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  1. Article ; Online: AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics.

    Kudo, Genki / Yanagisawa, Keisuke / Yoshino, Ryunosuke / Hirokawa, Takatsugu

    Journal of chemical information and modeling

    2023  Volume 63, Issue 24, Page(s) 7768–7777

    Abstract: Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI ...

    Abstract Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.
    MeSH term(s) Molecular Dynamics Simulation ; Solvents/chemistry ; Amino Acids/metabolism ; Proteins/chemistry ; Binding Sites ; Peptides/chemistry ; Protein Binding
    Chemical Substances Solvents ; Amino Acids ; Proteins ; Peptides
    Language English
    Publishing date 2023-12-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c01677
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Pocket to concavity: a tool for the refinement of protein-ligand binding site shape from alpha spheres.

    Kudo, Genki / Hirao, Takumi / Yoshino, Ryunosuke / Shigeta, Yasuteru / Hirokawa, Takatsugu

    Bioinformatics (Oxford, England)

    2023  Volume 39, Issue 4

    Abstract: Summary: Understanding the binding site of the target protein is essential for rational drug design. Pocket detection software predicts the ligand binding site of the target protein; however, the predicted protein pockets are often excessively estimated ...

    Abstract Summary: Understanding the binding site of the target protein is essential for rational drug design. Pocket detection software predicts the ligand binding site of the target protein; however, the predicted protein pockets are often excessively estimated in comparison with the actual volume of the bound ligands. This study proposes a refinement tool for the pockets predicted by an alpha sphere-based approach, Pocket to Concavity (P2C). P2C is divided into two modes: Ligand-Free (LF) and Ligand-Bound (LB) modes. The LF mode provides the shape of the deep and druggable concavity where the core scaffold can bind. The LB mode searches the deep concavity around the bound ligand. Thus, P2C is useful for identifying and designing desirable compounds in Structure-Based Drug Design (SBDD).
    Availability and implementation: Pocket to Concavity is freely available at https://github.com/genki-kudo/Pocket-to-Concavity. This tool is implemented in Python3 and Fpocket2.
    MeSH term(s) Protein Conformation ; Proteins/chemistry ; Binding Sites ; Protein Binding ; Software ; Ligands
    Chemical Substances Proteins ; Ligands
    Language English
    Publishing date 2023-04-22
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btad212
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes.

    Yanagisawa, Keisuke / Yoshino, Ryunosuke / Kudo, Genki / Hirokawa, Takatsugu

    International journal of molecular sciences

    2022  Volume 23, Issue 9

    Abstract: To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead ... ...

    Abstract To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing "inverse" mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.
    MeSH term(s) Ligands ; Molecular Dynamics Simulation ; Molecular Probes ; Proteins/chemistry ; Solvents/chemistry
    Chemical Substances Ligands ; Molecular Probes ; Proteins ; Solvents
    Language English
    Publishing date 2022-04-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms23094749
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Development of Machine Learning Model for Selecting the 1st Coil in the Treatment of Cerebral Aneurysms by Coil Embolization.

    Fujimura, Soichiro / Koshiba, Toshiki / Kudo, Genki / Takeshita, Kohei / Kazama, Masahiro / Karagiozov, Kostadin / Fukudome, Koji / Takao, Hiroyuki / Ohwada, Hayato / Murayama, Yuichi / Yamamoto, Makoto / Ishibashi, Toshihiro / Otani, K

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Volume 2023, Page(s) 1–4

    Abstract: To achieve good treatment outcomes in coil embolization for cerebral aneurysms, it is important to select an appropriate 1st coil for each aneurysm since it serves as a frame to support the subsequent coils to be deployed. However, its selection as ... ...

    Abstract To achieve good treatment outcomes in coil embolization for cerebral aneurysms, it is important to select an appropriate 1st coil for each aneurysm since it serves as a frame to support the subsequent coils to be deployed. However, its selection as appropriate size and length from a wide variety of lineups is not easy, especially for inexperienced neurosurgeons. We developed a machine learning model (MLM) to predict the optimal size and length of the 1st coil by learning information on patients and aneurysms that were previously treated with coil embolization successfully. The accuracy rates of the MLM for the test data were 86.3% and 83.4% in the prediction of size and length, respectively. In addition, the accuracy rates for the 30 cases showed good prediction by the MLM when compared with two different skilled neurosurgeons. Although the accuracy rate of the well-experienced neurosurgeon is similar to MLM, the inexperienced neurosurgeon showed a worse rate and can benefit from the method.Clinical Relevance- The developed MLM has the potential to assist in the selection of the 1st coil for aneurysms. A technically and cost efficient supply chain in the treatment of aneurysms may also be achieved by MLM application.
    MeSH term(s) Humans ; Intracranial Aneurysm/diagnostic imaging ; Intracranial Aneurysm/therapy ; Embolization, Therapeutic/adverse effects ; Treatment Outcome ; Blood Vessel Prosthesis
    Language English
    Publishing date 2023-12-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10341191
    Database MEDical Literature Analysis and Retrieval System OnLINE

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