目的采用数据非依赖采集(data-independent acquisition,DIA)蛋白质组学技术分析观察蕨麻胶囊对低温低氧下失血性休克大鼠血清蛋白差异表达的影响。方法22只雄性SD大鼠随机分为假手术组(n=8)、模型组(n=8)、治疗组(n=6)。3组大鼠适应性...目的采用数据非依赖采集(data-independent acquisition,DIA)蛋白质组学技术分析观察蕨麻胶囊对低温低氧下失血性休克大鼠血清蛋白差异表达的影响。方法22只雄性SD大鼠随机分为假手术组(n=8)、模型组(n=8)、治疗组(n=6)。3组大鼠适应性喂养1周,在造模前每日给予治疗组蕨麻胶囊稀释剂灌胃,假手术组和模型组大鼠灌胃同等剂量的生理盐水,连续灌胃7天。假手术组在另2组造模成功后同一时间段内搜集血清样本。所有血液样本通过蛋白质组学的方法,鉴定表达水平发生显著变化的蛋白质,并对其进行基因本体(gene ontology,GO)富集分析以确定其生物学功能,同时进行了京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路分析以探索它们所涉及的生物途径。结果共鉴定到3502个蛋白点。模型组与假手术组相比,差异表达明显的蛋白共196个,包含上调蛋白110个,下调蛋白86个。治疗组与模型组相比,差异表达明显的蛋白共104个,包括上调蛋白68个,下调蛋白36个。模型组与治疗组之间,Slfn2、Tex52、Fmod、Fabp5、Exoc3蛋白表达差异显著。通过差异蛋白的GO分析表明,差异蛋白主要涉及细胞过程、生物调节、应激反应、含蛋白复合物、催化活性、分子功能活性调节等。KEGG通路分析涉及核糖体、血小板活化、肌醇磷酸盐代谢、磷脂酰肌醇信号系统等通路。结论研究证实蕨麻胶囊可明显改变低温低氧下失血性休克大鼠血清相关免疫及炎性相关蛋白的表达,并可能通过核糖体代谢、血小板活化等代谢途径调节。展开更多
利用DNA重组技术在柞蚕蛹中表达重组人促红细胞生成素(Recombinant human erythropoietin,rhEPO),经过亲和层析纯化后,用SDS-PAGE分离纯化各组分,并通过电喷雾电离串联质谱技术(ESI-MS/MS)检测其糖基化修饰.结果显示,在柞蚕蛹中表达的rh...利用DNA重组技术在柞蚕蛹中表达重组人促红细胞生成素(Recombinant human erythropoietin,rhEPO),经过亲和层析纯化后,用SDS-PAGE分离纯化各组分,并通过电喷雾电离串联质谱技术(ESI-MS/MS)检测其糖基化修饰.结果显示,在柞蚕蛹中表达的rhEPO比活性约为1190 U/μg,其糖基化修饰位点与人体表达的EPO一致,有3个N-糖基化位点和1个O-糖基化位点.凝集素杂交实验结合质谱结果表明,柞蚕蛹表达的rhEPO的糖链中缺乏唾液酸修饰,而缺少唾液酸修饰的EPO通过鼻腔给药后在多种神经系统疾病的治疗中发挥着重要的作用.所得结果为进一步研究外源蛋白在柞蚕蛹-柞蚕核型多角体病毒(Anthraea pernyi nucleopolyhedrorirus,ApNPV)宿主载体表达系统表达后的糖基化与生物活性提供了依据.展开更多
Kinesin is an archetypal microtubule-based molecular motor that can generate force to transport cargo in cells. The load dependence of the detachment rate is an important factor of the kinesin motor, the determination...Kinesin is an archetypal microtubule-based molecular motor that can generate force to transport cargo in cells. The load dependence of the detachment rate is an important factor of the kinesin motor, the determination of which is critically related to the chemomechanical coupling mechanism of the motor. Here, we use three models for the load dependence of the detachment rate of the kinesin motor to study theoretically and numerically the maximal force generated and microtubuleattachment duration of the motor. By comparing the theoretical and numerical results with the available experimental data,we show that only one model can explain well the available experimental data, indicating that only this model can be applicable to the kinesin motor.展开更多
Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods.While experimental approaches are highly accurate,they often demand extensive time and ...Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods.While experimental approaches are highly accurate,they often demand extensive time and resources.To address these challenges,we present a computational ensemble learning framework designed to identify essential proteins more efficiently.Our method begins by using node2vec to transform proteins in the protein–protein interaction(PPI)network into continuous,low-dimensional vectors.We also extract a range of features from protein sequences,including graph-theory-based,information-based,compositional,and physiochemical attributes.Additionally,we leverage deep learning techniques to analyze high-dimensional position-specific scoring matrices(PSSMs)and capture evolutionary information.We then combine these features for classification using various machine learning algorithms.To enhance performance,we integrate the outputs of these algorithms through ensemble methods such as voting,weighted averaging,and stacking.This approach effectively addresses data imbalances and improves both robustness and accuracy.Our ensemble learning framework achieves an AUC of 0.960 and an accuracy of 0.9252,outperforming other computational methods.These results demonstrate the effectiveness of our approach in accurately identifying essential proteins and highlight its superior feature extraction capabilities.展开更多
