[目的]利用生物信息学方法预测绿脓杆菌外膜蛋白OprF的理化性质、高级结构和细胞表位。[方法]采用在线软件预测OprF蛋白的理化性质;Signal P 4.1软件预测OprF信号肽序列;利用TMHMM软件预测ACFA蛋白跨膜结构;SOPMA服务器预测蛋白的二级结...[目的]利用生物信息学方法预测绿脓杆菌外膜蛋白OprF的理化性质、高级结构和细胞表位。[方法]采用在线软件预测OprF蛋白的理化性质;Signal P 4.1软件预测OprF信号肽序列;利用TMHMM软件预测ACFA蛋白跨膜结构;SOPMA服务器预测蛋白的二级结构;Swiss-Model程序预测OprF三维结构;综合ABCpred与Bepi Pred方案预测OprF的B细胞表位;运用神经网络法预测OprF的CTL表位;使用MHC-Ⅱ类分子结合肽程序预测OprF的Th细胞表位。[结果]OprF为亲水性蛋白;1~24位氨基酸为信号肽序列;存在多个酶切位点;无跨膜结构并定位于细胞膜外;二级结构中含无规则卷曲34.36%、α-螺旋31.90%、β-转角11.66%、β-片层22.09%;并可能存在3个B细胞表位、2个CTL表位、4个Th细胞表位。[结论]系统分析了OprF蛋白的理化性质、信号肽、跨膜结构、二级与三级结构,以及B、T细胞抗原表位。展开更多
This paper considers the distributed online optimization(DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed on...This paper considers the distributed online optimization(DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed online one-point residual feedback(OPRF) optimization algorithm is proposed. This algorithm updates decision variables by leveraging one-point residual feedback to estimate the true gradient information. It can achieve the same performance as the two-point feedback scheme while only requiring a single function value query per iteration. Additionally, it effectively eliminates the effect of time-varying unbalanced graphs by dynamically constructing row stochastic matrices. Furthermore, compared to other distributed optimization algorithms that only consider explicitly unknown cost functions, this paper also addresses the issue of privacy information leakage of nodes. Theoretical analysis demonstrate that the method attains sublinear regret while protecting the privacy information of agents. Finally, numerical experiments on distributed collaborative localization problem and federated learning confirm the effectiveness of the algorithm.展开更多
文摘[目的]利用生物信息学方法预测绿脓杆菌外膜蛋白OprF的理化性质、高级结构和细胞表位。[方法]采用在线软件预测OprF蛋白的理化性质;Signal P 4.1软件预测OprF信号肽序列;利用TMHMM软件预测ACFA蛋白跨膜结构;SOPMA服务器预测蛋白的二级结构;Swiss-Model程序预测OprF三维结构;综合ABCpred与Bepi Pred方案预测OprF的B细胞表位;运用神经网络法预测OprF的CTL表位;使用MHC-Ⅱ类分子结合肽程序预测OprF的Th细胞表位。[结果]OprF为亲水性蛋白;1~24位氨基酸为信号肽序列;存在多个酶切位点;无跨膜结构并定位于细胞膜外;二级结构中含无规则卷曲34.36%、α-螺旋31.90%、β-转角11.66%、β-片层22.09%;并可能存在3个B细胞表位、2个CTL表位、4个Th细胞表位。[结论]系统分析了OprF蛋白的理化性质、信号肽、跨膜结构、二级与三级结构,以及B、T细胞抗原表位。
基金supported by the National Natural Science Foundation of China (62033010, U23B2061)Qing Lan Project of Jiangsu Province(R2023Q07)。
文摘This paper considers the distributed online optimization(DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed online one-point residual feedback(OPRF) optimization algorithm is proposed. This algorithm updates decision variables by leveraging one-point residual feedback to estimate the true gradient information. It can achieve the same performance as the two-point feedback scheme while only requiring a single function value query per iteration. Additionally, it effectively eliminates the effect of time-varying unbalanced graphs by dynamically constructing row stochastic matrices. Furthermore, compared to other distributed optimization algorithms that only consider explicitly unknown cost functions, this paper also addresses the issue of privacy information leakage of nodes. Theoretical analysis demonstrate that the method attains sublinear regret while protecting the privacy information of agents. Finally, numerical experiments on distributed collaborative localization problem and federated learning confirm the effectiveness of the algorithm.
