曹志伟
中药信息学,抗体信息学,功能基因组学
个性化签名
- 姓名:曹志伟
- 目前身份:
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学术头衔:
博士生导师, 教育部“新世纪优秀人才支持计划”入选者
- 职称:-
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学科领域:
物理海洋学
- 研究兴趣:中药信息学,抗体信息学,功能基因组学
曹志伟,女,中共党员,博士,同济大学生命科学与技术学院教授,兼上海生物信息技术研究中心功能基因课题组组长。上海市生物信息学会理事,中国青年科协会员. 目前已承担了科研项目共 13项,包括国家“973”项目2项,863 项目3项,自然基金项目2项,以及上海市科委重大项目和上海市教委项目等。先后获得2004年度上海市科委科技启明星计划, 2006年浦江人才项目,与 2007上海市教委曙光计划项目资助.获得2007年上海市新长征突击手表彰, 2008年教育部新世纪优秀人才。
到目前共建成生物大分子模拟软件2个,中药机理特色数据库3个,已发表了Pharmacological Reviews (IF>22.8), PNAS(IF>10),Molecular Cell Proteomics (IF>9.9 ), Nucleic acids research (IF>7.5),Clinical Pharmacology & Therapeutics (IF>7.5),Drug Discovery Today(IF>7.1), BMC bioinformatics(IF>4.9) 等SCI论文36篇,其中第一和通讯作者16篇。
研究方向:中药信息学,抗体信息学,功能基因组学
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成果阅读
138
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成果数
4
【期刊论文】Computer prediction of drug resistance mutations in proteins
曹志伟, Zhi Wei Cao, Lian Yi Han, Chan Juan Zheng, Zhi Lang Ji, Xin Chen, Hong Huang Lin and Yu Zong Chen
DDT • Volume 10, Number 7 • April 2005,-0001,():
-1年11月30日
Drug resistance is of increasing concern in the treatment of infectious diseases and cancer. Mutation in drug-interacting disease proteins is one of the primary causes for resistance particularly against anti-infectious drugs. Prediction of resistance mutations in these proteins is valuable both for the molecular dissection of drug resistance mechanisms and for predicting features that guide the design of new agents to counter resistant strains. Several protein structure-and sequence-based computer methods have been explored for mechanistic study and prediction of resistance mutations. These methods and their usefulness are reviewed here.
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曹志伟, Qing-Xi Yue‡§, Zhi-Wei Cao§¶, Shu-Hong Guan‡, Xiao-Hui Liu, Lin Tao¶, Wan-Ying Wu‡, Yi-Xue Li¶, Peng-Yuan Yang, Xuan Liu‡**, and De-An Guo‡ ‡‡
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-1年11月30日
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曹志伟, Consortium for Influenza Study at Shanghai (WU Di, , XU TianLei, SUN Jing, DAI JianXin, DING GuoHui, HE YunGang, ZHOU ZhengFeng, XIONG Hui, DONG Hui, JIN WeiRong, BIAN Chao, JIN Li, WANG HongYan, WANG XiaoNing, YANG Zhong, ZHONG Yang, WANG Hao, CHE XiaoYan, HUANG Zhong, LAN Ke, SUN Bing, WU Fan, YUAN ZhenAn, ZHANG Xi, ZHOU XiaoNong, ZHOU JiaHai, MA ZhiYong, TONG GuangZhi, GUO YaJun, ZHAO GuoPing, †, LI YiXue, † & CAO ZhiWei, †)
Consortium for Influenza Study at Shanghai. Chinese Science Bulletin | July 2009 | vol. 54 | no. 13,-0001,():
-1年11月30日
In recent months, a novel influenza virus H1N1 broke out around the world. With bioinformatics technology, the 3D structure of HA protein was obtained, and the epitope residues were predicted with the method developed in our group for this novel flu virus. 58 amino acids were identified as potential epitope residues, the majority of which clustered at the surface of the globular head of HA protein. Although it is located at the similar position, the epitope of HA protein for the novel H1N1 flu virus has obvious differences in the electrostatic potential compared to that of HA proteins from previous flu viruses.
novel H1N1 influenza virus,, HA protein,, spatial epitope,, bioinformatics
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【期刊论文】Reconstruction and Analysis of Human Liver-Specific Metabolic Network Based on CNHLPP Data
曹志伟, Jing Zhao, †, #, ⊥ Chao Geng, § Lin Tao, † Duanfeng Zhang, ‡ Ying Jiang, § Kailin Tang, † Ruixin Zhu, ‡ Hong Yu, † Weidong Zhang, # Fuchu He, *, § Yixue Li, | and Zhiwei Cao*, ‡, ♦
1648 Journal of Proteome Research 2010, 9, 1648-1658,-0001,():
-1年11月30日
Liver is the largest internal organ in the body that takes central roles in metabolic homeostasis, detoxification of various substances, as well as in the synthesis and storage of nutrients. To fulfill these complex tasks, thousands of biochemical reactions are going on in liver to cope with a wide range of foods and environmental variations, which are densely interconnected into an intricate metabolic network. Here, the first human liver-specific metabolic network was reconstructed according to proteomics data from Chinese Human Liver Proteome Project (CNHLPP), and then investigated in the context of the genome-scale metabolic network of Homo sapiens. Topological analysis shows that this organ-specific metabolic network exhibits similar features as organism-specific networks, such as power-law degree distribution, small-world property, and bow-tie structure. Furthermore, the structure of liver network exhibits a modular organization where the modules are formed around precursors from primary metabolism or hub metabolites from derivative metabolism, respectively. Most of the modules are dominated by one major category of metabolisms, while enzymes within same modules have a tendency of being expressed concertedly at protein level. Network decomposition and comparison suggest that the liver network overlays a predominant area in the global metabolic network of H. sapiens genome; meanwhile the human network may develop extra modules to gain more specialized functionality out of liver. The results of this study would permit a high-level interpretation of the metabolite information flow in human liver and provide a basis for modeling the physiological and pathological metabolic states of liver.
Metabolic network • Human Liver Proteome Project • Network reconstruction • Network topology • Network modularity
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