高分子量蛋白質のNMR構造解析を目指したスパース選択標識とNMR自動解析法の開発

2018 Fiscal Year Research-status Report

• Project/Area Number: 17K07312
• Research Institution: Tokyo Metropolitan University
• Principal Investigator: PETER GUENTERT 首都大学東京, 理学研究科, 客員教授 (20392110)
• Co-Investigator(Kenkyū-buntansha): 池谷 鉄兵 首都大学東京, 理学研究科, 助教 (30457840)
• Project Period (FY): 2017-04-01 – 2020-03-31
• Keywords: protein NMR / in-cell NMR / resonance assignment / automated assignment / methyl groups / isotope labeling

Outline of Annual Research Achievements

Proteins that are large, membrane-bound, or studied in living cells by in-cell NMR can in general not be assigned by the conventional solution NMR method that relies on uniform 13C/15N-labeling because the resonance lines become too broad and overlapping. Sparse labeling, in particular of methyl groups is an approach to restore interpretable spectra by drastically reducing relaxation rates and the number of signals. However, it remains difficult to establish resonance assignments, even if the three-dimensional structure of the protein is already known. In this project we develop computational methods to assign the spectra of sparsely labeled difficult proteins by structure-based automated assignment. On the basis of our existing FLYA algorithm for the assignment of uniformly labeled proteins, a new automated assignment method has been developed that can assign large proteins using methyl group labeling or other sparse labeling techniques, and NOESY spectra in conjunction with a known three-dimensional structure. The FLYA algorithm finds assignments by optimizing a mapping between the expected peaks, which one anticipates to see based on the protein sequence, and the measured peaks, which have been identified by peak picking. The general design of the algorithm makes it possible to exploit peak lists from virtually any type of NMR spectrum. In particular, we could show that FLYA is able to assign proteins using as input exclusively NOESY spectra. The new approach has been applied to large proteins up to 468 kDa size and to proteins in living cells.

Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

A full account of our methyl-FLYA method for structure-based assignment of large, specifically methyl-labeled proteins has been submitted to Nature Communications and is currently under revision. The method has been applied to the automated assignment of five proteins with sizes between 27 and 360 kDa, for which experimental NOESY spectra are available.
The first structure determinations of proteins directly in living eukaryotic cells, which use the automated assignment method, have been completed and published in Angewandte Chemie International Edition.

Strategy for Future Research Activity

We plan to apply our methods to the assignment and structure determination of proteins at near-physiological concentrations that are imported into human cells by electroporation.
We have established an integrated structure determination approach that simultaneously uses NMR and EM data to overcome the limits of each of these methods. The approach enables structure determination of the 468 kDa large dodecameric aminopeptidase TET2 to a precision and accuracy below 1 A. We expect that he resulting structure to exceed current standards of NMR and EM structure determination in terms of molecular weight and precision.

Causes of Carryover

新規計算機を購入予定であったが，目的の性能を満たす装置の年度内での発売がされなかったため，次年度に購入することとした．繰り越した費用は，新規計算機の購入と実験消耗品の購入に充てる予定である．

Research Products

• [Journal Article] High resolution protein 3D structure determination in living eukaryotic cells (2019)
  • Author(s): Tanaka, T., Ikeya, T., Kamoshida, H., Suemoto, Y., Mishima, M., Shirakawa, M., Guentert, P., Ito, Y.
  • Journal Title: Angewandte Chemie International Edition Volume: in press Pages: 1-7
  • DOI: 10.1002/anie.201900840
  • Peer Reviewed / Int'l Joint Research
• [Journal Article] Global response of diacylglycerol kinase towards substrate binding observed by 2D and 3D MAS NMR (2019)
  • Author(s): Moebius, K., Kazemi, S., Guentert, P., Jakob, A., Heckel, A., Becker-Baldus, J., Glaubitz, C.
  • Journal Title: Scientific Reporrts Volume: 9 Pages: 3995
  • DOI: 10.1038/s41598-019-40264-8
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks (2018)
  • Author(s): Kobayashi, N., Hattori, Y., Nagata, T., Shinya, S., Guentert, P., Kojima, C., Fujiwara, T.
  • Journal Title: Bioinformatics Volume: 34 Pages: 4300-4301
  • DOI: 10.1093/bioinformatics/bty581
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Fully automated assignment of methyl resonances of a 36 kDa protein dimer from sparse NOESY data (2018)
  • Author(s): Pritisanac, I., Wuerz, J. M., Guentert, P.
  • Journal Title: Journal of Physics: Conference Series Volume: 1036 Pages: 012008
  • DOI: 10.1088/1742-6596/1036/1/012008
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Extending the applicability of exact nuclear Overhauser enhancements to large proteins and RNA (2018)
  • Author(s): Nichols, P. J., Born, A., Henen, M. A., Strotz, D., Celestine, C., Riek, R., Guentert, P., Voegeli, B.
  • Journal Title: ChemBioChem Volume: 19 Pages: 1695-1701
  • DOI: 10.1002/cbic.201800237
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Presentation] Structure-based methyl resonance assignment and multi-state eNOE analysis with CYANA (2018)
  • Author(s): Peter GUENTERT
  • Organizer: 28th International Conference on Magnetic Resonance in Biological Systems (ICMRBS 2018)
  • Int'l Joint Research / Invited