为了开发适合吞咽困难患者食用的植物蛋白3D打印食品,本文将亚麻籽胶和魔芋胶按照质量比2:3进行复配形成复合多糖,并与豌豆分离蛋白(Pea protein isolate,PPI)进行剪切处理,探究了不同浓度的复合多糖(0.5%、0.7%、0.9%、1.1%和1.3%)对...为了开发适合吞咽困难患者食用的植物蛋白3D打印食品,本文将亚麻籽胶和魔芋胶按照质量比2:3进行复配形成复合多糖,并与豌豆分离蛋白(Pea protein isolate,PPI)进行剪切处理,探究了不同浓度的复合多糖(0.5%、0.7%、0.9%、1.1%和1.3%)对乳液凝胶粒径、流变学特性和3D打印性能的影响。并通过国际吞咽障碍标准化倡议(IDDSI)对乳液凝胶进行评估。结果表明:不同浓度的复合多糖改善乳液凝胶的效果不同。粒径测试表明随着复合多糖浓度的增加,PPI乳液凝胶的粒径呈现减小的趋势。流变学特性表明多糖的加入使PPI乳液凝胶具有粘弹性,且所有的乳液凝胶样品均显示出G'>G''。当复合多糖浓度为1.1%时,乳液凝胶的粒径最小,G'和G''值最高,粘弹性最好,打印的产品具有最清晰的纹理、最稳定的结构和较好的印刷适应性。IDDSI测试表明添加1.1%和1.3%浓度复合多糖的乳液凝胶可归类为Ⅳ级过渡性食品。综上所述,1.1%浓度的复合多糖对豌豆分离蛋白乳液凝胶3D打印性能的改善最显著,这为开发植物基3D打印油墨提供理论依据。展开更多
在过去的二十年里,随着IMiD和indisulam等MG降解剂的出现,分子胶(MGs)逐渐引起了制药界的关注。这些分子通过促进靶蛋白和E3连接酶之间的相互作用来降解靶蛋白。此外,MGs作为化学诱导剂,促进同源蛋白和异源蛋白的二聚化形成三元复合物,...在过去的二十年里,随着IMiD和indisulam等MG降解剂的出现,分子胶(MGs)逐渐引起了制药界的关注。这些分子通过促进靶蛋白和E3连接酶之间的相互作用来降解靶蛋白。此外,MGs作为化学诱导剂,促进同源蛋白和异源蛋白的二聚化形成三元复合物,在调节生物活性方面具有很大的前景。本文重点介绍MGs在药物开发领域的应用,包括蛋白质–蛋白质相互作用(PPI)稳定性和蛋白质降解。我们深入分析了各种MGs的结构以及MGs与各种生物活性分子之间的相互作用,从而为PPI稳定剂和新型降解剂的开发提供了新的视角。Over the past two decades, molecular glues (MGs) have gradually attracted the attention of the pharmaceutical community with the advent of MG degraders such as IMiDs and indisulam. Such molecules degrade the target protein by promoting the interaction between the target protein and E3 ligase. In addition, as a chemical inducer, MGs promote the dimerization of homologous proteins and heterologous proteins to form ternary complexes, which have great prospects in regulating biological activities. This review focuses on the application of MGs in the field of drug development including protein-protein interaction (PPI) stability and protein degradation. We thoroughly analyze the structure of various MGs and the interactions between MGs and various biologically active molecules, thus providing new perspectives for the development of PPI stabilizers and new degraders.展开更多
目的:采用网络药理学和分子对接技术,对防己茯苓汤治疗脓毒症AKI的作用机制进行预测性研究。方法:先通过TCMSP数据库收集防己茯苓汤中药物组成的活性成分,SwissTargetPrediction搜索活性成分所作用的靶点蛋白。再通过GeneCards、PharmGK...目的:采用网络药理学和分子对接技术,对防己茯苓汤治疗脓毒症AKI的作用机制进行预测性研究。方法:先通过TCMSP数据库收集防己茯苓汤中药物组成的活性成分,SwissTargetPrediction搜索活性成分所作用的靶点蛋白。再通过GeneCards、PharmGKb、数据库、TTD数据库收集脓毒症AKI靶点蛋白,选择药物成分以及对应的靶点蛋白,利用Cytoscape构建药物–成分–靶点–疾病网络,将药物–疾病交集靶点录入STRING分析后导入Cytoscape软件,进一步构建PPI网络图。在DAVID平台获取交集靶点的作图数据,在微生信平台进行GO富集分析和KEGG通路分析。最后使用MOE和Pymol进行分子对接和渲染。结果:筛选出防己茯苓汤活性成分126种及药物靶点993个,疾病靶点3301个,防己茯苓汤治疗脓毒症AKI作用靶点448个,其中联苯双酯、(E)-1-(2,4-二羟基苯基)-3-(2,2-二甲基色烯-6-基)丙-2-烯-1-酮、四脉银胶菊素A、β-谷甾醇、茯苓酸A为治疗疾病的核心成分,AKT1、TNF、TP53、CASP3、MAPK3为治疗疾病的核心靶点。GO富集分析得到1742个结果,主要富集于蛋白质磷酸化、磷酸化作用、炎症反应、ATP结合、蛋白丝氨酸激酶活性等过程,KEGG通路分析得到189条通路,主要通路为癌症通路、脂质和动脉粥样硬化、乙型肝炎等。分子对接验证了药物成分和疾病靶点蛋白有良好的结合能力。其中核心靶点TP53与β-谷甾醇的结合能最小,为−7.95 kg/mol。结论:本研究采用网络药理学和分子对接技术,预测性地研究了防己茯苓汤通过多成分–多靶点–多通路治疗脓毒症AKI的机制,为临床治疗脓毒症AKI提供了证明,并为进一步提高该类病症的临床治疗效果提供了参考。Objective: To predict the mechanism of action of Fangji Fuling Decoction in treating sepsis-induced AKI by using network pharmacology and molecular docking technology. Methods: The active ingredients of Fangji Fuling Decoction were first collected through the TCMSP database, and the target proteins acted by the active ingredients were searched by SwissTargetPrediction. Then, the target proteins of sepsis-induced AKI were collected through GeneCards, PharmGKb, database, and TTD database, and the drug ingredients and corresponding target proteins were selected. The drug-ingredient-target-disease network was constructed using Cytoscape. The drug-disease intersection targets were entered into STRING analysis and then imported into Cytoscape software to further construct the PPI network diagram. The mapping data of the intersection targets