项目编号: | 1603930
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项目名称: | Near-natural Amino Acid Mutagenesis for the Engineering and Study of Protein Function |
作者: | Alexander Deiters
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承担单位: | University of Pittsburgh
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批准年: | 2016
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开始日期: | 2016-08-01
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结束日期: | 2019-07-31
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资助金额: | 450000
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资助来源: | US-NSF
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项目类别: | Standard Grant
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国家: | US
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语种: | 英语
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特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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英文关键词: | protein
; near-natural amino acid
; near-natural
; amino acid
; unnatural amino acid
; protein function
; engineering
; function
; precise study
; protein structure
; near-natural protein
; protein engineering
; small protein
; biochemical engineering program
; endogenous protein biosynthesis
; orthogonal protein biosynthetic pathway
; group
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英文摘要: | 1603930 Deiters, Alexander
Most physiological processes rely on the function of proteins. In order to elucidate the precise biochemical mechanisms of protein function, researchers are increasingly turning to the tool set of unnatural amino acids that contain small structural or electronic changes which are very similar to Nature's set of common amino acids. These subtle differences, site-specifically incorporated into proteins, allow for the precise probing of protein structure and function. Within this project, new methodologies for the genetic encoding of these 'near-natural' amino acids will be developed and applied to the investigation of enzymatic mechanism.
In order to elucidate the precise biochemical mechanisms of protein function, unnatural amino acids that contain small structural or electronic changes, e.g., in pKa, polarity, redox potential, H-bonding ability, nucleophilicity, isotope composition, etc, are versatile molecular probes. Although these 'near-natural' amino acids are extremely useful to investigate protein function when located near or in an active site, they are difficult to apply to the engineering of a biological system for the following reasons: 1) The chemical synthesis of proteins containing near-natural amino acids is laborious, expensive, and can routinely only be applied to small proteins. 2) The semi-synthesis of proteins can be technically challenging. 3) The use of auxotrophic bacterial or yeast strains in vivo leads to global, non-specific incorporation of near-natural amino acids into proteins preventing precise studies. 4) Near-natural amino acids often display toxicity due to global incorporation into the proteome. Within this project, a robust and general methodology for protein engineering through site-specific near-natural amino acid mutagenesis will be developed by temporarily disguising these amino acids as completely unnatural amino acids. This will be achieved by introducing a transient structural change into near-natural amino acids, thus 'hiding' them from the cellular machinery responsible for endogenous protein biosynthesis, but simultaneously providing a handle for the engineering of an orthogonal biosynthetic pathway. Specifically, introduction of protecting groups (caging groups) on near-natural amino acids converts them temporarily into completely unnatural amino acids, effectively solving the issue of non-specific incorporation into proteins in vivo. After site-specific incorporation into the protein of interest, the caging group is removed through an external trigger providing the original near-natural amino acid and thus near-natural protein. For the engineering of an orthogonal protein biosynthetic pathway, new tRNA synthetases for the in vivo incorporation of near-natural amino acids into proteins in pro- and eukaryotic cells are generated. The developed methodology will be applied to the precise investigation of enzyme active sites with atomic resolution.
This award by the Biotechnology and Biochemical Engineering Program of the CBET Division is co-funded by the Division of Materials Research through BioMaPS funds. |
资源类型: | 项目
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标识符: | http://119.78.100.158/handle/2HF3EXSE/91681
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Appears in Collections: | 全球变化的国际研究计划 科学计划与规划
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Recommended Citation: |
Alexander Deiters. Near-natural Amino Acid Mutagenesis for the Engineering and Study of Protein Function. 2016-01-01.
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