3D-QSAR and receptor modeling of tyrosine kinase inhibitors with flexible atom receptor model (FLARM)

A set of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors was investigated with the aim of developing 3D-QSAR models using the Flexible Atom Receptor Model (FLARM) method. Some 3D-QSAR models were built with high correlation coefficients, and the FLARM method predicted the biological activities of compounds in test set well. The FLARM method also gave the pseudoreceptor model, which indicates the possible interactions between the receptor and the ligand. The possible interactions include two hydrogen bonds, one hydrophobic interaction, and one sulfur-aromatic interaction, which are in accord with those in the pharmacophore model given by the scientists at Novartis. This shows that the FLARM method can bridge 3D-QSAR and receptor modeling in computer-aided drug design. Pharmacophore can be obtained according to these results, and 3D searching can then be done with databases to find the lead compound of EGFR tyrosine kinase inhibitors.     

Pseudoreceptor models and 3D-QSAR for imidazobenzodiazepines at GABA A/BzR subtypes alphaxbeta3gamma2 [x = 1-3, 5, and 6] via flexible atom receptor model

Since benzodiazepines have been used widely in the treatment of anxiety, sleeplessness, and epilepsy, the receptor sites for the benzodiazepine are of prime importance. Quantitative structure-activity relationship (QSAR) studies and receptor modeling via Flexible Atom Receptor Model (FLARM) for the binding affinities of a series of imidazobenzodiazepines at five recombinant receptor subtypes were carried out successfully. The 3D-QSAR models for all five receptor subtypes were examined by a set of test set and demonstrated their high predictability for affinities of imidazobenzodiazepines at five receptor subtypes. The pseudoreceptors yielded by FLARM were compared to the united pharmacophore/receptor model. The result shows that two hydrogen bonds and other regions in the united pharmacophore/receptor model are presented in the pseudoreceptors, which demonstrates the receptor modeling capability of FLARM. The models and pseudoreceptors can help design high affinity ligands on the GABA(A)/BZ receptor and understand the GABA(A) receptor.     

柔性原子受性模型（FLARM）

柔性原子受体模型(Flexible Atom Receptor Model, FLARM)方法使用了改进 的遗传算法(IGA),比传统的受体模型方法具有更快的计算速度;FLARM受体模型中 的原子空间坐标在遗传演化过程中是可变的,避免了不合适的起始位点对模型的影 响;另外,FLARM用增加空原子权重的方法,可以生成不完全封闭的、允许有大段 空区域的受体模型,这样的模型能更好地模拟实际受体的结构,并且容易和药效团 模型找到对应关系。我们用FLARM方法分别计算了甾类化合物体系的CBG蛋白亲和性 和1,1,3-三氧-2H,4H-噻吩并[3,4-e] [1,2,4]噻二嗪衍生物(TTD)体系的抗HIV-1活 性,计算结果表明FLARM生成的虚拟受体模型对配体分子的活性具有较高的预测能 力,在此虚拟受体模型的基础上还可以进一步分析虚拟受体分子与配体分子的相互 作用机制,对真实受体分子结构的活性位点进行初步预测。     

GABA受体抑制剂的柔性原子受体模型研究

利用Flarm软件为GABA受体抑制剂建立了其抑制家蝇和大鼠GABA受体的柔性原子受体模型， 很好地模拟了两种受体与药物分子结合的情况，具有较好的预测能力，预测集的预测值与实验值的相关系数（r2）分别达到0.923和0.733，模型的结果与药效团模型有很大的一致性，为揭示药物与两种受体作用的区别提供了依据.     

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.     

Subtype selectivity of dopamine receptor ligands: insights from structure and ligand-based methods

Subtype selective dopamine receptor ligands have long been sought after as therapeutic and/or imaging agents for the treatment and monitoring of neurologic disorders. We report herein on a combined structure- and ligand-based approach to explore the molecular mechanism of the subtype selectivity for a large class of D?-like dopamine receptor ligands (163 ligands in total). Homology models were built for both human D(?L) and D? receptors in complex with haloperidol. Other ligands, which included multiple examples of substituted phenylpiperazines, were aligned against the binding conformations of haloperidol, and three-dimensional quantitative structure activity relationship (3D-QSAR) analyses were carried out. The receptor models show that although D? and D? share highly similar folds and 3D conformations, the slight sequence differences at their extracellular loop regions result in the binding cavity in D? being comparably shallower than in D?, which may explain why some larger ligands bind with greater affinity at D? compared to D? receptors. The QSAR models show excellent correlation and high predictive power even when evaluated by the most stringent criteria. They confirm that the origins of subtype selectivity for the ligands arise primarily due to differences in the contours of the two binding sites. The predictive models suggest that while both steric and electrostatic interactions contribute to the compounds' binding affinity, the major contribution arises from hydrophobic interactions, with hydrogen bonding conferring binding specificity. The current work provides clues for the development of more subtype selective dopamine receptor ligands. Furthermore, it demonstrates the possibility of being able to apply similar modeling methods to other subtypes or classes of receptors to study GPCR receptor-ligand interactions at a molecular level.     

