目的:从分子水平研究丹参黄芪配伍抗冠心病和心绞痛的分子机制。方法:采用中药系统药理学数据库和分析平台(Traditional Chinese Medicine Systems Pharmacology,TCMSP)获取丹参和黄芪活性成分,基于CTD(Comparative Toxicogenomics Data...目的:从分子水平研究丹参黄芪配伍抗冠心病和心绞痛的分子机制。方法:采用中药系统药理学数据库和分析平台(Traditional Chinese Medicine Systems Pharmacology,TCMSP)获取丹参和黄芪活性成分,基于CTD(Comparative Toxicogenomics Database,比较毒物基因组学数据库)筛选冠心病和心绞痛的关键靶标。借助STRING软件对心绞痛和冠心病的靶标基因进行相互作用分析,基于分子对接(Sybyl2.1)对筛选所得的丹参、黄芪的活性成分与心绞痛及冠心病靶点进行分子对接验证。借助Cytoscape3.5.1构建"活性成分-靶点"网络模型。结果:丹参黄芪共筛选出61个活性成分,其中丹参44个,黄芪17个。筛选出冠心病靶标25个,心绞痛靶标7个,通过靶蛋白PPI网络分析,肿瘤坏死因子、基质金属蛋白酶-9、Toll样受体4、载脂蛋白E、脂肪酸转运蛋白、血管紧张素Ⅰ转化酶、基质金属蛋白酶-3、尿激酶为冠心病和心绞痛疾病的关键靶标蛋白。分子对接发现黄芪单味药、丹参单味药、黄芪丹参配伍用药可能通过调节尿激酶(PLAU)、载脂蛋白E(APOE)、血管紧张素I转化酶(ACE)发挥抗冠心病及心绞痛的作用。结论:从分子层面筛选丹参黄芪配伍治疗冠心病、心绞痛疾病的关键活性成分及靶点,为其配伍后实验研究和临床应用提供合理解释。展开更多
[目的]探讨在男-男性行为(men sex with men,MSM)人群中开展环形接种从而阻断猴痘传播的可行性.[方法]将人群接触区分为固定与非固定接触两部分,建立描述环形接种的常微分方程猴痘传播数学模型.进而基于该模型的数值模拟,评估并探讨环...[目的]探讨在男-男性行为(men sex with men,MSM)人群中开展环形接种从而阻断猴痘传播的可行性.[方法]将人群接触区分为固定与非固定接触两部分,建立描述环形接种的常微分方程猴痘传播数学模型.进而基于该模型的数值模拟,评估并探讨环形接种在群体层面的防传播效果,以及接种过程中涉及的若干环节实施力度的影响.[结果]模拟显示,在基线场景的200 d传播模拟中,仅追踪接种80%和90%密接的环形接种方案可以分别使人群中平均每7.00和9.18 d产生一个病例,二者均大于病例的实际传染期,意味着发生传播阻断.密接追踪比例α1≥0.5时,继续提高α1可以减少疫苗消耗,提高有限疫苗资源的利用率.额外针对次密接的追踪接种将消耗与大规模接种类似的大量疫苗资源,效益较低,仅适用于疫情传播早期病例数极少的情形.[结论]对于猴痘这种自限性疾病,仅针对密切接触者的追踪接种可以很好地控制猴痘传播,同时对局部暴发具有较好的控制效果.然而为进一步减少聚集性暴发带来的额外负担,仍应预先提高MSM人群的疫苗覆盖率.展开更多
BACKGROUND Diabetic kidney disease(DKD)is the primary cause of end-stage renal disease.The Astragalus-Coptis drug pair is frequently employed in the management of DKD.However,the precise molecular mechanism underlying...BACKGROUND Diabetic kidney disease(DKD)is the primary cause of end-stage renal disease.The Astragalus-Coptis drug pair is frequently employed in the management of DKD.However,the precise molecular mechanism underlying its therapeutic effect remains elusive.AIM To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways.METHODS The ingredients of the Astragalus-Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform.The targets were predicted using the SwissTargetPrediction database,while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database.DKD targets were acquired from the GeneCards,Online Mendelian Inheritance in Man database,and DisGeNET databases,with common targets identified through the Venny platform.The protein-protein interaction network and the“disease-active ingredient-target”network of the common targets were constructed utilizing the STRING database and Cytoscape software,followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients.Gene Ontology(GO)function and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichments were performed using the DAVID database.The tissue and organ distributions of key targets were evaluated.PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets.Finally,molecular dynamics(MD)simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins.RESULTS A total of 27 active ingredients and 512 potential targets of the Astragalus-Coptis drug pair were identified.There were 273 common targets between DKD and the Astragalus-Coptis drug pair.Through protein-protein interaction network topology analysis,we identified 9 core active ingredients and 10 key targets.GO and KEGG pathway enrichment analyses revealed that Astragal展开更多
