平野　秀典 (ヒラノ　ヨシノリ)

Hirano, Yoshinori

写真a

所属（所属キャンパス）

理工学研究科 （矢上）

職名

特任准教授（有期）
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学歴 【 表示 ／ 非表示 】

  • 1997年03月

    明治薬科大学, 薬学部, 製薬学科

    大学, 卒業

  • 2002年03月

    千葉大学, 薬学研究科, 総合薬品科学専攻

    大学院, 修了, 博士

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研究分野 【 表示 ／ 非表示 】

  • ライフサイエンス / 薬系分析、物理化学 (Physical Pharmaceutical Science)

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論文 【 表示 ／ 非表示 】

  • Molecular Dynamics Study of Conformational Changes of Tankyrase 2 Binding Subsites upon Ligand Binding

    Hirano Y., Okimoto N., Fujita S., Taiji M.

    ACS Omega （ACS Omega)  6 （ 27 ） 17609 - 17620 2021年

    　概要を見る

    The interactions between proteins and ligands are involved in various biological functions. While experimental structures provide key static structural information of ligand-unbound and ligand-bound proteins, dynamic information is often insufficient for understanding the detailed mechanism of protein-ligand binding. Here, we studied the conformational changes of the tankyrase 2 binding pocket upon ligand binding using molecular dynamics simulations of the ligand-unbound and ligand-bound proteins. The ligand-binding pocket has two subsites: The nicotinamide and adenosine subsite. Comparative analysis of these molecular dynamics trajectories revealed that the conformational change of the ligand-binding pocket was characterized by four distinct conformations of the ligand-binding pocket. Two of the four conformations were observed only in molecular dynamics simulations. We found that the pocket conformational change on ligand binding was based on the connection between the nicotinamide and adenosine subsites that are located adjacently in the pocket. From the analysis, we proposed the protein-ligand binding mechanism of tankyrase 2. Finally, we discussed the computational prediction of the ligand binding pose using the tankyrase 2 structures obtained from the molecular dynamics simulations.

  • Drug binding dynamics of the dimeric SARS-CoV-2 main protease, determined by molecular dynamics simulation

    Komatsu T.S., Okimoto N., Koyama Y.M., Hirano Y., Morimoto G., Ohno Y., Taiji M.

    Scientific Reports （Scientific Reports)  10 （ 1 ）  2020年12月

    　概要を見る

    We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein–ligand complexes and suggest the possibilities of further drug optimisations.

  • Use of the Multilayer Fragment Molecular Orbital Method to Predict the Rank Order of Protein-Ligand Binding Affinities: A Case Study Using Tankyrase 2 Inhibitors

    Okimoto N., Otsuka T., Hirano Y., Taiji M.

    ACS Omega （ACS Omega)  3 （ 4 ） 4475 - 4485 2018年04月

    　概要を見る

    In computational drug discovery, ranking a series of compound analogues in the order that is consistent with the experimental binding affinities remains a challenge. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics (MM)-based force fields, although they cannot completely describe protein-ligand interactions. By contrast, quantum mechanics (QM) calculations play an important role in understanding the protein-ligand interactions; however, their huge computational costs hinder their application in drug discovery. In this study, we have evaluated the ability to rank the binding affinities of tankyrase 2 ligands by combining both MM and QM calculations. Our computational approach uses the protein-ligand binding energies obtained from a cost-effective multilayer fragment molecular orbital (MFMO) method combined with the solvation energy obtained from the MM-Poisson-Boltzmann/surface area (MM-PB/SA) method to predict the binding affinity. This approach enabled us to rank tankyrase 2 inhibitor analogues, outperforming several MM-based methods, including rescoring by molecular docking and the MM-PB/SA method alone. Our results show that this computational approach using the MFMO method is a promising tool for predicting the rank order of the binding affinities of inhibitor analogues.

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担当授業科目 【 表示 ／ 非表示 】

  • 看護のための薬理学

    2022年度

  • 看護のための薬理学

    2021年度

  • 看護のための薬理学

    2019年度

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