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Computational simulation for interactions of nano-molecules: The phospho-pivot modeling algorithm for prediction of interactions between a phospho-protein and its receptor

机译:纳米分子相互作用的计算模拟:用于预测磷蛋白与其受体之间相互作用的磷酸基团建模算法

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Direct docking of nanomolecules, such as proteins, is responsible for biological signal transduction in cells. This physiological interaction is mimicked by various biosensors in nanotechnology. In many cases phosphorylation of protein is involved in protein-protein interaction, and understanding phosphorylation-dependent interaction is necessary to design novel biosensors. Here, we developed and tested a specific method for studying on interaction of phospho-proteins in silico. The algorithm, named phospho-pivot modeling, consists of two parts: first is to generate a library of virtual complexes by pivoting phospho-ligand at the docking site on the receptor, and second is to grade them according to probability in atomic proximity between two molecules. After a 90-min computation by a personal computer, the phospho-pivot modeling yielded an in silico model for the complex of Ser/Thr phosphatase-1 (PP1) and calyculin A, an inhibitory compound of PP1, which was superimposed on the crystal structure in database with r.m.s.d. of 0.23 A. The phospho-pivot modeling was applied on the prediction for the complex of PP1 and phospho-CPI-17, an inhibitory protein, whose complex structure is unknown. A 1285-min computation selected one converged structure of the PP1·CPI-17 complex out of 186,624 models. The computation time was reduced to 400 min by adding a prescreening process, where virtual complexes with conflicts between main chains were dismissed from the grading process. Thus, phospho-pivot modeling algorithm is sufficient to predict complex structure of proteins, whose monomeric structures have been solved.
机译:纳米分子(例如蛋白质)的直接对接负责细胞中的生物信号转导。纳米技术中的各种生物传感器模仿了这种生理相互作用。在许多情况下,蛋白质的磷酸化参与蛋白质与蛋白质的相互作用,因此了解磷酸化依赖性相互作用对于设计新型生物传感器十分必要。在这里,我们开发并测试了一种用于研究磷酸蛋白在计算机中相互作用的特定方法。该算法称为磷酸化枢轴建模,由两部分组成:首先是通过使磷酸配体在受体的对接位点处枢转来生成虚拟络合物库,其次是根据两个原子之间原子接近的概率对它们进行分级。分子。在个人计算机上进行90分钟的计算后,磷酸化枢轴建模得到了计算机模拟的Ser / Thr磷酸酶1(PP1)和calyculin A(一种抑制PP1的化合物)的复合物,该复合物叠加在晶体上rmsd数据库中的结构0.23A。磷酸-枢轴模型用于预测PP1和磷酸-CPI-17(一种抑制蛋白,复合结构未知)的复合物。 1285分钟的计算从186,624个模型中选择了PP1·CPI-17复合体的一种聚合结构。通过添加预筛选过程,将计算时间减少到400分钟,在该过程中,从评分过程中消除了具有主链之间冲突的虚拟复合体。因此,磷酸-枢轴建模算法足以预测蛋白质的复杂结构,其单体结构已得到解决。

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