两类HPPD酶抑制剂的比较分子场分析研究

用比较分子场分析法（CoMFA）研究了环已二酮类及3-烷基酸-2-环已烯酯类化 合物的结构与活性的关系。本研究从蛋白酶与底物动力学模拟的复合物结构出发构 建两类抑制剂化合物分子的构象，并进行了全空间搜索，CoMFA分析得到了较好的 模型（交叉验证回归系数q~2 = 0.779，模型的线性回归系数r~2 = 0.989）。该方 程不仅可以帮助推测抑制剂与受体的结合方式，还可定量地预测结构相近的类似物 活性，为设计合成新的HPPD酶抑制剂提供了理论依据。     

q~2引导构象选择CoMFA方法研究HIV－1RT抑制剂

对未知受体结构的药物设计其主导方法CoMFA来说，柔性目标分子的多种构象 造成了问题的复杂性。本文介绍交叉验证参数R~2(q~2)引导的构象选择CoMFA方法 ，选择化合物的最佳构象。将一组47个HIV－1 RT抑制剂进行有、无构象选择的 CoMFA分析来作评价。根据化合物的活性、毒性、选择性指数（毒性／活性比）等 实验数据得到的模型，其交叉验证参数q~2为0.7以上，非交叉验证的相应参数为0. 94以上，最后，还经过试验集化合物验证该模型的预测能力，置信度（1－α）＞ 0.99。     

雌激素β受体喹啉类配体的分子对接及3D-QSAR研究

对33个喹啉衍生物的雌激素β受体活性进行了分子对接以及比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA). 对接结果显示氢键和疏水作用是配体与受体结合的主要因素,同时结果亦显示对接结合能与观测值pIC50具有极显著的线性相关性. 根据对接后各优势构象将33个样本进行叠合并进行CoMFA与CoMSIA研究,均得到了较优的结果,其中以选用立体场、静电场和疏水场建立的CoMSIA模型结果最优,其主成分数,r2,q2（LOO）和r2pred分别为2, 0.894, 0.708和0.802. 构效关系模型分析显示基团的空间位阻、电性及疏水作用是影响活性的主要因素     

家蝇与大鼠GABA受体抑制剂的药效团模型及其3D-QSAR研究

采用DISCOtech方法,用7个大鼠γ-氨基丁酸(GABA)A受体抑制剂和11个家蝇GABAA受体抑制剂分别建立了其药效团模型;用CoMFA方法建立了22个大鼠GABAA受体抑制剂和29个家蝇GABAA受体抑制剂的3D-QSAR模型,模型的交叉验证相关系数分别为0.526和0.679,验证了药效团模型的合理性,为设计更高活性和更高选择性的化合物提供了参考     

GABAA受体萜类抑制剂构效关系研究

采用DISCOtech方法建立了大鼠和家蝇GABAA受体萜类抑制剂的药效团模型, 根据药效团模型叠加规则建立了大鼠和家蝇GABAA受体萜类抑制剂CoMFA模型, 模型的交叉验证相关系数分别为0.713和0.738. 构效关系研究显示， 家蝇和大鼠受体抑制剂结合部位之间存在一个主要差别: 与受体作用的抑制剂的负电荷基团取代有利于保持其对哺乳动物的高抑制活性, 而保持对昆虫的高抑制活性是不需要的, 从而为寻找先导化合物和设计高选择性杀虫剂提供了理论指导.     

基于5-芳基乙内酰脲类化合物的CoMFA和HQSAR研究

用CoMFA和HQSAR两种QSAR方法研究了50个乙内酰脲类分子的定量构效关系.本研究从构象搜索所得的低能结构出发构建化合物分子的构象, 建立CoMFA模型,并进行了全空间搜索. HQSAR本质上是一种二维的QSAR方法,与CoMFA方法相比,该方法在数据处理方面,比CoMFA方法快捷,并且可重复性好.两种方法均得到了较好分析结果, CoMFA的交叉验证相关系数q2 值为0.815, HQSAR的q2值为0.893.这些方程有力地说明了该类分子在(R,R)-N-3,5-dinitrobenzoyl-1,2-diamine型手性固定相上拆分过程中的影响因素,对今后类似拆分的实验研究提供了理论支持.     