酪氨酸激酶抑制剂的三维定量构效关系研究

利用柔性原子受体模型(FLARM)方法对一系列的异黄酮和喹诺酮衍生物表皮生长因子受体酪氨酸激酶抑制剂进行了三维定量构效关系研究，得到了合理的构效关系模型.FLARM方法的计算结果还给出了虚拟的受体模型，该模型说明了抑制剂与受体之间可能的相互作用.由该虚拟受体模型得到的受体-配体相互作用与Novartis药效团模型比较类似.     

Quantitative structure-based design: formalism and application of receptor-dependent RD-4D-QSAR analysis to a set of glucose analogue inhibitors of glycogen phosphorylase

A method for performing quantitative structure-based design has been developed by extending the current receptor-independent RI-4D-QSAR methodology to include receptor geometry. The resultant receptor-dependent RD-4D-QSAR approach employs a novel receptor-pruning technique to permit effective processing of ligands with the lining of the binding site wrapped about them. Data reduction, QSAR model construction, and identification of possible pharmacophore sites are achieved by a three-step statistical analysis consisting of genetic algorithm optimization followed by backward elimination multidimensional regression and ending with another genetic algorithm optimization. The RD-4D-QSAR method is applied to a series of glucose inhibitors of glycogen phosphorylase b, GPb. The statistical quality of the best RI- and RD-4D-QSAR models are about the same. However, the predictivity of the RD- model is quite superior to that of the RI-4D-QSAR model for a test set. The superior predictive performance of the RD- model is due to its dependence on receptor geometry. There is a unique induced-fit between each inhibitor and the GPb binding site. This induced-fit results in the side chain of Asn-284 serving as both a hydrogen bond acceptor and donor site depending upon inhibitor structure. The RD-4D-QSAR model strongly suggests that quantitative structure-based design cannot be successful unless the receptor is allowed to be completely flexible.     

Insights into phenylalanine derivatives recognition of VLA-4 integrin: from a pharmacophoric study to 3D-QSAR and molecular docking analyses

The very late antigen-4 (VLA-4), also known as integrin alpha4beta1, is expressed on monocytes, T- and B-lympohocytes, basophils, and eosinophils and is involved in the massive recruitment of granulocytes in different pathological conditions such as multiple sclerosis and asthma. VLA-4 interacts with its endogenous ligand VCAM-1 during chronic inflammation, and blockade of VLA-4 /VCAM-1 interaction is a potential target for immunosuppression. Two classes of VLA-4 antagonists have so far been reported: beta-amino acid derivatives containing a diaryl urea moiety (BIO-1211) and phenylalanine derivatives (TR-14035). With the aim of clarifying the structural basis responsible for VLA-4 recognition by phenylalanine derivatives, we developed a combined computational study on a set of 128 antagonists available through the literature. Our computational approach is composed of three parts. (i) A VCAM-1 based pharmacophore was constructed with a restricted number of phenylalanine derivatives to identify the region of the protein that resembles synthetic antagonists. The pharmacophore was instrumental in constructing an alignment of a set of 128 compounds. This alignment was exploited to build a pseudoreceptor model with the RECEPTOR program. (ii) 3D-QSAR analysis was carried out on the computed electrostatic and steric interaction energies with the pseudoreceptor surface. The 3D-QSAR analysis yielded a predictive model able to explain much of the variance of the 128 antagonists. (iii) A homology modeling study of the headpiece of VLA-4 based on the crystal structure of alphavbeta3 was performed. Docking experiments of TR-14035 into the binding site of VLA-4 aided the interpretation of the 3D-QSAR model. The obtained results will be fruitful for the design of new potent and selective antagonists of VLA-4.     

马来酰胺类糖原合成酶激酶-3β抑制剂的分子对接和三维定量构效关系

通过分子对接和三维定量构效关系(3D-QSAR)两种方法来确定两类马来酰胺类的糖原合成酶激酶-3β(GSK-3β)抑制剂的结合方式.首先,用分子对接确定抑制剂与GSK-3β的结合模式及其相互作用;然后用比较分子力场分析法(CoMFA)与比较分子相似性指数分析法(CoMSIA)对48个化合物做三维定量构效关系的分析.两种方法得出的交互验证回归系数分别为0.669(CoMFA)和0.683(CoMSIA),证明该模型具有很好的统计相关性,同时也说明该模型具有较高的预测能力.根据该模型提供的信息,设计出9个预测性较好的分子.     

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.     

