Structure-aided drug development of potential neuraminidase inhibitors against pandemic H1N1 exploring alternate binding mechanism

Molecular Diversity - The rate of mutability of pathogenic H1N1 influenza virus is a threat. The emergence of drug resistance to the current competitive inhibitors of neuraminidase, such as...     

Design and one-pot synthesis of 2-thiazolylhydrazone derivatives as influenza neuraminidase inhibitors

Two series of novel 2-thiazolylhydrazone derivatives were designed and synthesized via one-pot reaction of benzaldehyde derivatives, \(\alpha \)-haloketones and thiosemicarbazide. The structures of compounds 1 and 2 were characterized by \(^{1}\hbox {H}\) NMR and \(^{13}\hbox {C}\) NMR, and compound 1g was further confirmed by X-ray crystallography. All of the target compounds were evaluated for their NA inhibitory activity against influenza viral neuraminidase (H1N1) in vitro, and the results showed that many compounds exhibited moderate to strong inhibitory activities against influenza viral neuraminidase (H1N1). Among them, compounds 1p, 1q and 2c showed the most potent inhibitory activities with \(\hbox {IC}_{50}\) values ranging from 10.50 to \(13.75\, \upmu \hbox {g}/\hbox {mL}\). Our structure–activity relationship analysis indicated that 2-thiazolylhydrazone is an effective scaffold for NA inhibitors and that introducing an ethoxycarbonyl group to the 5-position of thiazole ring could enhance inhibitory potency. Molecular docking was performed on the most active compounds 1p and 2c to provide more insight into their mechanism of interaction.     

分子模拟在生物化学中的应用实例

分子模拟是一种描述和模拟分子和分子体系运动状态和性质的方法.随着电子计算机技术的飞速发展,分子模拟进入了一个前所未有的新时代.在此之前,人们只能通过机械模型和纸笔计算进行简单的分子模拟,现在通过利用电子计算机人们可以做更为复杂、更为全面的分子模拟.本文通过两个实例来简单阐述了分子模拟在生物化学中的应用.一则是通过模拟膦酰基氧化腈和丙乙腈的1,3偶极环加成反应过程,用密度泛函理论方法在B3LYP/6-31G(d,p)水平上解释了得到2∶1的加成产物的现象,来解释1,3偶极环加成反应得到2:1加成产物的现象.一则是通过结构生物信息学的方法建立H5N1高致病性禽流感病毒蛋白的三维结构,模拟其与一些药物分子的相互作用,研究H5N1的活性中心.     

反刍动物源肉骨粉的近红外漫反射光谱判别分析

为了探讨利用近红外漫反射光谱判别分析技术快速鉴别非反刍动物源肉骨粉中是否掺有反刍动物源肉骨粉的可行性,收集了来源于国内不同地区的不同种类的肉骨粉样本39个,其中猪、鸡肉骨粉各15个,牛肉骨粉5个和羊肉骨粉4个。通过在非反刍动物源猪或鸡肉骨粉中人为掺入0%～48%的反刍类动物源牛或羊肉骨粉,制备了252个肉骨粉的样本。利用FOSS 6500型近红外光谱仪获取样本光谱,WINISI软件随机选取180个样本作为校正集,72个样本作为独立的验证集。采用基于偏最小二乘回归法(PLS)的判别分析技术,建立了判别分析模型,利用独立的验证集对判别分析模型进行了验证,最优判别分析模型正确判别率为90%。结果表明所建立的判别分析模型可以对非反刍动物源肉骨粉中含有的反刍动物成分进行快速鉴别,但对反刍动物成分含量低于2%的肉骨粉样本,模型的判别精度还有待提高。文章结果为反刍动物源性肉骨粉的鉴别分析提供了一种有效的快速筛选方法。     

