Design of novel focal adhesion kinase inhibitors using 3D-QSAR and molecular docking

Focal adhesion kinase (FAK) plays a primary role in regulating the activity of many signaling molecules. Increased FAK expression has been implicated in a series of cellular processes, including cell migration and survival. Inhibiting the activity of FAK for cancer therapy is currently under investigation. Hence, FAK and its inhibitors have been the subject of intensive research. To understand the structural factors affecting inhibitory potency, molecular docking and 3D-QSAR modeling were studied in this project. CoMFA and CoMSIA methods were used for deriving 3D-QSAR models, which were trained with 78 compounds and then were evaluated for predictive ability with additional 19 compounds. Two substructure-based 3D-QSAR models, including CoMFA model (r ² = 0.930; q ² = 0.629) and CoMSIA model (r ² = 0.952; q ² = 0.586), had a good quality to predict the biological activities of new compounds. Meanwhile, using the flexible docking strategy, two docking-based 3D-QSAR models (CoMFA with r ² = 0.914; q ² = 0.594; CoMSIA with r ² = 0.914; q ² = 0.524) were also constructed. The structure–activity relationship has been illustrated clearly by the contour maps gained from the 3D-QSAR models in combination with the docked binding structures. All the results indicated that it might be useful in the rational design of novel FAK inhibitors.     

Investigations and design of pyridine-2-carboxylic acid thiazol-2-ylamide analogs as methionine aminopeptidase inhibitors using 3D-QSAR and molecular docking

Methionine amino peptidases (MetAPs) are metalloproteases that remove co-translational N-terminal methionine from nascent polypeptide chains. Due to their essential role in protein synthesis, MetAPs are considered as potential targets for antibacterial drugs. In the present work, three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out on a series of pyridine-2-carboxylic acid thiazol-2-ylamide-based MetAP inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models were developed using 30 training set molecules. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients (q ²) of 0.799 and 0.704 and conventional correlation coefficients (r ²) of 0.989 and 0.954, respectively. These inhibitors were docked into MetAP active site. The CoMFA and CoMSIA field contour maps correlate well with the structural characteristics of the binding pocket of MetAP active site. Using the knowledge of structure–activity relationship and receptor–ligand interactions from 3D-QSAR model and the docked complexes, four new pyridine-2-carboxylic acid thiazol-2-ylamide analogs were designed. These analogs exhibit significantly better predicted activity than the reported molecules. The present work has implications for the development of novel antibiotics as potent MetAP inhibitors.     

3D-QSAR studies of chromone derivatives as iron-chelating agent

The metal-chelating activity of a series of 48 chromone compounds, evaluated by ferrous (Fe²⁺) chelating test, were subjected to 3D-QSAR studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA model obtained from HF/6-31G* geometry optimization and field fit alignment gave cross-validated r ² (q ²) = 0.582, non-cross-validated r ² = 0.975. The best CoMSIA model gave q ² = 0.617, non-cross-validated r ² = 0.917. The resulted CoMFA and CoMSIA contour maps proposed the Fe²⁺-chelating sites of chromone compounds compared with those of quercetin.     

Accurate Prediction of Glucuronidation of Structurally Diverse Phenolics by Human UGT1A9 Using Combined Experimental and In Silico Approaches

Purpose

Catalytic selectivity of human UGT1A9, an important membrane-bound enzyme catalyzing glucuronidation of xenobiotics, was determined experimentally using 145 phenolics and analyzed by 3D-QSAR methods.

Methods

Catalytic efficiency of UGT1A9 was determined by kinetic profiling. Quantitative structure activity relationships were analyzed using CoMFA and CoMSIA techniques. Molecular alignment of substrate structures was made by superimposing the glucuronidation site and its adjacent aromatic ring to achieve maximal steric overlap. For a substrate with multiple active glucuronidation sites, each site was considered a separate substrate.

Results

3D-QSAR analyses produced statistically reliable models with good predictive power (CoMFA: q²?=?0.548, r²?=?0.949, r _pred ² ?=?0.775; CoMSIA: q²?=?0.579, r²?=?0.876, r _pred ² ?=?0.700). Contour coefficient maps were applied to elucidate structural features among substrates that are responsible for selectivity differences. Contour coefficient maps were overlaid in the catalytic pocket of a homology model of UGT1A9, enabling identification of the UGT1A9 catalytic pocket with a high degree of confidence.