were obtained on the DAVID platform, and GO enrichment analysis and KEGG pathway analysis were performed on the Microbiological Information Platform. Finally, MOE and Pymol were used for molecular docking and rendering. Results: A total of 126 active ingredients and 993 drug targets, 3301 disease targets of Fangji Fuling Decoction were screened, and 448 targets of Fangji Fuling Decoction in the treatment of sepsis AKI were found, among which bifendate, (E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one,guaiacin A, β-sitosterol, and pachymic acid A were the core components for the treatment of diseases, and AKT1, TNF, TP53, CASP3, and MAPK3 were the core targets for the treatment of diseases. GO enrichment analysis obtained 1742 results, which were mainly enriched in protein phosphorylation, phosphorylation, inflammatory response, ATP binding, protein serine kinase activity, etc. KEGG pathway analysis obtained 189 pathways, and the main pathways were cancer pathways, lipids and atherosclerosis, hepatitis B, etc. Molecular docking verified that the drug components and disease target proteins had good binding ability. Among them, the binding energy of the core target TP53 with β-sitosterol was the smallest, which was −7.95 kg/mol. Conclusion: This study used network pharmacology and molecular docking technology to predictively study the mechanism of Fangji Fuling Decoction in treating sepsis AKI through multiple components, multiple targets, and multiple pathways, which provided evidence for the clinical treatment of sepsis AKI and provided a reference for further improving the clinical treatment effect of this type of disease.展开更多
Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional therapies.Autoph...Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional therapies.Autophagy is an important process for maintaining cellular homeostasis,and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors.In contrast to targeting protein activity,intervention with proteinprotein interaction(PPI)can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways.Methods:Here,we employed Naive Bayes,Decision Tree,and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNBC,aiming to uncover novel therapeutic targets.Meanwhile,the candidate proteins interacting with Beclin 2 were initially screened in MDA-MB-231 cells using Beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay,and the interaction relationship was verified by molecular docking and CO-IP experiments after intersection.Colony formation,cellular immunofluorescence,cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT)tests were used to predict the clinical therapeutic effects of manipulating candidate PPI.Results:By developing three PPI classification models and analyzing over 13,000 datasets,we identified 3733 previously unknown autophagy-related PPIs.Our network analysis revealed the central role of Beclin 2 in autophagy regulation,uncovering its interactions with 39 newly identified proteins.Notably,the CO-IP studies identified the substantial interaction between Beclin 2 and Ubiquilin 1,which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay results.Subsequently,in vitro investigations showed that overexpressing Beclin 2 increased Ubiquilin 1,promoted autophagy-dependent cell death,and inhibited proliferation and metastasis in MDA-MB-231 cells.Conclusions:This study not only enhances our understanding of autophagy regulation in TNBC but also identifies the Beclin 2-Ubiquilin 1 axis as a promising target for precision therapy.These findings open new avenues for drug discovery and offer inspiration for more effective treatments for this aggressive cancer subtype.展开更多
