人类ether-à-go-go相关基因(human ether-à-go-go related gene,hERG)亚家族H成员2(KCNH2)所编码的快速激活延迟整流钾离子通道,是许多药物心脏毒性的靶标。药物诱导性长QT间期综合征(drug-induced long QT syndrome,diLQTS)...人类ether-à-go-go相关基因(human ether-à-go-go related gene,hERG)亚家族H成员2(KCNH2)所编码的快速激活延迟整流钾离子通道,是许多药物心脏毒性的靶标。药物诱导性长QT间期综合征(drug-induced long QT syndrome,diLQTS)是由各类抗心律失常药、抗生素、抗组胺药、抗精神病药和血管扩张药等一个或多个脱靶相互作用而诱导的QT间期延长的病理状态。男性校正后的QT间期(QTc)>450 ms、女性QTc>460 ms是diLQTS心电图的临床表征之一。这种获得性长QT间期综合征容易诱发尖端扭转型室性心动过速,继而进展为心室颤动甚至心脏性猝死。本文从化学结构、心电图学、生物学、电生理学和分子生物学这几方面,综述hERG在diLQTS发生发展中的作用。展开更多
Under given conditions, two complexes of [Ni（H20）2BEDA]·2H2O 1 and [Ni（Py）2- BEDA]·6H2O 2 （BEDA = bis（3-methoxy-2-pyridyl）ether-6,6′-dicarboxylic acid） have been synthesized and characterized by ele...Under given conditions, two complexes of [Ni（H20）2BEDA]·2H2O 1 and [Ni（Py）2- BEDA]·6H2O 2 （BEDA = bis（3-methoxy-2-pyridyl）ether-6,6′-dicarboxylic acid） have been synthesized and characterized by elemental analysis and X-ray single-crystal diffraction. Crystal data for 1： C14H18NiN2O11, monoelinic C2/c, a = 14.3844（17）, b = 12.9900（15）, c = 9.6309（11） A, β = 104.3350（10）°, V= 1743.5（4） A3, Z = 4, Dc= 1.711 g/cm^3, F（000） = 928, μ = 1.179 mm^-1, Mr= 449.01, the final R = 0.0228 and wR = 0.0625. Crystal data for 2： C24H32NiN4O13, triclinic P1, a = 9.423（2）, b = 11.863（3）, c = 13.089（3） A, α = 91.511（3）, β = 92.465（3）, γ = 100.696（2）°, V = 1435.6（6） A^3, Z = 2, Dc= 1.488 g/cm^3, F（000） = 672, μ = 0.748 mm^-1, Mr= 643.25, the final R = 0.0400 and wR = 0.0975. Interestingly, in the two complexes, lattice water molecules dominate its crystal structures. Therefore, extensive intermolecular hydrogen bonds assemble 1 and 2 into 2D extended sheets and a 3D open framework, respectively. Furthermore, water molecules present in 2 are associated to form water clusters.展开更多
Cardiotoxicity is a critical issue in drug development that poses serious health risks,including potentially fatal arrhythmias.The human ether-à-go-go related gene(hERG)potassium channel,as one of the primary tar...Cardiotoxicity is a critical issue in drug development that poses serious health risks,including potentially fatal arrhythmias.The human ether-à-go-go related gene(hERG)potassium channel,as one of the primary targets of cardiotoxicity,has garnered widespread attention.Traditional cardiotoxicity testing methods are expensive and time-consuming,making computational virtual screening a suitable alternative.In this study,we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds,with the aim of improving prediction accuracy and efficiency.We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms,including Gaussian naive Bayes(NB),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),eXtreme gradient boosting(XGBoost),and Transformer models,to build predictive models.Our models demonstrated advanced predictive performance.The best machine learning model,XGBoost Morgan,achieved an accuracy(ACC)value of 0.84,and the deep learning model,Transformer_Morgan,achieved the best ACC value of 0.85,showing a high ability to distinguish between toxic and non-toxic compounds.On an external independent validation set,it achieved the best area under the curve(AUC)value of 0.93,surpassing ADMETlab3.0,Cardpred,and CardioDPi.In addition,we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods,such as voting and stacking,provided slight improvements in model stability.Furthermore,the SHapley Additive exPlanations(SHAP)explanations revealed the relationship between benzene rings,fluorine-containing groups,NH groups,oxygen in ether groups,and cardiotoxicity,highlighting the importance of these features.This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment.Using computational methods,this study facilitates a more efficient drug development process,reduces costs,and improves the safety of new drug candidates,ultimately benefiting medical and public health.展开更多
以商业果胶为原料依次采用酸水解、膜分离、酶水解和离子交换色谱分离方法制备具有不同分子质量及结构特征的果胶片段。将果胶一次或分次加入1.5 mol/L HCl溶液中,配制成240 g/L的溶液,于80℃下水解6 h,可分别得到分子质量250 k^270 k D...以商业果胶为原料依次采用酸水解、膜分离、酶水解和离子交换色谱分离方法制备具有不同分子质量及结构特征的果胶片段。将果胶一次或分次加入1.5 mol/L HCl溶液中,配制成240 g/L的溶液,于80℃下水解6 h,可分别得到分子质量250 k^270 k Da的均一半乳糖醛酸区(homogalacturonan region,HG区)大片段和分子质量约为30 k Da的鼠李半乳糖醛酸聚糖区(rhamnogalacturonan region,RG区)小片段,同时酸水解产物酯化度从原料的61%降为10%;水解液中和后用10 k Da超滤膜脱盐,之后用商业果胶酶Pectinex Ultra SP-L酶解,获得分子质量约70 k Da的果胶多糖片段,所得酶解液再经过DEAE Sepharose Fast Flow弱阴离子交换柱,依次以纯水、0.05~1.0 mol/L Na Cl溶液洗脱,得到果胶片段样品MCP-1,MCP-2,MCP-3,MCP-4,相应的峰位分子质量分别为12.39 k Da,125.33 k Da,146.49 k Da和193.31 k Da,单糖组成分析结果显示样品中半乳糖醛酸含量依次上升,即MCP-1中RG区含量最高,而MCP-4中HG区含量最高。展开更多