基于支持向量机、支持向量回归和分子对接的CYP450 1A2抑制剂的发现研究

支持向量机,支持向量回归和分子对接的计算方法已广泛应用于化合物的药理活性计算。为了提高计算的准确性和可靠性,拟以细胞色素P450酶1A2为研究载体,运用建立的联合SVM-SVR-Docking计算模型预测潜在的CYP1A2抑制剂。其中,建立的最优SVM定性模型训练集,内部测试集和外部测试集的准确率分别为99.432%,97.727%和91.667%。最优SVR定量模型训练集和测试集的R和MSE分别为0.763,0.013和0.753,0.056。实验表明两个模型具有较高的准确性和可靠性。通过对SVM和SVR模型结果的比较分析,发现连接性指数、分子构成描述符和官能团数目等分子描述符可能与CYP1A2抑制剂的辨识和活性预测密切相关。随后利用分子对接技术分析化合物与CYP1A2的结合构象及相互作用的稳定性。形成氢键相互作用的关键氨基酸包括THR124,ASP320;形成疏水相互作用的关键氨基酸包括ALA317和GLY316。所获得模型可用于天然产物化学成分中CYP1A2潜在抑制剂的活性计算及其介导的药物-药物相互作用预测提供理论指导,也为合理联合用药提供一定参考。共获得20个对CYP1A2具有潜在抑制活性的化合物。部分结果与文献结果相互印证,进一步说明了模型的准确性和联合计算策略的可靠性.     

Validation subset selections for extrapolation oriented QSPAR models

One of the most important features of QSPAR models is their predictive ability. The predictive ability of QSPAR models should be checked by external validation. In this work we examined three different types of external validation set selection methods for their usefulness in in-silico screening. The usefulness of the selection methods was studied in such a way that: 1) We generated thousands of QSPR models and stored them in 'model banks'. 2) We selected a final top model from the model banks based on three different validation set selection methods. 3) We predicted large data sets, which we called 'chemical universe sets', and calculated the corresponding SEPs. The models were generated from small fractions of the available water solubility data during a GA Variable Subset Selection procedure. The external validation sets were constructed by random selections, uniformly distributed selections or by perimeter-oriented selections. We found that the best performing models on the perimeter-oriented external validation sets usually gave the best validation results when the remaining part of the available data was overwhelmingly large, i.e., when the model had to make a lot of extrapolations. We also compared the top final models obtained from external validation set selection methods in three independent and different sizes of 'chemical universe sets'.     

Theoretical study of GSK−3<Emphasis Type="Italic">α</Emphasis>: neural networks QSAR studies for the design of new inhibitors using 2D descriptors

Glycogen synthase kinase-3 (GSK-3) targets encompass proteins implicated in AD and neurological disorders. The functions of GSK-3 and its implication in various human diseases have triggered an active search for potent and selective GSK-3 inhibitors. In this sense, QSAR could play an important role in studying these GSK-3 inhibitors. For this reason, we developed QSAR models for GSK−3α, linear discriminant analysis (LDA), and artificial neural networks (ANNs) from nearly 50,000 cases with more than 700 different GSK−3α inhibitors obtained from ChEMBL database server; in total we used more than 20,000 different molecules to develop the QSAR models. The model correctly classified 237 out of 275 active compounds (86.2%) and 14,870 out of 15,970 non-active compounds (93.2%) in the training series. The overall training performance was 93.0%. Validation of the model was carried out using an external predicting series. In these series, the model classified correctly 458 out of 549 (83.4%) compounds and 29,637 out of 31,927 non-active compounds (83.4%). The overall predictability performance was 92.7%. In this study, we propose three types of non-linear ANN as alternative to already existing models, such as LDA. Linear neural network: LNN: 236:236-1-1:1 which had an overall training performance of 96% proved to be the best model. In addition, we did a study of the different fragments of the molecules of the database to see which fragments had more influence in the activity. This can help design new inhibitors of GSK−3α. This study reports the attempts to calculate, within a unified framework probabilities of GSK−3α inhibitors against different molecules found in the literature.     

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure–activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set \({R}^{2}= 0.904\); test set: \({Q}^{2}= 0.676\); whole data set: stability \(s = 0.803\)). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.     