Abstract The aggregation of amyloid β-protein （Aβ） is tightly linked to the pathogenesis of Alzheimer＇s disease. Previous studies have found that three peptide inhibitors （i.e., KLVFF, VVIA, and LPFFD） can inhi...Abstract The aggregation of amyloid β-protein （Aβ） is tightly linked to the pathogenesis of Alzheimer＇s disease. Previous studies have found that three peptide inhibitors （i.e., KLVFF, VVIA, and LPFFD） can inhibit Aβ aggregation and alleviate Aβ-induced neurotoxicity. How- ever, atomic details of binding modes and binding affinities between these peptide inhibitors and Aβ have not been revealed. Here, using molecular dynamics simulations and molecular mechanics Poisson Boltzmann surface area （MM/PBSA） analysis, we examined the effect of three peptide inhibitors （KLVFF, VVIA, and LPFFD） on their sequence-specific interactions with Aβ and the molecular basis of their inhibition. All inhibitors exhibit varied binding affinity to Aβ, in which KLVFF has the highest binding affinity, whereas LPFFD has the least. MM/PBSA analysis further revealed that different peptide inhibitors have different modes of interaction with Aβ, consequently hotspot binding residues, and underlying driving forces. Specific residue-based interactions between inhibitors and Aβ were determined and compared for illustrating different binding and inhibition mechanisms. This work provides structure-based binding information for further modifica- tion and optimization of these three peptide inhibitors to enhance their binding and inhibitory abilities against Aβ aggregation.展开更多
文摘目的:从分子水平研究丹参黄芪配伍抗冠心病和心绞痛的分子机制。方法:采用中药系统药理学数据库和分析平台(Traditional Chinese Medicine Systems Pharmacology,TCMSP)获取丹参和黄芪活性成分,基于CTD(Comparative Toxicogenomics Database,比较毒物基因组学数据库)筛选冠心病和心绞痛的关键靶标。借助STRING软件对心绞痛和冠心病的靶标基因进行相互作用分析,基于分子对接(Sybyl2.1)对筛选所得的丹参、黄芪的活性成分与心绞痛及冠心病靶点进行分子对接验证。借助Cytoscape3.5.1构建"活性成分-靶点"网络模型。结果:丹参黄芪共筛选出61个活性成分,其中丹参44个,黄芪17个。筛选出冠心病靶标25个,心绞痛靶标7个,通过靶蛋白PPI网络分析,肿瘤坏死因子、基质金属蛋白酶-9、Toll样受体4、载脂蛋白E、脂肪酸转运蛋白、血管紧张素Ⅰ转化酶、基质金属蛋白酶-3、尿激酶为冠心病和心绞痛疾病的关键靶标蛋白。分子对接发现黄芪单味药、丹参单味药、黄芪丹参配伍用药可能通过调节尿激酶(PLAU)、载脂蛋白E(APOE)、血管紧张素I转化酶(ACE)发挥抗冠心病及心绞痛的作用。结论:从分子层面筛选丹参黄芪配伍治疗冠心病、心绞痛疾病的关键活性成分及靶点,为其配伍后实验研究和临床应用提供合理解释。
文摘[目的]探讨在男-男性行为(men sex with men,MSM)人群中开展环形接种从而阻断猴痘传播的可行性.[方法]将人群接触区分为固定与非固定接触两部分,建立描述环形接种的常微分方程猴痘传播数学模型.进而基于该模型的数值模拟,评估并探讨环形接种在群体层面的防传播效果,以及接种过程中涉及的若干环节实施力度的影响.[结果]模拟显示,在基线场景的200 d传播模拟中,仅追踪接种80%和90%密接的环形接种方案可以分别使人群中平均每7.00和9.18 d产生一个病例,二者均大于病例的实际传染期,意味着发生传播阻断.密接追踪比例α1≥0.5时,继续提高α1可以减少疫苗消耗,提高有限疫苗资源的利用率.额外针对次密接的追踪接种将消耗与大规模接种类似的大量疫苗资源,效益较低,仅适用于疫情传播早期病例数极少的情形.[结论]对于猴痘这种自限性疾病,仅针对密切接触者的追踪接种可以很好地控制猴痘传播,同时对局部暴发具有较好的控制效果.然而为进一步减少聚集性暴发带来的额外负担,仍应预先提高MSM人群的疫苗覆盖率.