文摘人类ether-à-go-go相关基因(human ether-à-go-go related gene,hERG)亚家族H成员2(KCNH2)所编码的快速激活延迟整流钾离子通道,是许多药物心脏毒性的靶标。药物诱导性长QT间期综合征(drug-induced long QT syndrome,diLQTS)是由各类抗心律失常药、抗生素、抗组胺药、抗精神病药和血管扩张药等一个或多个脱靶相互作用而诱导的QT间期延长的病理状态。男性校正后的QT间期(QTc)>450 ms、女性QTc>460 ms是diLQTS心电图的临床表征之一。这种获得性长QT间期综合征容易诱发尖端扭转型室性心动过速,继而进展为心室颤动甚至心脏性猝死。本文从化学结构、心电图学、生物学、电生理学和分子生物学这几方面,综述hERG在diLQTS发生发展中的作用。
基金supported by the Henan Innovation Project for University Prominent Research Talents (No. 2005KYCX021)Natural Science Foundation of Henan Province (No. 082300420040)
文摘Under given conditions, two complexes of [Ni（H20）2BEDA]·2H2O 1 and [Ni（Py）2- BEDA]·6H2O 2 （BEDA = bis（3-methoxy-2-pyridyl）ether-6,6′-dicarboxylic acid） have been synthesized and characterized by elemental analysis and X-ray single-crystal diffraction. Crystal data for 1： C14H18NiN2O11, monoelinic C2/c, a = 14.3844（17）, b = 12.9900（15）, c = 9.6309（11） A, β = 104.3350（10）°, V= 1743.5（4） A3, Z = 4, Dc= 1.711 g/cm^3, F（000） = 928, μ = 1.179 mm^-1, Mr= 449.01, the final R = 0.0228 and wR = 0.0625. Crystal data for 2： C24H32NiN4O13, triclinic P1, a = 9.423（2）, b = 11.863（3）, c = 13.089（3） A, α = 91.511（3）, β = 92.465（3）, γ = 100.696（2）°, V = 1435.6（6） A^3, Z = 2, Dc= 1.488 g/cm^3, F（000） = 672, μ = 0.748 mm^-1, Mr= 643.25, the final R = 0.0400 and wR = 0.0975. Interestingly, in the two complexes, lattice water molecules dominate its crystal structures. Therefore, extensive intermolecular hydrogen bonds assemble 1 and 2 into 2D extended sheets and a 3D open framework, respectively. Furthermore, water molecules present in 2 are associated to form water clusters.
基金supported by National Key Research and Development Project,China(Grant No.:2021YFA1500403).
文摘Cardiotoxicity is a critical issue in drug development that poses serious health risks,including potentially fatal arrhythmias.The human ether-à-go-go related gene(hERG)potassium channel,as one of the primary targets of cardiotoxicity,has garnered widespread attention.Traditional cardiotoxicity testing methods are expensive and time-consuming,making computational virtual screening a suitable alternative.In this study,we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds,with the aim of improving prediction accuracy and efficiency.We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms,including Gaussian naive Bayes(NB),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),eXtreme gradient boosting(XGBoost),and Transformer models,to build predictive models.Our models demonstrated advanced predictive performance.The best machine learning model,XGBoost Morgan,achieved an accuracy(ACC)value of 0.84,and the deep learning model,Transformer_Morgan,achieved the best ACC value of 0.85,showing a high ability to distinguish between toxic and non-toxic compounds.On an external independent validation set,it achieved the best area under the curve(AUC)value of 0.93,surpassing ADMETlab3.0,Cardpred,and CardioDPi.In addition,we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods,such as voting and stacking,provided slight improvements in model stability.Furthermore,the SHapley Additive exPlanations(SHAP)explanations revealed the relationship between benzene rings,fluorine-containing groups,NH groups,oxygen in ether groups,and cardiotoxicity,highlighting the importance of these features.This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment.Using computational methods,this study facilitates a more efficient drug development process,reduces costs,and improves the safety of new drug candidates,ultimately benefiting medical and public health.
文摘以商业果胶为原料依次采用酸水解、膜分离、酶水解和离子交换色谱分离方法制备具有不同分子质量及结构特征的果胶片段。将果胶一次或分次加入1.5 mol/L HCl溶液中,配制成240 g/L的溶液,于80℃下水解6 h,可分别得到分子质量250 k^270 k Da的均一半乳糖醛酸区(homogalacturonan region,HG区)大片段和分子质量约为30 k Da的鼠李半乳糖醛酸聚糖区(rhamnogalacturonan region,RG区)小片段,同时酸水解产物酯化度从原料的61%降为10%;水解液中和后用10 k Da超滤膜脱盐,之后用商业果胶酶Pectinex Ultra SP-L酶解,获得分子质量约70 k Da的果胶多糖片段,所得酶解液再经过DEAE Sepharose Fast Flow弱阴离子交换柱,依次以纯水、0.05~1.0 mol/L Na Cl溶液洗脱,得到果胶片段样品MCP-1,MCP-2,MCP-3,MCP-4,相应的峰位分子质量分别为12.39 k Da,125.33 k Da,146.49 k Da和193.31 k Da,单糖组成分析结果显示样品中半乳糖醛酸含量依次上升,即MCP-1中RG区含量最高,而MCP-4中HG区含量最高。
