We have developed an open-source cross-platform software toolkit entitled ACCEPT-NMR (Automated Crystal Contact Extrapolation/Prediction Toolkit for NMR) as a helpful tool to automate many of the complex tasks require...We have developed an open-source cross-platform software toolkit entitled ACCEPT-NMR (Automated Crystal Contact Extrapolation/Prediction Toolkit for NMR) as a helpful tool to automate many of the complex tasks required to find and visualize crystal contacts in structures of biomolecules and biomolecular assemblies. This toolkit provides many powerful features geared toward NMR spectroscopy and related disciplines, such as isotopic labeling, advanced visualization options, and reporting tools. Using this software, we have undertaken a survey of available chemical shift data in the literature and deposited in the BMRB, and show that the mere presence of one or more crystal contacts to a residue confers an approximately 65% likelihood of significant chemical shift perturbations (relative to solution NMR chemical shifts). The presence of each additional crystal contact subsequently increases this probability, resulting in predictive accuracies in excess of 80% in many cases. Conversely, the presence of a significant experimental chemical shift perturbation indicates a >60% likelihood of finding one or more crystal contacts to a particular residue. Pinpointing sites likely to experience large CSPs is critical to mapping solution NMR chemical shifts onto solid-state NMR data as a basis for preliminary assignments, and can thus simplify the assignment process for complex biomolecules. Mapping observed CSPs onto the molecular structure, on the other hand, can indicate the presence of crystal interfaces where no crystal structure is available. Finally, by detecting sites critical to intermolecular interfaces, ACCEPT-NMR can help guide experimental approaches (e.g. isotopic labeling schemes) to detect and probe specific inter-subunit interactions.展开更多
Atomic electronegativity interaction vector (AEIV) and atomic hybridization state index (AHSI) were used for establishing the quantitative structure-spectroscopy relationship(QSSR) model of 13C NMR chemical shifts of ...Atomic electronegativity interaction vector (AEIV) and atomic hybridization state index (AHSI) were used for establishing the quantitative structure-spectroscopy relationship(QSSR) model of 13C NMR chemical shifts of isodon diterpenoid compounds.Multiple linear regression (MLR) and computational neural network (CNN) were used to create the models,and the estimation stability and generalization ability of the models were strictly analyzed by both internal and external validations.The established MLR and CNN models were correlated with experimental values and the correlation coefficients of model estimation,leave-one-out (LOO)cross-validation (CV),and predicted values of external samples were Rcum=0.9724,RCV=0.9723,Qext=0.9738 (MLR);Rcum=0.9957,Qext=0.9956 (CNN),respectively.The results indicated that CNN gave significantly better prediction of 13C NMR chemical shifts for isodon diterpenoids than MLR.Satisfactory results showed that AEIV and AHSI were obviously good for modeling 13C NMR chemical shifts of isodon diterpenoid compounds.展开更多
人源转录调控因子MRG15(MORF4related gene on chromosome 15)是在胚胎发育,细胞增殖和衰老过程中具有重要作用的蛋白质.在分子水平上,MRG15的chromo结构域能够与组蛋白H3甲基化的肽段H3K36me3相互作用.为了确定二者的作用位点和方式,...人源转录调控因子MRG15(MORF4related gene on chromosome 15)是在胚胎发育,细胞增殖和衰老过程中具有重要作用的蛋白质.在分子水平上,MRG15的chromo结构域能够与组蛋白H3甲基化的肽段H3K36me3相互作用.为了确定二者的作用位点和方式,利用液体核磁共振的方法对MRG15 chromo结构域和H3K36me3多肽进行了研究.对MRG15chromo结构域进行归属,用化学位移扰动的方法研究了其与H3K36me3肽段的相互作用,确定了相互作用位点的关键氨基酸为H21,Y46,W49和W53,并计算了解离常数约为1mmol/L.研究结果对于阐明MRG15与组蛋白H3的结合和相互作用机制提供了重要的线索.展开更多
应用一种反映分子局部微环境描述子--原子电性相互作用矢量(vector of atomic elec-tronegative interaction,AEIV)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)对饱和脂肪酮类化合物的55种分子中的153个13C NMR谱建模模拟...应用一种反映分子局部微环境描述子--原子电性相互作用矢量(vector of atomic elec-tronegative interaction,AEIV)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)对饱和脂肪酮类化合物的55种分子中的153个13C NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系(QSSR)模型的复相关系数RMM=0.997,标准偏差为SDMM=7.155.采用留一法交互检验的结果是RCV=0.993,SDCV=10.195.并随机抽出三部分分子进行检验,得到的相关系数分别是RMM1=0.996,RMM2=0.996,RMM3=0.999.研究结果表明使用AEIV和AHSI所建模型预测能力是相当稳定的.展开更多
