2020 Fiscal Year Research-status Report
Use of contact prediction-based restraints for protein structure determination from sparse NMR data
| Project/Area Number |
20K06508
|
|---|
| Research Institution | Tokyo Metropolitan University |
|---|
Principal Investigator |
PETER GUENTERT 東京都立大学, 理学研究科, 客員教授 (20392110)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
池谷 鉄兵 東京都立大学, 理学研究科, 助教 (30457840)
伊藤 隆 東京都立大学, 理学研究科, 教授 (80261147)
|
|---|
| Project Period (FY) |
2020-04-01 – 2023-03-31
|
| Keywords | machine learning / NMR / protein structure / automated assignment |
|---|
| Outline of Annual Research Achievements |
Since the submission of the grant application, there have been very significant advances in protein structure prediction methods that have been revealed by the most recent CASP experiment (14th Community Wide Experiment on the
Critical Assessment of Techniques for Protein Structure Prediction; CASP14), which was held in 2020. One particular machine learning-based method, AlphaFold2, greatly outperformed all other approaches. We therefore decided to concentrate our project on using this approach, which is, however, unfortunately not accessible yet.
In the meantime, we have successfully applied machine learning methods to other parts of the NMR structure determination pipeline, namely peak picking and deconvolution, as well as the refinement of chemical shift assignments. Our method takes as input only the protein sequence and NMR spectra, producing as output: (a) peak lists for each spectrum, (b) a chemical shift list, (c) upper distance limit restraints, and (d) a protein structure in PDB format. The structure determination process does not require any human intervention and takes about 5 hours, making it possible to obtain a high-quality protein strucure shortly after completing the NMR measurements.
Using this approach, we have managed to automatically solve 100 protein structures of 35-175 residues with a median backbone RMSD of 1.27 A to the PDB reference structures. Moreover, the method correctly assigned 96.3% backbone and 85.5% side-chain chemical shifts (median accuracy), compared to BMRB depositions.
|
| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
Research on the use of distance predictions in NMR structure calculations has been adjusted in the light of new developments in order to make use of the best possible method available. On the other hand, machine learning methods for peak picking and deconvolution progressed fast and turned out to be highly successful in automated structure determination.
Research was negatively affected by travel restrictions due to the COVID-19 pandemic.
|
| Strategy for Future Research Activity |
We plan to adopt AlphaFold2, or a similar distance prediction approach, for generating additional distance restraints for CYANA structure calculations with sparse NMR data. Machine learning methods for peak picking, peak deconvolution, and extension of chemical shift assignments will be improved. Applications of the structure determination pipeline to data sets obtained by in-cell NMR are planned.
|
| Causes of Carryover |
The applicant could not travel between Switzerland and Japan because of COVID-19 travel restrictions. His on-site research at Tokyo Metropolitan University will be postponed and the corresponding budget transferred to FY2021.
|
