A structure-activity relationship study of catechol-O-methyltransferase inhibitors combining molecular docking and 3D QSAR methods

A panel of 92 catechol-O-methyltransferase (COMT) inhibitors was used to examine the molecular interactions affecting their biological activity. COMT inhibitors are used as therapeutic agents in the treatment of Parkinson's disease, but there are limitations in the currently marketed compounds due to adverse side effects. This study combined molecular docking methods with three-dimensional structure-activity relationships (3D QSAR) to analyse possible interactions between COMT and its inhibitors, and to incite the design of new inhibitors. Comparative molecular field analysis (CoMFA) and GRID/GOLPE models were made by using bioactive conformations from docking experiments, which yielded q2 values of 0.594 and 0.636, respectively. The docking results, the COMT X-ray structure, and the 3D QSAR models are in agreement with each other. The models suggest that an interaction between the inhibitor's catechol oxygens and the Mg2+ ion in the COMT active site is important. Both hydrogen bonding with Lys144, Asn170 and Glu199, and hydrophobic contacts with Trp38, Pro174 and Leu198 influence inhibitor binding. Docking suggests that a large R1 substituent of the catechol ring can form hydrophobic contacts with side chains of Val173, Leu198, Met201 and Val203 on the COMT surface. Our models propose that increasing steric volume of e.g. the diethylamine tail of entacapone is favourable for COMT inhibitory activity.     

Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors

Summary Inhibition of aromatase, a cytochrome P450 that converts androgens to estrogens, is relevant in the therapeutic control of breast cancer. We investigate this inhibition using a three-dimensional quantitative structure-activity relationship (3D QSAR) method known as Comparative Molecular Field Analysis, CoMFA [Cramer III, R.D. et al., J. Am. Chem. Soc., 110 (1988) 5959]. We analyzed the data for 50 steroid inhibitors [Numazawa, M. et al., J. Med. Chem., 37 (1994) 2198, and references cited therein] assayed against androstenedione on human placental microsomes. An initial CoMFA resulted in a three-component model for log(1/K_i), with an explained variance r² of 0.885, and a cross-validated q² of 0.673. Chemometric studies were performed using GOLPE [Baroni, M. et al., Quant. Struct.-Act. Relatsh., 12 (1993) 9]. The CoMFA/GOLPE model is discussed in terms of robustness, predictivity, explanatory power and simplicity. After randomized exclusion of 25 or 10 compounds (repeated 25 times), the q² for one component was 0.62 and 0.61, respectively, while r² was 0.674. We demonstrate that the predictive r² based on the mean activity (Y_m) of the training set is misleading, while the test set Y_m-based predictive r² index gives a more accurate estimate of external predictivity. Using CoMFA, the observed differences in aromatase inhibition among C6-substituted steroids are rationalized at the atomic level. The CoMFA fields are consistent with known, potent inhibitors of aromatase, not included in the model. When positioned in the same alignment, these compounds have distinct features that overlap with the steric and electrostatic fields obtained in the CoMFA model. The presence of two hydrophobic binding pockets near the aromatase active site is discussed: a steric bulk tolerant one, common for C4, C6-alpha and C7-alpha substitutents, and a smaller one at the C6-beta region.     

Structure-based 3D QSAR and design of novel acetylcholinesterase inhibitors

The paper describes the construction, validation and application of a structure-based 3D QSAR model of novel acetylcholinesterase (AChE) inhibitors. Initial use was made of four X-ray structures of AChE complexed with small, non-specific inhibitors to create a model of the binding of recently developed aminopyridazine derivatives. Combined automated and manual docking methods were applied to dock the co-crystallized inhibitors into the binding pocket. Validation of the modelling process was achieved by comparing the predicted enzyme-bound conformation with the known conformation in the X-ray structure. The successful prediction of the binding conformation of the known inhibitors gave confidence that we could use our model to evaluate the binding conformation of the aminopyridazine compounds. The alignment of 42 aminopyridazine compounds derived by the docking procedure was taken as the basis for a 3D QSAR analysis applying the GRID/GOLPE method. A model of high quality was obtained using the GRID water probe, as confirmed by the cross-validation method (q² _LOO=0.937, q² _{L50% O}=0.910). The validated model, together with the information obtained from the calculated AChE-inhibitor complexes, were considered for the design of novel compounds. Seven designed inhibitors which were synthesized and tested were shown to be highly active. After performing our modelling study the X-ray structure of AChE complexed with donepezil, an inhibitor structurally related to the developed aminopyirdazines, has been made available. The good agreement found between the predicted binding conformation of the aminopyridazines and the one observed for donepezil in the crystal structure further supports our developed model.     

