Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have b...Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.展开更多
In the present study, 28 Chinese medicinal herbs belonging to traditional Chinese medicine(TCM) for the treatment of type 2 diabetes were selected to explore the application of network pharmacology in developing new C...In the present study, 28 Chinese medicinal herbs belonging to traditional Chinese medicine(TCM) for the treatment of type 2 diabetes were selected to explore the application of network pharmacology in developing new Chinese herbal medicine formulae for the treatment of type 2 diabetes mellitus(T2DM). These herbs have the highest appearance rate in the literature, and their compounds are listed. The human protein–protein interaction network and the T2DM disease protein interaction network were constructed. Then, the related algorithm for network topology was used to perform interventions on the interaction network of disease proteins and normal human proteins to test different Chinese herbal medicine compound combinations, according to the information on the interaction of compounds–targets in two databases, namely TarN et and the Medicinal Plants Database. Results of the intervention scores indicate that the method proposed in this study can provide new effective combinations of Chinese herbal medicines for T2DM. Network pharmacology can effectively promote the modernization and development of TCM.展开更多
基金Project supported by the Gansu Province Industrial Support Plan (Grant No.2023CYZC-25)the Natural Science Foundation of Gansu Province (Grant No.23JRRA770)the National Natural Science Foundation of China (Grant No.62162040)。
文摘Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.
基金supported by the National Natural Sciences Foundation of China(No.81374011)
文摘In the present study, 28 Chinese medicinal herbs belonging to traditional Chinese medicine(TCM) for the treatment of type 2 diabetes were selected to explore the application of network pharmacology in developing new Chinese herbal medicine formulae for the treatment of type 2 diabetes mellitus(T2DM). These herbs have the highest appearance rate in the literature, and their compounds are listed. The human protein–protein interaction network and the T2DM disease protein interaction network were constructed. Then, the related algorithm for network topology was used to perform interventions on the interaction network of disease proteins and normal human proteins to test different Chinese herbal medicine compound combinations, according to the information on the interaction of compounds–targets in two databases, namely TarN et and the Medicinal Plants Database. Results of the intervention scores indicate that the method proposed in this study can provide new effective combinations of Chinese herbal medicines for T2DM. Network pharmacology can effectively promote the modernization and development of TCM.
