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  1. Article ; Online: Improvement of the Green-Red Förster Resonance Energy Transfer-Based Ca

    Matsuda, Tomoki / Sakai, Shinya / Okazaki, Kei-Ichi / Nagai, Takeharu

    ACS sensors

    2024  Volume 9, Issue 4, Page(s) 1743–1748

    Abstract: To monitor the ... ...

    Abstract To monitor the Ca
    MeSH term(s) Fluorescence Resonance Energy Transfer/methods ; Calcium/chemistry ; Calcium/metabolism ; Calcium/analysis ; Green Fluorescent Proteins/chemistry ; Luminescent Proteins/chemistry ; Humans ; Red Fluorescent Protein ; HEK293 Cells
    Chemical Substances Calcium (SY7Q814VUP) ; Green Fluorescent Proteins (147336-22-9) ; Luminescent Proteins ; Red Fluorescent Protein
    Language English
    Publishing date 2024-03-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2379-3694
    ISSN (online) 2379-3694
    DOI 10.1021/acssensors.3c02398
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Accelerated Molecular Dynamics and AlphaFold Uncover a Missing Conformational State of Transporter Protein OxlT.

    Ohnuki, Jun / Jaunet-Lahary, Titouan / Yamashita, Atsuko / Okazaki, Kei-Ichi

    The journal of physical chemistry letters

    2024  Volume 15, Issue 3, Page(s) 725–732

    Abstract: Transporter proteins change their conformations to carry their substrate across the cell membrane. The conformational dynamics is vital to understanding the transport function. We have studied the oxalate transporter (OxlT), an oxalate:formate antiporter ...

    Abstract Transporter proteins change their conformations to carry their substrate across the cell membrane. The conformational dynamics is vital to understanding the transport function. We have studied the oxalate transporter (OxlT), an oxalate:formate antiporter from
    MeSH term(s) Oxalates/chemistry ; Oxalates/metabolism ; Molecular Dynamics Simulation ; Membrane Transport Proteins/chemistry ; Antiporters/metabolism ; Formates/metabolism ; Protein Conformation
    Chemical Substances Oxalates ; Membrane Transport Proteins ; Antiporters ; Formates
    Language English
    Publishing date 2024-01-12
    Publishing country United States
    Document type Journal Article
    ISSN 1948-7185
    ISSN (online) 1948-7185
    DOI 10.1021/acs.jpclett.3c03052
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach.

    Okada, Kazushi / Kikutsuji, Takuma / Okazaki, Kei-Ichi / Mori, Toshifumi / Kim, Kang / Matubayasi, Nobuyuki

    The Journal of chemical physics

    2024  Volume 160, Issue 17

    Abstract: The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without explicit reference to dihedral angles. Conventionally, two stable states of ... ...

    Abstract The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without explicit reference to dihedral angles. Conventionally, two stable states of alanine dipeptide in vacuum, i.e., C7eq (β-sheet structure) and C7ax (left handed α-helix structure), have been primarily characterized using the main chain dihedral angles, φ (C-N-Cα-C) and ψ (N-Cα-C-N). However, our recent deep learning combined with the "Explainable AI" (XAI) framework has shown that the transition state can be adequately captured by a free energy landscape using φ and θ (O-C-N-Cα) [Kikutsuji et al., J. Chem. Phys. 156, 154108 (2022)]. In the perspective of extending these insights to other collective variables, a more detailed characterization of the transition state is required. In this work, we employ interatomic distances and bond angles as input variables for deep learning rather than the conventional and more elaborate dihedral angles. Our approach utilizes deep learning to investigate whether changes in the main chain dihedral angle can be expressed in terms of interatomic distances and bond angles. Furthermore, by incorporating XAI into our predictive analysis, we quantified the importance of each input variable and succeeded in clarifying the specific interatomic distance that affects the transition state. The results indicate that constructing a free energy landscape based on the identified interatomic distance can clearly distinguish between the two stable states and provide a comprehensive explanation for the energy barrier crossing.
    Language English
    Publishing date 2024-05-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3113-6
    ISSN 1089-7690 ; 0021-9606
    ISSN (online) 1089-7690
    ISSN 0021-9606
    DOI 10.1063/5.0203346
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Molecular mechanism on forcible ejection of ATPase inhibitory factor 1 from mitochondrial ATP synthase.

    Kobayashi, Ryohei / Ueno, Hiroshi / Okazaki, Kei-Ichi / Noji, Hiroyuki

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 1682

    Abstract: ... ...

