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.     

PDK1的吲哚酮类抑制剂定量构效关系研究

3'磷酸肌醇依赖的激酶1(3'-phosphoinositide-dependent kinase1,PDK1)是--个63kDa的丝氨酸/苏氨酸(Ser/Thr)蛋白酶,它是Akt1的上游活化激酶~([1,2]).PDK1可在磷酸化的Akt1的Thr308位点使Akt1的活性增加30倍以上.但这种活化以依赖PIP3或PIP2的方式完成,PDK1也就因此而得名~([3]).     

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.     

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-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.     

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.     

Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q² > 0.4, r² > 0.5 and r²_pred > 0.5. Based on better q² and r²_pred values, the best predictions were obtained for the CoMFA (model 5 q² = 0.488, r²_pred = 0.732), and CoMSIA (model 45 q² = 0.525, r²_pred = 0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.     

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.     

A combined CoMFA and CoMSIA 3D-QSAR study of benzamide type antibacterial inhibitors of the FtsZ protein in drug-resistant Staphylococcus aureus

A major problem today is bacterial resistance to antibiotics and the small number of new therapeutic agents approved in recent years. The development of new antibiotics capable of acting on new targets is urgently required. The filamenting temperature-sensitive Z (FtsZ) bacterial protein is a key biomolecule for bacterial division and survival. This makes FtsZ an attractive new pharmacological target for the development of antibacterial agents. There have been several attempts to develop ligands able to inhibit FtsZ. Despite the large number of synthesized compounds that inhibit the FtsZ protein, there are no quantitative structure–activity relationships (QSAR) that allow for the rational design and synthesis of promising new molecules. We present the first 3D-QSAR study of a large and diverse set of molecules that are able to inhibit the FtsZ bacterial protein. We summarize a set of chemical changes that can be made in the steric, electrostatic, hydrophobic and donor/acceptor hydrogen-bonding properties of the pharmacophore, to generate new bioactive molecules against FtsZ. These results provide a rational guide for the design and synthesis of promising new antibacterial agents, supported by the strong statistical parameters obtained from CoMFA (r²_pred = 0.974) and CoMSIA (r²_pred = 0.980) analyses.     

QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

The recent wide spreading of the H5N1 avian influenza virus (AIV) in Asia, Europe and Africa and its ability to cause fatal infections in human has raised serious concerns about a pending global flu pandemic. Neuraminidase (NA) inhibitors are currently the only option for treatment or prophylaxis in humans infected with this strain. However, drugs currently on the market often meet with rapidly emerging resistant mutants and only have limited application as inadequate supply of synthetic material. To dig out helpful information for designing potent inhibitors with novel structures against the NA, we used automated docking, CoMFA, CoMSIA, and HQSAR methods to investigate the quantitative structure-activity relationship for 126 NA inhibitors (NIs) with great structural diversities and wide range of bioactivities against influenza A virus. Based on the binding conformations discovered via molecular docking into the crystal structure of NA, CoMFA and CoMSIA models were successfully built with the cross-validated q (2) of 0.813 and 0.771, respectively. HQSAR was also carried out as a complementary study in that HQSAR technique does not require 3D information of these compounds and could provide a detailed molecular fragment contribution to the inhibitory activity. These models also show clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors. In addition, CoMFA and CoMSIA field distributions are found to be in well agreement with the structural characteristics of the corresponding binding sites. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction should be useful in developing novel potent NA inhibitors.     

CoMFA,CoMSIA, Topomer CoMFA,HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer

mTOR has become a promising target for many types of cancer like breast, lung and renal cell carcinoma. CoMFA, CoMSIA, Topomer CoMFA and HQSAR were performed on the series of 39 triazine morpholino derivatives. CoMFA analysis showed q² value of 0.735, r²_cv value of 0.722 and r²_pred value of 0.769. CoMSIA analysis (SEHD) showed q² value of 0.761, r²_cv value of 0.775 and r²_pred value of 0.651. Topomer CoMFA analysis showed q² value of 0.693, r² (conventional correlation coefficient) value of 0.940 and r²_pred value of 0.720. HQSAR analysis showed q²,r²and r²_pred values of 0.694, 0.920 and 0.750, respectively. HQSAR analysis with the combination of atomic number (A), bond type (B) and atomic connections showed q² and r² values of 0.655 and 0.891, respectively. Contour maps from all studies provided significant insights. Molecular docking studies with molecular dynamics simulations were carried out on the highly potent compound 36. Furthermore, four acridine derivatives were designed and docking results of these designed compounds showed the same interactions as that of the standard PI-103 which proved the efficiency of 3D-QSAR and MD/MS study. In future, this study might be useful prior to synthesis for the designing of novel mTOR inhibitors.     

Structural investigations of anthranilimide derivatives by CoMFA and CoMSIA 3D-QSAR studies reveal novel insight into their structures toward glycogen phosphorylase inhibition

In the present work, three-dimensional quantitative structure–activity relationship (3-D QSAR) studies on a set of 70 anthranilimide compounds has been performed using docking-based as well as substructure-based molecular alignments. This resulted in the selection of more statistically relevant substructure-based alignment for further studies. Further, molecular models with good predictive power were derived using CoMFA (r ^2?=?0.997; Q ^2?=?0.578) and CoMSIA (r ^2?=?0.976; Q ^2?=?0.506), for predicting the biological activity of new compounds. The so-developed contour plots identified several key features of the compounds explaining wide activity ranges. Based on the information derived from the CoMFA contour maps, novel leads were proposed which showed better predicted activity with respect to the already reported systems. Thus, the present study not only offers a highly significant predictive QSAR model for anthranilimide derivatives as glycogen phosphorylase (GP) inhibitors which can eventually assist and complement the rational drug-design attempts, but also proposes a highly predictive pharmacophore model as a guide for further development of selective and more potent GP inhibitors as anti-diabetic agents.     

