Common Substructure-based Alignment Research Articles

BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping.

Small molecule flexible alignment is a critical component of both ligand- and structure-based methods in computer-aided drug discovery. Despite its importance, the availability of high-quality flexible alignment software packages is limited. Here, we present BCL::MolAlign, a freely available property-based molecular alignment program. BCL::MolAlign accommodates ligand flexibility through a combination of pregenerated conformers and on-the-fly bond rotation. BCL::MolAlign converges on alignment poses by sampling the relative orientations of mutually matching atom pairs between molecules through Monte Carlo Metropolis sampling. Across six diverse ligand data sets, BCL::MolAlign flexible alignment outperforms MOE, ROCS, and FLEXS in recovering native ligand binding poses. Moreover, the BCL::MolAlign alignment score is more predictive of ligand activity than maximum common substructure similarity across 10 data sets. Finally, on a recently published benchmark set of 20 high quality congeneric ligand-protein complexes, BCL::MolAlign is able to recover a larger fraction of native binding poses than maximum common substructure-based alignment and RosettaLigand. BCL::MolAlign can be obtained as part of the Biology and Chemistry Library (BCL) software package freely with an academic license or can be accessed via Web server at http://meilerlab.org/index.php/servers/molalign .

3D-QSAR and Molecular Docking Study on Maleimide-Based Glycogen Synthase Kinase 3 (GSK-3) Inhibitors as Stimulators of Steroidogenesis

Glycogen synthase kinase-3 (GSK3) is a potential therapeutic target in bipolar disorder. Here, a three-dimensional quantitative structure–activity relationship (3D-QSAR) namely CoMFA, CoMFA-RF (region focusing), and CoMSIA has been carried out on a series (44 compounds) of GSK3 inhibitors on maleimide-based. The data set was divided into training set (32 compounds) and test set (12 compounds) using Kennard and Stone algorithm. An alignment rule for the training set was defined on the basis of common substructure-based alignment. The all-orientation search (AOS) was used to achieve the best orientation and minimize the effect of initial orientation of the structures. The statistical parameters from the models (CoMFA: q2 = 0.56, , CoMFA-RF: q2 = 0.70, , and CoMSIA: q2 = 0.60, ) indicate that the data are well fitted and have high predictive ability. Molecular docking was employed to explore the binding mode between these compounds and the receptor, as well as help understand the structure–activity relationship revealed by CoMFA and CoMSIA. The computer-aided design of new compounds as potential GSK-3 inhibitors with the application of defined structural alerts was presented.

3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modelling was conducted on a series of leucine-rich repeat kinase 2 (LRRK2) antagonists using CoMFA and CoMSIA methods. The data set, which consisted of 37 molecules, was divided into training and test subsets by using a hierarchical clustering method. Both CoMFA and CoMSIA models were derived using a training set on the basis of the common substructure-based alignment. The optimum PLS model built by CoMFA and CoMSIA provided satisfactory statistical results (q2 = 0.589 and r2 = 0.927 and q2 = 0.473 and r2 = 0.802, respectively). The external predictive ability of the models was evaluated by using seven compounds. Moreover, an external evaluation set with known experimental data was used to evaluate the external predictive ability of the porposed models. The statistical parameters indicated that CoMFA (after region focusing) has high predictive ability in comparison with standard CoMFA and CoMSIA models. Molecular docking was also performed on the most active compound to investigate the existence of interactions between the most active inhibitor and the LRRK2 receptor. Based on the obtained results and CoMFA contour maps, some features were introduced to provide useful insights for designing novel and potent LRRK2 inhibitors.

2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA.