    Abstract IF
    MeSH term(s) Animals ; Cattle ; Mitochondrial Proton-Translocating ATPases/genetics ; Mitochondrial Proton-Translocating ATPases/metabolism ; Proton-Translocating ATPases/genetics ; Proton-Translocating ATPases/chemistry ; Proteins/metabolism ; Mitochondria/metabolism ; Adenosine Triphosphate/metabolism
    Chemical Substances Mitochondrial Proton-Translocating ATPases (EC 3.6.3.-) ; Proton-Translocating ATPases (EC 3.6.3.14) ; Proteins ; Adenosine Triphosphate (8L70Q75FXE)
    Language English
    Publishing date 2023-03-31
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-37182-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Optimizing Gō-MARTINI Coarse-Grained Model for F-BAR Protein on Lipid Membrane.

    Mahmood, Md Iqbal / Poma, Adolfo B / Okazaki, Kei-Ichi

    Frontiers in molecular biosciences

    2021  Volume 8, Page(s) 619381

    Abstract: Coarse-grained (CG) molecular dynamics (MD) simulations allow us to access much larger length and time scales than atomistic MD simulations, providing an attractive alternative to the conventional simulations. Based on the well-known MARTINI CG force ... ...

    Abstract Coarse-grained (CG) molecular dynamics (MD) simulations allow us to access much larger length and time scales than atomistic MD simulations, providing an attractive alternative to the conventional simulations. Based on the well-known MARTINI CG force field, the recently developed Gō-MARTINI model for proteins describes large-amplitude structural dynamics, which has not been possible with the commonly used elastic network model. Using the Gō-MARTINI model, we conduct MD simulations of the F-BAR Pacsin1 protein on lipid membrane. We observe that structural changes of the non-globular protein are largely dependent on the definition of the native contacts in the Gō model. To address this issue, we introduced a simple cutoff scheme and tuned the cutoff distance of the native contacts and the interaction strength of the Lennard-Jones potentials in the Gō-MARTINI model. With the optimized Gō-MARTINI model, we show that it reproduces structural fluctuations of the Pacsin1 dimer from atomistic simulations. We also show that two Pacsin1 dimers properly assemble through lateral interaction on the lipid membrane. Our work presents a first step towards describing membrane remodeling processes in the Gō-MARTINI CG framework by simulating a crucial step of protein assembly on the membrane.
    Language English
    Publishing date 2021-02-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2814330-9
    ISSN 2296-889X
    ISSN 2296-889X
    DOI 10.3389/fmolb.2021.619381
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Editorial: Recent advances in computational modelling of biomolecular complexes.

    Poblete, Simón / Pantano, Sergio / Okazaki, Kei-Ichi / Liang, Zhongjie / Kremer, Kurt / Poma, Adolfo B

    Frontiers in chemistry

    2023  Volume 11, Page(s) 1200409

    Language English
    Publishing date 2023-04-11
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2711776-5
    ISSN 2296-2646
    ISSN 2296-2646
    DOI 10.3389/fchem.2023.1200409
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  7. Article: Updating view of membrane transport proteins by simulation studies.

    Sumikama, Takashi / Corry, Ben / Ono, Junichi / Kobayashi, Chigusa / Okazaki, Kei-Ichi

    Biophysics and physicobiology

    2023  Volume 20, Issue 4, Page(s) e200041

    Language English
    Publishing date 2023-11-02
    Publishing country Japan
    Document type Journal Article
    ISSN 2189-4779
    ISSN 2189-4779
    DOI 10.2142/biophysico.bppb-v20.0041
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Chemical-State-Dependent Free Energy Profile from Single-Molecule Trajectories of Biomolecular Motors: Application to Processive Chitinase

    Okazaki, Kei-ichi / Nakamura, Akihiko / Iino, Ryota

    Journal of physical chemistry. 2020 July 06, v. 124, no. 30

    2020  

    Abstract: The mechanism of biomolecular motors has been elucidated using single-molecule experiments for visualizing motor motion. However, it remains elusive that how changes in the chemical state during the catalytic cycle of motors lead to unidirectional ... ...