Conclusion

CoMFA/CoMSIA models can predict substrate selectivity and in vitro clearance of UGT1A9. Our findings also provide a possible molecular basis for understanding UGT1A9 functions and substrate selectivity.     

Pharmacophore-based 3D-QSAR of HIF-1 inhibitors

(Aryloxyamino)benzoic acids and nicotinic/isonicotinic acids represent an important new class of small molecules that inhibit the activation of Hypoxia-Inducible Factor (HIF)-1. In order to understand the factors affecting inhibitory potency of HIF-1 inhibitors, 3 dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed. Since no receptor structure are available, the pharmacophore-based alignment was used for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models gave reasonable statistics (CoMFA: q² = 0.564, r²=0.945; CoMSIA: q² = 0.575, r²=0.929). Both CoMFA and CoMSIA results indicate that the steric interaction is a major factor, while CoMSIA suggests importance of hydrogen bonding. These findings about steric and H-bonding effects can be useful to design new inhibitors. Equally contributed in this work.     

Molecular docking and 3D-QSAR studies on checkpoint kinase 1 inhibitors

Checkpoint kinase 1(Chk1) is a promising target for cancer treatment. Here three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on 174 1,4-dihydroindeno[1,2-c]pyrazole inhibitors of Chk1 using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Two satisfactory ligand-based QSAR models were built (CoMFA model: q ² = 0.541, r ² = 0.880, CoMSIA model: q ² = 0.590, r ² = 0.902). The docking-based studies presented a detailed understanding of interaction between the inhibitors and Chk1. The obtained QSAR models are highly predictable (CoMFA model: q ² = 0.567, r ² = 0.891, CoMSIA model: q ² = 0.596, r ² = 0.917). The models were further validated by an external testing set obtaining $ r_{\text{pred}}^{2} $ r pred 2 values 0.896 and 0.923 for CoMFA and CoMSIA, respectively. So our models might be helpful for further modification of 1,4-dihydroindeno[1,2-c]pyrazole derivatives.     

Cytotoxic Activity and Three-Dimensional Quantitative Structure Activity Relationship of 2-Aryl-1,8-naphthyridin-4-ones

A series of substituted 2-arylnaphthyridin-4-one analogues, which were previously synthesized in our laboratory, were evaluated for their in vitro cytotoxic activity against human lung cancer A549 and human renal cancer Caki-2 cells using MTT assay. Some compounds (11, 12, and 13) showed stronger cytotoxicity than colchicine against both tumor cell lines, and compound 13 exhibited the most potent activity with IC₅₀ values of 2.3 and 13.4 µM, respectively. Three-dimensional quantitative structure activity relationship (3D-QSAR) studies of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed. Predictive 3D-QSAR models were obtained with q² values of 0.869 and 0.872 and r²_ncv values of 0.983 and 0.993 for CoMFA and CoMSIA, respectively. These results demonstrate that CoMFA and CoMSIA models could be reliably used in the design of novel cytotoxic agents.     

A combined 3D QSAR and pharmacophore-based virtual screening for the identification of potent p38 MAP kinase inhibitors: an in silico approach

p38 kinase plays a vital role in inflammation mediated by tumor necrosis factor-α and interleukin-1β pathways. Inhibition of p38 kinase provides an effective way to treat inflammatory diseases. 3D-QSAR study was performed to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for a series of p38 inhibitors with three different alignment methods (Receptor based, atom by atom matching, and pharmacophore based). Among the different alignment methods, better statistics were obtained with receptor-based alignment (CoMFA: q ² = 0.777, r ² = 0.958; CoMSIA: q ² = 0.782, r ² = 0.927). Superposing CoMFA/CoMSIA contour maps on the p38 active site gave a valuable insight to understand physical factors which are important for binding. In addition, this pharmacophore model was used as a 3D query for virtual screening against NCI database. The hit compounds were further filtered by docking and scoring, and their biological activities were predicted by CoMFA and CoMSIA models.     

