Studies of febuxostat analogues as xanthine oxidase inhibitors through 3D-QSAR,Topomer CoMFA and molecular modeling

Xanthine oxidase, a complex molybdoflavoprotein, catalyzes the hydroxylation of xanthine to uric acid, which has emerged as an important target for gout and hyperuricemia. In this work, a combination of molecular modeling methods was performed on a series of febuxostat analogues as xanthine oxidase inhibitors to establish molecular models for new drug design, including three-dimensional quantitative structure–activity relationship, topomer comparative molecular field analysis (CoMFA), molecular docking and molecular dynamic simulations. The optimal CoMFA model yielded a leave-one-out correlation coefficient (q²) of 0.841 and a non-validated correlation coefficient (r²) of 0.985. The respective q² and r² of the best comparative molecular similarity indices analysis (CoMSIA) model were 0.794 and 0.972, respectively. The Topomer CoMFA model provided a q² of 0.915 and an r² of 0.977. 3D contour maps generated from CoMFA and CoMSIA have identified several key features responsible for the inhibition activity. Molecular modeling was taken to further elucidate the proposed binding conformations of the inhibitors to the protein. The obtained results can be served as a useful guideline for designing novel febuxostat derivatives with improved activity against xanthine oxidase.

     

3D-QSAR and Docking Studies of Pyrido[2,3-d]pyrimidine Derivatives as Wee1 Inhibitors

In order to investigate the inhibiting mechanism and obtain some helpful information for de-signing functional inhibitors against Wee1, three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies have been performed on 45 pyrido[2,3-d] pyrim-idine derivatives acting as Wee1 inhibitors. Two optimal 3D-QSAR models with significant statistical quality and satisfactory predictive ability were established, including the CoMFA model (q²=0.707, R²=0.964) and CoMSIA model (q²=0.645, R²=0.972). The external val-idation indicated that both CoMFA and CoMSIA models were quite robust and had high predictive power with the predictive correlation coefficient values of 0.707 and 0.794, essen-tial parameter r²_m values of 0.792 and 0.826, the leave-one-out r²_m(LOO) values of 0.781 and 0.809, r²_m(overall) values of 0.787 and 0.810, respectively. Moreover, the appropriate binding orientations and conformations of these compounds interacting with Wee1 were revealed by the docking studies. Based on the CoMFA and CoMSIA contour maps and docking analyses, several key structural requirements of these compounds responsible for inhibitory activity were identified as follows: simultaneously introducing high electropositive groups to the sub-stituents R1 and R5 may increase the activity, the substituent R2 should be smaller bulky and higher electronegative, moderate-size and strong electron-withdrawing groups for the substituent R3 is advantageous to the activity, but the substituent X should be medium-size and hydrophilic. These theoretical results help to understand the action mechanism and design novel potential Wee1 inhibitors.     

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.     

Strategies to design pyrazolyl urea derivatives for p38 kinase inhibition: a molecular modeling study

The p38 protein kinase is a serine–threonine mitogen activated protein kinase, which plays an important role in inflammation and arthritis. A combined study of 3D-QSAR and molecular docking has been undertaken to explore the structural insights of pyrazolyl urea p38 kinase inhibitors. The 3D-QSAR studies involved comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA). The best CoMFA model was derived from the atom fit alignment with a cross-validated r ² (q ²) value of 0.516 and conventional r ² of 0.950, while the best CoMSIA model yielded a q ² of 0.455 and r ² of 0.979 (39 molecules in training set, 9 molecules in test set). The CoMFA and CoMSIA contour maps generated from these models provided inklings about the influence of interactive molecular fields in the space on the activity. GOLD, Sybyl (FlexX) and AutoDock docking protocols were exercised to explore the protein–inhibitor interactions. The integration of 3D-QSAR and molecular docking has proffered essential structural features of pyrazolyl urea inhibitors and also strategies to design new potent analogues with enhanced activity.     

QSAR models of cytochrome P450 enzyme 1A2 inhibitors using CoMFA,CoMSIA and HQSAR

Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q² of 0.667 and a Pearson correlation coefficient r² of 0.976; a CoMSIA q² value of 0.616 and r² value of 0.985; and a HQSAR q² value of 0.652 and r² value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q² value of 0.5 and r² value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models.     

