DATA类逆转录酶抑制剂的三维定量构效关系

采用对接方法得到HIV-1抑制剂DATA(二芳基三嗪类)分子的活性构象, 进一步用比较分子场分析(CoMFA)和比较分子相似性分析(CoMSIA)法对DATA类逆转录酶抑制剂(RTIs)的三维定量构效关系(3D-QSAR)进行了研究, 建立3D-QSAR模型, 以指导进一步结构修饰. 用此模型预测了5个DATA类似物, 预测偏差较小, 表明了所建立的模型具有较强的预测能力.     

Homology model-guided 3D-QSAR studies of HIV-1 integrase inhibitors

In the present study, we report the exploration of binding modes of potent HIV-1 integrase (IN) inhibitors MK-0518 (raltegravir) and GS-9137 (elvitegravir) as well as chalcone and related amide IN inhibitors we recently synthesized and the development of 3D-QSAR models for integrase inhibition. Homology models of DNA-bound HIV-1 IN were constructed on the basis of the X-ray crystal structure of the foamy virus IN-DNA complex (PDB ID: 3L2T ) and used for docking. The binding modes of raltegravir and elvitegravir in our homology models are in accordance with those in the foamy virus structure revealing interactions important for inhibitor-IN binding. To gain further insights into the structural requirements for IN inhibition, three-dimensional quantitative structure activity relationship (3D-QSAR) studies were conducted using raltegravir, elvitegravir, and their analogs; our synthesized 3-keto salicylic acid IN inhibitor series; as well as other structurally related HIV-1 IN inhibitors. In the first part of the study with 103 compounds, atom-fit alignments, I and II, and docking-based alignment, III, were used to develop 3D-QSAR models 1, 2, and 3, respectively, each comprising comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSARs. This initial analysis indicated that the docking-based (structure-based) model 3 performed better than the atom-fit (ligand-based) models 1 and 2, in terms of statistical significance and robustness. Thus, the docking-based alignment was then subsequently used with an expanded data set of 296 compounds for building a more comprehensive 3D-QSAR, model 4. Model 4 afforded good q2 values of 0.70 and 0.75 for CoMFA and CoMSIA 3D-QSARs, respectively, and showed good predictive performance on an external validation test set of 59 compounds with predictive r2 values up to 0.71. The HIV IN-DNA homology model of biological relevance and the comprehensive 3D-QSAR models developed in the present study provide insights and new predictive tools for structure-based design and optimization of IN inhibitors.     

Use of 3D QSAR to investigate the mode of binding of pyrazinones to HIV-1 RT

Abstract  

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on the docked conformation were performed for 24 pyrazinone derivatives. All compounds were docked into the wild-type HIV-1 RT binding pocket and the lowest-energy docked configurations were used to construct the 3D QSAR models. The CoMFA and CoMSIA models enable good prediction of inhibition by the pyrazinones, with r_\textcv² r_{\text{cv}}^{2}  = 0.703 and 0.735. Results obtained from CoMFA and CoMSIA based on the docking conformation of the pyrazinones are, therefore, powerful means of elucidating the mode of binding of pyrazinones and suggesting the design of new potent NNRTIs.     

Use of 3D QSAR to investigate the mode of binding of pyrazinones to HIV-1 RT

Abstract  Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on the docked conformation were performed for 24 pyrazinone derivatives. All compounds were docked into the wild-type HIV-1 RT binding pocket and the lowest-energy docked configurations were used to construct the 3D QSAR models. The CoMFA and CoMSIA models enable good prediction of inhibition by the pyrazinones, with  = 0.703 and 0.735. Results obtained from CoMFA and CoMSIA based on the docking conformation of the pyrazinones are, therefore, powerful means of elucidating the mode of binding of pyrazinones and suggesting the design of new potent NNRTIs. Graphical Abstract        

Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism

Receptor-dependent (RD) 4D-QSAR models were constructed for a set of 39 4-hydroxy-5,6-dihydropyrone analogue HIV-1 protease inhibitors. The receptor model used in this QSAR analysis was derived from the HIV-1 protease (PDB ID ) crystal structure. The bound ligand in the active site of the enzyme, also a 4-hydroxy-5,6-dihydropyrone analogue, was used as the reference ligand for docking the data set compounds. The optimized RD 4D-QSAR models are not only statistically significant (r(2) = 0.86, q(2) = 0.80 for four- and greater-term models) but also possess reasonable predictivity based on test set predictions. The proposed "active" conformations of the docked analogues in the active site of the enzyme are consistent in overall molecular shape with those suggested from crystallographic studies. Moreover, the RD 4D-QSAR models also "capture" the existence of specific induced-fit interactions between the enzyme active site and each specific inhibitor. Hydrophobic interactions, steric shape requirements, and hydrogen bonding of the 4-hydroxy-5,6-dihydropyrone analogues with the HIV-1 protease binding site model dominate the RD 4D-QSAR models in a manner again consistent with experimental conclusions. Some possible hypotheses for the development of new lead HIV-1 protease inhibitors can be inferred from the RD 4D-QSAR 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.     

基于分子对接的6-萘甲基取代HEPT类HIV-1逆转录酶抑制剂构效关系研究

采用分子对接方法得到了一系列6-萘甲基取代HEPT类逆转录酶抑制剂分子与HIV-1逆转录酶复合物模型,从中抽取出抑制剂分子的活性构象,进一步应用CoMFA和CoMSIA方法建立了具有较好预测能力的3D-QSAR模型,深入探讨了这些化合物的定量构效关系,为进一步的药物设计奠定了良好的基础.另外,以化合物13及其相应的β异构体24为代表,结合量子化学从头算分子轨道理论方法考察了它们的前线轨道,为阐明α和β系列化合物的活性差异提供了理论依据.     

4D-QSAR analysis of a series of antifungal p450 inhibitors and 3D-pharmacophore comparisons as a function of alignment

A training set of 55 antifungal p450 analogue inhibitors was used to construct receptor-independent four-dimensional quantitative structure-activity relationship (RI 4D-QSAR) models. Ten different alignments were used to build the models, and one alignment yields a significantly better model than the other alignments. Two different methodologies were used to measure the similarity of the best 4D-QSAR models of each alignment. One method compares the residual of fit between pairs of models using the cross-correlation coefficient of their residuals of fit as a similarity measure. The other method compares the spatial distributions of the IPE types (3D-pharmacophores) of pairs of 4D-QSAR models from different alignments. Optimum models from several different alignments have nearly the same correlation coefficients, r(2), and cross-validation correlation coefficients, xv-r(2), yet the 3D-pharmacophores of these models are very different from one another. The highest 3D-pharmacophore similarity correlation coefficient between any pair of 4D-QSAR models from the 10 alignments considered is only 0.216. However, the best 4D-QSAR models of each alignment do contain some proximate common pharmacorphore sites. A test set of 10 compounds was used to validate the predictivity of the best 4D-QSAR models of each alignment. The "best" model from the 10 alignments has the highest predictivity. The inferred active sites mapped out by the 4D-QSAR models suggest that hydrogen bond interactions are not prevalent when this class of P450 analogue inhibitors binds to the receptor active site. This feature of the 4D-QSAR models is in agreement with the crystal structure results that indicate no ligand-receptor hydrogen bonds are formed.     

Molecular modeling on pyrimidine-urea inhibitors of TNF-α production: an integrated approach using a combination of molecular docking, classification techniques, and 3D-QSAR CoMSIA

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC(50) values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.