文摘为了开发适合吞咽困难患者食用的植物蛋白3D打印食品,本文将亚麻籽胶和魔芋胶按照质量比2:3进行复配形成复合多糖,并与豌豆分离蛋白(Pea protein isolate,PPI)进行剪切处理,探究了不同浓度的复合多糖(0.5%、0.7%、0.9%、1.1%和1.3%)对乳液凝胶粒径、流变学特性和3D打印性能的影响。并通过国际吞咽障碍标准化倡议(IDDSI)对乳液凝胶进行评估。结果表明:不同浓度的复合多糖改善乳液凝胶的效果不同。粒径测试表明随着复合多糖浓度的增加,PPI乳液凝胶的粒径呈现减小的趋势。流变学特性表明多糖的加入使PPI乳液凝胶具有粘弹性,且所有的乳液凝胶样品均显示出G'>G''。当复合多糖浓度为1.1%时,乳液凝胶的粒径最小,G'和G''值最高,粘弹性最好,打印的产品具有最清晰的纹理、最稳定的结构和较好的印刷适应性。IDDSI测试表明添加1.1%和1.3%浓度复合多糖的乳液凝胶可归类为Ⅳ级过渡性食品。综上所述,1.1%浓度的复合多糖对豌豆分离蛋白乳液凝胶3D打印性能的改善最显著,这为开发植物基3D打印油墨提供理论依据。
文摘在过去的二十年里,随着IMiD和indisulam等MG降解剂的出现,分子胶(MGs)逐渐引起了制药界的关注。这些分子通过促进靶蛋白和E3连接酶之间的相互作用来降解靶蛋白。此外,MGs作为化学诱导剂,促进同源蛋白和异源蛋白的二聚化形成三元复合物,在调节生物活性方面具有很大的前景。本文重点介绍MGs在药物开发领域的应用,包括蛋白质–蛋白质相互作用(PPI)稳定性和蛋白质降解。我们深入分析了各种MGs的结构以及MGs与各种生物活性分子之间的相互作用,从而为PPI稳定剂和新型降解剂的开发提供了新的视角。Over the past two decades, molecular glues (MGs) have gradually attracted the attention of the pharmaceutical community with the advent of MG degraders such as IMiDs and indisulam. Such molecules degrade the target protein by promoting the interaction between the target protein and E3 ligase. In addition, as a chemical inducer, MGs promote the dimerization of homologous proteins and heterologous proteins to form ternary complexes, which have great prospects in regulating biological activities. This review focuses on the application of MGs in the field of drug development including protein-protein interaction (PPI) stability and protein degradation. We thoroughly analyze the structure of various MGs and the interactions between MGs and various biologically active molecules, thus providing new perspectives for the development of PPI stabilizers and new degraders.
文摘目的:采用网络药理学和分子对接技术,对防己茯苓汤治疗脓毒症AKI的作用机制进行预测性研究。方法:先通过TCMSP数据库收集防己茯苓汤中药物组成的活性成分,SwissTargetPrediction搜索活性成分所作用的靶点蛋白。再通过GeneCards、PharmGKb、数据库、TTD数据库收集脓毒症AKI靶点蛋白,选择药物成分以及对应的靶点蛋白,利用Cytoscape构建药物–成分–靶点–疾病网络,将药物–疾病交集靶点录入STRING分析后导入Cytoscape软件,进一步构建PPI网络图。在DAVID平台获取交集靶点的作图数据,在微生信平台进行GO富集分析和KEGG通路分析。最后使用MOE和Pymol进行分子对接和渲染。结果:筛选出防己茯苓汤活性成分126种及药物靶点993个,疾病靶点3301个,防己茯苓汤治疗脓毒症AKI作用靶点448个,其中联苯双酯、(E)-1-(2,4-二羟基苯基)-3-(2,2-二甲基色烯-6-基)丙-2-烯-1-酮、四脉银胶菊素A、β-谷甾醇、茯苓酸A为治疗疾病的核心成分,AKT1、TNF、TP53、CASP3、MAPK3为治疗疾病的核心靶点。GO富集分析得到1742个结果,主要富集于蛋白质磷酸化、磷酸化作用、炎症反应、ATP结合、蛋白丝氨酸激酶活性等过程,KEGG通路分析得到189条通路,主要通路为癌症通路、脂质和动脉粥样硬化、乙型肝炎等。分子对接验证了药物成分和疾病靶点蛋白有良好的结合能力。其中核心靶点TP53与β-谷甾醇的结合能最小,为−7.95 kg/mol。结论:本研究采用网络药理学和分子对接技术,预测性地研究了防己茯苓汤通过多成分–多靶点–多通路治疗脓毒症AKI的机制,为临床治疗脓毒症AKI提供了证明,并为进一步提高该类病症的临床治疗效果提供了参考。Objective: To predict the mechanism of action of Fangji Fuling Decoction in treating sepsis-induced AKI by using network pharmacology and molecular docking technology. Methods: The active ingredients of Fangji Fuling Decoction were first collected through the TCMSP database, and the target proteins acted by the active ingredients were searched by SwissTargetPrediction. Then, the target proteins of sepsis-induced AKI were collected through GeneCards, PharmGKb, database, and TTD database, and the drug ingredients and corresponding target proteins were selected. The drug-ingredient-target-disease network was constructed using Cytoscape. The drug-disease intersection targets were entered into STRING analysis and then imported into Cytoscape software to further construct the PPI network diagram. The mapping data of the intersection targets were obtained on the DAVID platform, and GO enrichment analysis and KEGG pathway analysis were performed on the Microbiological Information Platform. Finally, MOE and Pymol were used for molecular docking and rendering. Results: A total of 126 active ingredients and 993 drug targets, 3301 disease targets of