比较分子力场分析法(CoMFA)的研究新进展

CoMFA是目前3D-QSAR 中应用最广且最为成功的一种方法, 但其在设计思想和实施过程中还存在一定缺陷, 主要表现在匹配规则、新场引入、变量选择及数据处理等几个方面。本文对近年来关于CoMFA 方法的各种改进研究做了较为系统的综述, 并就CoMFA 在药物设计中的作用进行了展望。     

QSAR and Pharmacophore Studies of Thiazolidine-4-carboxylic Acid Derivatives as Novel Influenza Neuraminidase Inhibitors Using HQSAR, Topomer CoMFA and CoMSIA

In order to understand the chemical-biological interactions governing their activities toward neuraminidase (NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. The obtained HQSAR (hologram quantitative structure activity relationship), Topomer CoMFA and CoMSIA (comparative molecular similarity indices analysis) models were robust and had good exterior predictive capabilities. Moreover, QSAR modeling results elucidated that hydrogen bonds highly contributed to the inhibitory activity, then electrostatic and hydrophobic factors. Squared multiple correlation coefficients (R2) of HQSAR, Topomer CoMFA and CoMSIA models were 0.994, 0.978 and 0.996, respectively. Squared cross-validated correlation coefficients (Q2) of HQSAR, Topomer CoMFA and CoMSIA models were in turn 0.951, 919 and 0.820. Furthermore, squared multiple correlation coefficients for the test set (R2test) of HQSAR, CoMFA and CoMSIA models were 0.879, 0.912 and 0.953, respectively. Squared cross-validated correlation coefficients for the test set (Q2ext) of HQSAR, Topomer CoMFA and CoMSIA models were 0.867, 0.884 and 0.899, correspondingly.     

QSAR studies on hepatitis C virus NS5A protein tetracyclic inhibitors in wild type and mutants by CoMFA and CoMSIA

ABSTRACT

Several 3D-QSAR models were built based on 196 hepatitis C virus (HCV) NS5A protein inhibitors. The bioactivity values EC₉₀ for three types of inhibitors, the wild type (GT1a) and two mutants (GT1a Y93H and GT1a L31V), were collected to build three datasets. The programs OMEGA and ROCS were used for generating conformations and aligning molecules of the dataset, respectively. Each dataset was randomly divided into a training set and a test set three times to reduce the contingency of only one random selection. QSAR models were computed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the datasets GT1a, GT1a Y93H, and GT1a L31V, the best models CoMFA-INDX, CoMSIA-SEHA, and CoMSIA-SEHA showed an r² value of 0.682 ± 0.033, 0.779 ± 0.036, and 0.782 ± 0.022 on the test sets, respectively. From the contour maps of the three best models, we summarized the favourable and unfavourable substituents on the tetracyclic core, the Z group, the proline group, and the valine group of inhibitors. We guessed the mutants could change the electrostatic surfaces of the wild type active pocket. In addition, we used ECFP analyses to find important substructures and could intuitively understand the results from QSAR models.     

A 3D‐QSAR Study of Celebrex‐Based Pdk1 Inhibitors Using Comfa Method

A 3D‐QSAR study of celebrex‐based compounds of PDK1 inhibitors using comparative molecular field analysis (CoMFA) was carried out. The structures of the compounds were obtained using quantum chemistry calculation. CoMFA calculations for a number of grouped subsets of compounds gave q² values of correlation in the range from 0 to 0.8. The low q² values should be mainly due to the narrow span of biological activity. Calculations for several subsets of 11–13 compounds gave high q² values, with 0.5–0.8. Factors affecting the results of the calculations are discussed. Calculated results with high q² values suggest that further chemical modifications of the compounds could lead to enhanced activity and could be an aid in the design of celebrex‐based cancer drugs.     

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of thiazolone derivatives as hepatitis C virus NS5B polymerase allosteric inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models for a series of thiazolone derivatives as novel inhibitors bound to the allosteric site of hepatitis C virus (HCV) NS5B polymerase were developed based on CoMFA and CoMSIA analyses. Two different conformations of the template molecule and the combinations of different CoMSIA field/fields were considered to build predictive CoMFA and CoMSIA models. The CoMFA and CoMSIA models with best predictive ability were obtained by the use of the template conformation from X-ray crystal structures. The best CoMFA and CoMSIA models gave q (2) values of 0.621 and 0.685, and r (2) values of 0.950 and 0.940, respectively for the 51 compounds in the training set. The predictive ability of the two models was also validated by using a test set of 16 compounds which gave r (pred) (2) values of 0.685 and 0.822, respectively. The information obtained from the CoMFA and CoMSIA 3D contour maps enables the interpretation of their structure-activity relationship and was also used to the design of several new inhibitors with improved activity.