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.     

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

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楝酰胺(Rocaglamide)类化合物的三维定量构效关系

采用比较分子力场分析(CoMFA)和比较分子相似因子分析(CoMSIA)方法,对训练集中的26个楝酰胺(Rocaglamide)类化合物进行了三维定量构效关系(3D-QSAR)研究,最终建立的CoMFA模型和CoMSlA模型的q<'2>分别为0.593和0.656.并对测试集中的5个化合物的生物活性进行了预测,结果表明...     

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.     

3D-QSAR study of hallucinogenic phenylalkylamines by using CoMFA approach

The three-dimensional quantitative structure–activity relationship (3D-QSAR) has been studied on 90 hallucinogenic phenylalkylamines by the comparative molecular field analysis (CoMFA). Two conformations were compared during the modeling. Conformation I referred to the amino group close to ring position 6 and conformation II related to the amino group trans to the phenyl ring. Satisfactory results were obtained by using both conformations. There were still differences between the two models. The model based on conformation I got better statistical results than the one about conformation II. And this may suggest that conformation I be preponderant when the hallucinogenic phenylalkylamines interact with the receptor. To further confirm the predictive capability of the CoMFA model, 18 compounds with conformation I were randomly selected as a test set and the remaining ones as training set. The best CoMFA model based on the training set had a cross-validation coefficient q ² of 0.549 at five components and non cross-validation coefficient R ² of 0.835, the standard error of estimation was 0.219. The model showed good predictive ability in the external test with a coefficient R _pre² of 0.611. The CoMFA coefficient contour maps suggested that both steric and electrostatic interactions play an important role. The contributions from the steric and electrostatic fields were 0.450 and 0.550, respectively.     

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.     

A 3D-QSAR CoMSIA study on 3-azolylmethylindoles as anti-leishmanial agents

A three-dimensional quantitative structure-activity relationship (3D-QSAR) study using Comparative Molecular Similarity Indices Analysis (CoMSIA) was conducted on a series of 3-azolylmethylindoles as anti-leishmanial agents. Evaluation of 24 compounds synthesized in our laboratory served to establish the model. A random search was performed on the library of compounds, and molecules of the training set were aligned on common elements of template molecule 13, one of the most active compounds. The best predictions were obtained from multifit procedure with a CoMSIA model combining steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (q ²?=?0.594, r ²?=?0.897). The model was validated using an external test set of 7 compounds giving a satisfactory predictive r ² value of 0.649. Information obtained from CoMSIA contour maps could be used for further design of more promising inhibitors.     

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.

     

3D-QSAR Studies of Dimethoxyphenoxyphenoxy pyrimidines and Analogues

1　 INTRODUCTIONPhotosynthesis is special and important physiology and biochemistry phe-nomenon for plant,so itisnecessary to selectphotosynthesis as a herbicidal targetinorder to get nontoxic pesticides. Recently,a number of 6 - ( 4- phenoxyphenoxy)pyrimidines and triazines which showed a strong Hill reaction inhibition were synthe-sized and their herbicidal activity was measured〔1〕. General structural formulas ofthese compounds are shown in Table1 .In this paper,we examined the three…     

3D-QSAR study on heterocyclic topoisomerase II inhibitors using CoMSIA

Selective topoisomerase II (Topo II) inhibitors have interested to a great extent for the design of new antitumoral compounds in recent years. Comparative molecular similarity indices analysis (CoMSIA) was performed on a series of previously synthesized benzoxazole, benzimidazole, and oxazolo(4,5-b)pyridine derivatives as eukaryotic Topo II inhibitors. A training set of 16 heterocyclic compounds was used to establish the CoMSIA model. They were constructed and geometrically optimized using SYBYL v7.0. The predictive ability of the model was assessed using a test set of 7 compounds. The best model has demonstrated a good fit having r2 value of 0.968 and cross-validated coefficient q2 value as 0.562 including steric and hydrophobic fields. The hydrophobic interactions showed a dominant role for increasing Topo II inhibitor activity and hydrophilic substituent was found more important than hydrophobic one on the 5 or 6 position of benzazole moiety. The model obtained from the present study can be useful for the modification and/or evaluation of the development of new Topo II inhibitors as potential antitumor compounds.     

A 3D-QSAR CoMSIA study on 3-azolylmethylindoles as anti-leishmanial agents

A three-dimensional quantitative structure-activity relationship (3D-QSAR) study using Comparative Molecular Similarity Indices Analysis (CoMSIA) was conducted on a series of 3-azolylmethylindoles as anti-leishmanial agents. Evaluation of 24 compounds synthesized in our laboratory served to establish the model. A random search was performed on the library of compounds, and molecules of the training set were aligned on common elements of template molecule 13, one of the most active compounds. The best predictions were obtained from multifit procedure with a CoMSIA model combining steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (q2 = 0.594, r2 = 0.897). The model was validated using an external test set of 7 compounds giving a satisfactory predictive r2 value of 0.649. Information obtained from CoMSIA contour maps could be used for further design of more promising inhibitors.     

