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.     

N-取代马来酰亚胺类hMGL抑制剂的3D-QSAR、分子对接和分子动力学研究

本文通过三维定量构效关系(3D-QSAR)建模、分子对接和分子动力学模拟,探讨了41个N-取代马来酰亚胺类衍生物与人单酰甘油脂肪酶(hMGL)的相互作用,并构建了相关模型。其中,比较分子力场分析模型(CoMFA、q₂ ^{= 0. 5}41、r₂ ^{= 0. 9}72)和比较分子相似性指数分析模型(CoMSIA、q₂ ^{= 0. 5}88、r₂ ^{= 0. 9}19)具有较好的预测能力。QSAR模型等势图阐明了该系列化合物生物活性与结构的关系,并依此设计了系列衍生物。采用分子对接和分子动力学模拟探讨了高活性化合物36、46与hMGL(PDB ID: 3PE6)的结合模式和稳定性,结果表明二者主要通过氢键和疏水相互作用与hMGL结合并且形成了稳定的复合物。随后对pIC₅₀预测值优于文献报道中最高活性化合物36的8个衍生物进行分子对接和ADMET预测,选择2个衍生物进行分子动力学模拟,结果表明2个衍生物分别与hMGL形成的复合物结合构象稳定。本研究为新型N-取代马来酰亚胺类单酰甘油脂肪酶抑制剂的开发提供了理论依据。     

3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments

A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B 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.     

Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

In this study, a combination of virtual screening methods were utilized to identify novel potential indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. A series of IDO1 potential inhibitors were identified by a combination of following steps: Lipinski's Rule of Five, Veber rules filter, molecular docking, HipHop pharmacophores, 3D-Quantitative structure activity relationship (3D-QSAR) studies and Pan-assay Interference Compounds (PAINS) filter. Three known categories of IDO1 inhibitors were used to constructed pharmacophores and 3D-QSAR models. Four point pharmacophores (RHDA) of IDO1 inhibitors were generated from the training set. The 3D-QSAR models were obtained using partial least squares (PLS) analyze based on the docking conformation alignment from the training set. The leave-one-out correlation (q²) and non-cross-validated correlation coefficient (r²_pred) of the best CoMFA model were 0.601 and 0.546, and the ones from the best CoMSIA model were 0.506 and 0.541, respectively. Six hits from Specs database were identified and analyzed to confirm their binding modes and key interactions to the amino acid residues in the protein. This work may provide novel backbones for new generation of inhibitors of IDO1.     

In silico design novel (5-imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine derivatives as inhibitors for glycogen synthase kinase 3 based on 3D-QSAR,molecular docking and molecular dynamics simulation

Glycogen Synthase Kinase 3 (GSK-3) is a member of cellular kinase with various functions, such as glucose regulation, cellular differentiation, neuronal function and cell apoptosis. It has been proved as an important therapeutic target in type 2 diabetes mellitus and Alzheimer's disease. To better understand their structure–activity relationships and mechanism of action, an integrated computational study, including three dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD), was performed on 79 (5-Imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine GSK-3 inhibitors. In this paper, we constructed 3D-QSAR using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) method. The results showed that the CoMFA model (q ² = 0.743, r² = 0.980) and the CoMSIA model (q² = 0.813, r² = 0.976) had stable and reliable predictive ability. The electrostatic and H-bond donor fields play important roles in the models. The contour maps of the model visually showed the relationship between the activity of compounds and their three-dimensional structure. Molecular docking was used to identify the key amino acid residues at the active site of GSK-3 and explore its binding mode with ligands. Based on 3D-QSAR models, contour maps and the binding feature between GSK-3 and inhibitor, we designed 10 novel compounds with good potential activity and ADME/T profile. Molecular dynamics simulation results validated that Ile62, Val70 and Lys85 located in the active site play a key role for GSK-3 complexed with inhibitors. These results might provide important information for designing GSK-3 inhibitors with high activity.     

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.

     

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.     

Design,Synthesis, Biological Evaluation,and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors

PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q² = 0.797 and r² = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q² = 0.567 and r² = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).     

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.     

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 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.     

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.     

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.     

Developing 13C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroid binding to the corticosteroid binding globulin

We have developed four quantitative spectrometric data-activity relationship (QSDAR) models for 30 steroids binding to corticosteroid binding globulin, based on comparative spectral analysis (CoSA) of simulated ¹³C nuclear magnetic resonance (NMR) data. A QSDAR model based on 3 spectral bins had an explained variance (r ²) of 0.80 and a cross-validated variance (q ²) of 0.78. Another QSDAR model using the 3 atoms from the comparative structurally assigned spectral analysis (CoSASA) of simulated ¹³C NMR on a steroid backbone template gave an explained variance (r ²) of 0.80 and a cross-validated variance (q ²) of 0.73. Positions 3 and 14 from the steroid backbone template have correlations with the relative binding activity to corticosteroid binding globulin that are greater than 0.52. The explained correlation and cross-validated correlation of these QSDAR models are as good as previously published quantitative structure-activity relationship (QSAR), self-organizing map (SOM) and electrotopological state (E-state) models. One reason that the cross-validated variance of QSDAR models were as good as the other models is that simulated ¹³C NMR spectral data are more accurate than the errors introduced by the assumptions and approximations used in calculated electrostatic potentials, E-states, HE-states, and the molecular alignment process of QSAR modeling. The QSDAR models developed provide a rapid, simple way to predict the binding activity of a steroid to corticosteroid binding globulin.