QM/MM based 3D QSAR models for potent B-Raf inhibitors

Three dimensional (3D) quantitative structure-activity relationship studies of 37 B-Raf inhibitors, pyrazole-based derivatives, were performed. Based on the co-crystallized compound (PDB ID: 3D4Q), several alignment methods were utilized to derive reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. Receptor-guided alignment with quantum mechanics/molecular mechanics (QM/MM) minimization led to the best CoMFA model (q ² = 0.624, r ² = 0.959). With the same alignment, a statistically reliable CoMSIA model with steric, H-bond acceptor, and hydrophobic fields was also derived (q ² = 0.590, r ² = 0.922). Both models were validated with an external test set, which gave satisfactory predictive r ² values of 0.926 and 0.878, respectively. Contour maps from CoMFA and CoMSIA models revealed important structural features responsible for increasing biological activity within the active site and explained the correlation between biological activity and receptor-ligand interactions. New fragments were identified as building blocks which can replace R1-3 groups through combinatorial screening methods. By combining these fragments a compound with a high bioactivity level prediction was found. These results can offer useful information for the design of new B-Raf inhibitors.     

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.     

The 3D Structure of the Binding Pocket of the Human Oxytocin Receptor for Benzoxazine Antagonists,Determined by Molecular Docking,Scoring Functions and 3D-QSAR Methods

Summary Molecular docking and 3D-QSAR studies were performed to determine the binding mode for a series of benzoxazine oxytocin antagonists taken from the literature. Structural hypotheses were generated by docking the most active molecule to the rigid receptor by means of AutoDock 3.05. The cluster analysis yielded seven possible binding conformations. These structures were refined by using constrained simulated annealing, and the further ligands were aligned in the refined receptor by molecular docking. A good correlation was found between the estimated ΔG_bind and the pK_i values for complex F. The Connolly-surface analysis, CoMFA and CoMSIA models q_CoMFA² = 0.653, q_CoMSA² = 0.630 and r_pred,CoMFA² = 0.852 , r_pred,CoMSIA² = 0.815) confirmed the scoring function results. The structural features of the receptor–ligand complex and the CoMFA and CoMSIA fields are in closely connected. These results suggest that receptor–ligand complex F is the most likely binding hypothesis for the studied benzoxazine analogs.     

3D-QSAR and docking studies on 2-arylbenzoxazole and linker-Y transthyretin amyloidogenesis inhibitors

Transthyretin (TTR), a plasma protein with a tetramer structure, could form amyloid fibril associated with several human diseases through the dissociation of tetramer and the misfolding of monomer. These amyloidogenesis can be inhibited by small molecules which bind to the central channel of TTR. A number of small molecules like 2-arylbenzoxazoles (ABZ) analogues are proposed as promising therapeutic strategy to treat amyloidosis. In this work, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies were performed on series of 2-arylbenzoxazoles (ABZ) and linker-Y analogues to investigate the inhibitory activities of TTR amyloidogenesis at atomic level. Significant correlation coefficients for ABZ series (CoMFA, r ² = 0.877, q ² = 0.431; CoMSIA, r ² = 0.836, q ² = 0.447) and those for linker-Y series (CoMFA, r ² = 0.828, q ² = 0.522; CoMSIA, r ² = 0.800, q ² = 0.493) were obtained, and the generated models were validated using test sets. In addition, docking studies on 6 compounds binding to TTR were performed to analyze the forward or reverse binding mode and interactions between molecules and TTR. These results from 3D-QSAR and docking studies have great significance for designing novel TTR amyloidogenesis inhibitors in the future.     

五元杂环并嘧啶类胸苷酸合成酶抑制剂的构效关系和分子对接

用比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)研究了38个五元杂环并嘧啶衍生物类胸苷酸合成酶抑制剂的三维定量构效关系(3D-QSAR), 建立了相关预测模型. CoMFA和CoMSIA模型的交互验证相关系数q²分别为0.662和0.672、非交互验证相关系数R²分别为0.921和0.884、外部交互验证相关系数Q_ext²分别为0.85和0.81. 分子对接得到的结合模式与三维定量构效关系得到的结果一致. 结果表明这两种模型都具有良好的预测能力, 可应用于指导化合物的设计和结构修饰, 为进一步设计新型胸苷酸合成酶抑制剂提供了理论依据.     

