Optimization of automated NMR data analysis for in-cell NMR structure determination

2015 Fiscal Year Final Research Report

• Project/Area Number: 25440032
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Structural biochemistry
• Research Institution: Tokyo Metropolitan University
• Principal Investigator: Peter Guentert 首都大学東京, 理工学研究科, 教授 (20392110)
• Co-Investigator(Kenkyū-buntansha): Ikeya Teppei 首都大学東京, 理工学研究科, 助教 (30457840)
• Co-Investigator(Renkei-kenkyūsha): Ito Yutaka 首都大学東京, 理工学研究科, 教授 (80261147)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: NMR立体構造計算 / NMR信号自動解析

Outline of Final Research Achievements

In-cell NMR has enabled the direct observation of proteins in living cells. We developed the CYANA software specifically for in-cell NMR. The automated chemical shift and NOE assignment algorithms in CYANA were adapted for better handling of in-cell NMR spectra processed with nonlinear sampling techniques.The automated chemical shifts assignment is significantly more robust and efficient than previous methods, and NMR structure calculation algorithm based on Bayesian inference are cornerstones for this. Compared to spectra of purified proteins, in-cell NMR ones typically show a significant number of background signals, low signal-noise ratio, and broadened signals that lead to much higher assignment ambiguity, which makes it difficult and cumbersome to check all possibilities manually. An algorithm can exhaustively search all candidates and determine assignments more quickly and more objectively. The viability of the in-cell CYANA approach was shown by applying it to in-cell NMR data.

Free Research Field

構造生物化学