| 研究課題/領域番号 |
22KF0153
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| 補助金の研究課題番号 |
22F22385 (2022)
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 基金 (2023)
補助金 (2022) |
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| 応募区分 | 外国 |
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| 審査区分 |
小区分43040:生物物理学関連
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| 研究機関 | 金沢大学 |
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研究代表者 |
古寺 哲幸 金沢大学, ナノ生命科学研究所, 教授 (30584635)
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| 研究分担者 |
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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| 研究期間 (年度) |
2023-03-08 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
2,100千円 (直接経費: 2,100千円)
2024年度: 500千円 (直接経費: 500千円)
2023年度: 900千円 (直接経費: 900千円)
2022年度: 700千円 (直接経費: 700千円)
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| キーワード | 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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| 研究開始時の研究の概要 |
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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| 研究実績の概要 |
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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
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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| 今後の研究の推進方策 |
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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