, and 4- position of the N-phenyl ring and N-(3-amino-2-methylphenyl) succinimide, were subjected to docking studies in order to inhibit voltage-gated sodium channels.
Materials and Methods
: Chemical structures of all compounds were designed using HYPERCHEM program and Conformational studies were performed through semi-empirical molecular orbital calculations method followed by PM3 force field. Total energy gradient calculated as a root mean square (RMS) value, until the RMS gradient was 0.01 kcal mol-1. Among all energy minima conformers, the global minimum of compounds was used in docking calculations. Using a model of the open pore of Na channels, docking study was performed by AUTODOCK4.2 program.
Results
: Docking studies have revealed that these types of ligands interacted mainly with II-S6 residues of NaV1.2 through making hydrogen bonds and have additional hydrophobic interactions with domain I, II, III and IV in the channel's inner pore.
Conclusion: These computational studies have displayed that these compounds are capable of inhibiting Na channel, efficiently.
