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【2月3日报告】Biomolecular scaffolds for enhanced biomass processing and cancer marker detection
  发布时间:2015-01-29    供稿部门:

  报告题目Biomolecular scaffolds for enhanced biomass processing and cancer marker detection   

  报 告 人:孙青博士  特拉华大学化学与生物分子工程系 

  报告时间23 (周二)10:00-11:00   

  报告地点:天津五分赛车APP下载 所 C408 报告厅   

  报告摘要Proteins inherently are not standalone entities. They function cohesively to finish complex biochemical reaction pathways or signaling pathways that trigger output actions upon input signal detection. Nature has evolved to co-localize proteins of the same pathway to ensure efficient communication of signals or intermediates. Inspired by nature, a variety of artificial scaffolds have been used to enhance reaction yields, increase effectiveness of signaling cascades, and create multi-functional nano-systems. Nucleic acids and proteins, executing genetic information flow of life, have recently been exploited to serve as efficient biomolecular scaffolds with their ability to assemble into one, two and three-dimensional complex structures. In this report, I am going to highlight the recent progress in our lab utilizing DNA and protein as scaffolds for enhanced biomass processing and biosensor assembly for cancer marker detection. We first demonstrated the multi-enzyme co-localization onto DNA scaffolds. Enhanced cellulose hydrolysis was achieved by organizing multiple cellulases and cellulose binding modules onto DNA scaffolds. DNA scaffolds have potential to expand cellulosome into complex structures for higher cellulose hydrolysis efficiency, thus eliminating the limitations of protein scaffolds based cellulosome. Secondly, we extended the idea to multi-component co-localization onto three-dimensional protein nanostructures. Using enzyme enabled post-translational modification, a protein nanoparticle was modified with purification tags, input domains, and output domains for cancer detection. Compared with traditional protein nanoparticle based biosensors, the sensors we constructed feature thermal-triggered easy purification, adjustable signal amplification levels and modular input-output domain decorations for context-driven sensor assembly.     

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