喹啉酮类小分子p53-MDM2结合抑制剂3D-QSAR研究

目的 设计、合成高活性的小分子p53-MDM2结合抑制剂,建立具有预测能力的3D-QSAR模型。方法 采用分子模拟软件Sybyl,利用比较分子场方法(CoMFA)、比较分子相似性指数法(CoMSIA),选择已报道的具有p53-MDM2结合抑制活性的一类有相同母核的21个异喹啉酮衍生物作为训练集,7个作为预测集进行3D-QSAR模型的建立和验证。结果 模型具有较高q²(q²_CoMFA=0.545,q²_CoMSIA=0.528)和r2(r²_CoMFA=0.984,r²_CoMSIA=0.972)值,表明2组模型具有较高的拟和能力及预测能力。结论 该模型具有较高的预测能力,为设计、合成高活性的小分子p53-MDM2结合抑制剂提供了理论依据。     

新型二芳基三嗪类抗锥体虫病化合物的三维定量构效关系研究

目的 建立具有预测能力的新型二芳基三嗪类抗锥体虫病化合物三维定量构效关系（3D-QSAR）模型。方法 通过对具有抗锥体虫活性的二芳基三嗪类化合物库进行结构分析,利用比较分子场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,建立3D-QSAR模型。结果 模型具有较高q²（q_CoMFA²=0.697,q_CoMSIA²=0.561）和r²（r_CoMFA²=0.998,r_CoMSIA²=0.966）值,表明2组模型具有较高的拟和能力及预测能力。结论 建立的CoMFA和CoMSIA模型均具有良好的预测能力,为设计更高活性的新型二芳基三嗪类抗锥体虫病化合物提供了理论依据和研究方向。     

Predictive 3D-QSAR and HQSAR model generation of isocitrate lyase (ICL) inhibitors by various alignment methods combined with docking study

Isocitrate lyase (ICL) is one of the most important targets in the treatment of Mycobacterium tuberculosis. In this study a diverse set of 2-benzanilide derivatives were aligned by two different methods for CoMFA, CoMSIA, and HQSAR analysis. The best CoMFA model was obtained with the internal validation value (q ²) of 0.730 and conventional coefficient (r ²) of 0.944. Various CoMSIA models were generated and cross-validated. The best cross-validation coefficient (q ²) value was found to be statistically satisfactory (0.688). Both the models were validated by test set of 10 compounds with satisfactory prediction value of (r ² _pred ) 0.725 and 0.631 for CoMFA and CoMSIA, respectively. Cross-validation coefficient value (q ²) of 0.694 and r ² of 0.856 were obtained for HQSAR study. The docking study reveals that large hydrophobic pockets occupy R substitutions of these compounds. An electronically negative surface is observed near R₁ substitution. The results of the 3D-QSAR analysis corroborate with the molecular docking results, and our findings will serve as a basis for further development of better allosteric inhibitors of ICL inhibitors against M. tuberculosis.     

3D-QSAR study using CoMFA and CoMSIA methods for a series of histone H3 phosphorylation inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis of inhibitory activities for a series of pyrrolotriazine derivatives against histone H3 phosphorylation (pHH3) was performed using comparative of molecular field analysis (CoMFA) and comparative of molecular similarities indices analysis (CoMSIA) techniques. 62 derivatives were used to establish and validate two models by considering a high deviation in biological activities and structural variations. Optimum CoMFA and CoMSIA models obtained from the training set were statistically significant with cross-validated correlation coefficients q ² of 0.551 and 0.621, and conventional correlation coefficients (r ²) of 0.999 and 0.995, respectively. The predicted correlation coefficients of test set (R ²) for CoMFA and CoMSIA were 0.835 and 0.918, respectively. Two models obtained provide guidelines to trace the features that really matter chiefly with respect to the design of novel pyrrolotriazine derivatives.     

3D-QSAR studies on the biological activity of juvenile hormone mimetic compounds for Culex pipiens Larvae

3D-QSAR modeling of juvenile hormone (JH) mimetic compounds of Culex pipiens (C. pipiens) larvae were analyzed. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were chosen for 3D-QSAR studies. These compounds act as potential insect control agents. The dataset containing 143 JH mimetic compounds considered in this study were divided into a training set of 129 compounds and a test set of 14 compounds. The CoMFA/CoMSIA model is obtained with the statistical significance r ² of 0.944 (0.773) with standard error of estimate of 0.313 (0.616). Both CoMFA and CoMSIA models provide valuable insight into the structural requirements for the enhanced activity of the JH inhibitors.     