    Abstract The mechanism of biomolecular motors has been elucidated using single-molecule experiments for visualizing motor motion. However, it remains elusive that how changes in the chemical state during the catalytic cycle of motors lead to unidirectional motions. In this study, we use single-molecule trajectories to estimate an underlying diffusion model with chemical-state-dependent free energy profile. To consider nonequilibrium trajectories driven by the chemical energy consumed by biomolecular motors, we develop a novel framework based on a hidden Markov model, wherein switching among multiple energy profiles occurs reflecting the chemical state changes in motors. The method is tested using simulation trajectories and applied to single-molecule trajectories of processive chitinase, a linear motor that is driven by the hydrolysis energy of a single chitin chain. The chemical-state-dependent free energy profile underlying the burnt-bridge Brownian ratchet mechanism of processive chitinase is determined. The novel framework allows us to connect the chemical state changes to the unidirectional motion of biomolecular motors.
    Keywords Gibbs free energy ; Markov chain ; chitin ; chitinase ; energy ; hydrolysis ; models ; motors ; physical chemistry
    Language English
    Dates of publication 2020-0706
    Size p. 6475-6487.
    Publishing place American Chemical Society
    Document type Article
    Note NAL-AP-2-clean
    ISSN 1520-5207
    DOI 10.1021/acs.jpcb.0c02698
    Database NAL-Catalogue (AGRICOLA)

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  9. Article ; Online: Chemical-State-Dependent Free Energy Profile from Single-Molecule Trajectories of Biomolecular Motors: Application to Processive Chitinase.

    Okazaki, Kei-Ichi / Nakamura, Akihiko / Iino, Ryota

    The journal of physical chemistry. B

    2020  Volume 124, Issue 30, Page(s) 6475–6487

    Abstract: The mechanism of biomolecular motors has been elucidated using single-molecule experiments for visualizing motor motion. However, it remains elusive that how changes in the chemical state during the catalytic cycle of motors lead to unidirectional ... ...

    Abstract The mechanism of biomolecular motors has been elucidated using single-molecule experiments for visualizing motor motion. However, it remains elusive that how changes in the chemical state during the catalytic cycle of motors lead to unidirectional motions. In this study, we use single-molecule trajectories to estimate an underlying diffusion model with chemical-state-dependent free energy profile. To consider nonequilibrium trajectories driven by the chemical energy consumed by biomolecular motors, we develop a novel framework based on a hidden Markov model, wherein switching among multiple energy profiles occurs reflecting the chemical state changes in motors. The method is tested using simulation trajectories and applied to single-molecule trajectories of processive chitinase, a linear motor that is driven by the hydrolysis energy of a single chitin chain. The chemical-state-dependent free energy profile underlying the burnt-bridge Brownian ratchet mechanism of processive chitinase is determined. The novel framework allows us to connect the chemical state changes to the unidirectional motion of biomolecular motors.
    MeSH term(s) Chitinases ; Diffusion ; Kinetics ; Molecular Motor Proteins/metabolism ; Motion
    Chemical Substances Molecular Motor Proteins ; Chitinases (EC 3.2.1.14)
    Language English
    Publishing date 2020-07-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1520-5207
    ISSN (online) 1520-5207
    DOI 10.1021/acs.jpcb.0c02698
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI).

    Kikutsuji, Takuma / Mori, Yusuke / Okazaki, Kei-Ichi / Mori, Toshifumi / Kim, Kang / Matubayasi, Nobuyuki

    The Journal of chemical physics

    2022  Volume 156, Issue 15, Page(s) 154108

    Abstract: A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing the product and reactant in complex molecular systems. Recently, abundant research has been devoted to obtaining reaction coordinates using ... ...

    Abstract A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing the product and reactant in complex molecular systems. Recently, abundant research has been devoted to obtaining reaction coordinates using artificial neural networks from deep learning literature, where many collective variables are typically utilized in the input layer. However, it is difficult to explain the details of which collective variables contribute to the predicted reaction coordinates owing to the complexity of the nonlinear functions in deep neural networks. To overcome this limitation, we used Explainable Artificial Intelligence (XAI) methods of the Local Interpretable Model-agnostic Explanation (LIME) and the game theory-based framework known as Shapley Additive exPlanations (SHAP). We demonstrated that XAI enables us to obtain the degree of contribution of each collective variable to reaction coordinates that is determined by nonlinear regressions with deep learning for the committor of the alanine dipeptide isomerization in vacuum. In particular, both LIME and SHAP provide important features to the predicted reaction coordinates, which are characterized by appropriate dihedral angles consistent with those previously reported from the committor test analysis. The present study offers an AI-aided framework to explain the appropriate reaction coordinates, which acquires considerable significance when the number of degrees of freedom increases.
    MeSH term(s) Alanine ; Artificial Intelligence ; Dipeptides/chemistry ; Isomerism ; Neural Networks, Computer
    Chemical Substances Dipeptides ; Alanine (OF5P57N2ZX)
    Language English
    Publishing date 2022-04-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3113-6
    ISSN 1089-7690 ; 0021-9606
    ISSN (online) 1089-7690
    ISSN 0021-9606
    DOI 10.1063/5.0087310
    Database MEDical Literature Analysis and Retrieval System OnLINE

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