Specific antibody response of mice after immunization with COS-7 cell derived avian influenza virus (H5N1) recombinant proteins

To develop avian influenza H5N1 recombinant protein, the hemagglutinin (HA), neuraminidase (NA), matrix (M), and non-structural (NS1) of avian influenza H5N1 isolates from Thailand were engineered to be expressed in prokaryotic (E. coli) and mammalian cell (COS-7) system. The plasmid pBAD-His and pSec-His were used as vectors for these inserted genes. Mice immunized with purified recombinant proteins at concentration 50–250 μg intramuscularly with Alum adjuvant at week 0, week 2, and week 3 showed a good immunogenicity measured by ELISA and neutralization assay. The HA and NS recombinant proteins produced in COS-7 cells can induce specific antibody titer detected by neutralization assay significantly higher than corresponding recombinant proteins produced in E. coli system. The antibody produced in immunized mice could neutralize heterologous avian influenza virus determined by micro-neutralization assay. This study shows that avian influenza virus H5N1 recombinant proteins produced in mammalian cell system were able to induce neutralizing antibody response.     

False Positive and False Negative Effects on Network Attacks

Robustness against attacks serves as evidence for complex network structures and failure mechanisms that lie behind them. Most often, due to detection capability limitation or good disguises, attacks on networks are subject to false positives and false negatives, meaning that functional nodes may be falsely regarded as compromised by the attacker and vice versa. In this work, we initiate a study of false positive/negative effects on network robustness against three fundamental types of attack strategies, namely, random attacks (RA), localized attacks (LA), and targeted attack (TA). By developing a general mathematical framework based upon the percolation model, we investigate analytically and by numerical simulations of attack robustness with false positive/negative rate (FPR/FNR) on three benchmark models including Erd?s-Rényi (ER) networks, random regular (RR) networks, and scale-free (SF) networks. We show that ER networks are equivalently robust against RA and LA only when FPR equals zero or the initial network is intact. We find several interesting crossovers in RR and SF networks when FPR is taken into consideration. By defining the cost of attack, we observe diminishing marginal attack efficiency for RA, LA, and TA. Our finding highlights the potential risk of underestimating or ignoring FPR in understanding attack robustness. The results may provide insights into ways of enhancing robustness of network architecture and improve the level of protection of critical infrastructures.     

基于Topomer CoMFA方法对芳基硫代吲哚衍生物的分子建模与设计

采用Topomer CoMFA方法对30个芳基硫代吲哚衍生物进行三维定量关系研究,建立了3D-QSAR模型,所得模型的交叉验证相关系数q~2,非交叉验证相关系数r~2,外部验证的复相关系数Q_(ext)~2分别为0.562,0.878,0.985,结果表明该模型具有较好的稳定性和预测能力.Topomer CoMFA模型等势面提供的立体场与静电场可视化图像,直观的揭示了这一系列化合物中不同取代基结构对其生物活性的影响,运用这些信息进行分子设计,在理论上获得了5个具有较高活性的新化合物,该QSAR的实验结果可为合成新药提供理论参考.     