3D-QSAR research of curcumin derivatives

Curcumin exhibits a great ability in various biological and pharmacological activities. Evaluation of curcumin derivatives served to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR) model which was validated by the evaluation of a serial of 22 compounds. Two favorable 3D-QSAR models (CoMFA with q ² = 0.539, R ² = 0.981; CoMSIA with q ² = 0.451, R ² = 0.907) had been developed to predict the biological activity of curcumin derivatives, and external metric q _pred ² (CoMFA with 0.79; CoMSIA with 0.78) and r _m ² _(overall) (CoMFA with 0.71; CoMSIA with 0.56) were applied to evaluate the ability of prediction. Comparing the results obtained from both standard models, we found that reducing the carbon chains of curcumin (S2 and A1 zones) could increase its MCF-7 cytotoxicity; exchanging acceptor/donor substituent on A2 and A4, A3 and D3 zones could turnover its cytotoxicity of MCF-7. These results help with understanding the specific activity of curcumin compounds and designing new specific MCF-7 inhibitors.     

QSAR studies on diclofenac analogues as potent cyclooxygenase inhibitors using CoMFA and CoMSIA

A series of 36 diclofenac analogues were analyzed for structure–activity relationship using CoMFA and CoMSIA. The CoMFA-based equation gave q ² = 0.625 and r ² = 0.973 compared to q ² = 0.773 and r ² = 0.959 for CoMSIA. The CoMSIA and CoMFA contours were analyzed and the structural features contributing to the enhancement of activity were identified. Based on the results obtained from these analysis two compounds were designed which show enhancement in activity compared to the parent compound. The new leads are predicted to be non-toxic through computational methods.     

噻唑类衍生物二氢乳清酸脱氢酶抑制剂的三维定量构效关系研究

目的 应用三维定量构效关系（3D-QSAR）研究噻唑类衍生物结构的二氢乳清酸脱氢酶抑制活性,为该类药物的设计和筛选提供可靠的理论依据。方法 针对38个以噻唑为基本骨架的二氢乳清酸脱氢酶抑制剂,分别应用分子力场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）2种经典的方法进行了三维定量构效关系（3D-QSAR）研究,建立相关模型,验证模型的预测能力,三维等势图分析噻唑类衍生物结构与活性的关系。结果 CoMFA模型的交叉验证系数q²为0.796,相关系数r²为0.978;CoMSIA模型的q²以及r²分别为0.721和0.976;2种模型对化合物的活性预测与实际值接近;三维等势图可以全面直观的分析化合物结构对其活性的影响。结论 该3D-QSAR模型三维等势图揭示了结构特征与抑制活性的关系,模型具有较好的预测能力和较强的稳定性,为进一步开发研究打下了较好的基础。     

Molecular modeling studies on 3,4-dihydroquinazolines as trypanothione reductase inhibitors using 3D-QSAR and docking approaches

The trypanothione reductase (TryR) has been used as a key validated target to guide drug discovery for human African trypanosomiasis (HAT). 3D-QSAR and docking studies were performed on a series of 3,4-dihydroquinazolines as TryR inhibitors to establish a molecular model for new drug design. The CoMFA and CoMSIA models resulted from 53 molecules gave r _cv ² values of 0.591 and 0.574, r ² values of 0.968 and 0.943, respectively. The external validation indicated that CoMSIA model with a valid r _m ² value of 0.864 exhibited better predictive power than CoMFA model. 3D contour maps generated from CoMFA and CoMSIA along with the docking analyses have identified several key features responsible for the activity. A set of analogs were proposed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The results can be served as a useful guideline for designing novel 3,4-dihydroquinazoline derivatives with improved activity against human African trypanosomes.     

Activity landscape analysis, CoMFA and CoMSIA studies of pyrazole CB1 antagonists

Obesity and the metabolic syndrome are pandemic diseases with high morbidity and mortality. With the aim of discovering novel therapies for those diseases, the cannabinoid receptor 1 (CB1), which has been validated as a target for treating appetitive disorders, is now considered a novel target for the design of anti-obesity compounds. Our main goal was to determine the activity landscape of pyrazole derivatives and to develop reliable three-dimensional quantitative structure–activity relationship (3D-QSAR) models. Structure–activity similarity (SAS) maps of pyrazole analogs acting as antagonists of CB1 were constructed in order to identify activity cliffs, compounds that have high structural similarity with the rest of the compound set, but low activity similarity. According to the SAS maps, one molecule was identified as an outlier and before comparative molecular field analysis (COMFA) and comparative molecular similarity analysis (CoMSIA) 3D-QSAR models were derived. The best models resulted in an r ² value of 0.992 and a q ² of 0.766 for CoMFA and an r ² of 0.983 and a q ² of 0.681 for CoMSIA. Contour plots identified that the R³ position at the pyrazole moiety is an important feature for the optimization of the binding affinity to the CB1 receptor. According to our results, these models can be a useful tool for the design and prediction of novel CB1 antagonists.