| Project/Area Number |
22KF0153
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| Project/Area Number (Other) |
22F22385 (2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2022) |
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| Section | 外国 |
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| Review Section |
Basic Section 43040:Biophysics-related
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| Research Institution | Kanazawa University |
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Principal Investigator |
古寺 哲幸 金沢大学, ナノ生命科学研究所, 教授 (30584635)
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| Co-Investigator(Kenkyū-buntansha) |
AMYOT ROMAIN 金沢大学, ナノ生命科学研究所, 外国人特別研究員
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| Project Period (FY) |
2023-03-08 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2024: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2023: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2022: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | 原子間力顕微鏡 / 生物物理学 / タンパク質 / 一分子イメージング / 生体分子 / 計算機科学 |
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| Outline of Research at the Start |
ABCA1は、生体内のコレステロールの恒常性の維持の鍵を握るトランポータータンパク質です。本研究では、機能中のタンパク質の構造形態変化を可視化できる高速AFMを用いて、機能中のABCA1の動態をリアルタイム観察することで、ABCA1の形態を捉えつつ、コレステロールを分泌している機能中の様子を捉えることを目指します。また、AFMが観察できるのはnmレベルの分子の表面形状ですが、申請者が得意とする計算科学的手法により、観察されたAFM画像を再現する構造を再構成します。これによりAFMだけでは分からない分子内部までも含んだ構造情報を原子レベルで理解することを目指します。
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| Outline of Annual Research Achievements |
We continued the observation of the mechanism of cholesterol exportation by the ATP-binding cassette transporter ABCA1 using the HS-AFM. We observed that, ABCA1 transports the cholesterol of the nanodisc in which it is embedded to its extracellular domain under the action of ATP. The accumulated cholesterol is then picked up by apoA-I molecules to be exported in the form of HDL.
The main goal of this year was to prepare our collaborative article to target a high scientific journal. In this perspective, we accumulated additional data to strengthen our message and we spent much effort in analyzing the experimental data by measuring volumes, surface areas, lengths and heights of our topographic images.
As for the computational part, we finished our work on the prediction of protein placement on AFM substrates based on the electrostatic properties. This method is intended to predict the orientation of the molecule which will most likely attach to a given AFM substrate. It can be applied before experiments and help to decide for an immobilization strategy. The method is not only applicable for this particular project but also for any projects involving AFM experiments.
In the meantime, we made an important step forward in accelerating the computations of simulated AFM images by developing a method relying on the parallelization of the graphics card. This acceleration (which can be up to two orders of magnitude) is particularly useful for computational methods requiring to iteratively compute simulated AFM images as it is the case in this project.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The experimental article about the mechanism of action of ABCA1 is currently in stand-by. The HS-AFM part is ready and the collaborators in Kyoto are now completing the article with cryo-electron microscope data. It is likely that this article will be submitted before the end of this year.
The two computational methods developed in the past year got their respective articles (Amyot et al., Frontiers in Molecular Biosciences, 2023 and Amyot et al., Algorithms, 2024) and presented at two Japanese conferences (The 61st Annual Meeting of the Biophysical Society of Japan and The Chubu Branch Meeting of the Biophysical Society of Japan).
We are now considering a method for atomic reconstruction across HS-AFM movies.
In addition, we have started a collaboration work with a French researcher. Our HS-AFM successfully visualized a protein complex structure between one of actin-binding proteins and a protein secreted from a bacteria. Currently, we are working on the image analysis for the obtained data.
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| Strategy for Future Research Activity |
In the remaining half a year, we intend to focus on developing automatized methods for atomic reconstruction of HS-AFM movies. We already developed such a method in the past but they do not include the internal conformational changes of the protein. Thanks to the accelerated method developed last year for AFM simulations, we are able to generate a large dataset of simulated AFM images in a short time for training supervised machine learning algorithms which will be used on experimental AFM data to assess the orientation and the conformation of the observed molecule frame by frame reconstructing the atomic movie corresponding to the experimental AFM movie. We think that a method based on machine learning would have a better potential to generalize beyond this ABCA1 project and have a deeper impact on the AFM analysis.
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