文摘BACKGROUND Diabetic kidney disease(DKD)is the primary cause of end-stage renal disease.The Astragalus-Coptis drug pair is frequently employed in the management of DKD.However,the precise molecular mechanism underlying its therapeutic effect remains elusive.AIM To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways.METHODS The ingredients of the Astragalus-Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform.The targets were predicted using the SwissTargetPrediction database,while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database.DKD targets were acquired from the GeneCards,Online Mendelian Inheritance in Man database,and DisGeNET databases,with common targets identified through the Venny platform.The protein-protein interaction network and the“disease-active ingredient-target”network of the common targets were constructed utilizing the STRING database and Cytoscape software,followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients.Gene Ontology(GO)function and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichments were performed using the DAVID database.The tissue and organ distributions of key targets were evaluated.PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets.Finally,molecular dynamics(MD)simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins.RESULTS A total of 27 active ingredients and 512 potential targets of the Astragalus-Coptis drug pair were identified.There were 273 common targets between DKD and the Astragalus-Coptis drug pair.Through protein-protein interaction network topology analysis,we identified 9 core active ingredients and 10 key targets.GO and KEGG pathway enrichment analyses revealed that Astragal
文摘Abstract The aggregation of amyloid β-protein （Aβ） is tightly linked to the pathogenesis of Alzheimer＇s disease. Previous studies have found that three peptide inhibitors （i.e., KLVFF, VVIA, and LPFFD） can inhibit Aβ aggregation and alleviate Aβ-induced neurotoxicity. How- ever, atomic details of binding modes and binding affinities between these peptide inhibitors and Aβ have not been revealed. Here, using molecular dynamics simulations and molecular mechanics Poisson Boltzmann surface area （MM/PBSA） analysis, we examined the effect of three peptide inhibitors （KLVFF, VVIA, and LPFFD） on their sequence-specific interactions with Aβ and the molecular basis of their inhibition. All inhibitors exhibit varied binding affinity to Aβ, in which KLVFF has the highest binding affinity, whereas LPFFD has the least. MM/PBSA analysis further revealed that different peptide inhibitors have different modes of interaction with Aβ, consequently hotspot binding residues, and underlying driving forces. Specific residue-based interactions between inhibitors and Aβ were determined and compared for illustrating different binding and inhibition mechanisms. This work provides structure-based binding information for further modifica- tion and optimization of these three peptide inhibitors to enhance their binding and inhibitory abilities against Aβ aggregation.