文摘目的采用数据非依赖采集(data-independent acquisition,DIA)蛋白质组学技术分析观察蕨麻胶囊对低温低氧下失血性休克大鼠血清蛋白差异表达的影响。方法22只雄性SD大鼠随机分为假手术组(n=8)、模型组(n=8)、治疗组(n=6)。3组大鼠适应性喂养1周,在造模前每日给予治疗组蕨麻胶囊稀释剂灌胃,假手术组和模型组大鼠灌胃同等剂量的生理盐水,连续灌胃7天。假手术组在另2组造模成功后同一时间段内搜集血清样本。所有血液样本通过蛋白质组学的方法,鉴定表达水平发生显著变化的蛋白质,并对其进行基因本体(gene ontology,GO)富集分析以确定其生物学功能,同时进行了京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路分析以探索它们所涉及的生物途径。结果共鉴定到3502个蛋白点。模型组与假手术组相比,差异表达明显的蛋白共196个,包含上调蛋白110个,下调蛋白86个。治疗组与模型组相比,差异表达明显的蛋白共104个,包括上调蛋白68个,下调蛋白36个。模型组与治疗组之间,Slfn2、Tex52、Fmod、Fabp5、Exoc3蛋白表达差异显著。通过差异蛋白的GO分析表明,差异蛋白主要涉及细胞过程、生物调节、应激反应、含蛋白复合物、催化活性、分子功能活性调节等。KEGG通路分析涉及核糖体、血小板活化、肌醇磷酸盐代谢、磷脂酰肌醇信号系统等通路。结论研究证实蕨麻胶囊可明显改变低温低氧下失血性休克大鼠血清相关免疫及炎性相关蛋白的表达,并可能通过核糖体代谢、血小板活化等代谢途径调节。
基金Project supported by Youth Project of Science and Technology Research Program of Chongqing Education Commission of China (Grant No. KJQN202404522)。
文摘Kinesin is an archetypal microtubule-based molecular motor that can generate force to transport cargo in cells. The load dependence of the detachment rate is an important factor of the kinesin motor, the determination of which is critically related to the chemomechanical coupling mechanism of the motor. Here, we use three models for the load dependence of the detachment rate of the kinesin motor to study theoretically and numerically the maximal force generated and microtubuleattachment duration of the motor. By comparing the theoretical and numerical results with the available experimental data,we show that only one model can explain well the available experimental data, indicating that only this model can be applicable to the kinesin motor.
基金financially supported by the National Key R&D Program of China(Grant No.2022YFF1202600)the National Natural Science Foundation of China(Grant No.82301158)+4 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Committee(Grant No.22015820100)Two-hundred Talent Support(Grant No.20152224)Translational Medicine Innovation Project of Shanghai Jiao Tong University School of Medicine(Grant No.TM201915)Clinical Research Project of Multi-Disciplinary Team,Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine(Grant No.201914)China Postdoctoral Science Foundation(Grant No.2023M742332)。
文摘Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods.While experimental approaches are highly accurate,they often demand extensive time and resources.To address these challenges,we present a computational ensemble learning framework designed to identify essential proteins more efficiently.Our method begins by using node2vec to transform proteins in the protein–protein interaction(PPI)network into continuous,low-dimensional vectors.We also extract a range of features from protein sequences,including graph-theory-based,information-based,compositional,and physiochemical attributes.Additionally,we leverage deep learning techniques to analyze high-dimensional position-specific scoring matrices(PSSMs)and capture evolutionary information.We then combine these features for classification using various machine learning algorithms.To enhance performance,we integrate the outputs of these algorithms through ensemble methods such as voting,weighted averaging,and stacking.This approach effectively addresses data imbalances and improves both robustness and accuracy.Our ensemble learning framework achieves an AUC of 0.960 and an accuracy of 0.9252,outperforming other computational methods.These results demonstrate the effectiveness of our approach in accurately identifying essential proteins and highlight its superior feature extraction capabilities.