Two and Three-dimensional quantitative structure-activity relationship (2D, 3D-QSAR) study was performed for some pyrazole-thiazolinone derivatives as EGFR kinase inhibitors using the CoMFA, CoMSIA and GA-MLR methods. The utilized data set was split into training and test set based on hierarchical clustering technique. From the five CoMSIA descriptors, electrostatic field presented the highest correlation with the activity. The statistical parameters for the CoMFA (r(2)=0.862, q(2)=0.644) and CoMSIA (r(2)=0.851, q(2)=0.740) were obtained for the training set with the common substructure-based alignment. The obtained parameters indicated the superiority of the CoMSIA model over the CoMFA model. A test set consisted of seven compounds was used to evaluate the proposed models. The results of contour maps which were presented by each method lead to some insights for increasing the inhibition activity of compounds. The 2D-QSAR model was built based on three descriptors selected by genetic algorithm and showed high predictive ability (R(2) train= 0.843, Q(2) LOO=0.787). Molecular docking study was also performed to understand the type interactions presented in binding site of the receptor and ligand. The developed models in parallel with molecular docking can be employed to design and derive novel compounds with the potent EGFR inhibitory activity.

Molecular docking and 3D-QSAR studies on the MAPKAP-K2 inhibitors

Mitogen-activated protein kinase-activated protein kinase 2 (MAPKAP-K2) has been identified as a drug target for the treatment of inflammatory diseases. Therefore, there is an urgent need to develop new classes of MAPKAP-K2 inhibitors. To understand the structure activity correlation of MAPKAP-K2 inhibitors, we have carried out a molecular docking study and three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling. Both comparative molecular field analysis (\(r_{\text{cv}}^{2}\) = 0.602, \(r_{\text{ncv}}^{2}\) = 0.955) and comparative molecular similarity indices analysis (\(r_{\text{cv}}^{2}\) = 0.546, \(r_{\text{ncv}}^{2}\) = 0.891) models were generated using the training set on the basis of the common substructure-based alignment and gave reasonable results. The structural insights obtained from both the 3D-QSAR contour maps and molecular docking help to better interpret the structure activity relationship. The results obtained from this study will be useful in the design of potent MAPKAP-K2 inhibitors.

3D-QSAR analysis of anti-cancer agents by CoMFA and CoMSIA

Three-dimensional quantitative structure–activity relationships were performed for a series of isatin derivatives as anti-cancer agents using the CoMFA and CoMSIA methods. Statistically significant CoMFA ( $$r_{\text{cv}}^{2} = 0.869,\;r_{\text{ncv}}^{2} = 0.962$$ ) and CoMSIA ( $$r_{\text{cv}}^{2} = 0.865,\;r_{\text{ncv}}^{2} = 0.959$$ ) models were generated using the training set on the basis of the common substructure-based alignment. Further, the predictive ability of the CoMFA and CoMSIA models was determined using a test set of nine compounds. Based on the information derived from CoMFA and CoMSIA contour maps, we have identified some key features for increasing the activity of compounds and have been used to design new anti-cancer agents. The newly designed molecules in this series of compounds may be more potent anti-cancer agents.

Design of potent human steroid 5α-reductase inhibitors: 3D-QSAR CoMFA, CoMSIA and docking studies

3D-QSAR CoMFA, CoMSIA and docking studies were performed on a set of 4-azasteroidal human steroid 5α-reductase inhibitors. The models developed using maximal common substructure-based alignment was found to be reliable and significant with good predictive r2 value. CoMSIA model developed using combination of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor features has shown rcv2 = 0.564 with six optimum components, rncv2 = 0.945, F value = 101.196, rPred2 = 0.693 and SEE = 0.209. The contour plots obtained has shown a favourable effect of bulkier groups at C-17 position. Docking studies indicates the importance of bulkier groups at C-17 position for favourable activity. The study further helps in design of potent inhibitors of the enzyme.