Comparative QSAR studies using HQSAR,CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors

The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC₅₀: 0.002–2.75 μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q² value of 0.693 and r² value of 0.981; a CoMFA q² value of 0.534 and r² value of 0.913; and a CoMSIA q² value of 0.512 and r² value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.     

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q ²) of 0.602 and 0.618, respectively, and conventional coefficients (r ²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r ² _pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.     

3D-QSAR studies and molecular docking on [5-(4-amino-1<Emphasis Type="Italic">H</Emphasis>-benzoimidazol-2-yl)-furan-2-yl]-phosphonic acid derivatives as fructose-1,6-biphophatase inhibitors

Fructose-1,6-biphophatase has been regarded as a novel therapeutic target for the treatment of type 2 diabetes mellitus (T2DM). 3D-QSAR and docking studies were performed on a series of [5-(4-amino-1H-benzoimidazol-2-yl)-furan-2-yl]-phosphonic acid derivatives as fructose-1,6-biphophatase inhibitors. The CoMFA and CoMSIA models using thirty-seven molecules in the training set gave r _cv² values of 0.614 and 0.598, r ² values of 0.950 and 0.928, respectively. The external validation indicated that our CoMFA and CoMSIA models possessed high predictive powers with r ₀² values of 0.994 and 0.994, r _m² values of 0.751 and 0.690, respectively. Molecular docking studies revealed that a phosphonic group was essential for binding to the receptor, and some key features were also identified. A set of forty new analogues were designed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The findings can be quite useful to aid the designing of new fructose-1,6-biphophatase inhibitors with improved biological response.     

Three-dimensional Quantitative Structure-Activity Relationship Analyses of a Series of Butenolide ETA Antagonists

Two kinds of Three-dimensional Quantitative Structure-activity Relationship(3D-QSAR) methods,comparative molecular filed analysis(CoMFA) and comparative molecular similarity indices analysis (CoMSIA) ,were applied to analyze the structure-activity relationship of a series of 63 butenolide ETA selective antagonists with respect to their inhibition against human ETA receptor,The CoMFA and CoMSIA models were developed for the conceivable alignment of the molecules based on a template structure from the crystallized data.The statistical results from the initial orientation of the aligned molecules show that the 3D-QSAR model from CoMFA(q^2=0.543) is obviously superior to that from the conventional CoMSIA(q^2=0.407).In order to refine the model,all-space search (ASS) was applied to minimize the field sampling process.By rotating and translating the molecular aggregate within the grid systematically,all the possible samplings of the molecular fields were tested and subsequently the one with the highest q^2 was picked out .The comparison of the sensitivity of CoMFA and CoMSIA to different space orientation shows that the CoMFA q^2 values are more sensitive to the translations and rotations of the aligned molecules with respect to the lattice than those of CoMSIA.The best CoMFA model from ASS was further refined by the region focused technique.The high quality of the best model is indicated by the high corss-validated correlation and the prediction on the external test set.The CoMFA coefficient contour plots identify several key features that explain the wide range of activities,which may help us to design new effective ETA selective antagonists.     

3D-QSAR (CoMFA,CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer

As per the World Health Organization (WHO), cancer is the second most leading cause of death after cardiovascular diseases in worldwide with around 9.88 million total new cases and 1.08 million were observed due to skin cancer in 2018. Amongst two types of skin cancer, progression of melanoma cancer is increasing day by day due to the environmental changes than non-melanoma cancer. Most of B-Raf mutation, specifically B-RafV600E, is responsible for the progression of the melanoma cancer. Here, various 3D-QSAR techniques like comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular hologram QSAR (HQSAR) and topomer CoMFA were used to design novel B-Raf inhibitors by using 28 synthetic B-Raf inhibitors. Except for topomer CoMFA model, remaining models were generated by three different alignment methods in which distil-based alignment method was found best and gave prominent statistical values. After performing N-fold statistical validation, in CoMFA, q², r² and r²_pred values were found to be 0.638, 0.969 and 0.848, respectively. Similarly, q², r² and r²_pred values were found to be 0.796, 0.978 and 0.891 in CoMSIA (SHD) and 0.761, 0.973 and 0.852 in CoMSIA (SH) by N-fold statistical validation. In HQSAR analysis, statistical values were found for q² as 0.984, r² as 0.999 and r²_pred as 0.634 with 97 as best hologram length (BHL). The results of topomer CoMFA showed the q² value of 0.663 and the r² value of 0.967. Important features of purinylpyridine were identified by contour map analysis of all 3D-QSAR techniques, which could be useful to design the novel molecules as B-Raf inhibitors for the treatment of melanoma cancer.