Fangji Fuling Decoction were screened, and 448 targets of Fangji Fuling Decoction in the treatment of sepsis AKI were found, among which bifendate, (E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one,guaiacin A, β-sitosterol, and pachymic acid A were the core components for the treatment of diseases, and AKT1, TNF, TP53, CASP3, and MAPK3 were the core targets for the treatment of diseases. GO enrichment analysis obtained 1742 results, which were mainly enriched in protein phosphorylation, phosphorylation, inflammatory response, ATP binding, protein serine kinase activity, etc. KEGG pathway analysis obtained 189 pathways, and the main pathways were cancer pathways, lipids and atherosclerosis, hepatitis B, etc. Molecular docking verified that the drug components and disease target proteins had good binding ability. Among them, the binding energy of the core target TP53 with β-sitosterol was the smallest, which was −7.95 kg/mol. Conclusion: This study used network pharmacology and molecular docking technology to predictively study the mechanism of Fangji Fuling Decoction in treating sepsis AKI through multiple components, multiple targets, and multiple pathways, which provided evidence for the clinical treatment of sepsis AKI and provided a reference for further improving the clinical treatment effect of this type of disease.
基金the National Natural Science Foundation of China(Nos.22307009,82374155,82073997,82104376)the Sichuan Science and Technology Program(Nos.2023NSFSC1108,2024NSFTD0023)+1 种基金the Postdoctoral Research Project of Sichuan Provincethe Xinglin Scholar Research Promotion Project of Chengdu University of TCM.
文摘Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional therapies.Autophagy is an important process for maintaining cellular homeostasis,and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors.In contrast to targeting protein activity,intervention with proteinprotein interaction(PPI)can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways.Methods:Here,we employed Naive Bayes,Decision Tree,and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNBC,aiming to uncover novel therapeutic targets.Meanwhile,the candidate proteins interacting with Beclin 2 were initially screened in MDA-MB-231 cells using Beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay,and the interaction relationship was verified by molecular docking and CO-IP experiments after intersection.Colony formation,cellular immunofluorescence,cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT)tests were used to predict the clinical therapeutic effects of manipulating candidate PPI.Results:By developing three PPI classification models and analyzing over 13,000 datasets,we identified 3733 previously unknown autophagy-related PPIs.Our network analysis revealed the central role of Beclin 2 in autophagy regulation,uncovering its interactions with 39 newly identified proteins.Notably,the CO-IP studies identified the substantial interaction between Beclin 2 and Ubiquilin 1,which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay results.Subsequently,in vitro investigations showed that overexpressing Beclin 2 increased Ubiquilin 1,promoted autophagy-dependent cell death,and inhibited proliferation and metastasis in MDA-MB-231 cells.Conclusions:This study not only enhances our understanding of autophagy regulation in TNBC but also identifies the Beclin 2-Ubiquilin 1 axis as a promising target for precision therapy.These findings open new avenues for drug discovery and offer inspiration for more effective treatments for this aggressive cancer subtype.