Triple-layered QSAR studies on substituted 1,2,4-trioxanes as potential antimalarial agents: superiority of the quantitative pharmacophore-based alignment over common substructure-based alignment

This study reports the utilization of three approaches – pharmacophore, CoMFA/CoMSIA and HQSAR studies – to identify the essential structural requirements in 3D chemical space for the modulation of the antimalarial activity of substituted 1,2,4-trioxanes. The superiority of quantitative pharmacophore-based alignment (QuantitativePBA) over global minima energy conformer-based alignment (GMCBA) has been reported in CoMFA and CoMSIA studies. The developed models showed good statistical significance in internal validation (q 2, group cross-validation and bootstrapping) and performed very well in predicting the antimalarial activity of test set compounds. Structural features in terms of their steric, electrostatic and hydrophobic interactions in 3D space have been found to be important for the antimalarial activity of substituted 1,2,4-trioxanes. Further, the HQSAR studies based on the same training and test set acted as an additional tool to find the sub-structural fingerprints of substituted 1,2,4-trioxanes for their antimalarial activity. Together, these studies may facilitate the design and discovery of new substituted 1,2,4-trioxanes with potent antimalarial activity.

3D‐QSAR and Docking Studies on the HEPT Derivatives of HIV‐1 Reverse Transcriptase

Three-dimensional Quantitative Structure Activity Relationship (3D-QSAR) has been derived for a set of HEPT derivatives of HIV-1 reverse transcriptase (RT) using Comparative Molecular Field Analysis (CoMFA). The CoMFA models have been developed using two different alignment procedures such as common substructure and bioactive conformation. The CoMFA model I is derived from a common substructure procedure that includes steric and electrostatic fields with the cross-validated q(2) and the non-cross-validated r(2) value of 0.86 and 0.97, respectively. The same for the CoMFA model II that is derived based on the bioactive conformation are 0.19 and 0.77, respectively. It is evident from the results that the common substructure-based alignment model has good statistical significance when compared with that of bioactive conformation for the selected systems in this study. The docking study revealed that the conformational flexibility observed at the R3 position favors different orientations of the substitution at the active site of HIV-1 RT and thereby leads to inconsistency in the CoMFA alignment based on bioactive conformation.

Pharmacophore Identification and Validation Study of CK2 Inhibitors Using CoMFA/CoMSIA

Protein kinase CK2, also known as casein kinase-2, has been found to be involved in cell growth, proliferation and suppression of apoptosis, which is related to human cancers. The series of compounds were identified as casein kinase-2 inhibitors and their inhibitory activities are a function of a variation of their structures. The current study deals with the pharmacophore identification and, accordingly, the three-dimensional quantitative structure-activity relationship model development using Pharmacophore Alignment and Scoring Engine. Several hypotheses were developed for the molecular alignments. On the basis of statistical values, the best-fitted model was identified and the same alignment was used for 3D-QSAR using comparative molecular field analysis/comparative molecular similarity index analysis. Both the CoMFA (R(2)(CV) = 0.58, R(2) = 0.82 and r(2)(pred) = 0.62) and the comparative molecular similarity index analysis (R(2)(CV) = 0.74, R(2) = 0.98 and r(2)(pred) = 0.81) gave reasonable results. Besides pharmacophore-based alignment, the maximum common substructure-based alignment was also used for the comparative molecular field analysis and comparative molecular similarity index analysis. The pharmacophore-based alignment was more prominent and it has provided important information for the modelling of potent inhibitors. The overall study implies that a highly positive and bulky group with H-bond donating property is desirable around the nitrogen atom adjacent to the pyrrolidine ring.