近红外光谱结合共识模型快速检测果酒的总酚含量

果酒发酵中的多酚是引起果酒口感、颜色变化的重要因素。为保证果酒品质,有必要开发一种快速监测发酵过程中多酚含量变化的技术。收集不同批次成熟期的蓝莓、桑葚为原料,分别碾压成汁,同时按比例混合二者,于小型发酵罐进行发酵。通过离线收集不同发酵时段的发酵液于离心管,高速离心后取上清液置于棕色瓶保存,共计得到48个果酒发酵样本。将上清液置于三个平行样比色皿,以傅里叶快速变换近红外光谱仪(FT-NIR)采集其透射光谱,取平均值作为该样本的光谱信号。然后将棕色瓶内的发酵液以国标法(即以标准液的吸光度值制定标准曲线)测定各样品的总酚含量,以duplex法计算样本光谱之间的距离且按2∶1的比例划分为训练集和预测集。采用间隔偏最小二乘法(iPLS)将训练集样本的透射光谱与总酚含量之间构建定量模型,间隔数从2依次变化到60个。该研究创新之处是使用共识方法融合多个已构建好的iPLS成员模型,按一定的共识规则分配权系数。通过各成员模型交互验证的残差及其残差之间的相关性来优化各成员模型的线性组合,以拉格朗日乘数法求解各成员模型的权系数,使间隔偏最小二乘-共识模型(consensual iPLS,CiPLS)的交互验证均方根误差最小。相比于全局PLS模型、划分不同间隔数量时的iPLS模型,CiPLS均具有较小的预测误差。当划分39个间隔时由三个iPLS成员模型(即14th,16th,18th)组成的共识模型误差最小为124.2,交互验证相关系数为0.944,对预测集样本的预测均方根误差为163.4,预测相关系数为0.931,预测性能均优于PLS和iPLS模型。另外,作为对比选用连续投影算法与无信息变量剔除法来优化光谱模型,其预测性能均不及本文提出的共识模型。分析各iPLS模型预测残差之间的相关性,发现共识模型主要是融合那些具有较高预测性能且模型间较低相关性的成员模型。结果表明,光谱分析结合共识方法可提高回归模型的预测精度、减少建模所需变量数,能够用于果酒总酚含量的离线快速检测。     

A Novel RBF Neural Network Training Methodology to Predict Toxicity to Vibrio Fischeri

Summary This work introduces a neural network methodology for developing QSTR predictors of toxicity to Vibrio fischeri. The method adopts the Radial Basis Function (RBF) architecture and the fuzzy means training strategy, which is fast and repetitive, in contrast to most traditional training techniques. The data set that was utilized consisted of 39 organic compounds and their corresponding toxicity values to Vibrio fischeri, while lipophilicity, equalized electronegativity and one topological index were used to provide input information to the models. The performance and predictive ability of the RBF model were illustrated through external validation and various statistical tests. The proposed methodology can be used to successfully model toxicity to Vibrio fischerifor a heterogeneous set of compounds.     

FTIR结合化学计量学对三七地下部位鉴别及皂苷含量预测

当今中药市场上掺假现象屡见不鲜，不良商贩利用三七须根粉末假冒主根和剪口粉末，严重影响三七的质量与药效。通过傅里叶变换红外光谱(FTIR)结合化学计量学建立三七主根、剪口和须根粉末鉴别及四种皂苷含量快速预测模型，为快速三七质量控制提供基础。采集三七主根、剪口和须根红外光谱，超高效液相色谱(UPLC)测量样品中三七皂苷R₁、人参皂苷Rg₁、人参皂苷Rb₁和人参皂苷Rd含量。采用纵坐标归一化及二阶导数对原始红外光谱进行预处理；Kennard-stone算法将60个样本分为2/3训练集与1/3预测集。训练集数据结合支持向量机(SVM)判别建立三七主根、剪口和须根粉末鉴别模型，最佳核函数c和g采用交叉验证进行网格式搜索，预测集数据用于对判别模型进行外部验证。正交信号校正偏最小二乘回归(OSC-PLSR)建立三七中四种皂苷含量预测模型，红外光谱采用一阶、二阶导数及Savitsky-Golay平滑5点、7点、9点、11点预处理。60个样本分为2/3训练集与1/3预测集，训练集数据建立OSC-PLSR模型，预测集数据对OSC-PLSR模型的预测结果进行外部验证。结果显示： (1)二阶导数可有效的分离原始谱图的叠合隐蔽谱峰，并提高谱图的分辨率；(2)交叉验证网格式搜索计算出最佳核函数c=2.828 43，g=4.882 81×10-4，此时训练集判别正确率为100%；(3)SVM判别模型核函数设置为最佳核函数，预测集数据外部验证正确率为100%，所有样本均被正确鉴别；(4)三七皂苷R₁、人参皂苷Rg₁、人参皂苷Rb₁和人参皂苷Rd最优含量预测模型预测值与UPLC检测值接近，预测效果良好。FTIR结合SVM判别能对三七主根、剪口和须根粉末快速鉴别，结合OSC-PLSR能对四种皂苷含量进行准确预测。该方法准确可靠，可为中药材三七提供快速有效的质量控制。