应用一种反映分子局部微环境描述子——原子电性相互作用矢量(vector of atomic electronegative interaction,AEIV)和原子杂化状态指数(atomic hybridation state index,AHSI)对喹啉类化合物的13种分子中的129个^(13)C-NMR谱建模模拟,...应用一种反映分子局部微环境描述子——原子电性相互作用矢量(vector of atomic electronegative interaction,AEIV)和原子杂化状态指数(atomic hybridation state index,AHSI)对喹啉类化合物的13种分子中的129个^(13)C-NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系模型的复相关系数(R_(MM1))为0.988,标准偏差(SD_(MM1))为5.317.采用留一法交互检验结果R_(CV1)为0.987,SD_(CV1)为5.630.随机抽出两部分分子进行检验,得到的相关系数R_(MM2)为0.993,R_(MM3)为0.987.结果表明,使用AEIV和AHSI所建模型具有相当的预测能力和稳定性.展开更多
文摘We have developed an open-source cross-platform software toolkit entitled ACCEPT-NMR (Automated Crystal Contact Extrapolation/Prediction Toolkit for NMR) as a helpful tool to automate many of the complex tasks required to find and visualize crystal contacts in structures of biomolecules and biomolecular assemblies. This toolkit provides many powerful features geared toward NMR spectroscopy and related disciplines, such as isotopic labeling, advanced visualization options, and reporting tools. Using this software, we have undertaken a survey of available chemical shift data in the literature and deposited in the BMRB, and show that the mere presence of one or more crystal contacts to a residue confers an approximately 65% likelihood of significant chemical shift perturbations (relative to solution NMR chemical shifts). The presence of each additional crystal contact subsequently increases this probability, resulting in predictive accuracies in excess of 80% in many cases. Conversely, the presence of a significant experimental chemical shift perturbation indicates a >60% likelihood of finding one or more crystal contacts to a particular residue. Pinpointing sites likely to experience large CSPs is critical to mapping solution NMR chemical shifts onto solid-state NMR data as a basis for preliminary assignments, and can thus simplify the assignment process for complex biomolecules. Mapping observed CSPs onto the molecular structure, on the other hand, can indicate the presence of crystal interfaces where no crystal structure is available. Finally, by detecting sites critical to intermolecular interfaces, ACCEPT-NMR can help guide experimental approaches (e.g. isotopic labeling schemes) to detect and probe specific inter-subunit interactions.
文摘Atomic electronegativity interaction vector (AEIV) and atomic hybridization state index (AHSI) were used for establishing the quantitative structure-spectroscopy relationship(QSSR) model of 13C NMR chemical shifts of isodon diterpenoid compounds.Multiple linear regression (MLR) and computational neural network (CNN) were used to create the models,and the estimation stability and generalization ability of the models were strictly analyzed by both internal and external validations.The established MLR and CNN models were correlated with experimental values and the correlation coefficients of model estimation,leave-one-out (LOO)cross-validation (CV),and predicted values of external samples were Rcum=0.9724,RCV=0.9723,Qext=0.9738 (MLR);Rcum=0.9957,Qext=0.9956 (CNN),respectively.The results indicated that CNN gave significantly better prediction of 13C NMR chemical shifts for isodon diterpenoids than MLR.Satisfactory results showed that AEIV and AHSI were obviously good for modeling 13C NMR chemical shifts of isodon diterpenoid compounds.
文摘应用一种反映分子局部微环境描述子--原子电性相互作用矢量(vector of atomic elec-tronegative interaction,AEIV)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)对饱和脂肪酮类化合物的55种分子中的153个13C NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系(QSSR)模型的复相关系数RMM=0.997,标准偏差为SDMM=7.155.采用留一法交互检验的结果是RCV=0.993,SDCV=10.195.并随机抽出三部分分子进行检验,得到的相关系数分别是RMM1=0.996,RMM2=0.996,RMM3=0.999.研究结果表明使用AEIV和AHSI所建模型预测能力是相当稳定的.
文摘应用一种反映分子局部微环境描述子——原子电性相互作用矢量(vector of atomic electronegative interaction,AEIV)和原子杂化状态指数(atomic hybridation state index,AHSI)对喹啉类化合物的13种分子中的129个^(13)C-NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系模型的复相关系数(R_(MM1))为0.988,标准偏差(SD_(MM1))为5.317.采用留一法交互检验结果R_(CV1)为0.987,SD_(CV1)为5.630.随机抽出两部分分子进行检验,得到的相关系数R_(MM2)为0.993,R_(MM3)为0.987.结果表明,使用AEIV和AHSI所建模型具有相当的预测能力和稳定性.