Synthesis, antifungal activity and CoMFA analysis of novel 1,2,4-triazolo[1,5- a]pyrimidine derivatives

In order to search novel agrochemicals with higher antifungal activity, a series of new 1,2,4-triazolo[1,5- a]pyrimidine derivatives bearing 1,3,4-oxadiazole moieties were designed and synthesized. Their antifungal activities against Rhizoctonia solani were evaluated in vitro. By determining the EC 50 values of all the newly synthesized compounds and 10 formerly synthesized compounds, compound 8r, 2-((5-( sec-butylthio)-1,3,4-oxadiazol-2-yl)-methylthio)-5-dimethyl-1,2,4-triazolo-[1,5- a]pyrimidine, was found to display the highest antifungal activity (EC 50 = 6.57 μg mL −1). Based on the quantitative structure–activity relationships analyses, 2-(1-(5-( sec-butylthio)-1,3,4-oxadiazol-2-yl)ethylthio)-5,7-dimethyl-1,2,4-triazolo[1,5- a]pyrimidine ( 9j) was designed and synthesized, which was found to display much higher activity (EC 50 = 3.34 μg mL −1) than compound 8r and the control. To further explore the comprehensive structure–activity relationships, a 3D-QSAR analysis using the method of comparative molecular field analysis (CoMFA) was performed and a statistically reliable model with good predictive power ( r 2 = 0.929, q 2 = 0.588) was achieved on the basis of the common substructure-based alignment. According to the CoMFA model, the structure–antifungal activity relationship was explained reasonably.

3D-QSAR and molecular docking studies of benzaldehyde thiosemicarbazone, benzaldehyde, benzoic acid, and their derivatives as phenoloxidase inhibitors

Phenoloxidase (PO), also known as tyrosinase, is a key enzyme in insect development, responsible for catalyzing the hydroxylation of tyrosine into o-diphenols and the oxidation of o-diphenols into o-quinones. Inhibition of PO may provide a basis for novel environmentally friendly insecticides. In the present study, we determined the inhibitory activities and IC 50 values of 57 compounds belonging to the benzaldehyde thiosemicarbazone, benzaldehyde, and benzoic acid families against phenoloxidase from Pieris rapae (Lepidoptera) larvae. In addition, the inhibitory kinetics of 4-butylbenzaldehyde thiosemicarbazone against PO was measured in air-saturated solutions for the oxidation of L-3,4-dihydroxyphenylalanine (L-DOPA). The results indicated that the compound is a reversible noncompetitive inhibitor. The bioactivity results were used to construct three-dimensional quantitative structure–activity relationship (3D-QSAR) models using two molecular field analysis techniques: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). After carrying out superimposition using common substructure-based alignment, robust and predictive 3D-QSAR models were obtained from CoMFA ( q 2 = 0.926, r 2 = 0.986) and CoMSIA ( q 2 = 0.933, r 2 = 0.984) with six optimum components. The 3D-QSAR model built here will provide hints for the design of novel PO inhibitors. The molecular interactions between the ligands and the target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the representative compound 4-butylbenzaldehyde thiosemicarbazone and the active site was elucidated in detail.

Binding Model Construction of Antifungal 2-Aryl-4-chromanones Using CoMFA, CoMSIA, and QSAR Analyses

Flavonoids, generated by plants upon attack by a range of pathogens, are demonstrated to have a role in biotic and abiotic stress response phenomena in plants, and there is increasing evidence for the antibacterial, antifungal, and antiviral activities of these compounds. Using the bioisosterism strategy, a series of 2-aryl-4-chromanone derivatives based upon the structure of flavanones, a kind of flavonoid phytoalexins, were synthesized and tested for the antifungal activity against Pyricularia grisea, which have been reported in our previous papers. To further explore the comprehensive structure-activity relationship and construct the binding model for the antifungal compounds, two kinds of molecular field analysis techniques, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were performed following a Hansch-Fujita QSAR study. Superimpositions were performed using three alignment rules, that is, centroid-based alignment, common substructure-based alignment, and field fit alignment, and statistically reliable models with good predictive power (CoMFA r(2) = 0.952, q(2) = 0.727; CoMSIA r(2) = 0.965, q(2) = 0.751) were achieved on the basis of the common substructure-based alignment. The combined results of CoMFA, CoMSIA, and former Hansch-Fujita QSAR analyses resulted in comprehensive understanding about the structure-activity relationships, which led to this construction of a plausible binding model of the title compounds.