A 3D-QSAR Study of HIV-1 Integrase Inhibitors Using RASMS and Topomer CoMFA

Acquired Immunodeficiency Syndrome(AIDS) is a significant human health threat around the world. Therefore, the study of anti-human immunodeficiency virus(HIV) drug design has become an important task for today's society. In this paper, a three-dimensional quantitative structure-activity relationships study(3 D-QSAR) was conducted on 53 HIV-1 integrase inhibitors(IN) using random sampling analysis on molecular surface(RASMS) and Topomer comparative molecular field analysis(Topomer CoMFA). The multiple correlation coefficients of fitting, cross-validation, and external validation of two models were 0.926, 0.815 and 0.908 and 0.930, 0.726 and 0.855, respectively. The results indicated that two models obtained had both favorable estimation stability and good prediction capability. Topomer Search was used to search appropriate R groups from ZINC database, and 28 new compounds were designed thereby. The Topomer CoMFA model was subsequently used to predict the biological activity of these compounds, showing that 24 of the new compounds were more active than the template molecule. Ligands of the template molecule and new designed compounds were used for molecular docking to study the interaction of these compounds with the protein receptor. The results show that the ligands would form hydrogen-bonding interactions with the residues LEU58, THR83, GLN62, MET155, LYS119 and ALA154 of the protein receptor generally, thereby providing additional insights for the design of even more effective HIV/AIDS drugs.     

CoMFA 3D-QSAR Analysis of Epothilones Based on Docking Conformation and Alignment

Epothilones belong to a class of novel microtubule stabilizing and anti-mitotic agents, which have a paclitaxel-like mechanism of action. A three-dimensional quantitative structure-activity relationship （3D-QSAR） model was built for epothilones by the method of comparative molecular field analysis （CoMFA） combined with the flexible docking technology. The docking CoMFA model gave a good cross-validated value of q2=0.784 with an optimized component of 6 and the conventional correlation coefficient of r^2=0.985. The statistical results show that the model has good ability to predict the activity of the studied compounds. At last, the docking CoMFA model was analyzed through contour maps complemented with MOLCAD-generated active site potential surface in the α,β-tubulin receptor, which can provide important information for the structure-based drug design.     

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.     

3 D-QSAR Analysis of Agonists of nAChRs： Epibatidine Analogues

A 3 D-QSAR about nAChRs agonists epibatidine analogues was performed using theCoMFA and CoMSIA. The correlation coefficients were R2cv = 0.546, R2cv = 0.907 in CoMFA andR2cv = 0.655, R2,~ = 0.962 in CoMSIA of the final model. The prediction using the final models tothe test set was r2 = 0.675 in CoMFA and r2 = 0.462 in CoMSIA. This model will be useful in thedesign of novel compounds with high affinity.     

Structure-based CoMFA and CoMSIA study of indolinone inhibitors of PDK1

Phosphoinositide-dependent protein kinase-1 (PDK1) is a Ser/Thr kinase which phosphorylates and activates members of the AGC kinase group known to control processes such as tumor cell growth, protection from apoptosis, and tumor angiogenesis. In this paper, CoMFA and CoMSIA studies were carried out on a training set of 56 conformationally rigid indolinone inhibitors of PDK1. Predictive 3D QSAR models, established using atom fit alignment rule based on crystallographic-bound conformation, had cross-validated (r _cv²) values of 0.738 and 0.816 and non-cross-validated (r _ncv²) values of 0.912 and 0.949 for CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 14 compounds, which gave predictive correlation coefficients (r _pred²) of 0.865 and 0.837, respectively. Structure-based interpretation of the CoMFA and CoMSIA field properties provided further insights for the rational design of new PDK1 inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

2D-QSAR Using MLR and 3D-QSAR Using CoMSIA Studies on the Toxicity of Aromatic Hydrocarbons on Larval Sinonvaculina Constricta

Aromatic hydrocarbons,one of the persistent organic pollutants(POPs),has been usually found in mussels,accumulated for their hard mobility and activities in harbours and estuaries.In this study,based on the 96 hr-LC50 of 12 aromatic hydrocarbons with larval sinonvaculina constricta,three-dimensional quantitative structure-activity relationship(3D-QSAR) technique:comparative molecular similarity indices analysis(CoMSIA) and 2D-QSAR technique:multiple linear regression(MLR) were described to obtain more detailed insight into the structure-activity relationships between the molecular structure and bio-activity.The results show the MLR model based on density functional theory(DFT) calculation carried out at the B3LYP/6-311** level with Gaussian 03 program yielded a very good correlation with a coefficient squared R2 of 0.716 and a cross-validated Q2 of 0.874.The dipole moment and enthalpy,as the thermodynamic parameters,were two important factors influencing pLC50.Correspondingly,CoMSIA based on the partial least-squares(PLS) methodology with steric,electrostatic,hydrophobic,H-bond donor and acceptor fields contributing simultaneously were employed and the values of R2 and the cross validation with leave-One-Out(LOO) Q2LOO were 0.585 and 0.990,respectively,which reveals the structure features,such as the electronegative substituent(nitro-group),hydrophobic groups(the benzene ring) and H-bond(nitro-group),related to the toxicity.The results of 2D-QSAR employing MLR model and 3D-QSAR employing CoMSIA model provide the useful information for predicting the toxicity of other aromatic hydrocarbons by comparing the molecular structures of similar compounds.