酪氨酸蛋白磷酸酯酶1B抑制剂的分子对接和三维定量构效关系研究

用一种柔性分子对接方法(FlexX)将12个2-草酰胺苯甲酸类抑制剂和酪氨酸蛋白磷酸酯酶(PTP1B)活性口袋进行分子对接,对接程序预测的抑制剂和酶之间的相互作用能与抑制活性之间有很好的相关性(非线性相关系数R²达0.859),这说明对接结果可以比较准确地预测抑制剂和PTP1B之间的结合模式.然后,将33个同类抑制剂的骨架叠合在分子对接预测的结合构象上,用比较分子力场分析方法(CoMFA)对其进行三维定量活性构效关系研究,得到的CoMFA模型具有很好的统计相关性(交互验证回归系数q²为0.650),并可以准确地预测测试集6个化合物的活性(平均标准偏差为0.177).同时,由CoMFA模型得出的抑制剂改造信息与用FlexX预测的结合模式是一致的,进一步证明我们预测的结合模式是正确的.为研究这类抑制剂和PTP1B的结合模式及对抑制剂进行结构改造提供了信息.     

皂甙的三维定量构效关系研究

针对目标分子柔性大的特点,在比较分子场分析(CoMFA)方法中采用交叉验证相关系数平方R^2引导的构象选择法。对12个皂甙分子的生物活性进行了三维定量构效关系研究。探讨了几种探针对构效关系结果的影响,并选择了一种较合理的“复合”探针方案。应用该复合探针构建CoMFA模型,发现影响药效的立体场与静电场的贡献分别为40%和40%,其它能量项的贡献为20%。该模型交叉验证的相关系数平方R^2为0.653,非交叉验证的R^2为0.991,方差比F(4,7)值130.195(即置信度99%以上),活性预计的标准偏差与极差比(s/△γ)为4.2%,表明模型具有较好的预测能力。根据该模型,预计在指定位置添加位阻较大的基团活性值提高将会比较明显。     

不同电荷场优化咪唑并吡啶类VEGFR-2抑制剂的三维定量构效关系研究

运用不同电荷场优化咪唑并吡啶类VEGFR-2抑制剂,挑选出最优电荷场Gasteiger-Marsili,用COMFA和CoMSIA两种方法建立3D-QSAR模型,以分析化合物结构与分子活性之间的关系。COMFA和CoMSIA模型的交叉验证系数分别为q2=0.673和q2=0.612,拟合验证系数分别为r~2=0.891和r~2=0.973,外部验证复相关系数分别为r2 pred=0.669和r2 pred=0.658,结果表明两种模型都有良好的可信度和预测能力。COMFA和CoMSIA模型在三维等视图上相互对照和验证,得到的结论与分子对接结果一致,确立了分子结构对抑制剂活性影响的具体作用方式,对指导药物的设计与改造,新VEGFR-2抑制剂的合成提供参考。     

五味子素类抑制HIV活性的三维定量构效关系研究

建立了五味子活性成分木脂素类和联苯类化合物抑制HIV活性的三维定量构效 方程。采用联苯环原子和苯环质心两种叠合方式，并区分联苯化合物的不同构型， 共建立了四类CoMSIA模型，其中训练集中联苯类为S构型并叠合联苯环原子建立的 CoMSIA模型相关性最好，交叉验证相关系数q~2为0.71，非交叉验证相关系数r~2 = 0.99，标准偏差SE = 0.051, F = 1000.6。CoMSIA方法采用Gaussian函数计算 场能，并在CoMFA方法的立体和静电场基础上加入疏水场，PLS分析结果更为准确。 该模型三维等势图证实了某些结构和活性规律，如联苯基共面性越好，活性越高， 同时给出了苯环上取代基的体积、电性和疏水性要求，为该类化合物的结构改造提 供了依据。     

Torsion angle preference and energetics of small-molecule ligands bound to proteins

