NMR backbone assignment of large proteins using 13Cα-only triple resonance experiments
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- 英文论文题名：NMR backbone assignment of large proteins using 13Cα-only triple resonance experiments
- 论文作者：Qingtao Wei ; Jiajing Chen ; Juan Mi ; Jiahai Zhang ; Ke Ruan ; Jihui Wu
- 英文论文作者：Qingtao Wei ; Jiajing Chen ; Juan Mi ; Jiahai Zhang ; Ke Ruan ; Jihui Wu ; School of Life Science ; University of Science and Technology of China
- 年：2016
- 作者机构：School of Life Science, University of Science and Technology of China;
- 会议召开时间：2016-04-27
- 会议录名称：第五届中国结构生物学学术讨论会摘要集
- 语种：英文
- 分类号：Q63-33
- 学会代码：ZGWI
- 会议名称：第五届中国结构生物学学术讨论会
- 会议地点：中国广东深圳
- 主办单位：中国生物物理学会分子生物物理学专业委员会
- 学会名称：中国生物物理学会
- 页数：1
- 文件大小：182k
- 原文格式：D
- 会议级别：全国
摘要
Nuclear Magnetic Resonance(NMR) is powerful to interrogate protein structures and dynamics residue by residue at the atomic resolution but impeded in large proteins due to fast relaxation of NMR signals and difficult in backbone assignment. Though signals of 13Cα correlated triple resonance experiments are visible for medium or even very large proteins, the backbone assignment remains a great challenge because of the severe chemical shift degeneracy of 13Cα. Here, we present for the first time a back-fitting algorithm integrated with previously presented covariance NMR method to discriminate these 13Cα degenerated peaks by utilizing peak shape similarity along the common 13Cα dimension in pairs of triple resonance experiments as a new filter. Even for two fully degenerated 13Cα nuclei in spectra, the peak shapes can still be different based on their slightly chemical shift differences and local structural properties. Our algorithm identifies over 90% artificial peaks caused by chemical shift degeneracy and allows sequential backbone assignment in the covariance spectrum without precisely peak picking. The versatility and advantage of this approach is demonstrated in the backbone assignment of a 42 k Da maltose binding protein(MBP) complexed with β-cyclodextrin with a very high accuracy of 97.7% using only HN(CO)CA and HNCA experiments. Our results pave the way to study structures and dynamics of large proteins residue by residue.
Nuclear Magnetic Resonance(NMR) is powerful to interrogate protein structures and dynamics residue by residue at the atomic resolution but impeded in large proteins due to fast relaxation of NMR signals and difficult in backbone assignment. Though signals of 13Cα correlated triple resonance experiments are visible for medium or even very large proteins, the backbone assignment remains a great challenge because of the severe chemical shift degeneracy of 13Cα. Here, we present for the first time a back-fitting algorithm integrated with previously presented covariance NMR method to discriminate these 13Cα degenerated peaks by utilizing peak shape similarity along the common 13Cα dimension in pairs of triple resonance experiments as a new filter. Even for two fully degenerated 13Cα nuclei in spectra, the peak shapes can still be different based on their slightly chemical shift differences and local structural properties. Our algorithm identifies over 90% artificial peaks caused by chemical shift degeneracy and allows sequential backbone assignment in the covariance spectrum without precisely peak picking. The versatility and advantage of this approach is demonstrated in the backbone assignment of a 42 k Da maltose binding protein(MBP) complexed with β-cyclodextrin with a very high accuracy of 97.7% using only HN(CO)CA and HNCA experiments. Our results pave the way to study structures and dynamics of large proteins residue by residue.
Nuclear Magnetic Resonance(NMR) is powerful to interrogate protein structures and dynamics residue by residue at the atomic resolution but impeded in large proteins due to fast relaxation of NMR signals and difficult in backbone assignment. Though signals of 13Cα correlated triple resonance experiments are visible for medium or even very large proteins, the backbone assignment remains a great challenge because of the severe chemical shift degeneracy of 13Cα. Here, we present for the first time a back-fitting algorithm integrated with previously presented covariance NMR method to discriminate these 13Cα degenerated peaks by utilizing peak shape similarity along the common 13Cα dimension in pairs of triple resonance experiments as a new filter. Even for two fully degenerated 13Cα nuclei in spectra, the peak shapes can still be different based on their slightly chemical shift differences and local structural properties. Our algorithm identifies over 90% artificial peaks caused by chemical shift degeneracy and allows sequential backbone assignment in the covariance spectrum without precisely peak picking. The versatility and advantage of this approach is demonstrated in the backbone assignment of a 42 k Da maltose binding protein(MBP) complexed with β-cyclodextrin with a very high accuracy of 97.7% using only HN(CO)CA and HNCA experiments. Our results pave the way to study structures and dynamics of large proteins residue by residue.
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