异甘草素抑制α-葡萄糖苷酶的分子机制

α-葡萄糖苷酶活性与糖尿病患者的餐后血糖水平有重要关联,寻找食源性的α-葡萄糖苷酶抑制剂是当前功能性食品研究的热点。异甘草素是甘草的重要活性成分,相关研究表明甘草提取物具有α-葡萄糖苷酶抑制活性,推测与异甘草素有关。鉴于此,本实验通过酶抑制、荧光猝灭以及分子对接等方法研究异甘草素抑制α-葡萄糖苷酶活性的机制。结果表明,异甘草素以竞争性与非竞争性相混合的方式抑制α-葡萄糖苷酶,其抑制效果明显优于阿卡波糖。荧光猝灭分析结果表明在疏水作用力驱动下异甘草素可与α-葡萄糖苷酶结合生成复合物,结合位点数为1。分子对接结果验证了相关实验结论:异甘草素位于酶的疏水口袋中,与残基Asp202和Arg400以氢键结合,并与周围众多的疏水残基存在疏水作用,共同维持该复合物结构。本研究对于开发新型的食源性α-葡萄糖苷酶抑制剂、推动异甘草素在功能性食品和医药领域的应用具有一定的参考意义。

基于Topomer CoMFA和Surflex-dock的黄酮类醛糖还原酶抑制剂的3D-QSAR与作用模式研究

醛糖还原酶与糖尿病并发症的发生有关,常见的糖尿病治疗药物常以该酶为作用靶标,但这些药物在应用中却存在毒副作用大的问题,寻找安全的醛糖还原酶抑制剂是目前功能性食品和医药研究的热点。源于植物的黄酮化合物虽具有较强的醛糖还原酶抑制活性,但其作用机制仍不明晰。鉴于此,本研究旨在运用分子模拟手段研究黄酮抑制醛糖还原酶的三维定量构效关系及作用模式。采用基于R基团搜索技术的比较分子场法(Topomer Co MFA)建立了39个类黄酮分子抑制醛糖还原酶的三维定量构效关系模型,并用包括12个样本的测试集验证模型的外部预测能力。所得模型的拟合、交互验证以及外部验证的相关系数分别为0.831,0.564和0.794。在此基础上,运用Surflex-dock分子对接法研究了黄酮与醛糖还原酶的结合模式。结果表明黄酮构型不同导致其在酶疏水性空腔中的取向不同,进而引起活性差异。当黄酮上的取代基分布符合立体场和静电场的修饰原则时,可显著改善黄酮与酶的结合,提高其醛糖还原酶抑制效果。对于开发新型的醛糖还原酶抑制剂,推动黄酮在功能性食品领域的应用具有一定的指导意义。

Using scores of amino acid topological descriptors for quantitative sequence-mobility modeling of peptides based on support vector machine

拓扑的描述符(SATD ) 从与 23 氨基酸有关的 1262 个结构的变量的一个矩阵的原则部件分析导出的氨基酸的分数被采用在不同长度表示 125 肽的结构。量的 sequence-mobilitymodelings (QSMM ) 用部分最少的平方被构造(请) 并且支持向量机器(SVM ) 分别地。作为新氨基酸规模，包括与生物活性有关的丰富的信息的 SATD 容易被操作。更好的结果与获得与的那些相比被获得请，它显示 SVM 介绍了柔韧的稳定性和优秀预兆的能力 forelectrophoretic 活动性。这些结果证明在 QSMM 为应用 ofSATD 和 SVM 回归有宽前景。

Support vector machine applied in QSAR modelling

Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural net- work (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) re- gression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel func- tion. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships(QSRR) models.First,the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties(SGBP),involving physicochemical,quantum chemical,topological,spatial structural properties,etc.;thereafter,the sequence data are transformed into a uniform matrix by auto cross covariance(ACC).ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence;hence,this method adequately takes the neighboring effect into account.Then,a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonucleotides.Finally,a support vector machine is used to develop QSRR models to predict chromatographic retention behavior.The whole dataset is divided into pairs of training sets and test sets with different proportions;as a result,it has been found that the QSRR models using more than 26 training samples have an appropriate external power,and can accurately represent the relationship between the features of sequences and structures,and the retention times.The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information,easy manipulation and high characterization competence.Moreover,the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

Scores of amino acid 0D-3D information as applied in cleavage site prediction and better specificity elucidation for human immunodeficiency virus type 1 protease

A new set of descriptors,namely score vectors of the zero dimension,one dimension,two dimensions and three dimensions(SZOTT),was derived from principle component analysis of a matrix of 1369 structural variables including 0D,1D,2D and 3D information for the 20 coded amino acids. SZOTT scales were then used in cleavage site prediction of human immunodeficiency virus type 1 protease. Linear discriminant analysis(LDA) and support vector machines(SVM) were applied to developing models to predict the cleavage sites. The results obtained by linear discriminant analysis(LDA) and support vector machines(SVM) are as follows. The Matthews correlation coefficients(MCC) by the resubstitution test,leave-one-out cross validation(LOOCV) and external validation are 0.879 and 0.911,0.849 and 0.901,0.822 and 0.846,respectively. The receiver operating characteristic(ROC) analysis showed that the SVM model possesses better simulative and predictive ability in comparison with the LDA model. Satisfactory results show that SZOTT descriptors can be further used to predict cleavage sites of human immunodeficiency virus type 1 protease.

Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.