A comparative QSAR analysis and molecular docking studies of phenyl piperidine derivatives as potent dual NK1R antagonists/serotonin transporter (SERT) inhibitors

Depression is a critical mood disorder that affects millions of patients. Available therapeutic antidepressant agents are associated with several undesirable side effects. Recently, it has been shown that Neurokinin 1 receptor (NK₁R) antagonists can potentiate the antidepressant effects of serotonin-selective reuptake inhibitors (SSRIs). In this study, a series of phenyl piperidine derivatives as potent dual NK₁R antagonists/serotonin transporter (SERT) inhibitors were applied to quantitative structure–activity relationship (QSAR) analysis. A collection of chemometrics methods such as multiple linear regression (MLR), factor analysis–based multiple linear regression (FA-MLR), principal component regression (PCR), and partial least squared combined with genetic algorithm for variable selection (GA-PLS) were applied to make relations between structural characteristics and NK₁R antagonism/SERT inhibitory of these compounds. The best multiple linear regression equation was obtained from GA-PLS and MLR for NK₁R and SERT, respectively. Based on the resulted model, an in silico-screening study was also conducted and new potent lead compounds based on new structural patterns were designed for both targets. Molecular docking studies of these compounds on both targets were also conducted and encouraging results were acquired. There was a good correlation between QSAR and docking results. The results obtained from validated docking studies indicate that the important amino acids inside the active site of the cavity that are responsible for essential interactions are Glu33, Asp395 and Arg26 for SERT and Ala30, Lys7, Asp31, Phe5 and Tyr82 for NK₁R receptors.     

Development of docking-based 3D QSAR models for the design of substituted quinoline derivatives as human dihydroorotate dehydrogenase (hDHODH) inhibitors

Abstract

This study has investigated docking-based 3D quantitative structure–activity relationships (QSARs) for a range of quinoline carboxylic acid derivatives by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A docking study has shown that most of the compounds formed H-bonds with Arg136 and Gln47, which have already been shown to be essential for the binding of ligands at the active site of the hydroorotate dehydrogenase adenovirus (hDHODH). Bioactive conformations of all the molecules obtained from the docking study were used for the 3D QSAR study. The best CoMFA and CoMSIA models were obtained for the training set and were found to be statistically significant, with cross-validated coefficients (q² ) of 0.672 and 0.613, r² _cv of 0.635 and 0.598 and coefficients of determination (r² ) of 0.963 and 0.896, respectively. Both models were validated by a test set of 15 compounds, giving satisfactory predicted correlation coefficients (r² _pred) of 0.824 and 0.793 for the CoMFA and CoMSIA models, respectively. From the docking-based 3D QSAR study we designed 34 novel quinoline-based compounds and performed structure-based virtual screening. Finally, in silico pharmacokinetics and toxicities were predicted for 24 of the best docked molecules. The study provides valuable information for the understanding of interactions between hDHODH and the novel compounds.     

A three-dimensional chemical phase pharmacophore mapping,QSAR modelling and electronic feature analysis of benzofuran salicylic acid derivatives as LYP inhibitors

Lymphoid tyrosine phosphatase (LYP), encoded by the PTPN22 gene, has a critical negative regulatory role in T-cell antigen receptor (TCR) and emerged as a promising drug target for human autoimmune diseases. A five-point pharmacophore with two hydrogen bond acceptors, one hydrogen bond donor and two aromatic ring features was generated for a series of benzofuran salicylic acid derivatives as LYP inhibitors in order to elucidate their anti-autoimmune activity. The generated pharmacophore yielded a significant 3D-QSAR model with r² of 0.9146 for a training set of 27 compounds. The model also showed excellent predictive power with Q² of 0.7068 for a test set of eight compounds. The investigation of the 3D-QSAR model has revealed the structural insights which could lead to more potent analogues. The most active and inactive compounds were further subjected to electronic structure analysis using density functional theory (DFT) at B3LYP/3?21^?G level to support the 3D-QSAR predictions. The results obtained from this study are expected to be useful in the proficient design and development of benzofuran salicylic acid derivatives as inhibitors of LYP.     