     

Molecular docking and 3D-quantitative structure activity relationship analyses of peptidyl vinyl sulfones: <Emphasis Type="Italic">Plasmodium Falciparum</Emphasis> cysteine proteases inhibitors

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were conducted on a series (39 molecules) of peptidyl vinyl sulfone derivatives as potential Plasmodium Falciparum cysteine proteases inhibitors. Two different methods of alignment were employed: (i) a receptor-docked alignment derived from the structure-based docking algorithm GOLD and (ii) a ligand-based alignment using the structure of one of the ligands derived from a crystal structure from the PDB databank. The best predictions were obtained for the receptor-docked alignment with a CoMFA standard model (q ² = 0.696 and r ² = 0.980) and with CoMSIA combined electrostatic, and hydrophobic fields (q ² = 0.711 and r ² = 0.992). Both models were validated by a test set of nine compounds and gave satisfactory predictive r ² _pred values of 0.76 and 0.74, respectively. CoMFA and CoMSIA contour maps were used to identify critical regions where any change in the steric, electrostatic, and hydrophobic fields may affect the inhibitory activity, and to highlight the key structural features required for biological activity. Moreover, the results obtained from 3D-QSAR analyses were superimposed on the Plasmodium Falciparum cysteine proteases active site and the main interactions were studied. The present work provides extremely useful guidelines for future structural modifications of this class of compounds towards the development of superior antimalarials.     

Docking-based CoMFA and CoMSIA study of azaindole carboxylic acid derivatives as promising HIV-1 integrase inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed based on a series of azaindole carboxylic acid derivatives that had previously been reported as promising HIV-1 integrase inhibitors. Docking studies to explore the binding mode were performed based on the highly active molecule 36. The best docked conformation of molecule 36 was used as template for alignment. The comparative molecular field analysis (CoMFA) model (including steric and electrostatic fields) yielded the cross validation q ² = 0.655, non-cross validation r ² = 0.989 and predictive r ² _pred = 0.979. The best comparative molecular similarity indices analysis (CoMSIA) model (including steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields) yielded the cross validation q ² = 0.719, non-cross validation r ² = 0.992 and predictive r ² _pred = 0.953. A series of new azaindole carboxylic acid derivatives were designed and the HIV-1 integrase inhibitory activities of these designed compounds were predicted based on the CoMFA and CoMSIA models.     

3D CoMFA,CoMSIA, topomer CoMFA and HQSAR studies on aromatic acid esters for carbonic anhydrase inhibitory activity

Molecular modelling studies [comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), topomer CoMFA and hologram quantitative structure–activity relationship (HQSAR)] have been performed on the series of 28 molecules belonging to the series of aromatic acid ester derivatives for their carbonic anhydrase inhibitory activity. The model exhibited good correlation coefficient (r²) and cross‐validated correlation coefficient (q²) for CoMFA, CoMSIA and HQSAR methods. On the basis of the findings from all these studies, a structure–activity relationship was established. Copyright © 2013 John Wiley & Sons, Ltd.     

靛玉红类CDK1抑制剂的同源模建、分子对接及3D-QSAR研究

细胞周期蛋白依赖性激酶1的异常表达会导致G2期的停滞及多种肿瘤的发生,故CDK1近年来已成为一个理想的治疗靶点. 本文以细胞分裂调控蛋白2的同源体为模板,同源模建了CDK1的结构,并与靛玉红类小分子抑制剂进行分子对接. 分别运用三种叠合方法进行分子叠合,并在此基础上采用Sybyl 7.1中的比较分子场分析(CoMFA)模块及Discovery Studio 3.0中的三维定量构效关系(3D-QSAR)模块(以下简称为DS)分别建立了3D-QSAR模型. 其中,将分子对接叠合与公共骨架叠合联合运用的叠合方法所得3D-QSAR模型的评价参数是最佳的(CoMFA：q²=0.681,r²=0.909,r_pred.²=0.836; DS：q²=0.579,r²=0.971,r_pred.²=0.795,其中q²为交叉验证系数,r²为非交叉验证系数). 本文的研究结果在对靛玉红类小分子进行结构修饰设计出新的CDK1抑制剂方面,可提供重要的理论基础.