Microscopic binding of butyrylcholinesterase with quinazolinimine derivatives and the structure–activity correlation

Butyrylcholinesterase (BChE) is not only an important protein for development of anti-cocaine medication but also an established drug target to develop new treatment for Alzheimer’s disease (AD). The molecular basis of interaction of a new series of quinazolinimine derivatives as BChE inhibitors has been studied by molecular docking and molecular dynamics (MD) simulations. The molecular docking and MD simulations revealed that all of these inhibitors bind with BChE in similar binding mode. Based on the similar binding mode, we have carried out three-dimensional quantitative structure–activity relationship (3D-QSAR) studies on these inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), to understand the structure–activity correlation of this series of inhibitors and to develop predictive models that could be used in the design of new inhibitors of BChE. The study has resulted in satisfactory 3D-QSAR models. We have also developed ligand-based 3D-QSAR models. The contour maps obtained from the 3D-QSAR models in combination with the simulated binding structures help to better interpret the structure–activity relationship and is consistent with available experimental activity data. The satisfactory 3D-QSAR models strongly suggest that the determined BChE-inhibitor binding modes are reasonable. The identified binding modes and developed 3D-QSAR models for these BChE inhibitors are expected to be valuable for rational design of new BChE inhibitors that may be valuable in the treatment of Alzheimer’s disease.     

Three-dimensional quantitative structure-activity relationship analysis of the new potent sulfonylureas using comparative molecular similarity indices analysis

The present study describes the implementation of a new three-dimensional quantitative structure-activity relationship (3D-QSAR) technique: comparative molecular similarity indices analysis (CoMSIA) to a set of novel herbicidal sulfonylureas targeted acetolactate synthase. Field expressions in terms of similarity indices in CoMSIA were applied instead of the usually used Lennard-Jones and Coulomb-type potentials in CoMFA. Two different kinds of alignment techniques including field-fit alignment and atom-by-atom fits were used to produce the molecular aggregate. The results indicated that those two alignment rules generated comparative 3D-QSAR models with similar statistical significance. However, from the predictive ability of the test set, the models from the alignment after maximal steric and electrostatic optimization were slightly better than those from the simple atom-by-atom fits. Moreover, systematic variations of some parameters in CoMSIA were performed to search the best 3D-QSAR model. A significant cross-validated q2 was obtained, indicating the predictive potential of the model for the untested compounds; meanwhile the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The CoMSIA coefficient contour plots identified several key features explaining the wide range of activities, which were very valuable for us in tracing the properties that really matter and getting insight into the potential mechanisms of the intermolecular interactions between inhibitor and receptor, especially with respect to the design of new compounds.     

马来酰胺类糖原合成酶激酶-3β抑制剂的分子对接和三维定量构效关系

通过分子对接和三维定量构效关系(3D-QSAR)两种方法来确定两类马来酰胺类的糖原合成酶激酶-3β(GSK-3β)抑制剂的结合方式. 首先, 用分子对接确定抑制剂与GSK-3β结合模式及其相互作用; 然后用比较分子力场分析法(CoMFA)与比较分子相似性指数分析法(CoMSIA)对48个化合物做三维定量构效关系的分析. 两种方法得出的交互验证回归系数分别为0.669(CoMFA)和0.683(CoMSIA), 证明该模型具有很好的统计相关性, 同时也说明该模型具有较高的预测能力.根据该模型提供的信息, 设计出9个预测活性较好的分子.     

苯并咪唑类缓蚀剂的3D-QSAR研究及分子设计

采用比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA), 对苯并咪唑衍生物抗盐酸腐蚀的缓蚀性能进行了三维定量构效关系研究, 并使用留一法交叉验证手段对3D-QSAR模型的稳定性及预测能力进行了分析. 结果表明, 立体场、静电场和氢键供体场(电子给体)是影响苯并咪唑缓蚀剂缓蚀性能的主要因素; 所构建的CoMFA模型(q2=0.541, R2=0.996)和CoMSIA模型(q2=0.581, R2=0.987)均具有较好的统计学稳定性和预测能力. 基于3D-QSAR等势图设计出了几种具有较好缓蚀性能的苯并咪唑化合物, 为油气田新型缓蚀剂的研发提供了一种新思路.     

QSAR modeling on benzo[c]phenanthridine analogues as topoisomerase I inhibitors and anti-cancer agents

Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, hologram-QSAR, 2D-QSAR and 3D-QSAR models were developed for BCPs on topoisomerase I inbibitory activity and cytotoxicity against seven tumor cell lines including RPMI8402, CPT-K5, P388, CPT45, KB3-1, KBV-1and KBH5.0. The hologram, 2D, and 3D-QSAR models were obtained with the square of correlation coefficient R2 = 0.58-0.77, the square of the crossvalidation coefficient q2 = 0.41-0.60 as well as the external set's square of predictive correlation coefficient r2 = 0.5-0.80. Moreover, the assessment method based on reliability test with confidence level of 95% was used to validate the predictive power of QSAR models and to prevent over-fitting phenomenon of classical QSAR models. Our QSAR model could be applied to design new analogues of BCPs with higher antitumor and topoisomerase I inhibitory activity.