3D－QSAR Study on Diindolylmethane and Its Analogues with Comparative Molecular Field Analysis(CoMFA)

Comparative molecular field analysis (CoMFA),a three dimensional quantitative structure-activity relationship (3D-QSAR) method was applied to a series of diindolylmethane(DIM) analogs to study the relationship between their structure and their induction of CYP 1A1-associated ethoxyresorufin-O-deethylase(EROD) activity.A DISCO model of pharmacophore was derved to guide the superposition of the compounds.The coefficient of cross-validation (q^2) and non cross-validation(r^2) for the model established by the study are 0.827 and 0.988 respectively,the value of variance ratio (F) is 103.53 and standard error estimate (SEE)is 0.044.These values indicate that the CoMFA model derived is significant and might have a good prediction for the catalytic activity of DIM compounds.As a consequence,the predicted activity values of new designed compounds were all higher than that of the reported value.     

Theoretical studies on QSAR and mechanism of 2‐indolinone derivatives as tubulin inhibitors

The theoretical studies on three‐dimensional quantitative structure activity relationship (3D‐QSAR) and action mechanism of a series of 2‐indolinone derivatives as tubulin inhibitors against human breast cancer cell line MDA‐MB‐231 have been carried out. The established 3D‐QSAR model from the comparative molecular field analysis (CoMFA) shows not only significant statistical quality but also predictive ability, with high correlation coefficient (R² = 0.986) and cross‐validation coefficient (q² = 0.683). In particular, the appropriate binding orientations and conformations of these 2‐indolinone derivatives interacting with tubulin are located by docking study, and it is very interesting to find that the plot of the energy scores of these compounds in DOCK versus the corresponding experimental pIC₅₀ values exhibits a considerable linear correlation. Therefore, the inhibition mechanism that 2‐indolinone derivatives were regarded as tubulin inhibitors can be theoretically confirmed. Based on such an inhibition mechanism along with 3D‐QSAR results, some important factors improving the activities of these compounds were discussed in detail. These factors can be summarized as follows: the H atom adopted as substituent R₁, the substituent R₂ with higher electropositivity and smaller bulk, the substituents R₄–R₆ (on the phenyl ring) with higher electropositivity and larger bulk, and so on. These results can offer useful theoretical references for understanding the action mechanism, designing more potent inhibitors, and predicting their activities prior to synthesis. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2009     

CoMFA and CoMSIA 3D-quantitative structure-activity relationship model on benzodiazepine derivatives, inhibitors of phosphodiesterase IV

Recently, we reported structurally novel PDE4 inhibitors based on 1,4-benzodiazepine derivatives. The main interest in developing bezodiazepine-based PDE4 inhibitors is in their lack of adverse effects of emesis with respect to rolipram-like compounds. A large effort has thus been made toward the structural optimization of this series. In the absence of structural information on the inhibitor binding mode into the PDE4 active site, 2D-QSAR (H-QSAR) and two 3D-QSAR (CoMFA and CoMSIA) methods were applied to improve our understanding of the molecular mechanism controlling the PDE4 affinity of the benzodiazepine derivatives. As expected, the CoMSIA 3D contour maps have provided more information on the benzodiazepine interaction mode with the PDE4 active site whereas CoMFA has built the best tool for activity prediction. The 2D pharmacophoric model derived from CoMSIA fields is consistent with the crystal structure of the PDE4 active site reported recently. The combination of the 2D and 3D-QSAR models was used not only to predict new compounds from the structural optimization process, but also to screen a large library of bezodiazepine derivatives.     

Development of biologically active compounds by combining 3D QSAR and structure-based design methods

One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved – namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It is expected that concepts from receptor-based 3D QSAR will be valuable tools for the analysis of high-throughput screening as well as virtual screening data     

类吗啡类拮抗物的结构与抑食活性的3D-QSAR研究

用比较力场分析研究了3,4-二甲基-4-(3-羟基苯基)哌啶及其衍生物类吗啡拮抗物的结构与抑食活性的关系,考察了网格结构和探针原子的影响.结果表明,立体效应和静电作用场是描述其抑食活性和进行结构性能关系研究的最重要的结构参数.     

The structure-activity relationship of inhibitors of serotonin uptake and receptor binding

Summary An analysis of five different datasets of inhibitors of serotonin uptake has yielded quantitative structure/ activity relationships (QSARs) which delineate the role of steric and hydrophobic properties essential for inhibition by phenylethylamine-type analogues.     

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.     

Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling,molecular docking, 3D-QSAR model and binding free energy calculation studies

B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG_bind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC₅₀ < 50 μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs.