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.     

Understanding and modulating cyclin-dependent kinase inhibitor specificity: molecular modeling and biochemical evaluation of pyrazolopyrimidinones as CDK2/cyclin A and CDK4/cyclin D1 inhibitors

Summary Cyclin-dependent kinases (CDKs) play a key role in regulating the cell cycle. The cyclins, their activating agents, and endogenous CDK inhibitors are frequently mutated in human cancers, making CDKs interesting targets for cancer chemotherapy. Our aim is the discovery of selective CDK4/cyclin D1 inhibitors. An ATP-competitive pyrazolopyrimidinone CDK inhibitor was identified by HTS and docked into a CDK4 homology model. The resulting binding model was consistent with available SAR and was validated by a subsequent CDK2/inhibitor crystal structure. An iterative cycle of chemistry and modeling led to a 70-fold improvement in potency. Small substituent changes resulted in large CDK4/CDK2 selectivity changes. The modeling revealed that selectivity is largely due to hydrogen-bonded interactions with only two kinase residues. This demonstrates that small differences between enzymes can efficiently be exploited in the design of selective inhibitors.     

Molecular dynamics simulations on the inhibition of Cyclin-Dependent Kinases 2 and 5 in the presence of activators

Interests in CDK2 and CDK5 have stemmed mainly from their association with cancer and neuronal migration or differentiation related diseases and the need to design selective inhibitors for these kinases. Molecular dynamics (MD) simulations have not only become a viable approach to drug design because of advances in computer technology but are increasingly an integral part of drug discovery processes. It is common in MD simulations of inhibitor/CDK complexes to exclude the activator of the CDKs in the structural models to keep computational time tractable. In this paper, we present simulation results of CDK2 and CDK5 with roscovitine using models with and without their activators (cyclinA and p25). While p25 was found to induce slight changes in CDK5, the calculations support that cyclinA leads to significant conformational changes near the active site of CDK2. This suggests that detailed and structure-based inhibitor design targeted at these CDKs should employ activator-included models of the kinases. Comparisons between P/CDK2/cyclinA/roscovitine and CDK5/p25/roscovitine complexes reveal differences in the conformations of the glutamine around the active sites, which may be exploited to find highly selective inhibitors with respect to CDK2 and CDK5.     

基于QSAR模型的有机磷化合物毒性预测

随着有机磷化合物(OPs)的广泛应用,其在越来越多的环境介质中被检测出来。大多数OPs具有毒性,但人们缺乏快速且有效的预测手段来对毒性进行评估。本文将结合E-Dragon软件计算的分子描述符,采用不同的QSAR模型对36个OPs的毒性进行预测。文中采用后退法作为描述符筛选方法,以均方根误差(RMSE)作为评价标准,共找到14个对线性核函数支持向量机(SVM)模型贡献较大的描述符;在最终得到的SVM模型交叉验证结果中,计算值与实际值的相关系数为0. 913,均方根误差为0. 388;外部测试验证结果中,平均相对误差为9. 10%。此外,采用多元线性回归(MLR)、人工神经网络(ANN)以及偏最小二乘回归(PLS)模型对OPs的毒性进行预测,交叉验证结果显示,三个模型的计算值与实际值的相关系数分别为0. 878、0. 686与0. 620,没有SVM模型的预测能力好。因此采用线性核函数的SVM模型对OPs进行毒性预测是一个行之有效的方法。