Small organic molecules can assume conformations in the protein-bound state that are significantly different from those in solution. We have analyzed the conformations of 21 common torsion motifs of small molecules extracted from crystal structures of protein-ligand complexes and compared them with their torsion potentials calculated by an ab initio DFT method. We find a good correlation between the potential energy of the torsion motifs and their conformational distribution in the protein-bound state: The most probable conformations of the torsion motifs agree well with the calculated global energy minima, and the lowest torsion-energy state becomes increasingly dominant as the torsion barrier height increases. The torsion motifs can be divided into 3 groups based on torsion barrier heights: high (>4 kcal/mol), medium (2-4 kcal/mol), and low (<2 kcal/mol). The calculated torsion energy profiles are predictive for the most preferred bound conformation for the high and medium barrier groups, the latter group common in druglike molecules. In the high-barrier group of druglike ligands, >95% of conformational torsions occur in the energy region <4 kcal/mol. The conformations of the torsion motifs in the protein-bound state can be modeled by a Boltzmann distribution with a temperature factor much higher than room temperature. This high-temperature factor, derived by fitting the theoretical model to the experimentally observed conformation occurrence of torsions, can be interpreted as the perturbation that proteins inflict on the conformation of the bound ligand. Using this model, it is calculated that the average strain energy of a torsion motif in ligands bound to proteins is approximately 0.6 kcal/mol, a result which can be related to the lower binding efficiency of larger ligands with more rotatable bonds. The above results indicate that torsion potentials play an important role in dictating ligand conformations in both the free and the bound states.     

Flexible matching of test ligands to a 3D pharmacophore using a molecular superposition force field: Comparison of predicted and experimental conformations of inhibitors of three enzymes

Summary A computer procedure TFIT, which uses a molecular superposition force field to flexibly match test compounds to a 3D pharmacophore, was evaluated to find out whether it could reliably predict the bioactive conformations of flexible ligands. The program superposition force field optimizes the overlap of those atoms of the test ligand and template that are of similar chemical type, by applying an attractive force between atoms of the test ligand and template which are close together and of similar type (hydrogen bonding, charge, hydrophobicity). A procedure involving Monte Carlo torsion perturbations, followed by torsional energy minimization, is used to find conformations of the test ligand which cominimize the internal energy of the ligand and the superposition energy of ligand and template. The procedure was tested by applying it to a series of flexible ligands for which the bioactive conformation was known experimentally. The 15 molecules tested were inhibitors of thermolysin, HIV-1 protease or endothiapepsin for which X-ray structures of the bioactive conformation were available. For each enzyme, one of the molecules served as a template and the others, after being conformationally randomized, were fitted. The fitted conformation was then compared to the known binding geometry. The matching procedure was successful in predicting the bioactive conformations of many of the structures tested. Significant deviation from experimental results was found only for parts of molecules where it was readily apparent that the template did not contain sufficient information to accurately determine the bioactive conformation.     

GalaxyDock3: Protein–ligand docking that considers the full ligand conformational flexibility

Predicting conformational changes of both the protein and the ligand is a major challenge when a protein–ligand complex structure is predicted from the unbound protein and ligand structures. Herein, we introduce a new protein–ligand docking program called GalaxyDock3 that considers the full ligand conformational flexibility by explicitly sampling the ligand ring conformation and allowing the relaxation of the full ligand degrees of freedom, including bond angles and lengths. This method is based on the previous version (GalaxyDock2) which performs the global optimization of a designed score function. Ligand ring conformation is sampled from a ring conformation library constructed from structure databases. The GalaxyDock3 score function was trained with an additional bonded energy term for the ligand on a large set of complex structures. The performance of GalaxyDock3 was improved compared to GalaxyDock2 when predicted ligand conformation was used as the input for docking, especially when the input ligand conformation differs significantly from the crystal conformation. GalaxyDock3 also compared favorably with other available docking programs on two benchmark tests that contained diverse ligand rings. The program is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2019 Wiley Periodicals, Inc.     

The MPSim-Dock hierarchical docking algorithm: application to the eight trypsin inhibitor cocrystals

To help improve the accuracy of protein-ligand docking as a useful tool for drug discovery, we developed MPSim-Dock, which ensures a comprehensive sampling of diverse families of ligand conformations in the binding region followed by an enrichment of the good energy scoring families so that the energy scores of the sampled conformations can be reliably used to select the best conformation of the ligand. This combines elements of DOCK4.0 with molecular dynamics (MD) methods available in the software, MPSim. We test here the efficacy of MPSim-Dock to predict the 64 protein-ligand combinations formed by starting with eight trypsin cocrystals, and crossdocking the other seven ligands to each protein conformation. We consider this as a model for how well the method would work for one given target protein structure. Using as a criterion that the structures within 2 kcal/mol of the top scoring include a conformation within a coordinate root mean square (CRMS) of 1 A of the crystal structure, we find that 100% of the 64 cases are predicted correctly. This indicates that MPSim-Dock can be used reliably to identify strongly binding ligands, making it useful for virtual ligand screening.     

Electrospray mass spectrometry for the direct accurate mass measurement of ligands in complex with the retinoid X receptor α ligand binding domain

Accurate mass measurements are often used in the structural determination of unknown compounds of low molecular mass (i.e., below approximately 500 Da). Recently, it has been shown that accurate mass measurements also can be made on small denatured proteins (i.e., M(r), approximately 17,000) to confirm their amino acid composition and identify the presence of isoforms. In the current report, we present nondenaturing electrospray (ES) mass spectrometry data on the direct accurate mass measurement of ligands in complex with the retinoid X receptor ligand binding domain (RXR LBD; M(r) 31,370.92). Average mass errors were below 0.198 Da, 6.3 ppm (standard deviation [SD], 0.146; n = 10) for low-affinity fatty acid agonists analyzed in complex with the RXR LBD. Protein consumption was less than 15 pmol, with fatty acid ligands present at concentrations corresponding to their median effective concentration value (low micromolar, determined in transfection assays). Although determination of fatty acid mass was only sufficiently accurate to give nominal mass values, measurements were of sufficient accuracy to assign fatty acid chain length, degree of unsaturation, or cyclization. Using 17beta-estradiol as a control, the ability to observe specific ligand binding is shown for both high- and low-affinity RXRalpha agonists. In addition, binding of a novel synthetic receptor agonist XCT0315908 to the RXRalpha LBD is reported. This compound showed a high degree of complex formation, and the receptor-ligand complex could be mass measured with an average mass error of -0.024 Da, 0.8 ppm (SD, 0.092; n = 9). Thus, specific binding of both nanomolar and micromolar affinity ligands to a nuclear receptor LBD can be directly observed using nondenaturing ES mass spectrometry and accurate mass measurements additionally can be made on intact complexes in the same experiment. This methodology also is applicable when ligands are present as components of mixtures.     

Conformational preferences of the axial ligands in some metalloporphyrins. A theoretical study

The conformational preferences of the axial ligands have been determined for several metalloporphyrins MPL and MPLL′ (M = Mo, Fe; P = porphine dianion; L and L′ being the axial ligands). For MoP(C₂H₂) a qualitative analysis indicates that the conformation with the acetylenic bond eclipsing two Mo-N bonds will be favored. Ab initio SCF calculations indicate that:

1. iron porphyrins with an axial imidazole ligand show a flat potential energy curve for the rotation of the imidazole ligand;
2. iron porphyrins with a dioxygen ligand prefer the staggered conformation with the O-O bond projecting along the bisectors of the Fe-N bonds;
3. in the cis-dinitrosyl molybdenum porphyrin, the nitrosyl ligands should be eclipsed with respect to the Mo-N_pyr bonds.

These theoretical predictions are compared with the experimental structures from the literature.     

Coordination to metal centers: a tool to fix high energy conformations in organic molecules. Application to 2,4,4-trimethyl-1,5,9-triazacyclododec-1-ene and related macrocycles

The solid state conformational preferences of ligand 2,4,4-trimethyl-1,5,9-triazacyclododec-1-ene (L1) and its 9-methyl derivative (L2) in transition metal complexes have been determined by a probabilistic method using data retrieved from the Cambridge Structural Database. These macrocyclic compounds, as ligands, tend to adopt a preferential conformation (85% of cases). The ring containing the C=N bond adopts a distorted half-chair conformation, the ring defined by both the N-sp(3) shows a distorted envelope conformation, and the remaining ring exhibits a chair conformation. This conformation corresponds to the enantiomer pair R(N5)S(N9)S(P)/S(N5)R(N9)R(P). Molecular mechanics calculations demonstrate that this is a high energy conformation for the organic molecule, far from the energy minimum. Two other enantiomer pairs are observed in experimental structures. The influence of the coordination on the conformation of the organic ligands has been studied by DFT calculations, and a clear correlation with the geometry of the coordination sphere has been found.