联合运用多种方法预测Pla a 1的亲水性及其二级结构,利用同源建模法建构Pla a 1的三维结构模型,在nb数据库中进行BLAST并构建同源进化树,在Scan Prosite数据库中进行Motif预测,对Pla a 1进行序列分析并进行三维结构建模。该蛋白是一个...联合运用多种方法预测Pla a 1的亲水性及其二级结构,利用同源建模法建构Pla a 1的三维结构模型,在nb数据库中进行BLAST并构建同源进化树,在Scan Prosite数据库中进行Motif预测,对Pla a 1进行序列分析并进行三维结构建模。该蛋白是一个主要为α+β结构的亲水性蛋白,预测其具有一个蛋白激酶C的磷酸化位点,一个N豆蔻酰化位点和三个酪蛋白激酶Ⅱ磷酸化位点。Pla a 1具有较强的信号转导作用,且与拟南芥的果胶(甲)酯酶抑制剂在进化上具有较近的亲缘关系;所预测的三维结构基本能反映出Pla a 1真实的空间构象,这将为今后进一步理解和掌握Pla a 1结构和功能上的关系打下理论基础。展开更多
An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 ...An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared with several phenomenological and physically based constitutive models. The ANN model has a much better applicability than the other models in characterization of the flow stress. The tempera- ture and the strain rate effects on the flow stress can be described successfully by the ANN model, with an average error of 1.78% for both quasi-static and dynamic loading conditions. Besides its high accuracy in prediction of the strain rate jump tests, the ANN model is more convenient in model es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.展开更多
文摘联合运用多种方法预测Pla a 1的亲水性及其二级结构,利用同源建模法建构Pla a 1的三维结构模型,在nb数据库中进行BLAST并构建同源进化树,在Scan Prosite数据库中进行Motif预测,对Pla a 1进行序列分析并进行三维结构建模。该蛋白是一个主要为α+β结构的亲水性蛋白,预测其具有一个蛋白激酶C的磷酸化位点,一个N豆蔻酰化位点和三个酪蛋白激酶Ⅱ磷酸化位点。Pla a 1具有较强的信号转导作用,且与拟南芥的果胶(甲)酯酶抑制剂在进化上具有较近的亲缘关系;所预测的三维结构基本能反映出Pla a 1真实的空间构象,这将为今后进一步理解和掌握Pla a 1结构和功能上的关系打下理论基础。
文摘An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared with several phenomenological and physically based constitutive models. The ANN model has a much better applicability than the other models in characterization of the flow stress. The tempera- ture and the strain rate effects on the flow stress can be described successfully by the ANN model, with an average error of 1.78% for both quasi-static and dynamic loading conditions. Besides its high accuracy in prediction of the strain rate jump tests, the ANN model is more convenient in model es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.
